Fiber Tractography in Diffusion Tensor Magnetic Resonance Imaging: A Survey and Beyond
Jun Zhang?, Hao Ji, Ning Kang, Ning Cao
Laboratory for High Performance Scientific Computing and Computer Simulation, Department of Computer Science, University of Kentucky,
Lexington, KY 40506-0046, USA
April 11, 2005
Abstract
Diffusion tensor magnetic resonance imaging (DT-MRI) is the first noninvasive in vivo imaging modality with the potential to generate fiber tract trajectories in soft fibrous tissues, such as the brain white matter. Several newly developed fiber reconstruction and tractography techniques based on DT-MRI are reviewed in this article, including streamline fiber tracking techniques, diffusion tensor deflection strategy, probabilistic Monte-Carlo method, fast marching tractography based on level set principles, and diffusion simulation-based tractography. We also discuss some potential applications of DT-MRI fiber tractography and list a few available tractography and visualization software packages.
Key words: Diffusion tensor magnetic resonance imaging, fiber tractography, streamline tracking, anisotropic diffusion simulation
1.Introduction to DT-MRI
The complexity of the human brain presents tremendous challenges in in vivo understanding its inter-structure and functionality. The nature of the human brain imposes substantial difficulty in noninvasive studies in which magnetic resonance imaging (MRI) plays an important role. Diffusion tensor magnetic resonance imaging, or DT-MRI [5,7,37], is an extension of the conventional MRI with the added capability of tracking and measuring the random motion of water molecules in all three dimensions, usually referred to as self-diffusion or “Brownian motion”. Since water diffusion is influenced by the microstructure, architecture, and physical properties of tissues, DT-MRI can render the information about how water diffuses in biological tissues containing a large number of fibers, like muscles or brain white matter, into intricate three
? Technical Report No. 437-05, Department of Computer Science, University of Kentucky, Lexington, KY, 2005. The research work of this author was supported in part by the U.S. National Science Foundation under grants
CCR-0092532 and ACR-0202934, and in part by the U.S. Department of Energy Office of Science under grant
DE-FG02-02ER45961. E-mail: jzhang@https://www.doczj.com/doc/a48678271.html,, URL: https://www.doczj.com/doc/a48678271.html,/~jzhang.
dimensional (3D) representations of tissue architecture. Thus, it can be exploited to visualize and extract information about the brain white matter and nerve fibers by probing and reconstructing the fiber pathways, which has raised promises for achieving a better comprehension of the fiber tract anatomy of the human brain [10]. In fact, one of the aims of using DT-MRI is the in vivo reconstruction of neural connectivity and the determination of fiber networks in the brain. In combination with functional MRI (fMRI), it might potentially bring tremendous improvements in our understanding of the crucial issue of anatomical connectivity and functional coupling between different regions of the brain [14,21,33,38]. As a result, the brain tissue diffusion properties and the neuro-anatomical knowledge on connectivity interpreted from the DT-MRI information have been playing an indispensable role in neurosurgery planning [15,71] and in tackling certain brain diseases and disorders, such as Alzheimer's disease [58,63], stroke [26], multiple sclerosis [17,66], schizophrenia [3,18], etc.
It is known that water diffusion is anisotropic in brain white matter. The significant anisotropy presenting in white matter reveals microscopic properties of the anatomy of the nerve fibers by the fact that water tends to diffuse predominantly along the long axis of the fibers, because the longitudinally oriented structures, the densely packed axons and the inherent axonal membranes, which are widely assumed to be the main barrier for water diffusion, hinder diffusion perpendicular to the fibers [10]. DT-MRI is sensitive to this anisotropy and is able to characterize it by noninvasively quantifying and assessing the self-diffusion of water in vivo . The information concerning the local orientation of fibers, extracted from the water anisotropic diffusion in white matter, forms the basis of utilizing DT-MRI to reconstruct fiber pathways and to build connectivity map in vivo . The water diffusion behavior elucidated by DT-MRI reflects the directional organization of the underlying white matter microstructure. DT-MRI characterizes the diffusion behavior on a voxel by voxel basis and for each voxel, the diffusion tensor yields the diffusion coefficient corresponding to any direction in space [5]. The direction of the greatest diffusion can be determined by evaluating the diffusion tensor in each voxel, which corresponds to the dominant axis of the white matter fiber bundles traversing the voxel.
The diffusion tensor at each voxel is a three-by-three symmetric (semi-positive definite) matrix [5],
xx xy xz yx
yy yz zx zy zz D D D D D D D D D D ????=??????
, which geometrically can be visualized as a 3D ellipsoid [74]. The three eigenvalues 0321≥≥≥λλλ and the associated eigenvectors 1e ,2e and 3e of D fully define the ellipsoid. The three eigenvectors are the three orthogonal axes of the ellipsoid, whose lengths are the magnitudes of the three eigenvalues. The eigenvalues and eigenvectors of D provide rich information about local orientation of the tissue structures.
To provide an intuition for the tensor data, derived from the measured diffusion-weighted and diffusion-unweighted images, an axial slice of a diffusion tensor volume from the human brain is depicted in Fig. 1. It shows a matrix of images, where each image is a color-coded representation of the corresponding component of the tensor matrix D .
Figure 1: An axial slice of a diffusion tensor data volume, which shows the individual diffusion tensor components, corresponding to the diffusion tensor matrix D .
Some of the most frequently used diffusion anisotropy parameters in DT-MRI studies are defined based on the eigenvalues of D . For example, the fractional anisotropy (FA ) value is defined as [7]
23
22212
32221)()()(23λλλλλλ++?+?+?=D D D FA , where 12311Trace()()33
D D λλλ==++. The FA value is 0 for diffusion in purely isotropic medium and 1 for diffusion in strongly anisotropic medium. Other parameters characterizing diffusion anisotropy, such as the mean diffusivity, lattice index, and intervoxel coherence, are also computed and compared in a few studies [55,56,58,77,78]. For excellent review articles on general aspects of diffusion tensor imaging, we refer to [4,38,39]. For more specific application-oriented reviews on diffusion tensor imaging, interested authors should consult [15,34,46,64].
The information in the DT-MRI data can be exploited to reconstruct the fiber pathways of the brain. A number of fiber tracking algorithms, or tractography, have been developed since the advent of DT-MRI [6,11,14,20,29,44,54,52]. In the following sections we will examine a few different fiber tracking techniques, explain their advantages, limitations, and potential applications, followed by a list of some available fiber tractography and visualization software packages. This survey focuses on accounting for the algorithmic difference of the various fiber tracking techniques, with an emphasis on diffusion simulation-based tractography. For recent reviews focusing on basic principles and assumptions and reliability analysis of fiber tractography, with an emphasis on streamline-based tractography, we refer to [42,45].
2. Streamline Fiber Tracking
The conventional white matter tractography reconstructs the pathways of white matter tracts by starting from a seed voxel and tracing down the trajectory in a voxel-by-voxel manner, using an estimate of the local fiber orientation determined by the principal eigenvector in each voxel. At
each voxel, the principal eigenvector, which is the one corresponding to the largest eigenvalue generated by the eigen decomposition of the diffusion tensor, is aligned with the mean fiber direction in that voxel. Hence, trajectories can be produced by integrating the principal eigenvector field, which are expected to coincide with white matter fiber bundles. Efforts have been made to trace the neural fiber pathways in white matter tracts by this method and its variants
[6,14,44,62,76,79] and very impressive results on major fiber structures have been demonstrated. The approaches to fiber reconstruction based on this idea are sometimes referred to as streamline tracking techniques [36,54], stemming from their similarity to computing flow streamlines from the velocity fields in fluid dynamics.
The most popular streamline tracking algorithm, proposed by Basser et al. [6], uses the direction of the eigenvector 1e associated with the largest eigenvalues 1λ as the direction of the local fiber orientation. Given an initial seed tracking point )0(s , we can solve a 3D path equation [6]
)()(t r dt
t ds =, (1) where )(t s is the fiber curve path position at t and )(t r is the local tangent direction of the path )(t s . We can set )()(1t e t r ε= with )(1t e being the eigenvector 1e at t and ε being a small positive number.
Eq. (1) can be solved numerically by either the Euler's method or some high order Runge-Kutta method. The integrated path is connected as the path of one fiber tract. A fiber bundle can be reconstructed by slightly perturbing the initial seed point and by generating multiple tracts following the same orientation and from the same region of interest (ROI).
In practice, care must be exercised to ensure that the DT-MRI data are properly collected and pre-processed. Since the resolution of most MRI data is usually not as good as the tracking algorithms prefer to have, mathematical techniques can be used to pre-process the DT-MRI data. Pajevic et al. [50] proposed to generate a continuous DT-MRI data field using interpolation, which allows integration step size to be smaller than the voxel size. Since the directions of both 1e and 1e ? are valid, tracks may be followed in both directions at the seed point (and along the forward path direction at the subsequent points) until certain termination conditions are met. Those termination conditions are set based on our understanding of the properties of the fiber tracts.
Figure 2: Two fiber tracts traced using a streamline-based tracking method in a healthy human brain (left panel) and their projection on an MR image (right panel).
The streamline fiber tracking algorithms, based on the ideas proposed by Basser et al. [6], have been implemented by a few groups. Fig. 2 shows two traced fiber tracts and their projection on a DT-MR image, produced in our lab using the streamline tractography.
The streamline-based tracking technique is the one most commonly used in white matter tractography studies and it appears to give excellent results in many instances if the principal eigenvector field is smooth and the fibers are strongly oriented along a certain direction. It is easy to implement and runs fast to trace individual fiber tracts. However, this technique suffers from a few major limitations. As noticed and summarized in [36,57], the vector field is error prone in the sense that the noise of DT-MRI data will influence the direction of the principal eigenvector, yielding an accumulation of orientational errors and thus an erroneous fork of the trajectory reconstruction process. Another major restriction is that it may also be affected by partial volume effects [75], leading to an unstable tracking of fibers through the primary eigenvector field. Since the current resolution of DT-MRI is 1-4mm while the diameter of the nerve fibers is in the
μ, in regions of fiber crossing, branching, or merging, the measured diffusion magnitude of m
tensor data is difficult to interpret, which represents a complicated averaging of multiple compartments within a voxel, making the principal eigenvector field not adequate to describe such entangled structures.
3.Diffusion Tensor Deflection
A variety of methods have been proposed aiming to overcome the difficulties to which the streamline tracking technique is exposed, with more information incorporated from the diffusion tensor data. In recent years, stochastic labeling method [65], bootstrap analysis [22], multi-ROI approach [23], and others have been proposed to enhance and assess DT-MRI fiber tracking. One improved approach to determining tract direction was proposed by Lazar et al. [36,73] using the
v direction:
entire diffusion tensor to deflect the incoming vector
in
in out v D v ?= (2)
The incoming vector represents the propagation direction from the previous integration step. The tensor operator deflects the incoming vector towards the major eigenvector direction, but limits the curvature of the deflection, which should result in smoother tract reconstructions. The algorithm is called TEND for tensor deflection.
In the TEND algorithm, the incoming vector, in v , can be conveniently described by using a linear combination of the three diffusion tensor eigenvectors:
332211e e e v in ααα++= (3)
where 1α, 2α and 3α are the relative vector weightings. By multiplying both sides of Eq. (3) with D , substituting i i e λ for 1e D ? (the characteristic matrix equation), and normalizing to the largest eigenvalue, Eq. (2) can be rewritten as:
)(331
32212111e e e v out αλλαλλαλ++= where out v is an unscaled vector, 1e , 2e , and 3e are unit vectors. Several specific cases can be treated separately. If the incoming vector coincides with one of the tensor eigenvectors, all α's will be zero except the one corresponding to the eigenvector direction and in v will not be deviated by the tensor (i.e., in out v v =). The degree of tensor deflection can be a function of the diffusion tensor anisotropy and the angle between in v and 1e .
For a highly prolated diffusion tensor (e.g.,
321,λλλ>>), and 1α not much less than 2α or 3α, the output vector of the TEND algorithm will be deflected towards the direction of the major eigenvector, 111111e e v out αλαλ≈?+=. However, for 21αα>> or 3α, the tract direction will not be highly deflected.
For an oblate shaped diffusion tensor (e.g., 321λλλ>>≈) and 3α small, the deflected vector is )(22111e e v out ααλ+=. In this case, both the major and medium eigenvectors affect the deflection. The incoming vector component perpendicular to the tensor plane (defined by 3e ) will be adjusted so that the incoming vector will be deflected toward the ellipsoid plane. This behavior may be desirable for voxels with planar diffusion where eigenvector degeneracy can occur and the direction of the fastest diffusivity is not well defined. The planar tensor shape may
appear in regions with crossing, fanning, or merging fibers [1,75]. However, where in v is roughly in the 3e direction, the incoming vector will be relatively undeflected.
For an isotropic tensor (e.g., 321λλλ≈≈), the incoming vector will not be significantly deviated, in out v e e e v ≡++≈)(3322111αααλ.
The tracking results reported in [36] show that the diffusion tensor deflection method is less sensitive to both image noise and lower tensor anisotropy. In general, the TEND algorithm will be able to trace longer fiber trajectories than the standard streamline-based tracking techniques do. However, tensor deflection method will underestimate the trajectory curvature for curved pathways [36]. This error is cumulative, but can be reduced by using smaller step sizes. Consequently, there is a tradeoff between lower error with the tensor deflection algorithm in straighter sections, but higher systematic errors in curved sections. One approach to balancing this tradeoff is to use a combined streamline tracking and tensor deflection algorithm. The tensorline algorithm, proposed by Weinstein et al. [73], dynamically adjusts a hybrid of the streamline and TEND algorithms in the form of
])1)[(1(1in in out v gD v g f fe v ?+??+=,
in which f and g are some user-defined weighting parameters that are between 0 and 1. Due to the flexibility in adjusting the parameters f and g , it is possible for the tensorline algorithm to map trajectories with different properties. The tensorline algorithm may be considered as a family of tractography algorithms that can be “tuned” to accommodate specific fiber tract behaviors [36]. The drawback, of course, is the need to handle these user-defined parameters, which makes the algorithm less automatic.
4. Probabilistic Monte-Carlo Method Based Tracking Parker et al. [51,52] proposed a probabilistic Monte-Carlo method to decide the mapping of brain connections for quantifying streamline-based diffusion fiber tracking methods. In this approach, the traditional streamline method is used to exploit the uncertainty in orientation of the principal direction of diffusion defined for each image voxel. By repeatedly running the streamline process the Monte-Carlo methods exploit the inherent uncertainty in the DT-MRI data to generate maps of connection probability. Uncertainty is defined by interpreting the shape of the diffusion orientation profile provided by the diffusion tensor in terms of the underlying microstructure [52].
Recall the introduction to streamline tracking algorithm in the earlier section, )(t r in
Eq. (1) is the local tangent direction of the path )(t s . It can be set to )(1t e ε with )(1t e being the
eigenvector 1e at t and ε being a small positive number. To define an anatomically reasonable interpretation of the diffusion tensor for probabilistic connectivity analyses, the probabilistic method exploits the uncertainty of the 1e orientation (principal eigenvector orientation) in the connectivity analysis. A general framework was proposed in [52] that allows the use of any probability distribution functions (PDFs) describing the fiber orientation distribution.
Two candidate PDFs are defined as the th 0 order
and the st 1 order to reflect their complexity. In the th 0 order case, uncertainty in the 1e is defined by tensor anisotropy, providing an isotropic normal distribution of orientation centered on the original estimate of 1e . In the st 1 order case, the orientations of the minor eigenvectors 2e and 3e , and their respective eigenvalues provide an uncertainty that is dependent upon the skewness of the tensor, thus providing a more accurate distribution of the orientation in the case of oblate tensor ellipsoids. Streamline propagation is then repeated in a Monte-Carlo process using the PDFs, thus establishing the confidence of connection to the starting point to a distributed area, from a fundamentally linear process.
Besides the choices of PDFs in [52], a number of alternative PDFs can also be chosen for the determination of a probabilistic index of connectivity. The experimental results in [51,52] show that this improved tracking method could somehow reveal more inherent voxel fiber tract topology and hopefully can improve the tracking result under the situation of fiber crossing and diverging.
5. Level Set-Based Fast Marching Tractography
A new algorithmic development trend in DT-MRI tractography is to make use of the front propagation algorithms [53,67]. Front propagation-based tractography methods have two advantages over the streamline-based methods. First, front propagation algorithms are capable of estimating branching and crossing tract structures in a more straightforward and computationally efficient manner than modified streamline techniques. Second, unlike streamline tracking, front propagation algorithms may estimate the likelihood of white matter connectivity between any two voxels.
Recently, level set theory, which models the evolution of a front over time, is utilized to find the fiber paths connecting different brain regions. Parker et al. [53, 54] proposed a fast marching tractography method (FMT). FMT is based on the level set theory and a fast marching algorithm
[60] in which a front interface propagates in directions normal to itself with a non-negative speed function. A time of arrival map is created for every point in a gridded space by recording the time at which the front passes each grid point. The relationship between the time, T , and the speed, F , during the front propagation, is given by the Eikonal equation [61] 1=?F T . The FMT algorithm begins at the seed point with the consideration of the nearest neighbors around the seed
point. In the 3D case, the 6 nearest neighbors are defined as being in the Up, Down, North, South, East and West directions. A potential time of front arrival is assigned to each of the neighboring candidate points (voxels) using the following equation
)()()(r F r r r T r T ′
?+′=,
where r is a position vector specifying the grid location of a candidate point. The position vector r ′ describes the grid location of the closest point to r along the normal direction to the front, )(?r n
?, that has already been passed by the front. The normal to the front may be calculated with 26 neighbor connectivity in 3D using Eqs. (4) - (6).
)
()()(?r f r f r n
??= (4) 111111()(,,)x i x
y z i j k f r C r i r j r k =?=?=??≈???∑∑
∑ (5) ,,,,:(,,)()(,,)0:(,,)()x y z i j k x y z x y z i j k r i r j r k S p C r i r j r k r i r j r k S p ???∈????=??????
(6) In Eq. (4), )(r f ? is a concentration gradient of past band points that have already been passed by the front, f , as seen from a local candidate band position r . The concentration gradient )(r f ? is a vector composed of three Cartesian components: )(r f x ?,)(r f y ?, and )(r f z ?. Eq. (6) discretely approximates )(r f ? using the concentration differences in the set of past band positions, )(p S , that have already passed by the front.
The seed point is considered to have already been passed by the front before the first iteration of the front propagation tractography algorithm. The speed function )(r F determines the speed of the propagation from r ′ to a candidate position r . The candidate point with the fastest time of arrival, )(r T , is selected as the next point of front arrival. The newest point is assigned a fixed time of arrival value )(r T , and a fixed speed of arrival value )(r F . The newest point of arrival at r is removed from the candidate band and is never considered again during the propagation. The propagation continues as new times of arrival are calculated from points within the 6 point neighborhood of r that have yet to be reached by the front. Times of arrival for unselected candidate points are modified if the time of arrival for the current iteration is less than the time of the previous iterations. The propagation continues until every point on the grid is assigned a time of arrival value.
The FMT algorithm uses the orientation and shape of the diffusion tensor D to define the speed function F . The premise of FMT is that the front propagation speed is controlled by using D such that the front propagates fastest within white matter tracts. An example of an FMT speed function developed by Parker et al. [54] is
1??()min((),()())F r F r v
r n r ′′=?, where the term )(r F ′ refers to the overall minimum historical speed of propagation to the point of the current iteration. The quantity )(?)(?1r n r v
?′ is the absolute value of the scalar product between the principal eigenvector at the latest point accepted into the past band and the unit
normal vector to the front from a candidate point at position r . The term )(?)(?1r n r v
?′ causes the front to propagate fastest in the direction of the principal eigenvector field. The historical minimum speed is used to establish connectivity between the starting point and all successive points of the propagation. Thus, the FMT algorithm does use the principal eigenvector information to define the speed and direction of the propagation.
In FMT, the time of arrival map from the seed point to other points in the DT-MRI volume is used to construct minimum cost paths between the seed point and other points on the grid. Because diffusion tensor orientation is embedded in the time of arrival map, maximum speed paths from an end point to the seed point in a DT-MRI volume are estimates of white matter tract connectivity between the two points. Diverging tract structures may be inferred using FMT by constructing maximum speed tracts to the seed point using each point on the grid as a potential tract end point. In the process of traversing maximum speed tracts back to the seed point, some tracts will converge or cross, indicating the presence of crossing and diverging structure.
The likelihood metric capability of FMT allows for confidence estimates of white matter connectivity between any two points in a volume of DT-MRI data. An example of an FMT likelihood function can be )))((min()(s F L γγ= where )(s γ is a minimum cost tract between an end point and the seed point parameterized by s . Because the front propagates fastest inside regions of homogeneous diffusion tensor orientation, higher speeds over the extent of a tract indicate that a putative tract is more likely to be a legitimate anatomical structure than an artifact.
Level set theory has also been used in [13,25] to implement a 3D geometrical flow to track the fiber bundles. The major disadvantage of this class of tractography is the difficulty to determine the true fiber trajectories from the possibly multiple constructed trajectories and the need to use the principal eigenvector direction to advance the fronts.
6. Diffusion Simulation-Based Fiber Tractography
As the measured quantity in DT-MRI is for water diffusion, an intuitive way to gain insights from the diffusion tensor data is to carry out a direct simulation of water diffusion, which is anisotropic and governed by the diffusion equation, over the brain. The idea of studying brain connectivity by
simulating the anisotropic diffusion has been preliminarily explored in [9,20,48], and [8] further extends the diffusion equation to a diffusion-convection equation by adding a convection term. However, in the above mentioned work based on this insight, there is no (or at best limited) attempt at determining the route of fiber pathways and connectivity between anatomical or functional regions in the brain, although the concentration or flow field over the brain is calculated and obtained. Kang et al. [29,31 ] proposed a new technique along this line, which relies on successive anisotropic diffusion simulations over the whole brain, and is utilized to construct 3D diffusion fronts.
6.1. Anisotropic diffusion in brain
In diffusion simulation-based fiber tractography, the whole 3D brain volume is treated as an anisotropic physical system, over which a virtual water diffusion process is simulated. A thorough discussion on the diffusion process and related transport mechanisms in brain can be found in
[35,47]. Anisotropic systems are those that exhibit a preferential spreading direction while isotropic systems are those that have no preference. According to Fick's macroscopic law of diffusion [16], which illustrates the physical behavior of the diffusion process, the flux, J , is related to the concentration, ρ, by the equation
J D ρ=?? (7)
This equation states that the concentration gradient ρ? causes the flux J which aims to compensate for this gradient. Substituting Eq. (7) into the continuity equation
0J t
ρ?+??=? which expresses the conservation law of mass. The equation governing the general anisotropic diffusion process can be written as
()D t
ρρ?=????, (8) where t is the independent time variable. Since the brain tissue is not only anisotropic but also heterogeneous, the diffusion tensor D in such a case is space-dependent and thus the spatial derivatives of the tensor components xx D , xy D , xz D , etc., must be utilized.
6.2. Constructing successive diffusion fronts
The fundamental idea behind the diffusion simulation-based fiber tracking algorithm (DFTA) is to perform successive diffusion simulations over the brain stemming from a series of selected starting voxels where a seed is placed. With certain thresholds satisfied, the starting voxels are dynamically picked up from the nodes on the 3D diffusion fronts produced by previous rounds of the diffusion simulation. Thus the first step to reconstruct fiber pathways starts with choosing a root node s . The virtual concentration seed of water spreads from the root node through neighboring nodes, within a limited amount of time, forming a diffusion front which is the surface
of a diffusion volume containing nodes with nonzero 1 concentration values. The expansion of the diffusion volume originated from the root voxel is achieved by integrating the anisotropic diffusion equation (8) through the whole brain over a certain amount of time, subject to the following initial condition,
01at the root voxel |0otherwise t ρ=?=??
(9) For the boundary condition of Eq. (8), we assume that the physical system containing the brain is insulated, i.e.,
()0D n ρ??=K (10)
which corresponds to the Neumann condition. Here n K is the direction normal to the brain
boundary. This condition implies that the normal part of the gradient of the concentration on the boundary is zero, in other words, nothing escapes out of the computational domain. An unsteady state anisotropic diffusion solver framework has been developed and implemented which is adapted to the cerebral circumstance and runs in both sequential and parallel computing environments [27,28].
In order to construct the diffusion front propagated from a root voxel, DFTA only integrates over a fine-tuned amount of time the equation (8) through the whole brain using the developed time-dependent diffusion solver framework [27,28]. The length of the integration time is determined in such a way that without the loss of revelations of the anatomical properties of the underlying nerve fibers, the number of voxels swept by the diffusion process should be as small as possible. This value is chosen according to several factors including the magnitude of the tensor data, the spatial resolution of the computational grid, and the initial concentration at the root voxel. Once the time integration procedure is completed, a discrete approximation to the diffusion front can be calculated in terms of whether the concentration value is zero in a voxel. Thus all nodes in the computational grid can be partitioned into two groups, one with zero concentration value and the other with nonzero value. Let )(r V denote the set of voxels that have nonzero concentration value, where r is the position of the root node. Since only a seed is diffused over the root node, the set )(r V is comprised of the nodes in the diffusion-swept volume. For each member of )(r V , we consider its surrounding 6 closest neighboring nodes in a 333×× kernel. Let i , j , k index the relative coordinates of the 6 nearest neighbors to r with }1,0,1{,,?∈k j i . If )(r F is the set of voxels that form the diffusion front of r , then for any node )(),,(r V p p p p z y x ∈≡, we define 1 Zero concentration values mean values close to zero. A threshold can be applied to determine zero and nonzero values.
,(,,)()or ()if and only if ,(,,)()or ,(,,)()x y z x y z x y z i p i p p V r p F r j p p j p V r k p p p k V r ?????∈?????????
(11)
Fig. 3 is an illustration of the discrete approximation to a diffusion front seed-diffused from a root voxel r .
Figure 3: An illustration of the discrete approximation to a diffusion front, which borders the diffusion-swept volume )(r V containing the node r (shown in red) as the root where a seed is diffused and the grey nodes with nonzero concentration value. Those originally grey nodes that satisfy condition (11) are colored black, which are used to approximate the front. The white nodes are the ones with zero concentration value.
A queue Q , a first-in first-out data structure, is set up to store and handle the dynamically produced front nodes. Q is initialized to contain just the starting node s ; thereafter, Q always contains the set of diffusion front nodes. We also define a set C to bear a number of criteria, which controls the connection of fiber pathways [29,31]. Once )(r F is computed for the root node r , the algorithm further applies the criteria in C to the nodes of )(r F and pick up those that meet the corresponding thresholds. Define )(r I to be the set of nodes selected from )(r F that satisfy the criteria in C . )(r I is then appended to the tail of the queue Q . The current head node of Q is removed off the queue and is considered to be a new root r ′ where a seed is diffused, and its diffusion front )(r F ′ is calculated in the same way as that of )(r F by solving and integrating the equation (8) over a certain amount of time through the whole brain.
The algorithm continues in this way by repeatedly taking off the head node of Q and
processing it as a new root to diffuse a seed over it, until the queue becomes empty.
6.3. Fiber tracking algorithm
During the construction of successive 3D diffusion fronts, the connection between a diffusion root node r and the nodes in its diffusion front )(r F may determine the possible routes of the fiber tract going through both r and )(r F . As mentioned earlier, the connectivity is regulated by the criteria set C , which determines the members of )(r I . The fiber pathways passing the node r and its diffusion front can be obtained by just connecting r and the nodes in )(r I . Obviously, fiber branching is allowed in this way since the size of the set )(r I can be greater than 1 such that the tracking route is allowed to split at r .
Aimed at recovering the fiber pathways after the construction of diffusion fronts, each voxel p in the voxel grid owns a memory of its predecessor voxel, )(p π, where ))((p I p π∈. In the current implementation of DFTA, )(p π is the sole predecessor of p if there is one. Thus, back propagation from the voxels on diffusion fronts by following continuously the corresponding predecessor voxels may lead to paths that merge to the starting voxel s . This merging corresponds to the procedure that can be viewed in the reverse direction as fiber tracts branch outwards from s .
6.4. Results
Kang et al. [29,30,31] demonstrated DFTA by exploring its ability to reproduce the well-known course of white matter structures. Fig. 4 shows the reconstructed fiber tracts stemming from two starting voxels located in the anterior and posterior portions of the corpus callosum, i.e., the genu and splenium, respectively. The tracking from the two starting voxels results in fibers traversing the genu and splenium and running toward the frontal and occipital poles of the two cerebral hemispheres, respectively. The trajectories are traced in parallel to the curvature of the genu and splenium, exhibiting an arch shape, which is consistent with the known anatomy. It is evident from Fig. 44 that DFTA allows tract branching since the generated fiber trajectories start from a single point and then end up with multiple points, forming a number of branching pathways from one single starting voxel. The corpus callosum commissural fibers reconstructed here are similar to those obtained in [6,14,36,76].
(a) (b) (c)
Figure 4: Fiber pathways computed from two starting voxels located in the genu and splenium of the corpus callosum (white arrow), as colored by blue and red, respectively. Fibers are incorporated into grey-scale FA maps for anatomical reference, where bright grey-scale regions reflect high diffusion anisotropy. (a) Fibers are viewed from the inferior direction, overlaid on an axial FA map. (b) and (c) Fibers are viewed from above in the anterior-left direction, shown together with a midline sagittal FA map as well as an axial one.
6.5. Advantages and limitations
Regarding the use of diffusion tensor data for unveiling the organizational patterns of white matter structures, the diffusion simulation-based tractography has at least three potential advantages. First, it has the capability to elucidate branching pathways naturally like the fast-marching approach, which is an advantage compared to the streamline type methods. It is impossible for the existing streamline-based tracking methods to generate diverging fiber trajectories when the tracing starts from a single point [54]. When multiple fiber bundles are generated by using these schemes with the same region of interest, the density of trajectories will be reduced and the subsequent connected regions are undersampled. There is no such undersampling problem for the branching process in the DFTA since the algorithm follows all branching pathways in the same manner, computing diffusion fronts consecutively. The fast marching tractography developed in [53,54] also presents the desired feature to allow tract branching, in which the fast marching technique is adopted in the context of the diffusion tensor field to propagate fronts that evolve at a rate controlled by the information contained in the principal eigenvector field. Compared to the fast marching approach, DFTA relies on direct simulation of the diffusion process to construct successive diffusion fronts, and more importantly, the diffusion simulation is more problem-oriented than the level set-based methods which are more general purpose oriented.
The second feature of the diffusion simulation-based method is that it is likely to enhance the robustness and reliability in fiber reconstruction. DFTA has a lower chance to get affected by the noise since more information from the diffusion simulation is involved in determining the fiber pathways, helping partially restrain the noise problem, although it does use the principal eigenvector to regulate the path curvature.
The third feature of DFTA is its generality in its formulation. The governing equation (8) can accommodate generalized diffusion formulation for more advanced imaging modalities, such as the high angular diffusion weighted imaging (HARDI) [68] and the generalized diffusion tensors [41]. Because DFTA simulates the underlying physical diffusion process, which is what the
diffusion-weighted imaging measures, it is virtually independent of the particular imaging modality used to acquire the images.
Further investigation and demonstrations are needed to determine if it is possible for DFTA to get through regions with low anisotropy to find fiber pathways, and thus to avoid dropouts (premature interrupts) of fiber tracking, and to partly overcome the partial volume effects. Although this class of techniques has this potential, it is difficult to validate the accuracy of the crossing fibers and rule out the possibility of false fiber courses. Another limitation of this method is that the spatial resolution of the measured diffusion tensor data likely affects its ability to track small fiber tracts and the fiber bundles with high curvatures, such as those in the reconstruction of the optic radiation.
7.Applications
DT-MRI fiber tractography provides unique quantitative and qualitative information to aid in visualizing and in in vivo studying fiber tract architecture inside the human brain. This field of research is still in its infancy but is developing very fast. There exist many possible and important applications for the fiber tractography, and more will appear in the future as DT-MRI and fiber tracking become standard clinical procedures. A few potentially important applications are:
z Brain surgery, which may cause damage to important fiber bundles. Knowledge of their extension can be used to avoid damage to the major fiber tracts and to minimize
the functional damage to the patients [71].
z White matter could be visualized using fiber tractography for a better understanding of the brain anatomy. The tractography can be used for segmentation of white matter
tissues [24,80]. Successive DT-MRI measurements at different ages can be used to
monitor the development of human brains [40,72] and the possible development of
certain brain disorders.
z Connectivity between different parts of the brain could be inferred, which is useful for functional and morphological research on the brain [43]. This is a very promising
potential research topic for the combination of DT-MRI and fMRI [21].
z Clinical applications to study the degeneration of white matter tissues and the disruption of fiber pathways due to different brain disorders [32,59,64].
To date, most clinical applications of DT-MRI are limited to the comparison of FA values or other anisotropy measures in some regions of interests of the brain [12,58,77,78]. It would be more interesting and more informative to visualize the disruption of the fiber pathways due to the disease progression, which can be done by using fiber tractography. For instance, Fig. 5 shows the reconstructed fiber tracts of the genu of the corpus callosum of a healthy person and an age-matched early Alzheimer's disease patient. It can be seen clearly that the full genu tract of the healthy person can be reconstructed, but the genu tract of the early Alzheimer's disease patient is
obviously disrupted and disconnected. By monitoring the integrity of certain fiber tracts, it may be possible to monitor the progress of this disease and to provide early warnings for possible interventions.
Figure 5: Reconstructed tracts of the genu of the corpus callosum of a healthy person (left) and an age-matched early Alzheimer's disease patient (right).
Also, DT-MRI fiber tractography could be applied to other fibrous tissues besides the human brain, such as the heart, whose fiber directional pattern and organization is critical in following its normal development and diagnosing diseases.
8.Fiber Tractography and Visualization Software
Packages
The following are some of the available fiber tractography and visualization software packages:
?3DMRI
3DMRI is developed at the Laboratory of Brain Anatomical Imaging, Johns Hopkins Medical Institute, Johns Hopkins University. It is a C++ application built on top of the Visualization Toolkit (VTK) to visualize fiber tracts. Fiber tracts are visualized inside an isosurface of human brain generated from brain MRI scan. Users can change the transparency of the isosurface and can assign colors and thickness to fiber tracks. Also, users can choose to project the fibers to the brain cortex to estimate possible contact area. This software is available at:
https://www.doczj.com/doc/a48678271.html,/DTIuser/DTIuser.asp
?3D Slicer
3D Slicer is a software tool for guiding biopsies and craniotomies in the operating room,
offering diagnostic visualization and surgical planning in the clinic, and facilitating research into brain shift and volumetric studies in the lab. It provides capabilities for automatic registration (aligning data sets), semi-automatic segmentation (extracting structures such as vessels and tumors from the data), generation of 3D surface models (for viewing the segmented structures), 3D visualization, and quantitative analysis (measuring distances, angles, surface areas, and volumes) of various medical scans. 3D slicer is developed by collaboration between the MIT Artificial Intelligence Lab and the Surgical Planning Lab at Brigham & Women's Hospital, an affiliate of Harvard Medical School. Windows, Solaris and Linux versions of the 3D Slicer are all provided. The download information is available at: https://www.doczj.com/doc/a48678271.html,
?DTIChecker
DTIChecker is a tool, written by P. Fillard2, to generate fiber tracts from diffusion tensor images. The latest stable release version is 1.2, which supports Windows, Linux and Solaris.
The original tracking algorithm and first C++ code were developed by Xu et al. [76]. The download information is available at:
https://www.doczj.com/doc/a48678271.html,/dev/download/fibertracking/index.htm
?DdDTI
DoDTI is a software toolkit developed for the analysis and quantification of diffusion tensor imaging, which includes techniques for visualization and fiber tractography on DT-MRI data.
It is a platform independent tool implemented in Matlab. The tracking algorithm embedded is based on the streamline tracking technique using a 4th order Runge-Kutta integration solver.
The current version of DoDTI is v.1.0. More information about the toolkit, including its download information can be accessed at:
http://neuroimage.yonsei.ac.kr/dodti.
?dTV
The diffusion tensor visualizer (dTV) is an extension program developed for the volume data view program VOLUME-ONE.3dTV performs analysis of DT-MRI data and transfers results to VOLUME-ONE for display. It is developed by Y. Masutani.4The download link is:
http://www.ut-radiology.umin.jp/people/masutani/dTV/dTV_download-e.htm
9.Beyond DT-MRI
Tissues with regularly ordered microstructure, such as skeletal muscle, spine, tongue, heart, cerebral white matter, exhibits anisotropic water diffusion due to the alignment of the diffusion compartments in the tissue. The direction of the preferred diffusion and the implied direction of the tissue orientation can be obtained in vivo by DT-MRI. DT-MRI measures the apparent water
2pierre.fillard@wanadoo.fr, Department of Computer Science, University of North Carolina, Chapel Hill, USA
3VOLUME-ONE is a free software for viewing volumetric image data such as X-ray CT and MRI. More information can be found at: https://www.doczj.com/doc/a48678271.html,
4Department of Radiology, University of Tokyo Hospital, http://www.ut-radiology.umin.jp/people/masutani/
self-diffusion tensor under the assumption of Gaussian diffusion. This assumption makes the tensor model incapable of resolving multiple fiber orientations within an individual voxel in the streamline-based fiber tracking algorithms [2]. However, DFTA can alleviate the fiber crossing problem, as we demonstrated in the previous section.
At the millimeter-scale resolution typical of DT-MRI, the volume of cerebral white matter may contain several fibers running along the same directions, given the widespread divergence and convergence of fascicles. Such phenomenon is called intervoxel orientational heterogeneity (IVOH) [68]. This shortcoming of DT-MRI motivates researchers to consider alternative imaging techniques to resolve IVOH, such as fiber crossing. New diffusion-weighted imaging techniques, such as the high angular resolution diffusion imaging (HARDI) [19,69,70], q-space imaging (QSI) [68], or generalized diffusion tensor imaging (GDTI) [41], etc. These imaging techniques make use of a large number of diffusion-weighted images taking along different angular directions. They hold the promise of distinguishing more complex fiber structures than DT-MRI, with the drawback of much longer imaging acquisition time and lower signal-to-noise ratios. Moreover, fiber tractography based on these new imaging techniques are still under development.
An outstanding feature of the fiber reconstruction technique using diffusion simulations is that it can be seamlessly adapted to the platform established by the new imaging techniques. Studies have shown that the generalized diffusion tensor model is able to not only accommodate HARDI and GDTI methods but QSI as well, due to the relationships among DT-MRI, HARDI, GDTI, and QSI [41,49]. This makes it possible for the diffusion simulation based tractography to become independent of the imaging techniques used, while the fiber tracking will require a more sophisticated diffusion simulation, which is governed by a generalized diffusion equation associated with generalized diffusion tensors, according to a generalization of Fick's second law.
References
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我们都期待她拥有良好的阅读习惯和阅读能力，可是早期阅读最为有效的是亲子阅读。 学前教育中的家庭教育比幼儿园教育来的更为重要。亲子阅读是最为有效的，最终我们想通过这些让幼儿养成自主阅读的习惯，提高幼儿自主阅读的能力。如果。。。可是亲子阅读的那方往往不是阅读专家，所以除了亲子阅读和自主阅读以外就还有阅读教学。那么什么是阅读教学呢，阅读教学就是激发阅读兴趣，提升阅读能力。当然兴趣是主要的，如果能力不提升，在浓厚的兴趣大概也是没什么指望了，兴趣和能力是相辅相成的。 所以我们在学校教幼儿养成良好的阅读习惯的的同时也要教给家长如何帮助幼儿养成良好的阅读习惯。那么什么是好的阅读习惯呢？ 读懂文本内容孩子至少要理解这个故事讲的是什么吧？ 理解主题思想，每一本书都有一个他所要表达的主题的思想和精神的传递的。 学习书面语言相同的目标，相同的内容呈现不同的教学效果却不同。----《灰姑娘》 那是因为形式不同。----我是歌手和中国好声音的例子。 形式很重要，阅读教学要从形式上吸引孩子投入到活动中从而让孩子爱上阅读。怎样的形式才能讨孩子喜欢又能达到我们的教学目标呢。那么我们所设计的形式就是要为目标服务的，是要有效 幼儿教师就是研究课堂，研究形式的，我们是怎么将一个美好的绘本呈现给我们的孩子的。一个绘本里的美好的主题思想，美好的语言我们都知道，可是我们怎么把这些美好的东西呈现给孩子呢，这就是形式的问题了。幼儿教师其实不用研究太多的理论，我们只要考虑怎样的形式呈现绘本。讨人喜欢的形式很重要。只要是他喜欢的，哪怕你稍微出错也是没有关系，就是语言有点问题也没有关系，只要孩子喜欢。-《哈林和李敏镐--情非得已》 形式为目标服务才有效，否则就形同虚设了 读懂文本内容首先是读懂图象符号图像符号有多重要，而我们中国人是不太注重图象符号的，而我们孩子的书是一图像为主的通过图像来储存想象力。我们想象一个事件是先出现图像还是字？ 阅读不仅仅是识字，在我们城市里到处充实着汉字，我们不用担心孩子识字问题而是读图的能力。 图像很重要《找死的兔子》 反复的说，怎么了，你才接下来发生什么事？发生什么事？但是从头至尾都是一样的问题时，孩子会说你烦死了。一般让孩子对看图讲述不超过四副让孩子保持对事件的热情。绘本阅读不是看图讲述的过程。---《一根羽毛都不能动》比游泳，比飞翔 看图讲述 把看到的说出来，把事件讲清楚，，其实语言学习不一定是复述和跟念。面对一个事件把事情讲清楚，不重复，比游泳，比飞翔，这个时候还看图讲述吗？接下来就是看图听赏了，教师读幼儿听。一根羽毛，蜜蜂，乌鸦，风来了，看图听赏也是一种提高读图能力的方式，只是这个时候的读图孩子是被动的，因为要听着老师的语言追随画面，如果说你要让这个图像符号让幼儿获得。主动的获得并且得到巩固，那么就在一个小结的地方回想讨论，在一个情节点上回想讨论一下，刚才。。。怎么了？孩子这个时候泛出的图像就是主动的并且得到了一项巩固。看图听赏之后做游戏接下来是看图判断，比赛没完没了，比赛要不要结束。 生命比比赛更重要。最后两个好伙伴喝着狐狸的热汤，心满意足的喝着。 如果一个绘本故事从头到位都是绘本讲述孩子看得会吐 一个绘本故事关只有图象符号也是不够的 有一个这样的笑话，有一天老师问小朋友你们看到了什么，小朋友说两条鲨鱼在游泳， 救命有鲨鱼，哥我们就是鲨鱼好不好。 绘本阅读必不可少的是翻阅书本，翻书的经验就是一页一页的翻书，在绘本阅读中必不可少的就是亲近书本。 不用一堂课上全部给孩子，细水长流 理解主题思想 主题思想没有准与不准，只是有你的文化背景和文化底蕴的不同，只要是对孩子认识世界有利的，绘本阅读的主题不一定是教学目标。依据年龄特点，原有经验，学习方式《小薇向前冲》改目标是勇气，和遗传。 理解主题的形式 常见的形式是讨论。但是一味的讨论会让孩子厌倦 怎样的形式让幼儿理解主题更为深刻呢？ 那就是体验 让孩子动一动，玩一玩 这样的玩一玩动一动是为主题的理解推波助澜，看似是情节的插科打诨却是为了主题服务的。理解主题的游戏中，融入多元领域的游戏。方格子的游戏<跳房子> ---《爱跳舞的小雅》可以让幼儿感受芭蕾舞的旋律。 学习书面语言
蓝印花布的起源 手工蓝印花布是中国传统的民间工艺品，是我国传统染织艺术花园中的一支奇葩，它以质朴的色彩，古拙的纹样，素雅的风韵，予人以清新雅致的魅力。然而她却又是我国传统手工艺中拥有“正在消亡的活化石”的称号，她的神秘、古朴、端庄，引来无数国外友人的热爱，却没有引起我们中学生的关注，传统中的优秀文化，凝聚了一个民族世世代代的智慧和创造，成为一个民族得以生存和延续的精神力量。民族的发展和复兴，必然包含着优秀传统文化的继承。 蓝印花布发展于民间，体现了中国浓厚的乡土气息，迎合了当今现代社会人们返璞归真的追求，以它特有的兰白分明、清新明丽、古朴深厚的风格赢得了一定文化修养人的喜爱。蓝印花布是一种按中国民间传统工艺制作的手工印花布，俗称“石灰拷花布”、“拷花蓝布”，是我国传统的民间工艺精品。又称“蓝染或草木染色。蓝印花布蓝白二色，色彩深沉明快，图案朴实典雅，具有浓厚的乡土味。千百年来蓝印花布做成的服饰、床被、围裙、桌布等成为百姓的日常家用品。 蓝印花布制作历史渊远流长。早在秦汉，在织物上印上花纹染色的面料称作“缬”，据《二仪实录》载“缬，秦汉间始有”，《周礼?考工记》在记载当时染色工艺状况时也写道“青与白相间也”。在秦汉造纸术未发明，棉花种植尚未引进，因此人们只能在木板上二面阴刻成花纹，然后把麻、丝织物夹在二块花版之间进行草木染色。用这种方法生产的印花布称作“夹缬”，这也可以说是今日蓝印花布之雏形。随着蔡伦发明造纸术，七世纪棉花种植从印度传入，十三世纪松江人黄道婆从海南把棉纺技术传授给江南织农，因而民间蓝印花布花版制作技术有了长足进步，老百姓对棉质蓝印花布的使用也得到了普及。 蓝印花布的纹样图案都来自于民间，反映了百姓的喜闻乐见，寄托着她们对美满生活的向往和朴素的审美情趣，在题材和内容上，老百姓那种健康和质朴的心灵，在民间蓝印花布上得到了形式和内容的完美统一，而蓝印花布真实地反映了一种深厚的文化和艺术积淀。桐乡蓝印花布是世界蓝染艺术苑中绚丽的花朵。 说到中国蓝印花布，好多人就会提到南通、桐乡的蓝染，贵州苗寨的蜡染扎染，因为那里至今还保留着一部分民间蓝坊，一些人仍以靛染为业。但很少有人知道无锡的蓝印花布生产也有着悠久的历史和曾经的辉煌，只是到了近代，无锡迅速崛起的民族工商业和飞速的城市化进程，才使民间蓝印花布和其他许多民间手工艺一起失去了赖以生存发展的空间和条件，逐渐远离人们的视野而消逝了。
样品确认制作流程（初稿） 1、目的： 规范样品制作管理流程，确保样品制作的交期和品质能满足客户和公司开发设计的要求。 2、范围： 该程序适用于本公司新产品和老产品的打样制作。 3、权责： 1）样品部负责样品的制作、检验、采购标准（品质部协同）的制定； 2）品质部负责样品的检查，确保制作好的样品能满足客人的品质要求； 3）采购部负责样品材料的准备； 4）仓库负责样品材料的发放； 5）注塑部负责样品箱壳的制作； 6）财务部（或采购、注塑、生产核算相关成本提供给样品部，由样品部统筹反馈至财务）负责核实样品的成本（材料、人工） 4、各环节作业流程、工作要求和完成时间规定 1）样品部确定与记录 a、根据样品单与业务确定样品的制作细节和客人的详细要求，展开样品单； b、记录样品单的接收时间，以及发出时间，及样品的详细要求，做统计便于后续追溯。 c、新样品制定初始BOM清单，并分发至相关部门（计划、生产、采购、车缝、仓库、注 塑、后勤），新产品做完第一次大货之后10日之内与生产部门核实BOM清单是否准确， 是否需要做出相应修改。 d、每一款特殊样品都要分业务员，分客户做相应的BOM清单，分发至计划、采购、仓库、 生产以保证大货生产同样品一致。 e、老产品记录清楚每张样品单的详细要求，分业务员，分客人做好相关记录，归档。 2）工作时间要求 a、新产品（30工作日） 特殊拉杆、轮子、锁、内装制作周期为30工作日，自样品单到工厂，所有物料需要重 新购买、箱壳需要重新上模具，调色，整体工作时间为30个工作日，外购材料需25
个工作日到工厂，注塑部上模到完工时间为15-20个工作日。 b、老产品 a）标准配置，仓库有剩余大货箱壳，有物料的，3个工作日完成； b）标准配置，仓库有剩余大货箱壳，需要重新购买物料，7个工作日完成； c）标准配置，仓库无箱壳，需重新上模具生产，有物料，15个工作日完成； d）非标准配置，需要重新采购特殊物料（如特殊内装，特殊拉杆、轮子等非标准物料），25个工作日完成 c、大货样 大货出货前7个工作日，采购、箱壳给予安排，样品部2日之内完成。 3）样品操作流程 a、业务下样品单，厂长签核，发放至样品部，样品部跟催物料以及箱壳，各个部门的完工 时间以4.2规定时间完成； b、样品部与业务确认库存箱壳的型号颜色，根据具体情形与业务协调样品的交货情况； c、各个部门给样品部回复确切物料及箱壳的完成时间； d、样品按照确认的要求制作； e、品质部检验合格后，寄出。 5、所需记录表单 拟定：审核：核准：
样品打样流程 版 本：A0 页 码：第1页,共2页 流程 部门 所用记录 事项说明 Y NG Y NG OK 需求单位 业务 业务 工程部 工程部 样板组 品保 业务 内部需求用样品制作单,客户用客户的方式;相关附件 样品制作通知单 样品制作通知单 样品图纸(1:1型版) ﹑打样进度表 BOM ﹑产品规格书表﹑生产作业指导书﹑打样进度表 BOM 样品检验报告 成本分析表 样品清单 客户提出用电话﹑E-mail ﹑传真或客户自己的格式 如是新客户和新产品时, 打样由工程部样板组进行，其他情况则不进行. 工程部上应注明不同打样阶段时的样品性质和数量(原样﹑初样﹑确认样和大货样) 工程部确认打样过程中的各项需求(物料﹑设备)和加工难度;如与业务对于打样有分歧,最终由副总裁决. 工程部在完成打样准备后需将打样进度回复业务,业务依此跟进打样进度 品质部须根据客户要求制定IQC 、IPQC 和OQC 的检验依据,必要时请工程协助 根据《样品制作通知单》制作,将相关材料状况填入《制样书》中，异常情况须及时通知业务人员。项目工程师在订单正式生产前参照样板组的手工BOM 制作正式BOM 和SOP. 样品测试合格后业务将样品送交客户时按客户要求附送相应资料. (接上页) 打样需求(内部提出和客户提出) 评估 制作打样单 制作评估 打样准备 样品制作 样品检验 样品交付 和送样、留样
样品打样流程版本：A0 页码：第1页,共2页流程部门所用记录事项说明 NG Y 客户、业务 业务业务 工程部封样卡 制样书、内部联 络单 客户用电话﹑E-mail﹑传真或客户自己的格式通知 业务样品确认结果.如不合格时确定是否需重新打样 只要客户通知打样终止，业务须要求相关部门停止 打样。 只要客户要求发生变更，业务须以重新制作制样书, 要求相关部门作出相应变更并确认变更所需时间.如 因内部原因不能完成打样，工程部需用内部联络单 通知业务，由业务与客户沟通。 当样品获得客户确认后，工程部须将客户原始图 纸、规格要求、BOM表存档备用。样品交由工程部 保管.客户下订单大货生产时,转交生管制作正式文 件,由文控人员按《文件和资料控制程序》的要求进 行发行. 客人评估 打新终止和 变更 资料归档 封样
教学资料参考范本 幼儿园大班社会教案：美丽的蓝印花 布
幼儿园大班社会教案：美丽的蓝印花布 _____ 年___月___日 ______________ 部门 一、活动背景：
在带领幼儿参观文化街的时候，孩子们发现了许多漂亮的蓝印花布，并对它产生了浓厚的兴趣，一幅幅美丽动人的壁画，一双双蓝色绣花小童鞋，一把把随着风儿转动着的小伞儿，一件件蓝底白花的美丽服装……都显得那样古色古香，吸引得小朋友停住了脚。于是，我们开始收集蓝印花布制品，并把他们布置成一个展览会，通过欣赏，让幼儿充分感受民族工艺的美，并能为此而感到自豪。接着，我设计了这节《蓝印花布》的综合活动，希望能从幼儿的兴趣出发，使他们对蓝印花布的由来，制作方法等有初步的了解，并在学习印染蓝印花布的过程中，体验成功的喜悦，感染家乡的文化气息，培养幼儿对家乡传统文化的兴趣，体会家乡人民的勤劳和智慧，同时激发幼儿热爱家乡的美好情感。 二、活动目标： 1、通过欣赏各种蓝印花布制品，让幼儿感受民族手工艺的美。 2、在欣赏的基础上，让幼儿亲自尝试蓝印花布的制作，体验成功的喜悦。 三、活动准备： 1、方形白布，白蜡笔，融化好的色颜料水六盘。 2、各种蓝印花布制品：桌布、壁画、伞，鞋、包、扇子、衣服等， 3、音机、磁带。 四、活动的重难点： 1、本次活动的重点是幼儿认识了解蓝印花布。 2、本次活动的难点是学习蓝印花布的制作。
五、活动流程图： 表演（引出课题）——欣赏蓝印花布制品——感受蓝印花布的古色古香——介绍蓝印花布的由来、制作方法——小朋友尝试制作 六、活动过程： 1、教师：“现在老师要请你们看一段表演，”音乐声中四个小朋友转动着小伞轻盈的走进教室。 （分析：课前我帮小朋友排练的这一段伞舞，虽然简短，但非常精彩，蓝印花布做成的小花伞在小朋友小手的旋转中，更显得美丽之致，在幽雅的音乐声中，身着蓝印花布漂亮服饰的小姑娘翩然起舞，把小朋友们的眼睛紧紧地吸引住了。让幼儿看表演的目的就是要吸引幼儿的注意，激发他们对蓝印花布的兴趣。） 2、教师：“她们表演得怎样?”“她们的打扮和那小伞跟我们平时看到的有什么不同?” （分析：提问是为了把小朋友从刚才的看表演拉回现实，从而点到主题，听“蓝印花布”能否从小朋友的口中说出，以便了解幼儿对蓝印花布的认识。） 3、老师：“你还知道用蓝印花布做成的其他东西吗?”（当幼儿讲到XX时，老师可以出示相关物品。”） （评析：让小朋友们讲了以后再展示，不但可以考考小朋友的记忆力，了解小朋友对这些工艺制品的印象如何。） 4、老师：“这些用蓝印花布做成的东西美不美?你觉得他们美在什么地方呢?” （从图案、颜色、不同的用途等方面。）“它们有相同的地方
样品制作管理流程[修订] 深圳市超视点科技有限公司 样品制作管理流程 WI-CSD-TH-01A 生效日期版本修改摘要编制批准 2014/010/10 A 首次发布 1.0目的: 为确保样品的制作能顺利进行，准时提供样品给客户测试及确认，以及为了预先评估生产工艺的稳定性。 2.0范围: 本公司样品制作送样确认。 3.0定义: 无 4.0职责: 4.1 业务部:负责收集市场资讯、情报、图样、规格，客户样品需求资料，提出样品评审申请及下 发生产订单，样品送客户确认及测试后续跟进工作。 4.2工程部:负责样品评审，交期确认及样品制作及测试，样品测试数据资料的保管及存档(包括 客供资料的保管)。 4.3仓库:样品电芯半成品及物料的提供，样品成品的收发管理。 4.4资材部:样品的材料采购。 4.5 生产部:协助样品制作。 4.6工程部:协助制作工装夹具。
5.0作业内容: 5.1 样品制作管理流程(见第4页/附件) 5.2 顾客沟通与样品订单评审 4.7品质部:负责样品出厂检验和相关包装确认。 5.2.1业务部人员负责与顾客充分沟通，接收顾客提供的样品、图纸、规格要求等，并搜集市场 资讯情况等，详细了解客户需求的各种工程参数及品质要求，与客户达成共识，同时须将顾客的 书面资料、讨论交流等信息，形成有效的质量记录凭证。 5.3样品订单评审 5.3.1业务部须将顾客相关信息资料，并确定样品数量要求、交期等事宜，以《合同、订单评审表》形式提交相关人员或部门进行样品评审，工程部负责工艺支持和跟进样品进度，并做好《样品制作跟踪表》的相关记录，其它部门进行配合。 5.4样品申请 业务部填写《样品申请单》及样品、图纸、规格要求等资料，并传达给工程部。 5.5 样品申请接收 5.6.1工程部人员接到《样品申请单》后详细了解客户要求细则，对样品进行设计评估与业务部 保持好沟通。 5.6.2工程部将顾客提供的样品、图纸、规格要求等资料进行有效保存管理。 5.7 样品制作 5.7.1 工程部从业务部提供半成品中挑选性能一致品质优良的半成品进行制作，并详细记录相关半成品检测数。
幼儿园大班早期阅读绘本教案:老鼠娶新娘 活动目标： 1、体验童话故事书传达的浓浓的民俗情，探知事物相克相生循环 往复的有趣现象。 2、自信地表达自己的长处，真诚地欣赏同伴的长处。 活动准备： 图书ppt，太阳、云、风、墙、老鼠、猫的图片。汉字：照、遮、吹、挡、打洞、抓、扬长补短的汉字。 活动过程： （一）激趣导入 1、欣赏唢呐演奏的音乐《过新年喜洋洋》 师：听了这段音乐，你觉得大家在做什么呢？ 师：放这样的音乐，一定是有什么喜事发生。那么，今天在这段音乐下，会发生了什么事？ 2、引发对故事的想象（出示花轿） 师：这是什么，它是做什么用的？ 师：对，这是结婚用的花轿，给谁坐呢？
师：新娘是老鼠美叮当，有新娘就会有新郎，美叮当想找个世界 上最强的新郞，她找到了······（出示有太阳、乌云、墙、风、老鼠、猫的图片）你们猜猜美叮当会选谁做自己最强的新郎。 （二）欣赏理解 1、边欣赏幻灯片，边听故事 2.理解事物之间的强弱关系 师：你们认为谁是世界上最强的新郎？（故事停在“村长迷糊了，到底谁是最强的新郎”。）师：世界上到底有没有最强的新郎？尽管他们不是世界上最强的新郎，可是，他们都有自己最强的地方，你们知道它们最强的地方分别是什么？（出现汉字照、遮、吹、 挡、打洞、抓） 3、讨论：你有没有最强的地方 鼓励幼儿自信地说出自己最强的地方。 请幼儿记住自己朋友的长处。 师：我们都说出了自己最强的地方。现在，我们来比一比，“记住朋友的长处”这件事谁最强？请你们说说记住了谁的长处？（这里是为 了让幼儿关注自己的同伴，并学会倾听，对幼儿来说，这是对其习惯 和心理的挑战。） 寻找“扬长补短”的朋友圈：你有什么地方不强？你想变得更强 吗？可以通过别人的帮忙使自己变得更强。
摘要 摘要 蓝印花布是我国重要的传统文化艺术之一，拥有浓郁的历史背景与文化特色。南通地区作为蓝印花布之乡，无论在传统工艺还是艺术特色方面，都是蓝印花布典型的产地与源头。南通蓝印花布凭借其严谨的纹样造型，讲究的艺术布局，成为该地区最具有代表性的艺术品之一，其以此类元素开发出的气体类型服装、工艺品，也为该地区艺术轻工领域的发展与文化经济的推动起到重要作用。 本文结合南通地区的经济、文化、艺术发展现状与规划，将该地区的蓝印花布作为主要研究对象，以对南通蓝印花布的纹样、价格和创新为基础，提出当前南通蓝印花布的发展存在的问题以及改进的方向，一方面对蓝印花布进行更全面的阐述和总结，另一方面也为南通蓝印花布的未来发展与市场拓展提供建议。 关键词：蓝印花布；南通；推广策略
摘要 (1) 前言 (3) 1、南通蓝印花布的概况 (3) 1.1蓝印花布的历史 (3) 1.2蓝印花布的民间文化 (3) 1.3蓝印花布的发展现状 (4) 2、南通蓝印花布推广存在的问题 (4) 2.1南通蓝印花布推广环境分析 (4) 2.2南通蓝印花布推广SWOT分析 (4) 2.2.1南通蓝印花布推广优势分析 (4) 2.2.2南通蓝印花布推广劣势分析 (5) 2.2.3南通蓝印花布推广机遇分析 (5) 2.2.4南通蓝印花布推广威胁分析 (5) 2.3南通蓝印花布推广存在问题 (6) 2.3.1蓝印花布的价格昂贵 (6) 2.3.2蓝印花布的纹样陈旧 (6) 2.3.3蓝印花布生产缺乏专业的人员 (6) 3、南通蓝印花布推广策略 (6) 3.1积极对蓝印花布纹样花型进行创新 (6) 3.2重视工艺传承，强化蓝印花布培训与激励机制 (7) 3.3增强体验式接触，协同当地特色产业发展 (7) 结论 (7) 致谢................................................... 错误！未定义书签。参考文献 (9)
早期阅读概念与图画书阅读教学 2013年第7期 ）（总第223期StudiesinPreschoolEducation学前教育研究No.7，2013S erialNo.223早期阅读概念与图画书阅读教学* 刘宝根1**李林慧2 2（1浙江师范大学杭州幼儿师范学院，杭州310012；上海师范大学教育学院，上海200234） ［摘要］图画书是幼儿园教育中的重要材料，教师在图画书阅读教学中面临诸多困惑：人手一本还是众人一本，读故事还是讲故事，片段读还是完整读，图画书是工具还是对象。以图画书为主要材料的早期阅读是培养儿童书面语言意识和能力的活动，儿童应该有人手一本的独立阅读机会；要有在阅读图画书的过程中倾听图画书故事的机会；要有完整阅读图画书的机会；要有首先接触、理解图画书本身的文学美、艺术美的机会。 ［关键词］早期阅读；图画书；阅读教学；关键经验 近年来，人们逐渐重视图画书在幼儿教育中的作用，许多幼儿园教师不仅在语言教育活动中开展图画书的阅读教学活动，也逐渐重视在其他领域活动中运用图画书。但在图画书阅读教学时，教师也出现了“到底该人手一书还是众人一书”的困惑，出现了“图画书是阅读的工具还是阅读的对象”的 [1][2][3]混淆了图画书教学争议，有的图画书阅读活动甚至走向了误区，如将早期阅读等同于早期识字，
[4]和故事教学之间的区别等。出现这些误区和困惑，主要原因还在于教师对早期阅读的基本观念认识 不清，对图画书在儿童早期阅读能力关键经验发展中的作用不明确。因此，要纠正现今图画书阅读的一些偏向，澄清图画书阅读教学中的困惑，需要重新回到早期阅读的基本概念上来。 一、早期阅读的基本概念 幼儿园教育实践中，对图画书的重视是在早期阅读兴起的浪潮中涌现出来的。早期阅读这一概念发端于萌发读写（emergentliteracy），最早由玛丽·克莱（MarieClay）提出，指的是“儿童在进入学校前获得的关于语言、阅读和书写方面的知识”。[5]我国学者认为幼儿园的早期阅读教育活动是在幼儿口头语言充分发展的基础上，接触有关书面语言的信息，获得有关书面语言意识、行为和初步能力的 [6]教育活动。2001年颁布的《幼儿园教育指导纲要（试行）》在“语言教育的基本要求”部分提出：“培养；“利用图书和绘画，引发幼儿对阅读和书写的兴幼儿对生活中常见的简单标记和文字符号的兴趣，培养前阅读和前书写技能”；最近颁布的《3～6岁儿童学习与发展指南（试行）》在儿童语言学习与发展中的“阅读和书写准备”中提出了两个目标：“具有初步的阅读理解能力”和“具有书面表达的愿。可见，无论是研究者还是幼儿园教育的指导文件都认为早期阅读是培养儿童书面语望和初步技能”言意识、行为和能力的教育活动，早期阅读聚焦的是儿童读写能力的发展。 总之，早期阅读的概念可从两方面来理解：一是早期阅读关注的是儿童书面语言意识和能力的发展。在幼儿语言学习和发展过程中，儿童要获得口头语言、
目的 规范样品的管理操作，确保样品得到有效的控制，从而确保工程，生产和检验有据可依。 1适用范围 适用于本公司所有样品的采集、制作、管理及使用全过程。 2定义 样品：由客户提供或由公司授权人员签发的，用于工程，生产或检验人员检验时作为参照使用的某种产品的认可实物。 3职责 3.1工程部 3.1.1负责供应商样品的确认、承认、测试测量和签署。 3.1.2负责提交客户样品的制作，试模样品，材料的内部评估、承认和签署。 3.1.3负责客户签署样件要求的信息获取，样品的验证确认。 3.1.4负责样品结构，性能，外观及颜色的确认。 3.1.5负责生产样品，限度样品的样品签署。 3.1.6负责工程样品档案的建立，保存管理。 3.1.7负责客户签署样品接受的登记,样品复稿的封存保护。 3.2质量部 3.2.1负责协助工程部对供应商样品的确认、承认、测试测量和签署。 3.2.2负责协助工程部向客户提交样品，材料的测试和检验。 3.2.3负责样品的使用，归还和有效周期的管理。 3.2.4负责质量样品档案的建立，保存管理。 3.3市场部 3.3.1负责样品提交客户的确认，客户产品的信息沟通，获取。 3.3.2负责客户签署样品接受的登记,样品原稿的封存保护。 3.4采购部 3.4.1负责供应商提交样品信息要求的沟通，获取。 3.4.2负责向相关部门（工程部/质量部等）送交供应商的提交样品，协助工程部和质量部建立 供应商提交样品的相关资料和信息。
5、作业程序内容 5.1 样品收集： 5.1.1 由客户或工程部/市场部提供样品来源。 5.1.2 工程部和品质部收到样品后，经工程部主管和质量部主管确认，然后进行封样。 5.1.3 由公司授权人签发的特殊情况下的让步接受，暂收样品。 5.2样品的分类： 5.2.1供应商样品：指由供应商制作提供，由我公司确认签发的样品，供供应商生产，检验和本公司工程、 质量检验和追溯时的参考依据。 5.2.2 客户样品：指经由客户确认签发的，用于指导本公司工程设计开发，生产及验收参考标准的样品。 包括：色板，结构，电路，功能等。(也称外来样品) 5.2.3 自制样品:指由本公司工程部、质量部确认签发的样品，供生产，检验参考使用（也称生产样品）。 5.2.4 临时样品：因生产异常时与客户样品存在差异时，经客户认可暂时接收，或由工程部，品质部认 可可让步接收的样品。 5.3 样品管理： 5.3.1 供应商样品 a. 当采购部开发新的供应商或供应商材料、生产工艺变更时，由采购部要求供应商附《自检报 告》，《材质证明》送样进行确认。 b. 采购部接到上述供应商的报告，材料样品时，转给工程部进行确认。工程部进行样品（5PCS 以上）检测，包括材料的适配动作，适时安排由生产制造部门进行试用，合格后工程部进行样品确认。 c. 工程部应把对样品的确认与测试记录在《样品确认单》上，工程部确认OK的话，须完成供应 商《样品确认单》的制作，并将《样品确认单》转给采购，若确认NG，工程部将该供应商提供的所有资料和样品退还给采购部，经确认变更影响客户产品特性或客户有要求材料变更时由工程部依据《工程更改通知单》交客户承认。 d. 若供应商材料样品经检验测试合格，工程部将《样品确认单》原件发回供应商，副本两份， 一份给采购部，一份给质量部。工程部与质量部依据要求建立《供应商样品清单》进行管理。若不合格，则将相关结果告之供应商并给予说明，若供应商要求样品全部退回，那么工程部应将所有供应商送样品资料与样品进行全部退回。 e. 质量部接到采购部转来的上述供应商的报告，环保资料应登记到《环保测试信息清单》，内 容包括供应商名称、物料名称、物料编号、测试日期、测试机构、报告编号、报告有效周期、报告有效期、测试项目、报告有效测试结果、下次索要报告时间、MSDS（化工品）、结合《供应 商样品清单》,作为以后供应商送货的依据。
----------有限公司工作指示
1．目的 旨在维护样品申请、制作、检验的顺利进行、样品项目管理的完整性及提高产品的性能和可靠性。2．范围 适用于新产品和客户特殊要求的样品申请、制作、检验及处理批核。 3．样品申请、制作、确认流程 3.1 样品申请、制作 3.1.1 所有部门或个人如需要样品,须填写样品制作申请单, 样品制作申请单须由样品审请部门 主管签字，样品的制作由生产部或采购部负责完成。 3.1.2 样品制作申请单须报财务部备案，如要收费，业务部负责收款；财务部负责跟催款项到帐。 3.2样品确认 3.2.1 品质部或研发部负责接收客户、供应商或本公司其它部门送来的样品并进行样品登记、样 品编号。 3.2.2 所有来样必须附上客户资料(如适用者) 、供应商自检报告(如适用者) 、材质证明报告(如 适用者) 、试模报告(如适用者) 或ECN(如适用者) 、样品制作申请单(如适用者), 否则罚款或不接收。 3.2.3样品确认由品质部和研发部共同参与.每次样品确认至少5个样品,其中品质部确认检测3 个;研发部确认检测2个。
3.2.4 每一件样品确认报告必须有报告编号，产品名称，型号/图号，制造单位，检验确认者、核 准者以及他们的签名和签名日期。 3.2.5 样品在完成检验测试后, 样品或样品确认报告必须在检测确认部门至少保留一年；所有保 留的样品资料如样品确认报告、客户/供应商资料,材质证明书等均要存档, 以便日后查阅。 3.2.6 样品检验确认报告为一式四份,分别发给品质部、研发部、客户(供应商或集团内公司) 、 采购部或外贸部。 3.2.7 如样品需外发加工或送交给客户确认的样品, 必须有由品质、研发、项目主管,有必要时须 客户代表,共同签名后方能生效。 3.2.8 所有样品必须有品质部和研发部共同检验、确认；未经品质部和研发部共同检验、确认的 样品，申请部门不得寄出或使用，否则承担一切后果。 4．记录 附件1. 样品确认流程图 附件2. 样品制作申请单 5．相关文件 无 附件1.样品申请制作确认流程图 申请部门申请 -------------------------------------------------------财 务部备案 负 责制样部门制作 品 质部和研发部检验
物料采购及样品确认程序 表格格式 Prepared on 22 November 2020
1、目的 1.1给控制和确认采购文件提供指引。 提供足够和正确的采购资料而确保物料品质得到保障。 2、概述 2.１采购对象包括物料及加工工作。 2.2采购全部在本公司执行。 一般物料的采购是根据产品BOM及客户生产通知单要求的物料进行。3、职责 3.1采购人员负责发出和确认物料采购单（P/O）及跟进返货情况。 3.2采购人员负责加工工作的安排。 3.3工程部负责确认采购物料样板。 4、程序 4.1物料控制部收到生产单后，作出[物料需求单]。若需加工的物料，由 各加工部门作出[加工物料需求单]，经由物料控制部确认。 采购人员根据[物料需求单]和[加工物料需求单]发出[采购单]。 采购人员首先检阅[合格供应商清单]，确保采购对象于清单内。 产品BOM及规格图纸由工程部负责人起草及确认。有需要时，采购人员必须保证最新受控图纸交至物料供应商。 物料采购单至少包括以下必须内容： A.P/O编号 B.供应商名称 C.物料编号/名称 D.采购数量 E.送货日期 F.规格、说明 G.结算方式 P/O由采购人员确认后，发出给供应商。
新物料的采购必须收到[物料确认书]方可执行。 若P/O资料有更改时，采购人员须再次发出及确认一份新的P/O给供应商，如果只是送货期改变，只须以FAX或口头形式通知便可（口头通知须记录于P/O上）。 不能向未经认可的供应商采购物料（请看供应商评审程序）。若紧急情况下，未完成评审程序而要向某未经认可的供应商采购物料，必须经工程部负责人确认P/O后，方可执行。 物料的确认程序 1998年12月31日前采购的物料，将根据其使用的经验和客户反映及改善，确认其品质达到要求，可以继续采用。 1998年12月31日后采购的物料，须于收货前完成以下确认程序： 1.工程部提供样板或资料给采购部，用于寻找潜在供应商。 A.有需要时，采购部将样板或资料交给潜在供应商进行确认并制 作样品。 B.潜在供应商制造的物品由采购部转呈工程部负责人判定并签署 于[物料确认书] 上。 C.[合格]和[有条件]接受的[物料确认书]副本，交IQC作来料检验 使用，有需要时，物料图纸须交IQC保存用作检查标准（看第 外来文件之管理）。 D.一些普通类物料，毋须进行以上确认程序。但对于胶水、油漆 类化工物料，采购部在采购前需要求厂家提供有关规格文件及 检测报告，确认该物料在使用过程中对环境、人员及产品满足 法律法规要求。
幼儿园大班早期阅读绘本公开课教案: 我爸爸 一、师：每年6月的第三个星期天是父亲节,今天我们聊聊爸爸。 老师带来一本书，想看吗？《我爸爸》投影出示： 1、爸爸的图片，看看怎么样？（爱睡觉，贪吃的，发呆的）在哪里？（家里）不爱工作的爸爸。 2、在我眼里他真的很酷。他什么也不怕？连大野狼也不怕。 3、他一跳能飞过月亮。我爸爸敢跟月亮比高低。 4、我爸爸还敢走钢索，蓝天象大熊。 5、他还敢跟巨人摔跤，巨人膀大腰圆，但爸爸不怕。 6、参加运动会他轻松地得第一名。大家看这名黑人选手是谁？（美国著名短跑名将刘易斯） 师：看了几页，看到这儿说说我爸爸怎么样？（勇敢）你真勇敢，第一个举手发言。 还怎么样？有这样的爸爸好安全呀！ 二、还想继续看吗？ 1、出示下一幅图：爸爸呢？请你来给图画配上一句话。（变成了马。爸爸很胖，所以很能吃。）我爸爸吃得象马一样多。
2、再看，爸爸像鱼会游泳，游泳得很快。游泳的时候像鱼一样灵活。板书：灵活 3、再看几幅图（两幅） 任意选择一幅说说：我爸爸像……一样…… 我爸爸像大猩猩一样强壮。 我爸爸像河马一样跳舞，很愉快。 看作者是怎样说的？ 他像笑眯眯的河马一样快乐。 4、在说说我爸爸怎么样？（开心果，载歌载舞，德智体全面发展） 5、有这样的爸爸怎么样？（好温暖） 6、在我的眼里我爸爸真酷！还想看吗？有个条件，咱得要猜一猜，想想还会画些爸爸的什么呢？（爸爸像章鱼一样游得快，我爸爸像小蜜蜂忙碌，爸爸像猪一样吃得多，像企鹅一样不怕冷……） 7、好，带着你的猜想往下看： ①有时候，他又很温柔，像我的泰迪熊一样 ②我爸爸像猫头鹰一样聪明（传说猫头鹰是智慧的化身） ③有时也会做点傻事（把头发梳得像扫把） 8、看了这些我爸爸又给你留下了怎样的影响？你看他举着扫帚等着你的评价呢（聪明，板书：耳聪目明……爸爸
启发精选好书： 1.我喜欢书 2.大卫不可以 3.三只小猪的真实故事 4.鲸鱼 5.奥莉薇 6.丽塔和鳄鱼去钓鱼 7.丽塔和鳄鱼迷路了 8.大脚跳芭蕾浦蒲兰文库绘本世界： 1.这是什么形状 2.我的颜色是什么？3.莎娜的雪火车蒲蒲兰绘本馆： 1.换一换 2.彩虹色的花 3.蚂蚁和南瓜 4.谁藏起来了(互动) 5.爸爸 6.普的淘气的妹妹 7.荷花镇的早市 8.猫太噼哩噗噜在海里 10.你真好 9.三只山羊嘎啦嘎啦 11.小斑普大冒险海豚绘本花园： 1.动物绝对不该穿衣服 2.小猪的爱情 3.我也可以飞 4.弗洛拉的花 5.青蛙王子历险记 6.小魔怪要上学 7.小绿狼 8.达芬奇想飞 9.小朵朵和胖沃克 10.小棕熊的梦 11.小猪闹闹 12.小象欧利找弟弟 13.给爸爸的吻 14.凯，能行 15.小熊不刷牙 16.我爸爸 17.我妈妈 18.有个性的羊 19.我想要爱 20.大嘴狗 21.四个苹果 22.我的爸爸叫焦尼 23.三个怪物迪克布鲁纳丛书： 1.米菲在海边 2.做客 3.米菲在学校 4.米菲的梦 5.米菲在动物园 6.米菲和梅儿 7.米菲做飞机嘻嘻哈哈系列： 1.呼噜，呼噜，哞 2.吱咕，吱咕，嘎 3.咔嗒，咔嗒，哞 4.鸭子当总统噼里啪啦系列： 1.我要拉巴巴 2.我去刷牙 3.我要洗澡 4.你好 5.草莓点心 6.车来了 7.我喜欢游泳我的猫咪系列: 1.我的猫咪就知道睡觉 2.小猫该睡觉了小熊和最好的爸爸： 1.我长大了 2.看世界 3.聚会 4.当厨师 5.坐游戏 6.世界上最好的爸爸 7.搬家梦幻有声图画书：别 1.坏坏狼 2.小熊睡觉了 3.小熊和月亮 4.遥远的家 5.小刺猬的麻烦 6.小家伙，害怕 7.我想有只猫 8.月亮麦姬 9.特别的礼物 10.下雪的冬夜 11.我看见圣诞老人啦 12.小不点儿 13.特别的客人 14.小心的戴西 15.雪天使蒲公英图画书馆: 1.因为我爱你 2.我会永远伴着你 3.幸福到了鼻子尖 4.星期四要去哪里呢？ 5.懒奥西 6.跟上那头熊 7.一口袋的吻 8.五个小怪物 9.鼹鼠与小鸟阿罗系列：1.阿罗漫游太空 2.阿罗在北极 3.阿罗有枝彩笔 4.阿罗房间要挂 5.阿罗的 ABC 6.阿罗在马戏团小兔汤姆系列： 1.汤姆的外公去世了 2.汤姆住院 3.汤姆尿床了 4.汤姆和伤心的鲁鲁 5.汤姆挨罚 6.汤姆无聊的时候 7.汤姆搬家 8.汤姆的小妹妹 9.汤姆去海滩 10.汤姆恋爱了 11.汤姆去农场 12.汤姆走丢了 13.汤姆最好的朋友 14.汤姆上幼儿园 15.汤姆的噩梦16.汤姆的生日聪明豆绘本系列： 1.忘了说我爱你 2.长大做个好爷爷 3.小熊孵蛋 4.我永远爱你 5.愿望树 6.小猪变形记 7.火龙爸爸戒烟记 8.猫头鹰呜呜呼 9.乱七八糟变色龙聪明豆系列：第二辑 1.咕噜牛 2.小房子变大房子 3.女巫扫帚挪挪坐 4.小海螺和大鳄鱼 5.城里最漂亮的巨人 6.咕噜牛小妞聪明豆绘本系列：第三辑 1.狐狸爸爸鸭儿子 2.小狗阿疤想变羊一家 3.狮子烫头发 4.我们是一家 5.你是我最好的朋友 6.美宝的魔法花园 7.小乖，抱抱 8.小老鼠分果果神奇立体书☆☆☆找朋友系列 1.查理 2.水獭奥斯卡 3.小兔比利 4.猫头鹰奥齐神奇立体书☆☆☆忙碌的虫子 1.蚂蚁的职责 2.甲壳虫的工具箱 3.忙碌的蜘蛛4.毛虫长大后乐乐趣☆☆☆机器奥秘系列 1.教授的机器 2.机器是怎样工作的乐乐趣☆☆☆梦幻来信系列 1.美人鱼的手链各种各样系列☆☆☆乐乐趣图书 1.各种各样的人 2.各种各样的身体 3.各种各样的感觉 4.各种各样的害怕 5.各种各样的房屋 6.各种各样的交通工具乐乐趣☆☆☆自信的鲁比鼠系列 1.音乐之星鲁比鼠 2.星鲁比鼠创意连连☆☆☆乐趣翻翻 1.大森林 2.来到农场 3.动物园 4.趣骑马吧 5.坐火车喽 6.我的城市 7.我们盖房子 8.警笛响了青蛙弗洛格的成长故事： 1. 弗洛格找宝藏 2.找一个好朋友 3.冬天里的弗洛格 4.弗洛格是个英雄 5.特别的日子 6.弗洛格去旅行 7.鸟儿在歌唱 8.爱的奇妙滋味 9.弗洛格和陌生人 10.我就是喜欢我 11.弗洛格吓坏了 12.难过的弗洛格悦读阅美绘本馆： 1.一根羽毛也不能动 2.是蜗牛开始的 3.一只想当爸爸的熊 4.皮皮放屁屁 5.还有一只羊嘟嘟和巴豆： 1.独一无二 2.我会回家过圣诞节 3.快下雪吧 4.新朋友 5.世界之巅 6.真希望你们在这二 7.两个好伙伴 8.迷人的奥碧儿 9.你是我的阳光 10.送给嘟嘟的礼物斯凯瑞：第三辑 1.警察局的一天 2.飞机场的一天 3.消防站的一天 斯凯瑞金色童书系列 1.忙忙碌碌镇 2.轱辘轱辘转 3.会讲故事的单词书 4.最受欢迎的
关于南通蓝印花布工艺的文化特征及“现代”精 神研究 来源：中国论文下载中心 [ 10-03-27 09:07:00 ] 作者：何韵潇编辑： studa090420 [论文关键词] 南通蓝印花布工艺文化特征“现代”精神 [论文摘要] 南通蓝印花布是最具代表性的民间工艺品之一，制造工艺十分讲究；蓝印花布的最大特征在有意无意间关合浓烈的生命意识；从色思维学本身来看，可以从蓝色具有的情感和心理特征方面寻找原因；南通蓝印花布的内涵在任何历史时代都包含着的那个时代的“现代”精神，使南通蓝印花布历久弥新，成为不朽的工艺品门类。 南通蓝印花布是印染艺术的奇葩，研究南通蓝印花布的历史发展、制造工艺与文化特征，对于认识我国的文化传统，促进南通蓝印花布艺术发展并世代承传，具有深刻的文化意义。 南通素有“崇川福地”之称，吴越文化、荆楚文化和齐鲁文化在这里撞击融汇，形成了江海平原地区独具特色的地域文化风情。经过一代代民间艺人的不懈努力，蓝印花布已经从单一的土布制品走向多种面料的制品，从生活实用型走向实用、装饰多种类型、从阡陌田畴走向都会城市。国人利用蓝草的色素染色，可追溯到春秋战国时期。苟子目睹绿色“蓝草”的色素转化过程及染出由黄变绿、由绿变蓝、再变青的过程，发出“青，取之于蓝，而青于蓝”的感叹。北魏
贾思勰记述了从蓝草中撮蓝淀的方法：“七月中作坑，令受百许束，作麦秆泥泥之，令深五寸，以苫蔽四壁。刈蓝倒竖于坑中，下水，以木石镇压令没。热时一宿，冷时再宿，漉去菱，内汁于瓮中，率十石瓮，著石灰一斗五升，急手摔之，一食顷止。澄清，泻去水，别作小坑，贮蓝淀著坑中。候如强粥，还出瓮中，蓝淀成矣。” 古代漏浆防染印花历史悠久。早在北朝就出现了用镂空花版和防染剂的蓝底白花布，原称“蜡缬”。唐代统称“染缬”，包括“绞缬”和“夹缬”。夹缬工艺是用特定木板缕刻而成，然后把布匹对折夹在两片刻有同样花纹的木板中间，捆扎后注人需要的颜色或者投入染缸中染色，待去掉夹板后，便显出蓝底白花图纹。夹缬工艺所刻花版费工费时且容易变形，南宋加以改进，用于印染蓝花布，又名为“药斑布”，俗名“浇花布”。史籍中记载了嘉定及安亭镇的印染情形：“宋嘉定中(1208--1224)有归姓者创为之。以布抹灰药而染色、候干、去灰药，则青白相间。有人物、花鸟作被面、帐帘之用。”“药”即染色原料蓝草，“斑”是防染浆剂印后构成的纹样大小斑点，故称“药斑布”。 随着棉纺手工业的发展，“药斑布”简单、粗糙的图形已不能满足民众的审美和生活的需求，民间艺人大胆吸收剪纸、刺绣、木雕等传统艺术图案，不断地丰富药斑布的纹样。民间蓝印花布的广泛应用，促进“印花担”队伍的迅速发展，他们只印花、括浆，不染色，为农家提供各种形式的花版。这种“印花担”在江南也称“秃印作”，他们走街串巷走乡串村，担子一头装的黄豆及石灰粉，另一头装有刮印工具和花版，任凭客户挑选花型加工。清末，南通地区“印花担”
----------有限公司 工作指示 1．目的 旨在维护样品申请、制作、检验的顺利进行、样品项目管理的完整性及提高产品的性能和可靠性。 2．范围 适用于新产品和客户特殊要求的样品申请、制作、检验及处理批核。 3．样品申请、制作、确认流程 3.1 样品申请、制作
3.1.1 所有部门或个人如需要样品,须填写样品制作申请单, 样品制作申请单须由样品 审请部门主管签字，样品的制作由生产部或采购部负责完成。 3.1.2 样品制作申请单须报财务部备案，如要收费，业务部负责收款；财务部负责跟催款项到帐。 3.2样品确认 3.2.1 品质部或研发部负责接收客户、供应商或本公司其它部门送来的样品并进行样品 登记、样品编号。 3.2.2 所有来样必须附上客户资料(如适用者) 、供应商自检报告(如适用者) 、材质证 明报告(如适用者) 、试模报告(如适用者) 或ECN(如适用者) 、样品制作申请单(如适用者), 否则罚款或不接收。 3.2.3样品确认由品质部和研发部共同参与.每次样品确认至少5个样品,其中品质部确 认检测3个;研发部确认检测2个。 3.2.4 每一件样品确认报告必须有报告编号，产品名称，型号/图号，制造单位，检验确 认者、核准者以及他们的签名和签名日期。 3.2.5 样品在完成检验测试后, 样品或样品确认报告必须在检测确认部门至少保留一年； 所有保留的样品资料如样品确认报告、客户/供应商资料,材质证明书等均要存档, 以便日后查阅。 3.2.6 样品检验确认报告为一式四份,分别发给品质部、研发部、客户(供应商或集团内 公司) 、采购部或外贸部。 3.2.7 如样品需外发加工或送交给客户确认的样品, 必须有由品质、研发、项目主管, 有必要时须客户代表,共同签名后方能生效。 3.2.8 所有样品必须有品质部和研发部共同检验、确认；未经品质部和研发部共同检验、 确认的样品，申请部门不得寄出或使用，否则承担一切后果。 4．记录 附件1. 样品确认流程图 附件2. 样品制作申请单 5．相关文件 无 附件1.样品申请制作确认流程图 -------------------------------------------------------申请部门申请 -------------------------------------------------------财务部备案