SPM8图像分析操作手册：时间与空间处理详解

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SPM8 Manual 是一个专注于图像数据分析的工具，由功能成像实验室（Functional Imaging Laboratory）的Wellcome Trust中心神经影像学研究所（Wellcome Trust Centre for Neuroimaging）的多个知名专家团队共同编撰，包括 John Ashburner、Gareth Barnes 等。该手册详细阐述了在 SPM8 中进行时空处理和空间处理的核心方法。 **第一部分：Temporal Processing (时空处理)** 1. **Slice Timing (片层同步)**: SPM8 考虑到MRI数据中的时间轴偏差，提供了片层同步功能。用户需要指定每个扫描序列的TR（重复时间）、TA（回波时间）、片层数以及可能的片层顺序，以便正确对齐数据，确保时间线上的同步性。参考片层的选择也会影响同步过程。 2. **Realign (重采样)**: 这个阶段涉及到数据的校准，包括数据预处理，如运动估计，旨在纠正因受试者头部运动引起的图像位移。用户可以选择不同的估计选项，如参数估计，来计算头动参数，并根据这些参数对图像进行重新采样。 **第二部分：Spatial Processing (空间处理)** 2.1 **Realign: Estimate (重采样估计)**: 这部分详述了如何利用原始数据进行头动参数估计，用户需提供数据文件，选择不同的估计参数来优化头动校正效果。 2.2 **Realign: Reslice (重采样)**: 在这个步骤，已校准的数据被重新排列到标准空间模板上，以消除空间差异。用户可以设置重采样选项，如空间分辨率、插值方式等。 2.3 **Realign: Estimate & Reslice (同时估计与重采样)**: 该操作结合了前面两步，同时进行头动参数估计和图像重采样，通常作为预处理流程的标准环节。 3. **Realign & Unwarp (重采样与去扭曲)**: 这部分涉及更复杂的处理，可能包括头部变形校正（unwarping），这对于处理如梯度场不均匀导致的空间失真等问题非常关键。数据准备时需要明确扫描会话，因为校正的效果会依赖于整个扫描系列的一致性。通过SPM8 Manual，使用者能全面了解如何运用这一工具高效地处理和分析脑部影像数据，从基本的时间同步到高级的空间校正，以确保研究结果的准确性和可靠性。这个手册对于神经科学研究人员、临床实践者以及数据分析师来说，是不可或缺的参考资料。

16 CONTENTS

Chapter 1

Slice Timing

Contents

1.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.1.1 Session . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.2 Number of Slices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.3 TR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.4 TA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.5 Slice order . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.6 Reference Slice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1.7 Filename Preﬁx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Correct diﬀerences in image acquisition time between slices. Slice-time corrected ﬁles are

prepended with an ’a’.

Note: The sliceorder arg that speciﬁes slice acquisition order is a vector of N numbers, where

N is the number of slices per volume. Each number refers to the position of a slice within the

image ﬁle. The order of numbers within the vector is the temporal order in which those slices

were acquired. To check the order of slices within an image ﬁle, use the SPM Display option and

move the cross-hairs to a voxel co-ordinate of z=1. This corresponds to a point in the ﬁrst slice

of the volume.

The function corrects diﬀerences in slice acquisition times. This routine is intended to correct

for the staggered order of slice acquisition that is used during echo-planar scanning. The correction

is necessary to make the data on each slice correspond to the same point in time. Without

correction, the data on one slice will represent a point in time as far removed as 1/2 the TR from

an adjacent slice (in the case of an interleaved sequence).

This routine ”shifts” a signal in time to provide an output vector that represents the same

(continuous) signal sampled starting either later or earlier. This is accomplished by a simple shift

of the phase of the sines that make up the signal. Recall that a Fourier transform allows for a

representation of any signal as the linear combination of sinusoids of diﬀerent frequencies and

phases. Eﬀectively, we will add a constant to the phase of every frequency, shifting the data in

time.

Shifter - This is the ﬁlter by which the signal will be convolved to introduce the phase shift.

It is constructed explicitly in the Fourier domain. In the time domain, it may be described as

an impulse (delta function) that has been shifted in time the amount described by TimeShift.

The correction works by lagging (shifting forward) the time-series data on each slice using sinc-

interpolation. This results in each time series having the values that would have been obtained

had the slice been acquired at the same time as the reference slice. To make this clear, consider

a neural event (and ensuing hemodynamic response) that occurs simultaneously on two adjacent

slices. Values from slice ”A” are acquired starting at time zero, simultaneous to the neural event,

20 CHAPTER 1. SLICE TIMING

while values from slice ”B” are acquired one second later. Without correction, the ”B” values

will describe a hemodynamic response that will appear to have began one second EARLIER on

the ”B” slice than on slice ”A”. To correct for this, the ”B” values need to be shifted towards

the Right, i.e., towards the last value.

This correction assumes that the data are band-limited (i.e. there is no meaningful information

present in the data at a frequency higher than that of the Nyquist). This assumption is support

by the study of Josephs et al (1997, NeuroImage) that obtained event-related data at an eﬀective

TR of 166 msecs. No physio-logical signal change was present at frequencies higher than our

typical Nyquist (0.25 HZ).

When using the slice timing correction it is very important that you input the correct slice

order, and if there is any uncertainty then users are encouraged to work with their physicist to

determine the actual slice acquisition order.

One can also consider augmenting the model by including the temporal derivative in the

informed basis set instead of slice timing, which can account for +/- 1 second of changes in

timing.

Written by Darren Gitelman at Northwestern U., 1998. Based (in large part) on ACQCOR-

RECT.PRO from Geoﬀ Aguirre and Eric Zarahn at U. Penn.

1.1 Data

Subjects or sessions. The same parameters speciﬁed below will be applied to all sessions.

1.1.1 Session

Select images to slice-time correct.

1.2 Number of Slices

Enter the number of slices.

1.3 TR

Enter the TR (in seconds).

1.4 TA

Enter the TA (in seconds). It is usually calculated as TR-(TR/nslices). You can simply enter

this equation with the variables replaced by appropriate numbers.

1.5 Slice order

Enter the slice order. Bottom slice = 1. Sequence types and examples of code to enter are given

below.

ascending (ﬁrst slice=bottom): [1:1:nslices]

descending (ﬁrst slice=top): [nslices:-1:1]

interleaved (middle-top):

for k = 1:nslices,

round((nslices-k)/2 + (rem((nslices-k),2) * (nslices - 1)/2)) + 1,

end

interleaved (bottom -> up): [1:2:nslices 2:2:nslices]

interleaved (top -> down): [nslices:-2:1, nslices-1:-2:1]

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