ADNI-MRI-PET-ANALYSIS

MRI-ANALYSIS

Laboratory of Neuro Imaging, USC

× Co-investigator: Paul Thompson

Diffusion Tensor Imaging Summary Statistics of White Matter Regions of Interest 感兴趣区域的白质的扩散张量成像摘要统计

Diffusion tensor imaging (DTI) allows for the study of microstructural properties of white matter tracts. Regional summary measures were calculated from DTI to include measures of diffusion and anisotropy of various fiber tracts within the brain. A standard DTI template, with a corresponding white matter tract atlas, was registered to each individual subject. Registrations were subsequently applied to the segmented atlas. Visual inspection of the images ensured adequate registration. The mean of all voxels from each of the regions of interest from the atlas were obtained from maps of fraction (FA) and mean diffusivity (MD). 扩散张量成像（DTI）可以研究白质束的微结构特性。根据DTI计算得出的区域汇总指标包括大脑内各种纤维束的扩散和各向异性。将标准的DTI模板和相应的白质图谱图集注册到每个个体受试者。随后将配准应用于细分的地图集。目视检查图像可确保充分套准。从分数（FA）和平均扩散率（MD）的图获得来自地图集的每个感兴趣区域的所有体素的平均值。

Tensor-Based Morphometry Protocol 基于张量的形态计量协议

Tensor Based Morphometry (`TBM`) is applied to `cross-sectional MRI data` for local volumetric comparisons between two or more groups of subjects, based on nonlinearly registering individual brain scans to a common anatomical template. Moreover, when TBM is applied to `a longitudinal MRI study`, the derived `Jacobian maps` reflect `the percentage of tissue change over time`.	基于张量形态计量学（`TBM`）应用于`横截面MRI数据`，以基于将单个脑部扫描非线性地配准的解剖模板的基础上，在两组或更多组受试者之间进行局部体积比较。此外，将TBM用于`纵向MRI研究`时，派生的`雅可比图`反映了`组织随时间变化的百分比`。

Center for Imaging of Neurodegenerative Diseases, UCSF

＊ Co-investigator: Norbert Schuff

FreeSurfer Overview and Quality Control FreeSurfer概述和质量控制

`Cortical reconstruction` and `volumetric segmentation` is performed with the FreeSurfer image analysis suite. FreeSurfer analysis was completed using Version 4.3 for ADNI1 `cross-sectional data`[UCSFFSX], Version 4.4 for ADNI1 `longitudinal data`[UCSFFSL], and Version 5.1 for ADNI GO and 2 data[UCSFFSX51].	使用FreeSurfer图像分析套件执行`皮质重建`和`体积分割`。 FreeSurfer分析是使用4.3版的ADNI1`横截面数据`[UCSFFSX]，4.4版的ADNI1`纵向数据`[UCSFFSL]，5.1版的ADNI GO和2个数据[UCSFFSX51]完成的。

ASL Perfusion Processing Methods ASL灌注处理方法

The Center for Imaging of Neurodegenerative Diseases (CIND) processing pipeline for Arterial Spin Label (ASL) imaging, prepares perfusion-weighted images (`PWI`) and computes `a quantitative map of cerebral blood flow (CBF)` and `a regional analysis`.	神经退行性疾病成像中心（CIND）处理管道以进行动脉自旋标记（ASL）成像，准备`灌注加权图像`（PWI）并计算`脑血流量（CBF）的定量图`和`区域分析`。

Hippocampal Voluming Analysis 海马体积分析

Semi-automated hippocampal volumetry was carried out using a commercially available high-dimensional brain mapping tool: Medtronic Surgical Navigation Technologies (SNT). This method of hippocampal voluming has a documented `reliability of an intraclass coefficient better than .94`.	使用商业可用的高维脑图工具：Medtronic 外科导航技术（SNT）进行半自动海马体积测量。这种海马体积的方法已证明`其组内系数的可靠性优于0.94`。

Alzheimer’s Disease Center, UC Davis

＊ Co-investigator: Charles S. DeCarli

Total Cranial Vault Segmentation: Method and Grading Rubric 整体颅骨分割：方法和评分标准

The quality of the total cranial vault segmentation using DSE T2 weighted MRI brain scans has been verified and individually graded.	使用DSE T2加权MRI脑部扫描进行的总颅穹顶分割的质量已得到验证并单独分级。

4-Tissue Segmentation Methods for ADNI MR Scans ADNI MR扫描的4种组织分割方法

This document describes the `4-Tissue segmentation` methods used for ADNI scans to produce segmentations of each image into four tissue types: `White Matter`, `Gray Matter`, `Cerebrospinal Fluid`, and `White Matter Hyperintensity`.	本文档介绍了用于ADNI扫描的`4-组织分割`方法，以将每个图像分割为四种组织类型：`白质`，`灰质`，`脑脊液`和`白质高强度`。

Institute of Neurology, University College London

＊ Co-investigator: Nick Fox

Brain and Ventricular Boundary Shift Integral 脑和心室边界移位积分

We describe the processing methods in the brain and ventricular boundary shift integral (BSI). The brain and ventricles were first semi-automatically delineated from the T1-weighted MRI scans. The repeat scans were then registered to the baseline scans using 9-degree-of-freedom registration. The intensity inhomogeneity between the baseline and registered repeat scans was corrected using the differential bias correction. Finally, BSI was calculated over the boundaries of the brain and ventricles respectively using the registered and corrected scans. 我们描述了大脑和心室边界移位积分（BSI）中的处理方法。首先通过T1加权MRI扫描对大脑和心室进行半自动描绘。然后使用9自由度配准将重复扫描配准到基线扫描。使用微分偏差校正校正基线和已记录的重复扫描之间的强度不均匀性。最后，使用配准和校正的扫描分别计算了脑和心室边界的BSI。

Biomedical Image Analysis, University of Pennsylvania 宾夕法尼亚大学生物医学图像分析

＊　Co-investigator: Christos Davatzikos

Spatial Patter of Abnormalities for Recognition of Early AD 识别早期AD的异常空间格局

The `SPARE-AD score` was calculated for each individual, using a specific pattern classification method. This score indicates the presence of an `AD-like spatial pattern of brain atrophy`, if positive, and otherwise if negative.	使用特定的模式分类方法为每个人计算`SPARE-AD分数`。该分数表明存在`脑萎缩的类AD空间模式`（如果为阳性，否则为阴性）。

PET-ANALYSIS

BANNER ALZHEIMER’S INSTITUTE

＊ CO-INVESTIGATOR: ERIC REIMAN

The Banner Alzheimer’s Institute (Arizona) of the ADNI PET Core analyzes the FDG-PET data using the computer package SPM5 to examine the progression that correlate with changes in cognition and to evaluate cross-sectional differences among three diagnostic groups: patients with AD, patients with MCI and normal healthy controls. All PET from the post-processed group-4 images were downloaded from the ADNI data archive in NIFTI format. ADNI PET核心的banner阿尔茨海默氏症研究所（亚利桑那州）使用计算机软件包SPM5分析了FDG-PET数据，以检查与认知变化相关的进展并评估三个诊断组（AD患者、MCI患者和正常健康对照者）的横断面差异 。后处理的第4组图像中的所有PET均以NIFTI格式从ADNI数据档案中下载。

* Summary of Statistical Parametric Mapping (SPM) Image Analysis 统计参数映射（SPM）图像分析摘要

* SPM Methods, Results and SPM-based Global Indices SPM方法，结果和基于SPM的全局指标

The Banner Alzheimer’s Institute (Arizona) of the ADNI PET Core analyzes the FDG-PET data using the computer package `SPM5` to examine the progression that correlate with changes in `cognition` and to evaluate `cross-sectional differences` among three diagnostic groups: patients with `AD`, patients with `MCI` and `normal` healthy controls. All PET from the post-processed group-4 images were downloaded from the ADNI data archive in NIFTI format.	ADNI PET核心的banner阿尔茨海默氏症研究所（亚利桑那州）使用计算机软件包`SPM5`分析了FDG-PET数据，以检查与`认知变化相关`的进展并评估三个诊断组（AD患者、MCI患者和正常健康对照者）的`横断面差异` 。后处理的第4组图像中的所有PET均以NIFTI格式从ADNI数据档案中下载。
* Summary of Statistical Parametric Mapping (SPM) Image Analysis 统计参数映射（SPM）图像分析摘要
* SPM Methods, Results and SPM-based Global Indices SPM方法，结果和基于SPM的全局指标

JAGUST LAB, UC BERKELEY

＊ CO-INVESTIGATOR: WILLIAM JAGUST

Processed FDG Data Methods

A literature search on PubMed was conducted using permutations of six terms relating to FDG-PET, AD, and MCI in order to identify studies that carried out direct whole-brain contrasts of FDG data and reported Z-scores or T-values corresponding to MNI or Talairach coordinates that represented regions in which FDG uptake differed significantly between patients (AD or MCI) and controls. This resulted in a total of 15 studies involving a cross-sectional comparison of AD, MCI, and/or Normal groups and 178 MNI and Talairach coordinates. A spreadsheet of these coordinates, associated t-values or z-scores was created. Talairach coordinates were transformed into MNI space, and t-values were converted to z-scores, which were then mapped to the space of the MNI template brain as intensity values. There were 14 overlapping coordinates, and their z-scores were added. Resulting images were smoothed with a standard 14 mm FWHM kernel. The values of all voxels in the image were then normalized, resulting in an image with values between 0 and 1. 使用与FDG-PET，AD和MCI相关的六个关键词的排列对PubMed进行文献搜索，以鉴定进行FDG数据直接全脑对比并报告与MNI或Talairach坐标对应的Z得分或T值的研究表示患者（AD或MCI）与对照组之间FDG摄取明显不同的区域。总共进行了15项研究，涉及AD，MCI和/或正常组的横断面比较以及178个MNI和Talairach坐标。创建了这些坐标，关联的t值或z分数的电子表格。将Talairach坐标转换为MNI空间，然后将t值转换为z分数，然后将z分数作为强度值映射到MNI模板大脑的空间。有14个重叠的坐标，并且添加了它们的z得分。用标准的14毫米FWHM内核对所得图像进行平滑处理。然后将图像中所有体素的值归一化，从而得到图像的值在0到1之间。

Processed Florbetapir (AV45) Data Methods

The Florbetapir methods description contains an explanation of the Freesurfer-based processing stream, and also a description of the methods used to derive our Florbetapir cutoff for this dataset. These processing methods were developed at Dr. William Jagust’s laboratory of the Helen Wills Neuroscience Institute, UC Berkeley and Lawrence Berkeley National Laboratory.	Florbetapir方法描述包含对基于`Freesurfer`的处理流的说明，还包含用于导出此数据集的`Florbetapir临界值`的方法的说明。这些加工方法是由加州大学伯克利分校海伦威尔斯神经科学研究所的William Jagust博士实验室和劳伦斯伯克利国家实验室开发的。

PET FACILITY, UNIVERSITY OF PITTSBURGH 匹兹堡大学

＊ CO-INVESTIGATOR: CHESTER MATHIS

An automated template-based method was used to sample multiple regions-of-interest (ROIs) on the ADNI PIB SUVR image. The PIB SUVR was downloaded from the ADNI website along with its corresponding ADNI Processed #3 MR image. The MR image choice was scanner dependent. The #4 PIB SUVR image has been co-registered to the first frame of the raw image file and averaged across frames (for dynamic acquisitions only), reoriented to Talairach space, intensity normalized so that the average of voxels within the mask was exactly 1, and smoothed to achieve a uniform isotropic resolution of 8 mm FWHM. Fourteen ROIs were generated. 一种基于模板的自动方法用于对ADNI PIB SUVR图像上的多个感兴趣区域（ROI）进行采样。从ADNI网站下载了PIB SUVR及其相应的、经过ADNI处理的＃3 MR图像。 MR图像选择取决于扫描仪。＃4 PIB SUVR图像已被配准到原始图像文件的第一帧，并在各帧取平均值（仅用于动态采集），重新定向到Talairach空间，强度归一化，从而使蒙版内的体素的平均值恰好为1 ，并进行平滑处理，以实现8 mm FWHM的均匀各向同性分辨率。 14个ROI被生成。

SUVR Re-Normalized to CER

Results were compiled with the following data:	使用以下数据编译结果：
Subject ID, Group, Sex, Age, Study Date, Study UID, Series UID, Image UID, ACG (Anterior Cingulate), FRC (Frontal Cortex), LTC (Lateral Temporal Cortex), PAR (Parietal Cortex), PRC (Precuneus Cortex), MTC (Mesial Temporal Cortex), OCC (Occipital Cortex), OCP (Occipital Pole), PON (Pons), AVS (Anterior Ventral Striatum), CER (Cerebellum), SMC (Sensory Motor Cortex), SWM (Sub-cortical White Matter), THL (Thalmus)	受试者ID，组，性别，年龄，研究日期，研究UID，系列UID，图像UID，ACG（前扣带），FRC（额叶皮层），LTC（侧颞叶皮层），PAR（顶叶皮层），PRC（足前皮层）），MTC（中颞叶皮层），OCC（枕骨皮层），OCP（枕骨皮层），PON（脑桥），AVS（前腹纹状体），CER（小脑），SMC（感觉运动皮层），SWM（皮层下层）白色物质），THL（丘脑）

Results were compiled with the following data:

使用以下数据编译结果：

Subject ID, Group, Sex, Age, Study Date, Study UID, Series UID, Image UID, ACG (Anterior Cingulate), FRC (Frontal Cortex), LTC (Lateral Temporal Cortex), PAR (Parietal Cortex), PRC (Precuneus Cortex), MTC (Mesial Temporal Cortex), OCC (Occipital Cortex), OCP (Occipital Pole), PON (Pons), AVS (Anterior Ventral Striatum), CER (Cerebellum), SMC (Sensory Motor Cortex), SWM (Sub-cortical White Matter), THL (Thalmus)

受试者ID，组，性别，年龄，研究日期，研究UID，系列UID，图像UID，ACG（前扣带），FRC（额叶皮层），LTC（侧颞叶皮层），PAR（顶叶皮层），PRC（足前皮层）），MTC（中颞叶皮层），OCC（枕骨皮层），OCP（枕骨皮层），PON（脑桥），AVS（前腹纹状体），CER（小脑），SMC（感觉运动皮层），SWM（皮层下层）白色物质），THL（丘脑）

Delineated ROIs	划定的感兴趣区域
AD Region of Interest PIB PET: Images of the scans with ROIs delineated. Related to Alzheimer’s disease (AD)	AD感兴趣区域PIB PET：描绘了带有ROI的扫描图像。与阿尔茨海默氏病（AD）相关
MCI Region of Interest PIB PET: Images of the scans with ROIs delineated. Related to Mild Cognitive Impairment (MCI)	MCI感兴趣区域PIB PET：描绘了带有ROI的扫描图像。与轻度认知障碍（MCI）相关
Normal Subjects Region of Interest PIB PET: Images of the scans with ROIs delineated. Related to Normal Subjects (NL)	正常对象感兴趣区域PIB PET：描绘了ROI的扫描图像。有关正常组（NL）

UNIVERSITY OF UTAH CENTER FOR ALZHEIMER’S CARE, IMAGING AND RESEARCH (CACIR) 犹他大学阿尔泽米尔护理，影像和研究中心

＊ CO-INVESTIGATOR: NORMAN L. FOSTER

The focus of The University of Utah component of the PET Imaging Core is on the individual image analysis and processing of molecular imaging data 分子成像数据 using 3-dimensional stereotactic surface projections 3维立体定向表面投影 (3D-SSP) computed by Neurostat, developed by Satoshi Minoshima [Minoshima et al., J Nucl Med 1995; 36:1238-48].

We have uploaded baseline ADNI1 FDG-PET images warped into Talairach space in dicom format. We have provided a document with 3D-SSP images of AD subjects showing a pattern of glucose hypometabolism that is consistent with frontotemporal dementia. We submit 6 numeric summary values that encapsulate information on the spatial extent and the severity of hypometabolism from FDG-PET and amyloid uptake values from amyloid-PET scans. 我们已经上载了以dicom格式扭曲到Talairach空间中的基线ADNI1 FDG-PET图像。我们提供了包含AD受试者的3D-SSP图像的文档，该图像显示了与额颞痴呆相一致的葡萄糖低代谢模式。我们提交了6个数字摘要值，这些摘要值封装了有关FDG-PET代谢不足的程度和严重性的空间范围信息以及来自淀粉样PET扫描的淀粉样摄取值的信息。

我们针对通常与阿尔茨海默氏病相关的区域：额叶和缔合皮质，计算18F-FDG，18F-AV45和11C-PIB图像中的平均摄取值。我们还通过Talairach空间中Z分数的像素级计算，计算出与对照组相比受试者的差异。

We have uploaded baseline `ADNI1 FDG-PET images` warped into Talairach space in dicom format. We have provided a document with 3D-SSP images of AD subjects showing a pattern of `glucose hypometabolism` that is consistent with `frontotemporal dementia`. We submit 6 numeric summary values that encapsulate information on `the spatial extent and the severity of hypometabolism from FDG-PET` and `amyloid uptake values from amyloid-PET` scans.	我们已经上载了以dicom格式扭曲到Talairach空间中的基线`ADNI1 FDG-PET图像`。我们提供了包含AD受试者的3D-SSP图像的文档，该图像显示了与`额颞痴呆`相一致的`葡萄糖低代谢`模式。我们提交了6个数字摘要值，这些摘要值封装了`有关FDG-PET代谢不足的程度和严重性的空间范围信息`以及`来自淀粉样PET扫描的淀粉样摄取值`的信息。
我们针对通常`与阿尔茨海默氏病相关的区域：额叶和缔合皮质，计算18F-FDG，18F-AV45和11C-PIB图像中的平均摄取值`。我们还`通过Talairach空间中Z分数的像素级计算，计算出与对照组相比受试者的差异。`

对于FDG扫描，我们提交了与一组正常老人相比明显代谢不足的像素数。这些以脑桥进行标准化。
对于淀粉样蛋白扫描，我们提交了与一组淀粉样蛋白阴性受试者相比摄取显着增加的像素数。这些以小脑和全体白质进行标准化。

我们的数字摘要值报告了重大变化的空间范围，计算出了重要的Z分数。报告具有空间范围偏差严重性的数字摘要值将有效的Z分数相加:

对于FDG扫描，这些值报告了低代谢的程度。
对于淀粉样蛋白扫描，这些值报告了示踪剂摄取的增加。

CENTER FOR BRAIN HEALTH, NYU SCHOOL OF MEDICINE 纽约大学医学院脑健康中心

＊ MONY J. DE LEON

Hippocampal Glucose Metabolism Sampling, the NYU HIPMASK 海马葡萄糖代谢采样NYU HIPMASK

We developed, validated, and published the `HIPMASK` technique for measurement of the `HIP` and other structures. HIPMASK generates a 3-D HIP sampling mask to accurately sample true HIP tissue with `approximately 95% anatomical overlap` between the HIPMASK and the co-registered MRI in normal elderly, MCI and AD groups.	我们开发，验证并发布了`HIPMASK`技术，用于`HIP`和其他结构的测量。在正常的老年人，MCI和AD组中，HIPMASK生成3-D HIP采样mask，以在HIPMASK和配准的MRI之间的`解剖重叠大约95％`的情况下，对真实的HIP组织进行精确采样。

QUALITY CONTROL

* Every Florbetapir and FDG PET scan is reviewed for protocol compliance by the ADNI PET QC team ADNI PET QC团队对每次Florbetapir和FDG PET扫描均进行了协议符合性审查
* If a correctable problem is identified, the PET QC team contacts the PET technologist directly 如果发现可纠正的问题，PET QC团队将直接与PET技术人员联系
* If a problem with the scan is identified and it is not fixable, the PET QC team provides the PET technologist with protocol guidance to apply to future PET scans 如果发现扫描问题并且无法解决，则PET QC团队会为PET技术人员提供方案指导，以应用于将来的PET扫描

Scans that fail the PET QC and are deemed unusable due to participant motion or non-compliance are `documented` with the reason as identified by the participant and the technician on the PET Scan Information Form. In these instances, `rescans` are only scheduled if the participant’s motion is believed to be correctable and is not a result of chronic illness or deteriorated cognitive ability.	如果PET QC失败且由于参与者的运动或不合规而被认为无法使用的扫描，则在PET扫描信息表上记录参与者和技术人员所标识的`原因`。在这种情况下，仅在认为参与者的动作是可以纠正的并且不是慢性病或认知能力下降的结果时才安排`重新扫描`。

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