3D Multi-Contrast MRI with Automatic Brain Tissue and Lesion Segmentation

项目摘要 这项工作的目标是开发和评估一种新的策略，多对比成像和自动病变 神经胶质瘤的分割，将显著影响临床成像工作流程，并实现稳健的测量， 监测治疗反应。所得到的图像、定量图和分割区域将具有 1 mm各向同性分辨率，全脑覆盖，6分钟扫描时间，方便日常使用， 缓解评估的客观标准，并揭示病变生长随时间的细微变化， 通过目测评估或横截面肿瘤直径的测量而遗漏。除了产生 定量T1、T2和大分子质子分数图沿着与常规T2-、FLAIR-和T1- 加权图像，我们的自动分割区域，对应于对比度增强和T2- 将在不注射基于钆的造影剂的情况下进行高密度损伤。 我们的策略涉及使用具有多个反转脉冲的高度加速的3D MRI序列来实现 通过在不完全反转恢复期间连续采集数据进行全脑多对比度成像， 平衡稳态自由旋进这种独特的方法克服了传统方法固有的局限性， 在有限的窗口期间采集数据并且需要通过结合 在MR指纹识别方法中使用的字典搜索。我们最近的研究表明 通过利用增加的对比度， 自动分割对比增强病变、浸润性肿瘤和水肿，并强调需要 以进一步细化参数并对患者进行评估。具体而言，在目标1中，我们将开发和 评价：1）基于由水组成的多室模型的定量多参数绘图 和大分子质子池，并包括磁化转移效应; 2）患者特异性自动 利用定量组织T1、T2和大分子质子分数映射的组织分割，3） 使用运动补偿来改善组织和病变分割。由此产生的定量地图， 将通过与其单独参考进行比较来评价合成图像和组织分割 在正常脑组织和病变中。然后，目标2将利用最佳参数集来评估结果 增强高级别胶质瘤患者的分段病变。从自动化衍生而来的 我们的多对比度扫描的分割前和注射钆后将进行比较，手动 为了确定造影剂注射前多重造影扫描是否可以 准确地：1）描绘对比增强病变和2）从水肿中分离浸润性肿瘤。

PROJECT SUMMARY The goal of this work is to develop and evaluate a novel strategy for multi-contrast imaging and automatic lesion segmentation of gliomas that will significantly impact clinical imaging workflow and enable robust measures for monitoring therapeutic response. The resulting images, quantitative maps, and segmented regions will have 1mm isotropic resolution, full brain coverage, in 6 minutes scan time, facilitating their routine use in providing objective criteria for response assessment and revealing subtle changes in lesion growth over time that can be missed by visual assessment or measurements of cross-sectional tumor diameter. Besides generating quantitative T1, T2, and macro-molecular proton fraction maps along with conventional T2-, FLAIR-, and T1- weighted images, our automatic segmentation of regions that correspond to contrast-enhancing and T2- hyperinense lesions will be performed without the injection of a gadolinium-based contrast agent. Our strategy involves using a highly accelerated 3D MRI sequence with multiple inversion pulses to achieve whole brain, multi-contrast imaging by continuously acquiring data during incomplete inversion recovery with balanced steady state free precession. This unique approach overcomes the limitations inherent in conventional anatomical imaging that acquire data during a limited window and require full inversion recovery by incorporating dictionary searching that is used in the MR fingerprinting approach. Our recent studies demonstrate the potential of this sequence in imaging patients with brain tumors by taking advantage of the added contrasts to automatically segment the contrast-enhancing lesion, infiltrative tumor, and edema, as well as highlight the need for further refinement of parameters and evaluation in patients. Specifically, in Aim 1 we will develop and evaluate: 1) quantitative multi-parametric mapping based on a multiple-compartment model comprised of water and macromolecular proton pools and includes magnetization transfer effects; 2) a patient-specific automated tissue segmentation that utilizes quantitative tissue T1, T2, and macromolecular proton fraction mapping, 3) using motion compensation to improve tissue and lesion segmentation. The resulting quantitative maps, synthetic images, and tissue segmentations will be evaluated through comparison with their individual references in normal brain tissue and lesions. Aim 2 will then utilize the best set of parameters to evaluate the resulting segmented lesions in patients with enhancing high-grade gliomas. Volumes derived from the automatic segmentation of our multi-contrast scan pre- and post-injection of gadolinium will be compared to manually defined regions of interest in order to determine whether the pre-contrast injection multi-contrast scan can accurately: 1) delineate the contrast-enhancing lesion and 2) separate infiltrative tumor from edema.