Accurate MR-based PET Attenuation Correction for Quantitative Clinical Trials

用于定量临床试验的基于 MR 的准确 PET 衰减校正

• 批准号: 9759831
• 负责人: RAYMOND F MUZIC
• 金额: $ 43.94万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9759831
• 关键词: Abdomen; Address; Air; American College of Radiology Imaging Network; Anatomy; Area; Atlases; Attenuated; Brain; Cancer Patient; Cerebellum; Chest; Childhood; Choline; Clinic; Clinical; Clinical Research; Clinical Trials; Clinical Trials Cooperative Group; Clinical Trials Network; Collaborations; Complex; Data; Data Set; Dementia; Detection; Diagnostic; Electrons; Enrollment; Environment; Exhibits; FOLH1 gene; Fatty acid glycerol esters; Goals; Head and neck structure; Hybrids; Hydrogen; Image; Image Analysis; Imagery; Industrialization; Institution; Legal patent; Lesion; Liver; Localized Lesion; Lung; Magnetic Resonance Imaging; Malignant neoplasm of prostate; Measurement; Metastatic breast cancer; Methods; Molecular; Monitor; National Cancer Institute; New Agents; Operative Surgical Procedures; PET/CT scan; Patients; Pattern Recognition; Pelvis; Performance; Photons; Physiologic pulse; Population; Positron-Emission Tomography; Radiation Oncology; Randomized; Reference Standards; Reproducibility; Scanning; Signal Transduction; Staging; Structure of parenchyma of lung; Support System; System; Techniques; Therapeutic Intervention; Time; Tissues; Translating; Tumor Pathology; Vendor; Water; Work; attenuation; base; bone; clinical translation; density; diagnostic accuracy; falls; gray matter; image processing; imaging modality; improved; in vivo; lymph nodes; malignant breast neoplasm; metabolomics; new technology; novel; off-patent; patient population; prevent; programs; public health relevance; recruit; soft tissue; symposium; treatment response; whole body imaging; working group

 DESCRIPTION (provided by applicant): PET/MR is a hybrid imaging modality that combines the exquisite soft tissue contrast of MR with the molecular information of PET. In order to utilize PET/MR in a clinical trial setting, images must be quantitatively accurate, and be reproducible across vendor platforms and institutions. Accurate MR-based attenuation correction (MR- AC) is currently a technical barrier to accomplishing these goals. A specific challenge is differentiating bone from air. While these tissue types have dramatic differences in the degree to which they attenuate photons, they both have negligible signal with conventional MR pulse sequences. Consequently, current MR-AC methods exhibit SUV errors of 20% or greater, particularly in areas within and adjacent to bone, and therefore, current PET/MR scanners do not meet the SUV accuracy required by NCI/ACRIN for clinical trials qualification. Ultra-short echo time (UTE) MR can capture signal in bone prior to its rapid signal decay and is a promising approach to achieve more accurate MR-AC. However, current UTE approaches have low image quality, clinically impractical acquisition times, and a field of view that is too limited for whole-body imaging. The goal of this academic-industrial collaboration is to address these current limitations of UTE by developing accurate and clinically practical methods for whole-body MR-AC, further refining novel and patented methods developed by our working group. The specific aims to realize the goal: 1) Develop novel MR acquisition methods that maximize tissue information regarding photon attenuation for whole-body imaging. We will use our preliminary work in brain as a starting point, which employs an undersampled UTE-Dixon acquisition. 2) Establish image processing methods for determining photon attenuation on a voxel-level. Pattern recognition methods will be developed to analyze the combination of features extracted from the UTE- Dixon data sets. The photon attenuation will be estimated on a continuous scale reflecting the fractional composition of different tissue types within each voxel and also by directly mapping to CT values. 3) Demonstrate clinical feasibility of the above proposed MR-based attenuation correction methods. Clinical scanning with a commercial PET/MR system will be performed in a cancer patient population comparing the developed MR-AC methods to CT-AC values for SUV accuracy, image quality, and diagnostic accuracy. By bringing together cutting-edge advances in both MR acquisition and image analyses, the successful completion of these aims will achieve SUVs that are within 5% of those obtained with PET/CT (reference standard) with clinically appropriate acquisition time, image quality, and diagnostic accuracy, capable of supporting quantitative clinical trials with commercial PET/MR systems.

 描述（由申请人提供）：PET/MR 是一种混合成像方式，将 MR 的精致软组织对比度与 PET 的分子信息结合起来。 在临床试验环境中，PET/MR 图像必须在定量上准确，并且能够在平台供应商和机构之间重现。基于 MR 的精确衰减校正 (MR-AC) 目前是实现这些目标的技术障碍。 虽然这些组织类型对光子的衰减程度存在显着差异，但使用传统 MR 脉冲序列检查时，它们的信号都可以忽略不计，但当前的 MR-AC 方法表现出 20% 或更高的 SUV 误差，特别是在某些区域。超短回波时间 (UTE) MR 可以在快速信号之前捕获骨骼中的信号。然而，目前的 UTE 方法图像质量低，采集时间不切实际，而且视野对于全身成像来说过于有限。产业合作旨在解决当前的这些限制 通过准确且临床实用的全身 MR-AC 方法，进一步完善我们工作组开发的新颖且专利的方法，以实现以下目标： 1) 开发新颖的 MR 采集方法，最大限度地提高有关光子衰减的组织信息。我们将以我们在大脑中的初步工作为起点，采用欠采样 UTE-Dixon 采集 2) 建立用于确定光子衰减的图像处理方法。将开发模式识别方法来分析从 UTE-Dixon 数据集中提取的特征组合，并将在反映每个体素内不同组织类型的分数组成的连续尺度上进行估计。 3) 证明上述基于 MR 的衰减校正方法的临床可行性，将在癌症患者群体中进行临床扫描，比较所开发的 MR-AC 方法与 CT-AC 值。为了通过将 MR 采集和图像分析方面的尖端技术结合起来，SUV 准确度、图像质量和诊断准确度将与 PET/CT（参考标准）相比，成功实现 SUV 的误差在 5% 以内。具有临床上适当的采集时间、图像质量和诊断准确性，能够支持商业 PET/MR 系统的定量临床试验。

项目成果

Knowledge-leveraged transfer fuzzy C-Means for texture image segmentation with self-adaptive cluster prototype matching.

知识杠杆转移模糊 C 均值用于具有自适应集群原型匹配的纹理图像分割。

• 发表时间: 2017-08-15
• 影响因子: 8.8
• 作者: Qian, Pengjiang;Zhao, Kaifa;Jiang, Yizhang;Su, Kuan;Deng, Zhaohong;Wang, Shitong;Muzic Jr, Raymond F
• 通讯作者: Muzic Jr, Raymond F

Transforming UTE-mDixon MR Abdomen-Pelvis Images Into CT by Jointly Leveraging Prior Knowledge and Partial Supervision.

通过联合利用先验知识和部分监督，将 UTE-mDixon MR 腹部骨盆图像转换为 CT。

• 发表时间: 2021
• 作者: Qian, Pengjiang;Zheng, Jiamin;Zheng, Qiankun;Liu, Yuan;Wang, Tingyu;Al Helo, Rose;Baydoun, Atallah;Avril, Norbert;Ellis, Rodney J;Friel, Harry;Traughber, Melanie S;Devaraj, Ajit;Traughber, Bryan;Muzic, Raymond F
• 通讯作者: Muzic, Raymond F

mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

基于 mDixon 的合成 CT 生成，联合使用转移模糊聚类和基于主动学习的分类，对腹部和骨盆进行 PET 衰减校正。

• 发表时间: 2020-04
• 影响因子: 10.6
• 作者: Qian, Pengjiang;Chen, Yangyang;Kuo, Jung;Zhang, Yu;Jiang, Yizhang;Zhao, Kaifa;Al Helo, Rose;Friel, Harry;Baydoun, Atallah;Zhou, Feifei;Heo, Jin Uk;Avril, Norbert;Herrmann, Karin;Ellis, Rodney;Traughber, Bryan;Jones, Robert S;Wang
• 通讯作者: Wang

Abdominopelvic MR to CT registration using a synthetic CT intermediate.

使用合成 CT 中间体进行腹盆腔 MR 到 CT 配准。

• 发表时间: 2022-09
• 作者: Heo, Jin Uk;Zhou, Feifei;Jones, Robert;Zheng, Jiamin;Song, Xin;Qian, Pengjiang;Baydoun, Atallah;Traughber, Melanie S;Kuo, Jung;Helo, Rose Al;Thompson, Cheryl;Avril, Norbert;DeVincent, Daniel;Hunt, Harold;Gupta, Amit;Faraji, Navid;Kharo
• 通讯作者: Kharo

Cross-domain, soft-partition clustering with diversity measure and knowledge reference.

具有多样性度量和知识参考的跨域软分区聚类。

• 发表时间: 2016-02
• 影响因子: 8
• 作者: Qian, Pengjiang;Sun, Shouwei;Jiang, Yizhang;Su, Kuan;Ni, Tongguang;Wang, Shitong;Muzic Jr, Raymond F
• 通讯作者: Muzic Jr, Raymond F