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是一种混合成像模式，将MR的精细软组织对比与PET的分子信息相结合。为了利用 在临床试验环境中，PET/MR图像必须定量准确，并且在供应商平台和机构之间可重现。准确的基于MR的衰减校正（MR-AC）目前是实现这些目标的技术障碍。一个具体的挑战是差异化 空气中的骨头虽然这些组织类型在它们衰减光子的程度上具有显著差异，但是它们在常规MR脉冲序列下都具有可忽略的信号。因此，当前的MR-AC方法表现出20%或更大的SUV误差，特别是在骨内和骨附近的区域中，因此，当前的PET/MR扫描仪不满足NCI/ACRIN对临床试验鉴定所要求的SUV精度。超短回波时间（UTE）MR可以在信号快速衰减之前捕获骨骼中的信号，是实现更准确MR-AC的一种有前途的方法。然而，当前的UTE方法具有低图像质量、临床上不切实际的采集时间以及对于全身成像而言太有限的视场。这种学术-工业合作的目标是解决目前的这些限制 通过为全身MR-AC开发准确和临床实用的方法，进一步完善我们工作组开发的新颖和专利方法，实现该目标的具体目标是：1）开发新的MR采集方法，最大限度地提高全身成像光子衰减的组织信息。我们将以大脑中的初步工作为起点，其中采用欠采样的UTE-Dixon采集。2)建立用于在体素水平上确定光子衰减的图像处理方法。将开发模式识别方法，以分析从UTE-狄克逊数据集提取的特征组合。光子衰减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估计，并且还通过直接映射到CT值来估计。3)证明上述提出的基于MR的衰减校正方法的临床可行性。将在癌症患者人群中使用市售PET/MR系统进行临床扫描，比较开发的MR-AC方法与CT-AC值的SUV准确度、图像质量和诊断准确度。通过将MR采集和图像分析的前沿技术结合在一起，这些目标的成功实现将使SUV达到PET/CT（参考标准）获得的SUV的5%以内，具有临床适当的采集时间、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

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

• DOI: 10.1109/tcbb.2020.2979841
• 发表时间: 2021-01
• 期刊: IEEE/ACM transactions on computational biology and bioinformatics
• 作者: Qian P;Zheng J;Zheng Q;Liu Y;Wang T;Al Helo R;Baydoun A;Avril N;Ellis RJ;Friel H;Traughber MS;Devaraj A;Traughber B;Muzic RF
• 通讯作者: Muzic RF

Cluster Prototypes and Fuzzy Memberships Jointly Leveraged Cross-Domain Maximum Entropy Clustering.

聚类原型和模糊隶属度联合利用跨域最大熵聚类。

• DOI: 10.1109/tcyb.2015.2399351
• 发表时间: 2016-01
• 期刊: IEEE transactions on cybernetics
• 影响因子: 11.8
• 作者: Qian P;Jiang Y;Deng Z;Hu L;Sun S;Wang S;Muzic RF Jr
• 通讯作者: Muzic RF Jr

SSC-EKE: Semi-Supervised Classification with Extensive Knowledge Exploitation.

SSC-EKE：具有广泛知识利用的半监督分类

• DOI: 10.1016/j.ins.2017.08.093
• 发表时间: 2018-01
• 期刊: Information sciences
• 影响因子: 8.1
• 作者: Qian P;Xi C;Xu M;Jiang Y;Su KH;Wang S;Muzic RF Jr
• 通讯作者: Muzic RF Jr

Abdominopelvic MR to CT registration using a synthetic CT intermediate.

• DOI: 10.1002/acm2.13731
• 发表时间: 2022-09
• 期刊: JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS
• 影响因子: 2.1
• 作者: Heo, Jin Uk;Zhou, Feifei;Jones, Robert;Zheng, Jiamin;Song, Xin;Qian, Pengjiang;Baydoun, Atallah;Traughber, Melanie S.;Kuo, Jung-Wen;Al Helo, Rose;Thompson, Cheryl;Avril, Norbert;DeVincent, Daniel;Hunt, Harold;Gupta, Amit;Faraji, Navid;Kharouta, Michael Z.;Kardan, Arash;Bitonte, David;Langmack, Christian B.;Nelson, Aaron;Kruzer, Alexandria;Yao, Min;Dorth, Jennifer;Nakayama, John;Waggoner, Steven E.;Biswas, Tithi;Harris, Eleanor;Sandstrom, Susan;Traughber, Bryan J.;Muzic, Raymond F., Jr.
• 通讯作者: Muzic, Raymond F., Jr.

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

自适应聚类原型匹配的知识杠杆传递模糊 C 均值纹理图像分割

• DOI: 10.1016/j.knosys.2017.05.018
• 发表时间: 2017-08-15
• 期刊: Knowledge-based systems
• 影响因子: 8.8
• 作者: Qian P;Zhao K;Jiang Y;Su KH;Deng Z;Wang S;Muzic RF Jr
• 通讯作者: Muzic RF Jr