TOPIC 417: GPU-ACCELERATED 3D MONTE CARLO SPECT RECONSTRUCTION ALGORITHM FOR PERSONALIZED RADIOPHARMACEUTICAL THERAPY

主题 417：用于个性化放射药物治疗的 GPU 加速 3D MONTE CARLO SPECT 重建算法

• 批准号: 10496814
• 负责人: PAUL WICKRE
• 金额: $ 39.74万
• 依托单位: VOXIMETRY, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2022-06-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10496814
• 关键词: 3-Dimensional; Adoption; Algorithms; Clinical; Code; Computer software; Contracts; Deposition; Dose; Drug Kinetics; Effectiveness; Funding; Hour; Kidney; Liver neoplasms; Lymphoma; Measures; Metastatic Prostate Cancer; Neuroendocrine Tumors; Normal tissue morphology; Outcome; Patients; Radiation; Radiopharmaceuticals; Site; Small Business Innovation Research Grant; Spleen; Statistical Data Interpretation; Testing; Tumor Tissue; Work; base; dosimetry; improved; in vivo; reconstruction; side effect; single photon emission computed tomography

Although radiopharmaceutical therapy (RPT) has worked well in patients with lymphoma, late-stage, metastatic prostate cancer, and neuroendocrine tumors, it is well known that because of variability in patient pharmacokinetics, standard dosing leads to clinical outcomes that are difficult to predict and drastically vary. However, it is possible to make RPT safer and more effective by first measuring the radiation emitted by the RPT agent in vivo using quantitative SPECT imaging and then calculating the radiation energy deposited in tumors and normal tissues using dosimetry software. A personalized RPT prescription can be derived that maximizes effectiveness and minimizes side effects. However, because commercially available SPECT reconstruction algorithms do not accurately correct for scatter, they are insufficient for determining personalized RPT prescriptions. Therefore, there is a clinically unmet need for better reconstruction algorithms which enable true quantitative SPECT imaging. The aim of this contract proposal is to build a graphics processing unit (GPU) software platform that can perform SPECT reconstruction with Monte Carlo (MC)-based scatter correction within 5 minutes, making it clinically viable. Fast and accurate GPU MC-based SPECT reconstruction (Torch Recon) will further improve the accuracy of RPT dosimetry and thereby accelerate the clinical adoption of our current SBIR-funded MC dose engine (Torch).

尽管放射性药物治疗（RPT）在淋巴瘤，晚期，前列腺癌和神经内分泌肿瘤的患者中表现良好，但众所周知，由于患者药代动力学的变异性，标准给药会导致很难预测和发生巨大变化的临床剂量。但是，通过首先使用定量SPECT成像测量RPT代理在体内发出的辐射，然后使用剂量图软件计算沉积在肿瘤和正常组织中的辐射能，可以使RPT更安全，更有效。可以得出个性化的RPT处方，以最大化有效性并最大程度地减少副作用。但是，由于市售的SPECT重建算法无法准确纠正散布，因此它们不足以确定个性化的RPT处方。因此，在临床上需要更好地重建算法，从而实现真正的定量SPECT成像。该合同提案的目的是建立一个图形处理单元（GPU）软件平台，该平台可以在5分钟内使用蒙特卡洛（MC）基于蒙特卡洛（MC）的散射校正执行SPECT重建，从而使其在临床上可行。基于GPU MC的快速精确的SPECT重建（Torch Recon）将进一步提高RPT剂量测定的准确性，从而加速我们当前SBIR资助的MC剂量发动机（Torch）的临床采用。