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

尽管放射性药物治疗 (RPT) 在淋巴瘤、晚期、转移性前列腺癌和神经内分泌肿瘤患者中效果良好，但众所周知，由于患者药代动力学的可变性，标准剂量会导致难以预测且差异巨大的临床结果。然而，首先使用定量 SPECT 成像测量体内 RPT 制剂发出的辐射，然后使用剂量测定软件计算沉积在肿瘤和正常组织中的辐射能量，可以使 RPT 更安全、更有效。可以得出个性化的 RPT 处方，以最大限度地提高有效性并最大限度地减少副作用。然而，由于商用 SPECT 重建算法不能准确校正散射，因此它们不足以确定个性化 RPT 处方。因此，临床上对能够实现真正定量 SPECT 成像的更好重建算法的需求尚未得到满足。该合同提案的目的是构建一个图形处理单元（GPU）软件平台，可以在5分钟内通过基于蒙特卡罗（MC）的散射校正进行SPECT重建，使其在临床上可行。快速、准确的基于 GPU MC 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 剂量引擎 (Torch) 的临床采用。

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).