Low-Field High-Performance MRI for Screening Clinically Significant Prostate Cancer

项目总结/摘要 前列腺癌（PCa）是最常见的恶性肿瘤，是癌症相关死亡的第二大原因 在美国的男性中。尽管PCa的发病率高，对人类和经济造成巨大影响， 对于有临床意义的前列腺癌没有好的筛选试验。前列腺特异性血液检查 抗原（PSA）是廉价的，但缺乏特异性的临床显着PCa。在过去的十年里，多... 参数前列腺MRI已被证明在检测中具有越来越重要的作用， 具有临床意义的PCa的定位。尽管前列腺MRI的价值已得到证实，但仍有两个主要问题 在人群水平上，前列腺MRI作为一线筛查工具的临床实施面临的挑战：（1）高 扫描仪和检查的成本，这降低了可访问性，以及（2）MRI检查缺乏鲁棒性。 为了克服这些限制，我们建议建立一个低场，强大的，广泛访问，因此低- 用于PCa筛查的高性价比MRI系统。在较低场强下成像的主要挑战 与1.5 T和3.0 T相比，信噪比（SNR）更低，因此采集时间更长。在这 我们的建议旨在解决这些效率和扫描时间的挑战，在低场。我们的临床团队 成像科学家将带来新的采集和重建技术来恢复信噪比， 特别是随机矩阵理论（RMT-我们中心最近推出的技术）和深度学习- 基于图像重建（我们小组帮助开拓的领域）。我们将评估这些技术， 健康志愿者和前列腺癌患者。随后，我们将进行低场的头对头比较， 在30例已知或疑似PCa患者中进行常规临床1.5 T或3.0 T MRI检查的双参数检查。

Project Summary/Abstract Prostate cancer (PCa) is the most common malignancy and is the 2nd leading cause of cancer-related mortality in males in the United States. Despite high incidence and tremendous human and economic impact of PCa, there is no good screening test for clinically significant PCa. Commonly used blood test for Prostate Specific Antigen (PSA) is inexpensive but lack specificity for clinically significant PCa. Over the last decade, multi- parametric prostate MRI has been shown to have an increasingly important role in the detection and localization of clinically significant PCa. Despite the demonstrated value of prostate MRI, there are two major challenges in clinical implementation of prostate MRI as a first-line screening tool at a population level: (1) high cost of the scanner and the exam, which decreases accessibility, and (2) lack of robustness of the MRI exam. To overcome these limitations we propose establishing a low-field, robust, widely accessible, and thus low- cost high-performance MRI system for PCa screening. Major challenges for imaging at lower field strength is the lower signal to noise (SNR) and thus longer acquisition times compared to 1.5 and 3.0 T. In this proposal we aim to address these efficiency and scan time challenges at low field. Our team of clinical and imaging scientists will bring to bear novel acquisition and reconstruction techniques to restore SNR, specifically random matrix theory (RMT- a technique our center has recently introduced), and deep-learning- based image reconstruction (an area our group has helped to pioneer). We will evaluate these techniques in healthy volunteers and patients with PCa. Subsequently, we will perform head-to-head comparison of low-field bi-parametric exam to routine clinical 1.5 T or 3.0 T MRI exams in 30 patients with known or suspected PCa.