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

低场高性能 MRI 用于筛查有临床意义的前列腺癌

基本信息

批准号：
10306404
负责人：
Hersh Chandarana
金额：
$ 19.41万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-12-01 至 2022-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10306404
关键词：
Acoustics Address Advisory Committees American Cancer Society Amplifiers Area Biopsy Blood Tests Cancer Etiology Clinical Deposition Detection Diagnosis Diagnostic Diagnostic Imaging Diffusion Diffusion Magnetic Resonance Imaging Disease Early Diagnosis Early treatment Evaluation Fellowship Funding Gases Helium Hip region structure Human Image Image Enhancement Incidence Intestines Literature Magnetic Resonance Imaging Malignant Neoplasms Malignant neoplasm of prostate Measurement Methodology Methods Noise Ownership PSA screening Pathway interactions Patients Performance Play Population Predisposition Preventive Prostate Prostate-Specific Antigen Protocols documentation Reporting Research Resolution Role Safety Sampling Scanning Screening for Prostate Cancer Screening procedure Sensitivity and Specificity Signal Transduction Source Specificity System T2 weighted imaging Techniques Testing Time Training United States United States National Institutes of Health Ursidae Family aged base cancer care clinical implementation clinically significant contrast enhanced cost cost estimate deep learning denoising design economic impact head-to-head comparison healthy volunteer image reconstruction imaging scientist imaging system improved innovation light weight male men mortality novel operation prostate lesions prototype radiologist reconstruction screening screening guidelines theories tumor volunteer

项目摘要

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.

项目概要/摘要前列腺癌 (PCa) 是最常见的恶性肿瘤，也是癌症相关死亡的第二大原因在美国男性中。尽管 PCa 发病率高且对人类和经济产生巨大影响，对于具有临床意义的前列腺癌，目前尚无良好的筛查试验。前列腺特异性的常用血液检查抗原 (PSA) 价格低廉，但缺乏对具有临床意义的 PCa 的特异性。在过去的十年里，多参数化前列腺 MRI 已被证明在检测和诊断方面发挥着越来越重要的作用。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，但仍存在两个主要问题：前列腺 MRI 作为人群一线筛查工具在临床实施中面临的挑战：(1) 高扫描仪和检查的成本，这降低了可及性，(2) MRI 检查缺乏稳健性。为了克服这些限制，我们建议建立一个低场、鲁棒、广泛可访问的、因此低场用于 PCa 筛查的成本高性能 MRI 系统。较低场强成像的主要挑战与 1.5 和 3.0 T 相比，信噪比 (SNR) 较低，因此采集时间较长。我们的提案旨在解决低场下的这些效率和扫描时间挑战。我们的临床团队成像科学家将采用新颖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一项技术）和深度学习—— 基于图像重建（我们小组帮助开拓的一个领域）。我们将评估这些技术健康志愿者和 PCa 患者。随后我们将进行低场的头对头对比对 30 名已知或疑似 PCa 患者进行双参数检查与常规临床 1.5 T 或 3.0 T MRI 检查。