Non-invasive neurosurgical planning with Random Matrix Theory MRI

利用随机矩阵理论 MRI 进行无创神经外科规划

• 批准号: 10541655
• 负责人: Grigoriy Lemberskiy
• 金额: $ 5.5万
• 依托单位: MICROSTRUCTURE IMAGING, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-28 至 2022-04-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10541655
• 关键词: Algorithms; Anatomy; Award; Brain; Brain Mapping; Brain Neoplasms; Clinic; Clinical; Computer software; Data; Diagnosis; Diagnostic Imaging; Diffusion; Diffusion Magnetic Resonance Imaging; Excision; Feasibility Studies; Functional Magnetic Resonance Imaging; Future; Goals; Gold; Hospitals; Image; Innovation Corps; Investments; Joints; Location; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of brain; Medical Imaging; Modality; Modernization; Neurosurgeon; New York; Noise; Operative Surgical Procedures; Outcome; Patients; Perfusion; Persons; Phase; Price; Protocols documentation; Resolution; Risk; Scanning; Sensitivity and Specificity; Services; Signal Transduction; Small Business Technology Transfer Research; Time; Translating; United States National Institutes of Health; Universities; base; brain magnetic resonance imaging; clinical translation; clinically relevant; cost; denoising; image processing; image reconstruction; improved; improved outcome; innovation; multimodality; noninvasive diagnosis; prospective; radio frequency; radiologist; reconstruction; soft tissue; software as a service; survival prediction; theories; tumor; volunteer; white matter

PROJECT SUMMARY This application is intended for I-CORPs at NIH. Summary of the associated NIH NCI STTR Phase I About 23,830 people in the US are diagnosed per year with primary malignant brain tumors, and 200,000- 300,000 with metastatic brain tumors (10-30% of all cancers). Maximizing surgical resection of tumor is a major predictor of survival, but must be balanced against the risk of injuring eloquent white matter and cortical regions. To improve outcomes, the unmet need is to radically increase quality of noninvasive preoperative brain mapping. As the brain mapping gold standard, MRI offers unique soft-tissue contrast, anatomical and functional information of the brain, yet is inherently signal-to-noise ratio (SNR)-starved. The majority of brain mapping relies on diffusion (dMRI) and functional (fMRI), which are both especially severely limited by SNR. The MRI signal can be increased with higher-field; however, scanner prices scale with the field strength: 1.5T ~ $1.5M, 7T ~ $7M, as do installation and service costs. Since 90% of the MRIs in the US are 1.5T or below, it appears that the majority of hospitals cannot justify or afford high field MRI. SNR increase by the signal averaging is impractical from the scan time perspective, as brain tumor patients rarely tolerate scan times above 45 min. Our company, Microstructure Imaging (MICSI), is an award-winning New York University (NYU) spinoff that offers a software-as-a-service for medical image processing. Our product dramatically enhances the SNR of MRI brain mapping, which translates into increased resolution, image quality, sensitivity and specificity. Here we employ random matrix theory (RMT) to achieve an order-of-magnitude gain in SNR purely in software at the image reconstruction level, by utilizing the information across multiple radiofrequency coils and MRI contrasts within a single protocol. Our overarching goal is to optimize our RMT/MP-PCA image reconstruction algorithm for the clinical translation in brain mapping preoperative studies. Our Specific Aims are: Aim 1: Enabling lower field / higher resolution. We will develop and evaluate a multimodal (dMRI/fMRI) RMT denoising and reconstruction protocol in 6 volunteers on 1.5T and 3T with different image resolutions, and retrospectively in 30 preoperative brain mapping MRI patients. This data will be used to justify prospectively altering clinical MRI protocols during the anticipated Phase II of the STTR. Aim 2: Clinical feasibility study. 15 minutes of additional scan time for dMRI and 2 fMRI tasks at 1.2 mm isotropic resolution will be prospectively added to 10 brain mapping cases at 3T. The image quality with and without denoising will be assessed quantitatively, and qualitatively by radiologists and neurosurgeons. While the Phase-I STTR will optimize RMT in preoperative planning for brain tumors, in the future we will optimize protocols for any tumor type or location by joint RMT reconstruction of variety of MRI modalities (perfusion, T1/T2, dMRI, fMRI) to help them denoise each other and maximize the overall information content. RMT image reconstruction will open MRI to the developing world by bringing high-field quality to inexpensive low-field MRI.

项目摘要 此应用程序适用于NIH的I-Corps。 相关的NIH NCI STTR I期摘要 美国每年大约有23,830人患有原发性恶性脑肿瘤，200,000- 300,000转移性脑肿瘤（占所有癌症的10-30％）。最大化肿瘤手术切除是主要的 生存的预测指标，但必须与受伤雄辩的白质和皮质区域的风险保持平衡。 为了改善预后，未满足的需求是从根本上提高非侵入性术前脑图的质量。 作为大脑映射黄金标准，MRI提供了独特的软组织对比度，解剖和功能信息 大脑的却是固有的信噪比（SNR）。大多数大脑映射依赖于扩散 （DMRI）和功能性（fMRI），它们均受SNR的严重限制。 MRI信号可以通过高场所增加；但是，扫描仪的价格随现场强度而缩小：1.5T〜 150万美元，700万美元，安装和服务成本也是如此。由于美国90％的MRI为1.5T或以下 看来大多数医院无法证明或负担高领域的MRI合理。 SNR通过平均信号增加 从扫描时间的角度来看，由于脑肿瘤患者很少在45分钟以上耐受扫描时间，因此是不切实际的。 我们的公司微观结构成像（MICSI）是屡获殊荣的纽约大学（纽约大学）的衍生产品 提供用于医疗图像处理的软件服务。我们的产品大大增强了MRI的SNR 大脑映射，这转化为分辨率，图像质量，灵敏度和特异性的提高。 在这里，我们采用随机矩阵理论（RMT）来实现SNR中纯粹在软件中的速度增益 在图像重建级别，通过多个射频线圈和MRI的信息利用信息 在单个协议中对比。我们的总体目标是优化我们的RMT/MP-PCA图像重建 用于脑图术前研究的临床翻译算法。我们的具体目的是： 目标1：启用较低场 /更高分辨率。我们将开发和评估多模式（DMRI/FMRI）RMT 具有不同图像分辨率的1.5T和3T的6位志愿者中的denoising和重建协议，以及 在30个术前大脑映射MRI患者中回顾性。这些数据将用于前瞻性证明 在STTR的预期II期期间改变临床MRI方案。 目标2：临床可行性研究。 DMRI的15分钟额外扫描时间和2个FMRI任务为1.2 mm 各向同性分辨率将前瞻性地添加到3T时的10例大脑映射病例中。图像质量和 放射科医生和神经外科医生将对不脱糖性进行定量评估。 I期STTR将在脑肿瘤的术前计划中优化RMT，但将来我们将优化 通过各种MRI模态的联合RMT重建任何肿瘤类型或位置的方案（灌注， T1/T2，DMRI，fMRI），以帮助他们互相降级并最大化整体信息内容。 RMT图像 重建将通过为廉价的低场MRI带来高场质量，向发展中国家开放MRI。