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NIH NCI STTR I期总结 在美国，每年约有23，830人被诊断患有原发性恶性脑肿瘤， 30万例转移性脑肿瘤（占所有癌症的10-30%）。最大限度地手术切除肿瘤是一个主要的 是生存的预测因素，但必须与损伤明显的白色物质和皮质区域的风险相平衡。 为了改善结果，未满足的需求是从根本上提高无创术前脑标测的质量。 作为脑成像的黄金标准，MRI提供了独特的软组织对比度，解剖和功能信息 然而，它天生就缺乏信噪比（SNR）。大多数的大脑映射依赖于扩散 （dMRI）和功能性（fMRI），这两者都特别严重地受到SNR的限制。 MRI信号可以随着更高的场而增加;然而，扫描仪价格与场强成比例：1.5T ~ 150万美元，7 T ~ 700万美元，安装和服务费用也是如此。由于美国90%的MRI都是1.5T或以下， 似乎大多数医院不能证明或负担得起高场MRI。通过信号平均提高SNR 从扫描时间的角度来看，这是不切实际的，因为脑肿瘤患者很少能忍受超过45分钟的扫描时间。 我们的公司，微结构成像（MICSI），是一个屡获殊荣的纽约大学（NYU）分拆， 为医学图像处理提供软件即服务。我们的产品显著提高了MRI的SNR 大脑映射，这转化为提高分辨率，图像质量，灵敏度和特异性。 在这里，我们采用随机矩阵理论（RMT），以实现一个数量级的增益，在信噪比纯粹的软件 在图像重建水平，通过利用多个射频线圈和MRI之间的信息 在一个单一的协议。我们的首要目标是优化我们的RMT/MP-PCA图像重建 用于脑映射术前研究的临床翻译算法。我们的具体目标是： 目标1：实现低场/高分辨率。我们将开发和评估多模式（dMRI/fMRI）RMT 6名志愿者在1.5T和3 T上使用不同图像分辨率进行降噪和重建，以及 回顾性研究了30例术前脑标测MRI患者。该数据将用于前瞻性证明 在预期的STTR第二阶段期间改变临床MRI方案。 目标2：临床可行性研究。1.2 mm处dMRI和2个fMRI任务的额外扫描时间为15分钟 将在3 T下对10例脑标测病例前瞻性添加各向同性分辨率。图像质量， 将由放射科医生和神经外科医生定量和定性地评估没有去噪的情况。 虽然I期STTR将优化脑肿瘤术前计划中的RMT，但未来我们将优化 通过各种MRI模态（灌注， T1/T2，dMRI，fMRI），以帮助它们相互降噪，并最大限度地提高整体信息含量。RMT图像 重建将使MRI向发展中国家开放，为廉价的低场MRI带来高场质量。