Non-invasive neurosurgical planning with Random Matrix Theory MRI

项目总结 在美国，每年约有23,830人被诊断出患有原发性恶性脑瘤，20万人- 30万人患有转移性脑瘤(占所有癌症的10%-30%)。最大限度地手术切除肿瘤是一项重要的 这是生存的预测因素，但必须与损伤雄辩的脑白质和皮质区域的风险进行权衡。 为了改善结果，尚未得到满足的需求是从根本上提高非侵入性术前脑标测的质量。 作为脑成像的黄金标准，MRI提供了独特的软组织对比、解剖和功能信息 然而，它天生就缺乏信噪比(SNR)。大部分的脑图都依赖于扩散 (DMRI)和功能性(FMRI)，两者都受到信噪比的特别严重限制。 MRI信号随场强的增加而增大，但扫描仪的价格随场强的变化而变化：1.5T~ 150万美元，700万美元~700万美元，安装和服务成本也是如此。由于美国90%的核磁共振成像是1.5T或以下，它 似乎大多数医院无法证明或负担得起高场磁共振成像。通过信号平均提高信噪比 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容忍超过45分钟的扫描时间。 我们的公司微结构成像(MICSI)是纽约大学(NYU)的一个获奖分支， 为医学图像处理提供软件即服务。我们的产品极大地提高了磁共振成像的信噪比 脑成像，这转化为更高的分辨率、图像质量、灵敏度和特异度。 在这里，我们使用随机矩阵理论(RMT)来纯软件地实现数量级的SNR增益 在图像重建层面，通过利用跨多个射频线圈和磁共振成像的信息 在单一协议中进行对比。我们的首要目标是优化我们的RMT/MP-PCA图像重建 脑电地形图术前研究中的临床翻译算法。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3T不同图像分辨率下的去噪和重建方案 回顾性分析30例术前脑电地形图检查结果。这些数据将被用来前瞻性地证明 在预期的STTR第二阶段期间改变临床MRI方案。 目的2：临床可行性研究。1.2 mm的dMRI和2个fMRI任务的额外扫描时间为15分钟 各向同性分辨率将在3T时被预期添加到10个脑图病例中。具有和的图像质量 如果没有去噪，放射科医生和神经外科医生将对其进行定量和定性评估。 虽然第一阶段STTR将在脑肿瘤的术前计划中优化RMT，但在未来我们将优化 通过多种MRI模式的联合RMT重建用于任何肿瘤类型或位置的方案(灌注， T1/t2、dMRI、fMRI)，以帮助它们彼此去噪并最大化整体信息内容。RMT图像 重建将把高场质量带给廉价的低场MRI，从而向发展中国家开放MRI。

PROJECT SUMMARY 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.