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Interactive Feature Space Explorer© for Multi–ModalMagnetic Resonance Imaging

机译:用于多模式的交互式要素空间浏览器©磁共振成像

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摘要

Wider information content of multi–modal biomedical imaging is advantageous for detection, diagnosis and prognosis of various pathologies. However, the necessity to evaluate a large number images might hinder these advantages and reduce the efficiency. Herein, a new computer aided approach based on the utilization of feature space (FS) with reduced reliance on multiple image evaluations is proposed for research and routine clinical use.The method introduces the physician experience into the discovery process of FS biomarkers for addressing biological complexity, e.g., disease heterogeneity. This, in turn, elucidates relevant biophysical information which would not be available when automated algorithms are utilized. Accordingly, the prototype platform was designed and built for interactively investigating the features and their corresponding anatomic loci in order to identify pathologic FS regions.While the platform might be potentially beneficial in decision support generally and specifically for evaluating outlier cases, it is also potentially suitable for accurate ground truth determination in FS for algorithm development. Initial assessments conducted on two different pathologies from two different institutions provided valuable biophysical perspective. Investigations of the prostate magnetic resonance imaging data resulted in locating a potentialaggressiveness biomarker in prostate cancer. Preliminary findings on renal cellcarcinoma imaging data demonstrated potential for characterization of diseasesubtypes in the FS.
机译:多模态生物医学成像的信息内容更广泛,对于各种病理的检测,诊断和预后是有利的。但是,评估大量图像的必要性可能会阻碍这些优势并降低效率。本文提出了一种新的基于特征空间(FS)的利用的计算机辅助方法,该方法减少了对多个图像评估的依赖,用于研究和常规临床使用。该方法将医生的经验引入了FS生物标志物的发现过程中,以解决生物复杂性例如疾病异质性。反过来,这阐明了相关的生物物理信息,当使用自动算法时这些信息将不可用。因此,设计并构建了原型平台,用于交互式研究特征及其相应的解剖位点,以识别病理性FS区域,尽管该平台可能在总体上支持决策支持,尤其是在评估异常病例方面,但也可能适用FS中用于算法开发的准确地面真相确定。从两个不同机构对两种不同病理进行的初步评估提供了宝贵的生物物理观点。对前列腺磁共振成像数据的研究导致了潜在的定位前列腺癌的侵袭性生物标志物。肾细胞的初步发现癌影像数据显示出表征疾病的潜力FS中的子类型。

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