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Purpose Reduced gray-white matter contrast along the central sulcus has been described on T1- and T2-weighted magnetic resonance imaging (MRI). The purpose of this study was to assess the gray-white matter contrast of the motor cortex on double inversion recovery (DIR), a sequence with superior gray-white matter differentiation. Methods The gray-white matter signal on DIR was retrospectively compared to T1-weighted magnetization-prepared rapid gradient echo (T1-MPRAGE) using normal ( n  = 25) and abnormal ( n  = 25) functional MRI (fMRI) exams. Quantitative gray-white matter contrast ratios (CR) of the precentral and adjacent gyri were obtained on normal exams. Two neuroradiologists qualitatively rated reduced gray-white matter contrast of the hemispheres of both normal and abnormal exams. Hand motor functional mapping was used as a reference. Results In normal hemispheres ( n  = 50), the mean CR was significantly lower on DIR (0.44) vs T1-MPRAGE (0.63, p  < 0.001). Reduced gray-white matter contrast was categorized as “definitely present” more frequently on DIR than T1-MPRAGE by reviewers in both normal ( n  = 50; reviewer 1 DIR 88% and MPRAGE 68%, p  = 0.02; reviewer 2 DIR 86% and T1-MPRAGE 64%; p =0.01) and abnormal hemispheres ( n  = 50; reviewer 1 DIR 80% and T1-MPRAGE 38%, p  < 0.001; reviewer 2 DIR 74% and T1-MPRAGE 46%, p  = 0.005). Conclusion Reduced gray-white matter contrast of the motor cortex is more pronounced on DIR compared to T1-MPRAGE on quantitative and qualitative assessments of normal MRI exams. In abnormal cases, reviewers more definitively identified the motor cortex on DIR. In cases with distorted brain anatomy, DIR may be a useful adjunct sequence to localize the motor cortex.

The aim of this study is to develop an automated classification method for Brain Tumor Reporting and Data System (BT-RADS) categories from unstructured and structured brain magnetic resonance imaging (MR) reports. This retrospective study included 1410 BT-RADS structured reports dated from January 2014 to December 2017 and a test set of 109 unstructured brain MR reports dated from January 2010 to December 2014. Text vector representations and semantic word embeddings were generated from individual report sections (i.e., “History,” “Findings,” etc.) using Tf-idf statistics and a fine-tuned word2vec model, respectively. Section-wise ensemble models were trained using gradient boosting (XGBoost), elastic net regularization, and random forests, and classification accuracy was evaluated on an independent test set of unstructured brain MR reports and a validation set of BT-RADS structured reports. Section-wise ensemble models using XGBoost and word2vec semantic word embeddings were more accurate than those using Tf-idf statistics when classifying unstructured reports, with an f1 score of 0.72. In contrast, models using traditional Tf-idf statistics outperformed the word2vec semantic approach for categorization from structured reports, with an f1 score of 0.98. Proposed natural language processing pipeline is capable of inferring BT-RADS report scores from unstructured reports after training on structured report data. Our study provides a detailed experimentation process and may provide guidance for the development of RADS-focused information extraction (IE) applications from structured and unstructured radiology reports.