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A Machine Learning Approach for Creating Texture-Preserved MRI Tumor Models From Clinical Sequences

M Vallières

M Vallieres*, A Diamant , S Laberge , I R. Levesque , I El Naqa , McGill University, Montreal, QC, Canada


SU-E-J-250 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: We hypothesize that MRI texture-based tumor outcome prediction models could be optimized via numerical simulations of image acquisitions. These simulations require knowledge of T1 and T2 relaxation times as inputs. The goal of this study is to evaluate the feasibility of using machine learning techniques to infer T1 and T2 tumor maps with accurate texture preservation for simulation inputs from clinical sequences.

Methods: Clinical T1-weighted (T1w) and T2-weighted fat-saturated (T2FS) scans, and measured T1 and T2 maps from eight patients with soft-tissue sarcomas were used in this study. Measured T1 and T2 maps were computed using pulse sequences with variable flip angles and echo times, respectively. General regression neural networks (GRNNs) were trained on these data to infer T1 and T2 relaxation times from T1w and T2FS images. Four texture features were extracted to evaluate texture preservation: GLCM/Entropy, GLRLM/Gray-Level Variance (GLV), GLSZM/Zone Size Variance (ZSV) and NGTDM/Complexity. The GRNN ability to estimate T1 and T2 relaxation times was assessed using leave-one-out cross-validation.

Results: The average T1 and T2 relaxation times within the tumor region of all patients were (1515 ± 542) ms and (226 ± 151) ms in the measured cases, and (1546 ± 546) ms and (249 ± 145) ms in the estimated cases, respectively. The average root-mean-square errors between measured and estimated relaxation times were 573 ms for T1 and 160 ms for T2. The average absolute percentage differences between measured and estimated GLCM/Entropy, GLRLM/GLV, GLSZM/ZSV and NGTDM/Complexity features were 5.1%, 0.02%, 0.0% and 16.2% for T1 maps, and 7.7%, 0.04%, 0.0% and 10.9% for T2 maps, respectively.

Conclusion: From a texture preservation perspective, this work demonstrates the feasibility to create MRI numerical models using GRNNs from T1w and T2FS clinical scans. Further work is required to obtain higher accuracy for T1 and T2 absolute relaxation times.

Funding Support, Disclosures, and Conflict of Interest: This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada under the scholarship CGSD3-426742-2012, as well as it was supported by the Canadian Institutes of Health Research (CIHR) under grant MOP-136774.

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