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Improved characterization of diffusion in normal and cancerous prostate tissue through optimization of the restriction spectrum imaging signal model

By Christopher Charles Conlin, Christine H. Feng, Ana E. Rodríguez-Soto, Roshan Karunamuni, Joshua M. Kuperman, Dominic Holland, Rebecca Rakow-Penner, Michael E. Hahn, Tyler M Seibert, Anders M. Dale

Posted 30 Mar 2020
medRxiv DOI: 10.1101/2020.03.27.20042069

BackgroundOptimizing a restriction spectrum imaging (RSI) model for the prostate could lead to improved characterization of diffusion in the prostate and better discrimination of tumors. PurposeTo determine optimal apparent diffusion coefficients (ADCs) for prostate RSI models and evaluate the number of tissue compartments required to best describe diffusion in prostate tissue. Study TypeRetrospective. Population/SubjectsThirty-six patients who underwent an extended MRI examination for suspected prostate cancer; 13 had prostate tumors and 23 had no detectable cancer. Field strength/Sequence3T multi-shell diffusion weighted sequence. AssessmentRSI models with 2-5 tissue compartments were fit to multi-shell DWI data from the prostate to determine optimal compartmental ADCs. Signal fractions from the different tissue compartments were computed using these ADCs and compared between normal tissues (peripheral zone, transition zone, seminal vesicles) and tumors. Statistical TestsThe Bayesian Information Criterion was used to evaluate the optimality of the different RSI models. Model-fitting residual (as percent variance) was recorded for the optimal model to assess its goodness-of-fit and whether it varied between anatomical regions of the prostate. Two-sample t-tests (=0.05) were used to determine the statistical significance of any differences observed in compartmental signal-fraction between normal prostate tissue and tumors. ResultsThe lowest BIC was observed from the 5-compartment model. Optimal ADCs for the 5 compartments were 0, 8.9e-4, 1.7e-3, 2.7e-3, and >>3.0e-3 mm2/s. The fitting residual from the in 5-compartment model was 0.05% across all voxels. Tumor tissue showed the largest reduction fitting residual by increasing model order. Prostate tumors had a significantly (P<<0.05) greater proportion of signal from compartments 1 and 2 than normal tissue. Data ConclusionAmong the examined RSI models, the 5-compartment model best described the diffusion-signal characteristics of the prostate. Compartmental signal fractions revealed by such a model may improve discrimination between cancerous and benign prostate tissue.

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