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Infection with Trypanosoma cruzi causes mega-syndromes of the gastrointestinal (GI) tract in humans and animals. In the present study, we employed magnetic resonance imaging to noninvasively monitor the effect of selenium supplementation on alterations in the GI tract of T. cruziinfected mice. CD1 mice infected with T. cruzi (Brazil strain) exhibited dilatation of the intestines similar to that we recently reported in infected C57Bl/6 mice. The average intestine lumen diameter increased by 65% and the increase was reduced to 29% in mice supplemented with 2 ppm selenium in the drinking water. When supplemented with 3 ppm selenium in chow the lumen diameter was also significantly reduced although the difference between the infected and infected supplemented mice was smaller. Intestinal motility in infected mice fed with selenium-enriched chow was increased

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Background: There is increasing interest in non-contrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI.Purpose: To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted breast MRIs from pre-contrast MRIs in biopsy-proven invasive breast cancer using deep learning. Methods and Materials:Women with invasive breast cancer and contrast-enhanced breast MRI performed for initial evaluation of extent of disease were retrospectively identified between January 2015 and December 2019 at a single academic institution. A three-dimensional, fully convolutional deep neural network simulated contrast-enhanced T1-weighted breast MRIs from five pre-contrast sequences (T1-weighted non-fat-suppressed [FS], T1-weighted FS, T2-weighted FS, apparent diffusion coefficient, and diffusion-weighted imaging). For qualitative assessment, four blinded breast radiologists (3 to 15 years of experience) assessed image quality (excellent/ acceptable/good/poor/unacceptable), presence of tumor enhancement, and maximum index mass size using 22 pairs of real and simulated contrast-enhanced MRIs. Quantitative comparison was performed using whole breast similarity and error metrics and Dice coefficient analysis of enhancing tumor overlap.Results: 96 MRIs from 96 women (mean age, 52 years ± 12 [SD]) were evaluated. The readers assessed all simulated MRIs as having the appearance of a real MRI with tumor enhancement. Index mass sizes on real and simulated MRIs demonstrated good-to-excellent agreement (intraclass correlation coefficient, 0.73-0.86; P<.001) without significant differences (mean differences −0.8-0.8 mm, P=.36-.80). Almost all simulated MRIs (84 of 88; 95%) were considered of diagnostic quality (ratings of excellent/acceptable/good). Quantitative analysis demonstrated strong similarity (structural similarity index, 0.88 ± 0.05), low voxel-wise error (symmetric mean absolute percent error, 3.26%), and Dice coefficient of enhancing tumor overlap of 0.75 ± 0.25. Conclusions:It is feasible to generate simulated contrast-enhanced breast MRI using deep learning. Simulated and real contrast-enhanced MRI demonstrated comparable tumor sizes,