The proposition to use non-invasive thermometry based on magnetic resonance diffusion imaging for applications in therapeutic hyperthermia is examined. The measurement of proton motion predominantly associated with the self-diffusion of water can be characterized by a Boltzmann temperature dependence (i.e. e-Ea/kT). The activation energy (Ea) is on the order of 0.2 eV and, for a restricted range (approximately 30 degrees) at a base temperature of approximately 300 K, the relationship between the effective diffusion coefficient and temperature is approximately linear. This response has been empirically demonstrated in water-based gel phantoms using magnetic resonance imaging (MRI). Additionally, it is feasible to have compatibility between radiofrequency (RF) heating devices and MRI equipment. An MRI-compatible heating applicator that includes a hexagonal array of coherently phased dipoles was assembled. This heating array easily fits into a standard 1.5 T head imaging coil (diameter 28 cm). The RF fields associated with heating (130 MHz) and imaging (64 MHz) were decoupled using bandpass filters providing isolation in excess of 100 dB. This isolation was sufficient to allow simultaneous imaging and RF heating without deterioration of the image signal-to-noise ratio. In this report temperature, spatial and time resolution achieved in phantom are examined in order to assess the potential for using this non-invasive temperature measurement in applications of hyperthermic oncology. Using this system and conventional multi-slice imaging techniques, 0.5 degrees C resolution in a voxel size of less than 1 cm3 has been achieved using an acquisition time of 4.15 min.

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Background The number of new HIV diagnoses in the United States continues to slowly decline; yet, transgender women and men who have sex with men remain disproportionately affected. Key to improving the quality of prevention services are providers who are comfortable broaching the subjects of sexual health and HIV prevention with people across the spectrum of gender identities and sexual orientations. Preservice training is a critical point to establish HIV prevention and sexual health education practices before providers’ practice habits are established. Objective The study aimed to develop participative web-based educational modules and test their impact on HIV prevention knowledge and awareness in future providers. Methods Sexual health providers at an academic hospital, research clinicians, community engagement professionals, and New York City community members were consulted to develop 7 web-based educational modules, which were then piloted among medical students. We assessed knowledge of HIV and sexually transmitted infection prevention and comfort assessing the prevention needs of various patients via web-based questionnaires administered before and after our educational intervention. We conducted exploratory factor analysis of the items in the questionnaire. Results Pre- and postmodule surveys were completed by 125 students and 89 students, respectively, from all 4 years of training. Before the intervention, the majority of students had heard of HIV pre-exposure prophylaxis (122/123, 99.2%) and postexposure prophylaxis (114/123, 92.7%). Before the training, 30.9% (38/123) of the students agreed that they could confidently identify a patient who is a candidate for pre-exposure prophylaxis or postexposure prophylaxis; this increased to 91% (81/89) after the intervention. Conclusions Our findings highlight a need for increased HIV and sexually transmitted infection prevention training in medical school curricula to enable future providers to identify and care for diverse at-risk populations. Participative web-based modules offer an effective way to teach these concepts.

Multiple door interlock switches in commercial microwave ovens are designed to prevent accidental exposure and injury. We report a) heating rate (degree/sec) measurements in a phantom of the human upper extremity in a 2450 MHz microwave oven having interlock switches deliberately bypassed; b) skin temperature measurements on the upper extremity of a human volunteer similarly exposed; c) perception of warmth and pain experienced by the volunteer during exposure; d) thermographic camera recordings of the volunteer's skin; and e) finite element modeling of specific absorption rate (SAR) in the volunteer's hand. Moderately severe pain was experienced at the fingertips after 5 sec of exposure, consistent with the modeled SAR, measured heating rates, and published data on the temperature threshold for pain. We estimate that an additional 9 sec of exposure would be required to produce irreversible injury, consisting of focal thermal injury in the fingertips and possibly the thenar and hypothenar eminences.

e16600 Background: Prostate cancer is the most common cancer of men in the United States, with over 200,000 new cases diagnosed in 2018. Multiparametric MRI of the prostate (mpMRI) has emerged as valuable adjunct for the detection and characterization of prostate cancer as well as for guidance of prostate biopsy. As mpMRI progresses towards widespread clinical use, major challenges have been identified, arising from the need to increase accuracy of mpMRI localization of prostate lesions, improve in lesion categorization, and decrease the time and technical complexity of mpMRI evaluation by radiologists or urologists. Deep learning convolutional neural networks (CNN) for image recognition are becoming a more common method of machine learning and show promise in evaluation of complex medical imaging. In this study we describe a deep learning approach for automatic localization and segmentation of prostates organ on clinically acquired mpMRIs. Methods: This IRB approved retrospective review included patients who had a prostate MRI between September 2014 and August 2018 and an MR-guided transrectal biopsy. For each mpMRI the prostate was manually segmented by a board-certified abdominal radiologist on T2 weighted sequence. A hybrid 3D/2D CNN based on U-Net architecture was developed and trained using these manually segmented images to perform automated organ segmentation. After training, the CNN was used to produce prostate segmentations autonomously on clinical mpMRI. Accuracy of the CNN was assessed by Sørensen–Dice coefficient and Pearson coefficient. Five-fold validation was performed. Results: The CNN was successfully trained and five-fold validation performed on 411 prostate mpMRIs. The Sørensen–Dice coefficient from the five-fold cross validation was 0.87 and the Pearson correlation coefficient for segmented volume was 0.99. Conclusions: These results demonstrate that a CNN can be developed and trained to automatically localize and volumetrically segment the prostate on clinical mpMRI with high accuracy. This study supports the potential for developing an automated deep learning CNN for organ segmentation to replace clinical manual segmentation. Future studies will look towards prostate lesion localization and categorization on mpMRI.