Background Ultrasound was first introduced in clinical dermatology in 1979. Since that time, ultrasound technology has continued to develop along with its popularity and utility. Main text summary Today, high-frequency ultrasound (HFUS), or ultrasound using a frequency of at least 10 megahertz (MHz), allows for high-resolution imaging of the skin from the stratum corneum to the deep fascia. This non-invasive and easy-to-interpret tool allows physicians to assess skin findings in real-time, enabling enhanced diagnostic, management, and surgical capabilities. In this review, we discuss how HFUS fits into the landscape of skin imaging. We provide a brief history of its introduction to dermatology, explain key principles of ultrasonography, and review its use in characterizing normal skin, common neoplasms of the skin, dermatologic diseases and cosmetic dermatology. Conclusion As frequency advancements in ultrasonography continue, the broad applications of this imaging modality will continue to grow. HFUS is a fast, safe and readily available tool that can aid in diagnosing, monitoring and treating dermatologic conditions by providing more objective assessment measures.

Purpose Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image‐guided radiotherapy because it provides a foundation for advanced image‐guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. However, CBCT is currently limited only to patient setup in the clinic because of the severe issues in its image quality. In this study, we develop a learning‐based approach to improve CBCT's image quality for extended clinical applications. Materials and methods An auto‐context model is integrated into a machine learning framework to iteratively generate corrected CBCT (CCBCT) with high‐image quality. The first step is data preprocessing for the built training dataset, in which uninformative image regions are removed, noise is reduced, and CT and CBCT images are aligned. After a CBCT image is divided into a set of patches, the most informative and salient anatomical features are extracted to train random forests. Within each patch, alternating RF is applied to create a CCBCT patch as the output. Moreover, an iterative refinement strategy is exercised to enhance the image quality of CCBCT. Then, all the CCBCT patches are integrated to reconstruct final CCBCT images. Results The learning‐based CBCT correction algorithm was evaluated using the leave‐one‐out cross‐validation method applied on a cohort of 12 patients’ brain data and 14 patients’ pelvis data. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), normalized cross‐correlation (NCC) indexes, and spatial nonuniformity (SNU) in the selected regions of interest (ROIs) were used to quantify the proposed algorithm's correction accuracy and generat the following results: mean MAE = 12.81 ± 2.04 and 19.94 ± 5.44 HU, mean PSNR = 40.22 ± 3.70 and 31.31 ± 2.85 dB, mean NCC = 0.98 ± 0.02 and 0.95 ± 0.01, and SNU = 2.07 ± 3.36% and 2.07 ± 3.36% for brain and pelvis data. Conclusion Preliminary results demonstrated that the novel learning‐based correction method can significantly improve CBCT image quality. Hence, the proposed algorithm is of great potential in improving CBCT's image quality to support its clinical utility in CBCT‐guided adaptive radiotherapy.

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Background: Glioblastoma is the most aggressive brain tumor with poor prognosis. The purpose of this study is to improve the tissue characterization of these highly heterogeneous tumors using delta-radiomic features of images from dynamic susceptibility contrast enhanced (DSC) magnetic resonance imaging (MRI).Methods: Twenty-five patients with histopathologically confirmed to be 13 high-grade (HG) and 12 lowgrade (LG) gliomas who underwent the standard brain tumor MRI protocol, including DSC MRI, were included. Tumor regions on all DSC MRI images were registered to and contoured in T2-weighted fluidattenuated inversion recovery (FLAIR) images. These contours and its contralateral regions of the normal tissue were used to extract delta-radiomic features before applying feature selection. The most informative and non-redundant features were selected to train a random forest to differentiate HG and LG gliomas.Then a leave-one-out cross-validation random forest was applied to classify these tumors for grading. Finally, a majority-voting method was applied to reduce binarization bias and to combine the results of various feature lists.Results: Analysis of the predictions showed that the reported method consistently predicted the tumor grade of 24 out of 25 patients correctly (0.96). Finally, the mean prediction accuracy was 0.950±0.091 for HG and 0.850±0.255 for LG. The area under the receiver operating characteristic curve (AUC) was 0.94. Conclusions:This study shows that delta-radiomic features derived from DSC MRI data can be used to characterize and determine the tumor grades. The radiomic features from DSC MRI may be used to elucidate the underlying tumor biology and response to therapy.

We develop a learning-based method to generate patient-specific pseudo computed tomography (CT) from routinely acquired magnetic resonance imaging (MRI) for potential MRI-based radiotherapy treatment planning. The proposed pseudo CT (PCT) synthesis method consists of a training stage and a synthesizing stage. During the training stage, patch-based features are extracted from MRIs. Using a feature selection, the most informative features are identified as an anatomical signature to train a sequence of alternating random forests based on an iterative refinement model. During the synthesizing stage, we feed the anatomical signatures extracted from an MRI into the sequence of well-trained forests for a PCT synthesis. Our PCT was compared with original CT (ground truth) to quantitatively assess the synthesis accuracy. The mean absolute error, peak signal-to-noise ratio, and normalized cross-correlation indices were 60.87 AE 15.10 HU, 24.63 AE 1.73 dB, and 0.954 AE 0.013 for 14 patients' brain data and 29.86 AE 10.4 HU, 34.18 AE 3.31 dB, and 0.980 AE 0.025 for 12 patients' pelvic data, respectively. We have investigated a learning-based approach to synthesize CTs from routine MRIs and demonstrated its feasibility and reliability. The proposed PCT synthesis technique can be a useful tool for MRI-based radiation treatment planning.

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ABSTRACT:Various output heaters were extruded with acetylene black-filled platinum-catalyzed silicone rubber. The resistivity-temperature behavior of extruded heaters exhibited a positive-temperature coefficient (PTC) effect without any negative-temperature coefficient (NTC) effect. Resistivity and thermal reproducibility of the extruded heaters were investigated during heating and cooling cycles at an applied voltage of 220 V. These heaters initially showed poor reproducibility of resistivity during the repeated cycles and this effect increased significantly as the acetylene black content decreased. PTC effect and electrical reproducibility were improved significantly during the thermal ageing process.

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