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.

No abstract

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.

No abstract