Publications by authors named "R K Ausman"
Background: Radiomics is an approach to medical imaging that quantifies the features normally translated into visual display. While both radiomic and clinical markers have shown promise in predicting response to neoadjuvant chemoradiation therapy (nCRT) for rectal cancer, the interrelationship is not yet clear.
Methods: A retrospective, single-institution study of patients treated with nCRT for locally advanced rectal cancer was performed.
Objective: The aim of this study was to evaluate image quality in vascular and oncologic dual-energy computed tomography (CT) imaging studies performed with a deep learning (DL)-based image reconstruction algorithm in patients with body mass index of ≥30.
Methods: Vascular and multiphase oncologic staging dual-energy CT examinations were evaluated. Two image reconstruction algorithms were applied to the dual-energy CT data sets: standard of care Adaptive Statistical Iterative Reconstruction (ASiR-V) and TrueFidelity DL image reconstruction at 2 levels (medium and high).
Article Synopsis
- The study aimed to compare deep learning (DL) image reconstruction with conventional MRI methods, focusing on noise reduction settings of 50% and 75% among orthopedic MRI datasets.
- It involved 54 patients and utilized quantitative metrics like edge sharpness and signal-to-noise ratio, alongside expert radiologist rankings to assess image quality and diagnostic confidence.
- Results revealed that both DL settings yielded better image quality than conventional methods, but there was less consensus among radiologists on which DL setting was superior.
We analyzed the adequacy of pain control for 17 trauma patients during the initial part of their stay in the intensive care unit, and assessed reasons for inadequate analgesia, if it occurred. Patients, and physicians, and nurses were interviewed. A verbal pain intensity scale was used to determine whether patients received adequate analgesia.
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