Transfer Learning for Automated COVID-19 B-Line Classification in Lung Ultrasound.

Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung pathologies. B-lines are a predominant feature in COVID-19, however LUS requires a skilled clinician to interpret findings. To facilitate the interpretation, our main objective was to develop automated methods to classify B-lines as pathologic vs.

Subchondral Bone Length in Knee Osteoarthritis: A Deep Learning-Derived Imaging Measure and Its Association With Radiographic and Clinical Outcomes.

Objective: To develop a bone shape measure that reflects the extent of cartilage loss and bone flattening in knee osteoarthritis (OA) and test it against estimates of disease severity.

Methods: A fast region-based convolutional neural network was trained to crop the knee joints in sagittal dual-echo steady-state magnetic resonance imaging sequences obtained from the Osteoarthritis Initiative (OAI). Publicly available annotations of the cartilage and menisci were used as references to annotate the tibia and the femur in 61 knees.

Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma.

Objective: To develop machine learning (ML) models capable of predicting ICU admission and extended length of stay (LOS) after torso (chest, abdomen, or pelvis) trauma, by using clinical and/or imaging data.

Materials And Methods: This was a retrospective study of 840 adult patients admitted to a level 1 trauma center after injury to the torso over the course of 1 year. Clinical parameters included age, sex, vital signs, clinical scores, and laboratory values.

Detecting Intracranial Hemorrhage with Deep Learning.

Initial results are reported on automated detection of intracranial hemorrhage from CT, which would be valuable in a computer-aided diagnosis system to help the radiologist detect subtle hemorrhages. Previous work has taken a classic approach involving multiple steps of alignment, image processing, image corrections, handcrafted feature extraction, and classification. Our current work instead uses a deep convolutional neural network to simultaneously learn features and classification, eliminating the multiple hand-tuned steps.

Breath of relief: apparent pulmonary nodule due to heterotopic ossification.

Extrapulmonary heterotopic ossification appears similarly to pulmonary nodules on CXR, and is in the differential for pulmonary nodules. It occurs following the bone trauma, and in early stages appears similarly to tumors. Heterotopic ossification is diagnosed by its calcification pattern via MRI or ultrasound and managed conservatively unless symptoms develop.

Texture analysis as a radiomic marker for differentiating renal tumors.

Purpose: To evaluate the utility of texture analysis for the differentiation of renal tumors, including the various renal cell carcinoma subtypes and oncocytoma.

Materials And Methods: Following IRB approval, a retrospective analysis was performed, including all patients with pathology-proven renal tumors and an abdominal computed tomography (CT) examination. CT images of the tumors were manually segmented, and texture analysis of the segmented tumors was performed.

Characterization of gradient echo signal decays in healthy and cancerous prostate at 3T improves with a Gaussian augmentation of the mono-exponential (GAME) model.

A biomarker of cancer aggressiveness, such as hypoxia, could substantially impact treatment decisions in the prostate, especially radiation therapy, by balancing treatment morbidity (urinary incontinence, erectile dysfunction, etc.) against mortality. R2 (*) mapping with Mono-Exponential (ME) decay modeling has shown potential for identifying areas of prostate cancer hypoxia at 1.

Multiexponential T(2) analyses in a murine model of hepatic fibrosis at 11.7 T MRI.

This study evaluated the effects of hepatic fibrosis on the multiexponential T(2) (MET(2) ) relaxation of ex vivo murine liver specimens using an 11.7 T MRI. This animal study was approved by the Institutional Animal Care and Use Committee.

Quantitative MR imaging: physical principles and sequence design in abdominal imaging.

Quantitative magnetic resonance (MR) imaging seeks to quantify fundamental biologic and MR-inducible tissue properties, in contrast to the routine application of MR imaging in the clinic, in which differences in MR parameters are used to generate contrast for subsequent subjective image analysis. Fundamental parameters that are commonly quantified by using MR imaging include proton density, diffusion, T1 relaxation, T2 and T2* relaxation, and magnetization transfer. Applications of these MR imaging-quantifiable parameters to abdominal imaging include oncologic imaging, evaluation of diffuse liver disease, and assessment of splenic, renal, and pancreatic disease.