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Abdomen

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Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: Comparison with conventional diffusion weighted imaging.

European journal of radiology
PURPOSE: To assess the clinical feasibility of accelerated deep learning-reconstructed diffusion weighted imaging (DWI) and to compare its image quality and acquisition time with those of conventional DWI.

Robot-assisted anterior resection for rectal cancer with double inferior vena cava: A case report.

Asian journal of endoscopic surgery
Double inferior vena cava (DIVC) is a rare but generally asymptomatic condition that is often detected incidentally by radiological examinations such as computed tomography (CT). Here, we describe the case of a 73-year-old woman with DIVC, who underw...

Deep learning for emergency ascites diagnosis using ultrasonography images.

Journal of applied clinical medical physics
PURPOSE: The detection of abdominal free fluid or hemoperitoneum can provide critical information for clinical diagnosis and treatment, particularly in emergencies. This study investigates the use of deep learning (DL) for identifying peritoneal free...

Natural Language Processing for Imaging Protocol Assignment: Machine Learning for Multiclass Classification of Abdominal CT Protocols Using Indication Text Data.

Journal of digital imaging
A correct protocol assignment is critical to high-quality imaging examinations, and its automation can be amenable to natural language processing (NLP). Assigning protocols for abdominal imaging CT scans is particularly challenging given the multiple...

Image Quality Evaluation in Dual-Energy CT of the Chest, Abdomen, and Pelvis in Obese Patients With Deep Learning Image Reconstruction.

Journal of computer assisted tomography
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...

Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T.

Academic radiology
To evaluate the clinical performance of a deep learning-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE)-sequence for T2-weighted fat-suppressed MRI of the abdomen at 1.5 T and 3 T in comparison to standard ...

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.

BMC medical informatics and decision making
BACKGROUND: There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) comp...

Medical lesion segmentation by combining multimodal images with modality weighted UNet.

Medical physics
PURPOSE: Automatic segmentation of medical lesions is a prerequisite for efficient clinic analysis. Segmentation algorithms for multimodal medical images have received much attention in recent years. Different strategies for multimodal combination (o...