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Abdomen

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A Telerobotic Ultrasound Clinic Model of Ultrasound Service Delivery to Improve Access to Imaging in Rural and Remote Communities.

Journal of the American College of Radiology : JACR
OBJECTIVE: Patients living in many rural and remote areas do not have readily available access to ultrasound services because of a lack of sonographers and radiologists in these communities. The objective of this study was to determine the feasibilit...

Automatic segmentation of paravertebral muscles in abdominal CT scan by U-Net: The application of data augmentation technique to increase the Jaccard ratio of deep learning.

Medicine
Sarcopenia, characterized by a decline of skeletal muscle mass, has emerged as an important prognostic factor for cancer patients. Trunk computed tomography (CT) is a commonly used modality for assessment of cancer disease extent and treatment outcom...

ACTIVITY CONCENTRATION ESTIMATION IN AUTOMATED KIDNEY SEGMENTATION BASED ON CONVOLUTION NEURAL NETWORK METHOD FOR 177LU-SPECT/CT KIDNEY DOSIMETRY.

Radiation protection dosimetry
For 177Lu-DOTATATE treatments, dosimetry based on manual kidney segmentation from computed tomography (CT) is accurate but time consuming and might be affected by misregistration between CT and SPECT images. This study develops a convolution neural n...

Development and Evaluation of Deep Learning-Accelerated Single-Breath-Hold Abdominal HASTE at 3 T Using Variable Refocusing Flip Angles.

Investigative radiology
OBJECTIVE: Deep learning (DL) reconstruction enables substantial acceleration of image acquisition while maintaining diagnostic image quality. The aims of this study were to overcome the drawback of specific absorption rate (SAR)-related limitations ...

Computer-assisted technology for enhanced abdominal surgery.

The British journal of surgery
The application of computer-based technology to surgery has the potential to enhance the accuracy and outcomes of surgical procedures and perioperative care. Such innovative technologies include the integration of artificial intelligence into surgica...

Deep learning for abdominal ultrasound: A computer-aided diagnostic system for the severity of fatty liver.

Journal of the Chinese Medical Association : JCMA
BACKGROUND: The prevalence of nonalcoholic fatty liver disease is increasing over time worldwide, with similar trends to those of diabetes and obesity. A liver biopsy, the gold standard of diagnosis, is not favored due to its invasiveness. Meanwhile,...

Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging: An Analysis of Image Quality, Diagnostic Confidence, and Lesion Conspicuity.

Investigative radiology
OBJECTIVES: The aim of this study was to investigate the impact of a deep learning-based superresolution reconstruction technique for T1-weighted volume-interpolated breath-hold examination (VIBESR) on image quality in comparison with standard VIBE i...

The potential for reduced radiation dose from deep learning-based CT image reconstruction: A comparison with filtered back projection and hybrid iterative reconstruction using a phantom.

Medicine
The purpose of this phantom study is to compare radiation dose and image quality of abdominal computed tomography (CT) scanned with different tube voltages and tube currents, reconstructed with filtered back projection (FBP), hybrid iterative reconst...

Diagnostic Confidence and Feasibility of a Deep Learning Accelerated HASTE Sequence of the Abdomen in a Single Breath-Hold.

Investigative radiology
OBJECTIVE: The aim of this study was to evaluate the feasibility of a single breath-hold fast half-Fourier single-shot turbo spin echo (HASTE) sequence using a deep learning reconstruction (HASTEDL) for T2-weighted magnetic resonance imaging of the a...

[The Clinical Effectiveness of Neural Network-based Boundary Recognition of Upper Abdominal Organs on CT Images].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To assess the clinical effectiveness of boundary recognition of upper abdomen organs on CT images based on neural network model and the combination of different slices.