AIMC Topic: Abdomen

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New biomarkers to predict the need for surgery of necrotizing enterocolitis: a study based on abdominal X-ray radiomics and machine learning.

Biomedical engineering online
BACKGROUND: Necrotizing enterocolitis (NEC) is an inflammatory intestinal disease that primarily affects premature infants and is a major cause of death in the neonatal period. Approximately half of the affected infants require surgical intervention,...

Automating prostate volume acquisition using abdominal ultrasound scans for prostate-specific antigen density calculations.

Scientific reports
Proposed methods for prostate cancer screening are currently prohibitively expensive (due to the high costs of imaging equipment such as magnetic resonance imaging and traditional ultrasound systems), inadequate in their detection rates, require high...

Optimized deep learning-accelerated single-breath-hold abdominal HASTE with and without fat saturation improves and accelerates abdominal imaging at 3 Tesla.

BMC medical imaging
BACKGROUND: Deep learning-accelerated single-shot turbo-spin-echo techniques (DL-HASTE) enable single-breath-hold T2-weighted abdominal imaging. However, studies evaluating the image quality of DL-HASTE with and without fat saturation (FS) remain lim...

Lung and abdominal ultrasound accuracy for tuberculosis: An Indian prospective cohort study.

PloS one
BACKGROUND: Tuberculosis (TB) diagnosis remains a challenge, particularly in low-resource settings. Point-of-care ultrasound (POCUS) has shown promise, but most studies focus on HIV-infected populations. In the case of TB, data on lung ultrasound (LU...

Image quality and radiation dose of reduced-dose abdominopelvic computed tomography (CT) with silver filter and deep learning reconstruction.

Scientific reports
To assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruction (DLR) using SilverBeam filter and standard dose with iterative reconstruction (IR) in abdominopelvic CT. In total, 182 patients (mean age ± stand...

A preprocessing method based on 3D U-Net for abdomen segmentation.

Computers in biology and medicine
Deep learning methods have made significant progress in the field of biomedical automatic segmentation but still open to developments, especially due to the insufficient use of preprocessing methods. In this study, a pre-processing step is proposed b...

Feasibility study of a general model for synthetic CT generation in MRI-guided extracranial radiotherapy.

Biomedical physics & engineering express
This study aims to investigate the feasibility of a single general model to synthesize CT images across body sites, thorax, abdomen, and pelvis, to support treatment planning for MRI-only radiotherapy. A total of 157 patients who received MRI-guided ...

TCGAN: Temporal Convolutional Generative Adversarial Network for Fetal ECG Extraction Using Single-Channel Abdominal ECG.

IEEE journal of biomedical and health informatics
Noninvasive fetal ECG (FECG) monitoring holds significant importance in ensuring the normal development of the fetus. Since FECG is usually submerged by maternal ECG (MECG) and background noise in abdominal ECG (AECG), it is challenging to exactly re...

Development of a Low-Profile, Piezoelectric Robot for MR-Guided Abdominal Needle Interventions.

Annals of biomedical engineering
PURPOSE: Minimally invasive needle-based interventions are commonly used in cancer diagnosis and treatment, including procedures, such as biopsy, brachytherapy, and microwave ablation. Although MR-guided needle placement offers several distinct advan...

Deep-learning synthetized 4DCT from 4DMRI of the abdominal site in carbon-ion radiotherapy.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To investigate the feasibility of deep-learning-based synthetic 4DCT (4D-sCT) generation from 4DMRI data of abdominal patients undergoing Carbon Ion Radiotherapy (CIRT).