Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Introduction: Diagnostic reference level (DRL) values for computed tomography (CT) based on clinical indication are warranted since imaging protocols are indication-dependent. This study proposes clinical DRL values using the CT dose metrics and five patient size-related parameters while considering image quality.
Methods: The volumetric CT dose index (CTDI), dose-length product (DLP) and five size-related parameters of size-specific dose estimates (SSDE), namely the anterior-posterior (AP) dimension, lateral (LAT) dimension, sum dimension, effective diameter, and the body mass index (BMI), were used to calculate DRL values for CT chest-abdomen-pelvis (CAP) and abdomen-pelvis (AbP) protocols. DRL values of the clinical indications for cancer, urinary system stones and other pathologies were assessed based on the BMI classifications using the median and 75th percentile. An image subtraction algorithm was used to assess the image quality metrics (IQM) of the CT images.
Results: The 75th percentile for SSDE for CAP cancer was 19.7, 14.9 and 12.7 mGy at Hospitals A, C and E, respectively. The median DLP for other AbP pathologies was 556.3, 1452.0 and 1960.7 mGy.cm for normal weight, overweight and obese patients, respectively, at Hospital A. The image quality varied among BMI classifications for different clinically indicated examinations. Although the dose increased with BMI, the image quality index was consistent because automatic tube current modulation (ATCM) was used.
Conclusion: DRL values are influenced by patient size-related parameters and the clinical indication protocols, while the image quality index is independent of the BMI.
Implications For Practice: Size-related clinical DRL values and image quality index can be used to monitor and optimise dose and image quality. Acquisition parameters and image quality indexes should be investigated and adjusted when unusually high DRL values are noted.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.radi.2023.07.011 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Clinical Role of the Noninvasive Abdominal Fetal ECG in the Detection and Monitoring of Fetal Tachycardia.
Circ Arrhythm Electrophysiol
September 2025
Department of Congenital Heart Disease, Evelina London Children's Hospital, United Kingdom (S. Chivers, T.V., V.Z., S.M., G.M., W.R., E.R., D.F.A.L., T.G.D., O.I.M., G.K.S., J.M.S.).
Background: Fetal tachycardias can cause adverse fetal outcomes including ventricular dysfunction, hydrops, and fetal demise. Postnatally, ECG is the gold standard, but, in fetal practice, echocardiography is used most frequently to diagnose and monitor fetal arrhythmias. Noninvasive extraction of the fetal ECG (fECG) may provide additional information about the electrophysiological mechanism and monitoring of intermittent arrhythmias.
View Article and Find Full Text PDFLeveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data.
ACS Sens
September 2025
Institute of Applied Mechanics, National Taiwan University, Taipei 106, Taiwan.
In recent AI-driven disease diagnosis, the success of models has depended mainly on extensive data sets and advanced algorithms. However, creating traditional data sets for rare or emerging diseases presents significant challenges. To address this issue, this study introduces a direct-self-attention Wasserstein generative adversarial network (DSAWGAN) designed to improve diagnostic capabilities in infectious diseases with limited data availability.
View Article and Find Full Text PDFFew-shot learning for highly accelerated 3D time-of-flight MRA reconstruction.
Magn Reson Med
September 2025
Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Purpose: To develop a deep learning-based reconstruction method for highly accelerated 3D time-of-flight MRA (TOF-MRA) that achieves high-quality reconstruction with robust generalization using extremely limited acquired raw data, addressing the challenge of time-consuming acquisition of high-resolution, whole-head angiograms.
Methods: A novel few-shot learning-based reconstruction framework is proposed, featuring a 3D variational network specifically designed for 3D TOF-MRA that is pre-trained on simulated complex-valued, multi-coil raw k-space datasets synthesized from diverse open-source magnitude images and fine-tuned using only two single-slab experimentally acquired datasets. The proposed approach was evaluated against existing methods on acquired retrospectively undersampled in vivo k-space data from five healthy volunteers and on prospectively undersampled data from two additional subjects.
Improving magnetic field homogeneity in prostate MR imaging and spectroscopy using an add-on local external shim coil array.
MAGMA
September 2025
Department of Medical Imaging, (766), Radboud University Medical Center, Geert Grooteplein 10Radboudumc, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands.
Objective: To improve B field homogeneity in prostate MR imaging and spectroscopy using a custom-designed 16-channel external local shim coil array.
Methods: In vivo prostate imaging was performed in seven healthy volunteers (mean age: 40.7 years) without bowel preparation.
MRI-based radiomics signatures for predicting the efficacy of targeted therapy with lenvatinib in hepatocellular carcinoma: a retrospective cohort study.
J Cancer Res Clin Oncol
September 2025
Department of Radiology, Guizhou Provincial People's Hospital, No. 83 East Zhongshan Road, Guiyang, 550002, Guizhou, China.
Purpose: Targeted therapy with lenvatinib is a preferred option for advanced hepatocellular carcinoma, however, predicting its efficacy remains challenging. This study aimed to build a nomogram integrating clinicoradiological indicators and radiomics features to predict the response to lenvatinib in patients with hepatocellular carcinoma.
Methods: This study included 211 patients with hepatocellular carcinoma from two centers, who were allocated into the training (107 patients), internal test (46 patients) and external test set(58 patients).