Objective: Fat deposition is an important marker of many metabolic diseases. As a noninvasive and convenient examination method, CT has been widely used for fat quantification. With the clinical application of photon-counting detector (PCD)-CT, we aimed to investigate the accuracy, stability, and dose level of PCD-CT using various scan settings for fat quantification.
Materials And Methods: Eleven agar-based lipid-containing phantoms (vials with different fat fractions [FFs]; range: 0 %-100 %) were scanned using PCD-CT. Three scanning types (sequence scan, regular spiral scan with a pitch of 0.8, and high-pitch spiral scan with a pitch of 3.2), four tube voltages (90, 120, 140, and 100 kV with a tin filter), and three image quality (IQ) levels (IQ levels of 20, 40, and 80) were alternated, and each scan setting was used twice. For each scan, a 70-keV image was generated using the same reconstruction parameters. A regular spiral scan at 120 kV with IQ80 was used to transfer the CT numbers of all scans to the FF. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were implemented for accuracy and agreement evaluation, and group differences were compared using analysis of variance.
Results: Excellent agreement and accuracy of FF derived by PCD-CT with all scan settings was demonstrated by high ICCs (>0.9; range: 0.929-0.998, p < 0.017) and low bias (<5% range: -2.9 %-5%). The root mean square error (RMSE) between the PCD-CT-acquired FF and the reference standard ranged from 1.0 % to 5.0 %, among which the high-pitch scan at 120 kV with IQ20 accounted for the lowest RMSE (1.0 %). The spiral scan at 120 kV with IQ20 and IQ80 yielded the lowest bias (mean value: 1.19 % and 1.23 %, respectively).
Conclusion: Fat quantification using PCD-CT reconstructed at 70 keV was accurate and stable under various scan settings. PCD-CT has great potential for fat quantification using ultralow radiation doses.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.ejrad.2024.111545 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Liver function and Alzheimer's brain pathologies: A longitudinal study: Liver and Alzheimer's pathologies.
J Prev Alzheimers Dis
January 2025
Department of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea; Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, 03080, Republic of Korea; Interdisciplinary Program of Cognitive Science, Seoul National University College of Humanities, Seoul, 08826, Republic of Korea. Electronic address:
Importance: The neuropathological links underlying the association between changes in liver function and AD have not yet been clearly elucidated.
Objective: We aimed to examine the relationship between liver function markers and longitudinal changes in Alzheimer's disease (AD) core pathologies.
Design: Data from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease, a longitudinal cohort study initiated in 2014, were utilized.
INTERCEPT-AD, a phase 1 study of intravenous sabirnetug in participants with mild cognitive impairment or mild dementia due to Alzheimer's disease.
J Prev Alzheimers Dis
January 2025
CenExel iResearch, Atlanta, GA, USA.
Background: Soluble species of multimeric amyloid-beta including globular amyloid-beta oligomers (AβOs) and linear amyloid-beta protofibrils are toxic to neurons. Sabirnetug (ACU193) is a humanized monoclonal antibody, raised against globular species of soluble AβO, that has over 650-fold greater binding affinity for AβOs over monomers and appears to have relatively little binding to amyloid plaque.
Objectives: To assess safety, pharmacokinetics, and exploratory measures including target engagement, biomarker effects, and clinical efficacy of sabirnetug in participants with early symptomatic Alzheimer's disease (AD; defined as mild cognitive impairment and mild dementia due to AD).
Do-it-yourself instrument integration into an existing mammalian cell line development automation platform.
SLAS Technol
January 2025
Cell Line Development, WuXi Biologics, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China.
Laboratory automation in the biopharmaceutical industry as a rule requires contracted service from highly professional automation solution provider, at times involving the purchase and development of specialized or customized hardware and software, which can be proprietary and expensive. Alternatively, with the availability of open-source software customized for automation, it is possible to automate existing laboratory instruments in a do-it-yourself (DIY), low-cost, and flexible fashion. In this work, we used an open-source scripting language, AutoIt, to integrate an existing microplate imager into an existing automation platform that is already equipped with a 4-axis robotic arm and an automated incubator, to achieve automation of the imaging procedure in our cell line development workflow.
View Article and Find Full Text PDFThe Role of Lung Ultrasound Scan in Different Heart Failure Scenarios: Current Applications and Lacks of Evidences.
Diagnostics (Basel)
December 2024
Cardiovascular Diseases Unit, Cardio Thoracic and Vascular Department, Le Scotte Hospital, University of Siena, 53100 Siena, Italy.
Pulmonary congestion is a critical factor influencing the clinical presentation, therapeutic decisions, and outcomes of heart failure (HF) patients. Lung ultrasound (LUS) offers a simple, rapid, and accurate method for assessing pulmonary congestion, surpassing the diagnostic capabilities of traditional clinical evaluation and chest radiography. Due to the wide availability of ultrasound equipment, congestion can be evaluated in multiple settings, ranging from emergency departments to intensive care units, including outpatient settings.
View Article and Find Full Text PDFAutomated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning.
Diagnostics (Basel)
December 2024
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan.
Background/objectives: To develop and validate a model system using deep learning algorithms for the automatic detection of type A aortic dissection (AD), and differentiate it from normal and type B AD patients.
Methods: In this retrospective study, a deep learning model is developed, based on aortic computed tomography angiography (CTA) scans of 498 patients using training, validation and test sets of 398, 50 and 50 patients, respectively. An independent test set of 316 patients is used to validate and evaluate its performance.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!