The objective assessment of atrophy and the measurement of brain volume is important in the early diagnosis of dementia and neurodegenerative diseases. Recently, several MR-based volumetry software have been developed. For their clinical application, several issues arise, including the standardization of image acquisition and their validation of software. Additionally, it is important to highlight the diagnostic performance of the volumetry software based on expert opinions. We instituted a task force within the Korean Society of Neuroradiology to develop guidelines for the clinical use of MR-based brain volumetry software. In this review, we introduce the commercially available software and compare their diagnostic performances. We suggest the need for a standard protocol for image acquisition, the validation of the software, and evaluations of the limitations of the software related to clinical practice. We present recommendations for the clinical applications of commercially available software for volumetry based on the expert opinions of the Korean Society of Neuroradiology.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9432367 | PMC |
http://dx.doi.org/10.3348/jksr.2020.0174 | DOI Listing |
Transplant Proc
January 2025
Department of Hepatopancreatobiliary & Transplant Surgery, Singapore General Hospital, Singapore; SingHealth Duke-NUS Transplant Centre, Singapore; Duke-NUS Medical School, Singapore.
Background: Accurately assessing graft volume is crucial for donor and recipient safety in living donor liver transplantation. This can be performed using manual computed tomography volumetry (CTvol) or semiautomated methods (MeVis). We aimed to compare CTvol and MeVis in estimating the actual graft weight during LDLT, and analyse any differences in weight between laparoscopic and open donor hepatectomy.
View Article and Find Full Text PDFRadiol Adv
January 2025
Department of Radiology, Duke University Medical Center, Durham, NC 27710, United States.
Purpose: To assess agreement between CT volumetry change classifications derived from Quantitative Imaging Biomarker Alliance Profile cut-points (ie, QIBA CTvol classifications) and the Response Evaluation Criteria in Solid Tumors (RECIST) categories.
Materials And Methods: Target lesions in lung, liver, and lymph nodes were randomly chosen from patients in 10 historical clinical trials for various cancers, ensuring a balanced representation of lesion types, diameter ranges described in the QIBA Profile, and variations in change magnitudes. Three radiologists independently segmented these lesions at baseline and follow-up scans using 2 software tools.
Clin Neuroradiol
January 2025
Department of Radiology and Neuroradiology, University Hospital of Schleswig-Holstein, Arnold-Heller-Str. 3, Hs D (Neurozentrum), 24105, Kiel, Germany.
Purpose: Magnetic Resonance Imaging based brain segmentation and volumetry has become an important tool in clinical routine and research. However the impact of the used hardware is only barely investigated. This study aims to assess the influence of scanner manufacturer, field strength and head-coil on volumetry results.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Background: Post-traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD), and Adjustment Disorder (AdjD) are highly prevalent among military personnel, often presenting diagnostic challenges due to overlapping symptoms and reliance on self-reporting. The amygdala, particularly the basolateral complex involved in fear-related memory formation and extinction recall, plays a crucial role in emotional processing. Abnormalities in these amygdala nuclei are implicated in PTSD and may distinguish it from other disorders like MDD and AdjD, where these mechanisms are less central.
View Article and Find Full Text PDFAtherosclerosis
December 2024
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address:
Background And Aims: The significance of left ventricular mass and chamber volumes from non-contrast computed tomography (CT) for predicting major adverse cardiovascular events (MACE) has not been studied. Our objective was to evaluate the role of artificial intelligence-enabled multi-chamber cardiac volumetry from non-contrast CT for long-term risk stratification in asymptomatic subjects without known coronary artery disease.
Methods: Our study included 2022 asymptomatic individuals (55.
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