Objective: To investigate the effect of a novel motion-correction algorithm (Snap-short Freeze, SSF) on image quality and diagnostic accuracy in patients undergoing prospectively ECG-triggered CCTA without administering rate-lowering medications.
Materials And Methods: Forty-six consecutive patients suspected of CAD prospectively underwent CCTA using prospective ECG-triggering without rate control and invasive coronary angiography (ICA). Image quality, interpretability, and diagnostic performance of SSF were compared with conventional multisegment reconstruction without SSF, using ICA as the reference standard.
Results: All subjects (35 men, 57.6 ± 8.9 years) successfully underwent ICA and CCTA. Mean heart rate was 68.8±8.4 (range: 50-88 beats/min) beats/min without rate controlling medications during CT scanning. Overall median image quality score (graded 1-4) was significantly increased from 3.0 to 4.0 by the new algorithm in comparison to conventional reconstruction. Overall interpretability was significantly improved, with a significant reduction in the number of non-diagnostic segments (690 of 694, 99.4% vs 659 of 694, 94.9%; P<0.001). However, only the right coronary artery (RCA) showed a statistically significant difference (45 of 46, 97.8% vs 35 of 46, 76.1%; P = 0.004) on a per-vessel basis in this regard. Diagnostic accuracy for detecting ≥50% stenosis was improved using the motion-correction algorithm on per-vessel [96.2% (177/184) vs 87.0% (160/184); P = 0.002] and per-segment [96.1% (667/694) vs 86.6% (601/694); P <0.001] levels, but there was not a statistically significant improvement on a per-patient level [97.8 (45/46) vs 89.1 (41/46); P = 0.203]. By artery analysis, diagnostic accuracy was improved only for the RCA [97.8% (45/46) vs 78.3% (36/46); P = 0.007].
Conclusion: The intracycle motion correction algorithm significantly improved image quality and diagnostic interpretability in patients undergoing CCTA with prospective ECG triggering and no rate control.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4646467 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142796 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
The Venus score for the assessment of the quality and trustworthiness of biomedical datasets.
BioData Min
January 2025
Fondazione Bruno Kessler, Trento, Italy.
Biomedical datasets are the mainstays of computational biology and health informatics projects, and can be found on multiple data platforms online or obtained from wet-lab biologists and physicians. The quality and the trustworthiness of these datasets, however, can sometimes be poor, producing bad results in turn, which can harm patients and data subjects. To address this problem, policy-makers, researchers, and consortia have proposed diverse regulations, guidelines, and scores to assess the quality and increase the reliability of datasets.
View Article and Find Full Text PDFEvaluation of One Step Nucleic Acid Amplification for detection of lymph node metastases compared to histopathological ultrastaging in women with endometrial cancer: a protocol for a diagnostic accuracy study.
BMC Womens Health
January 2025
Department of Gynaecology and Obstetrics, University Hospital Pilsen, Charles University, Pilsen, Czech Republic.
Background: This is a multicentre, European, prospective trial evaluating the diagnostic accuracy of One Step Nucleic Acid Amplification (OSNA) compared to sentinel lymph nodes histopathological ultrastaging in endometrial cancer patients.
Methods: Centres with expertise in sentinel lymph node mapping in endometrial cancer patients in Europe will be invited to participate in the study. Participating units will be trained on the correct usage of the OSNA RD-210 analyser and nucleic acid amplification reagent kit LYNOAMP CK19 E for rapid detection of metastatic nodal involvement, based on the cytokeratin 19 (CK19) mRNA detection.
Biopsy location and tumor-associated macrophages in predicting malignant glioma recurrence using an in-silico model.
NPJ Syst Biol Appl
January 2025
Center for Interdisciplinary Digital Sciences (CIDS), Department Information Services and High-Performance Computing (ZIH), Dresden University of Technology, 01062, Dresden, Germany.
Predicting the biological behavior and time to recurrence (TTR) of high-grade diffuse gliomas (HGG) after maximum safe neurosurgical resection and combined radiation and chemotherapy plays a pivotal role in planning clinical follow-up, selecting potentially necessary second-line treatment and improving the quality of life for patients diagnosed with a malignant brain tumor. The current standard-of-care (SoC) for HGG includes follow-up neuroradiological imaging to detect recurrence as early as possible and relies on several clinical, neuropathological, and radiological prognostic factors, which have limited accuracy in predicting TTR. In this study, using an in-silico analysis, we aim to improve predictive power for TTR by considering the role of (i) prognostically relevant information available through diagnostics used in the current SoC, (ii) advanced image-based information not currently part of the standard diagnostic workup, such as tumor-normal tissue interface (edge) features and quantitative data specific to biopsy positions within the tumor, and (iii) information on tumor-associated macrophages.
View Article and Find Full Text PDFAccurate colorectal cancer detection using a random hinge exponential distribution coupled attention network on pathological images.
Abdom Radiol (NY)
January 2025
Department of Computer Engineering, Vishwakarma Institute of Information Technology, Pune, India.
Colorectal cancer (CRC) is one of the most common and deadly forms of cancer worldwide, necessitating accurate and early detection to improve treatment outcomes. Traditional diagnostic methods often rely on manual examination of pathological images, which can be time-consuming and prone to human error. This study presents an advanced approach for colorectal cancer detection using a Random Hinge Exponential Distribution coupled Attention Network (RHED-CANet) on pathological images.
View Article and Find Full Text PDFDeep Learning-Based Super-Resolution Reconstruction on Undersampled Brain Diffusion-Weighted MRI for Infarction Stroke: A Comparison to Conventional Iterative Reconstruction.
AJNR Am J Neuroradiol
January 2025
Department of Radiology (M.Z., N.W., S.H., X.L., H.Z., C.Y., Q.S.), The First Affiliated Hospital of Dalian Medical University, Dalian, China
Background And Purpose: DWI is crucial for detecting infarction stroke. However, its spatial resolution is often limited, hindering accurate lesion visualization. Our aim was to evaluate the image quality and diagnostic confidence of deep learning (DL)-based super-resolution reconstruction for brain DWI of infarction stroke.
View Article and Find Full Text PDFWant 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!