The purpose of this study was to develop strategies for optimal image reconstruction in multidetector-row cardiac CT and to discuss the results in the context of individual heart rate, cardiac physiology, and technical prerequisite. Sixty-four patients underwent multidetector-row cardiac CT. Depending on the heart rate either a single-segmental reconstruction (SSR) or an adaptive two-segmental reconstruction (ASR) was applied. Image reconstruction was done either antegrade (a) or retrograde (r) in relation to the R-peak. Reconstruction of all data sets was performed at multiple time points within the t-wave/p-wave interval, differing from each other by 50 ms. In addition, each reconstruction was assigned to one of six reconstruction intervals (A-F), each corresponding to a specific event in the cardiac cycle. While no significant time points were found for absolute values, the following interval/reconstruction technique combinations provided significant better image quality: F/r at HR <60 bpm for all coronary segments ( p=0.004) and at HR 60-65 bpm for segments 5-10 ( p=0.001); B/a at HR 60-65 bpm for segments 1-4 and 11-15 ( p<0.001) and at HR >65 bpm for all segments ( p=0.002). The results show that in order to achieve optimal image quality, image reconstruction has to be adjusted to each patient's ECG curve and heart rate individually. The moment of reconstruction should be determined as absolute rather than as relative distance from the previous R-peak.
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http://dx.doi.org/10.1007/s00330-002-1553-5 | DOI Listing |
J Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual scoring is time-consuming and subject to variability. This study proposes a multistage deep learning model to predict the Overall Sharp Score (OSS) from hand X-ray images.
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January 2025
Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Dusseldorf, Germany.
Aim of this study was to proof the concept of optimizing the contrast between prostate cancer (PC) and healthy tissue by DWI post-processing using a quadrature method. DWI post-processing was performed on 30 patients (median age 67 years, prostate specific antigen 8.0 ng/ml) with PC and clear MRI findings (PI-RADS 4 and 5).
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Department of Data Science and Artificial Intelligence, Sunway University, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the complexity of vessel structures, and the potential for confusion with lesions. In this paper, we introduce a novel approach, the MSMA Net model, which overcomes these challenges by replacing traditional convolution blocks and skip connections with an improved multi-scale squeeze and excitation block (MSSE Block) and Bottleneck residual paths (B-Res paths) with spatial attention blocks (SAB).
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January 2025
Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan.
Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. STAIG can integrate tissue slices without prealignment and remove batch effects.
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