Purpose: To prospectively evaluate non-contrast-enhanced 7-Tesla (T) MRA for delineation of unruptured intracranial aneurysms (UIAs) in comparison with DSA.
Material And Methods: Forty patients with single or multiple UIAs were enrolled in this IRB-approved trial. Sequences acquired at 7 T were TOF MRA and non-contrast-enhanced MPRAGE. All patients additionally underwent 3D rotational DSA. Two neuroradiologists individually analysed the following aneurysm and image features on a five-point scale in 2D and 3D image reconstructions: delineation of parent vessel, dome and neck; overall image quality; presence of artefacts. Interobserver accordance was assessed by the kappa coefficient.
Results: A total of 64 UIAs were detected in DSA and in all 2D and 3D MRA image reconstructions. Ratings showed comparable results for DSA and 7-T MRA when considering all image reconstructions. Highest ratings for individual image reconstructions were given for 2D MPRAGE and 3D TOF MRA. Interobserver accordance was almost perfect for the majority of ratings.
Conclusion: This study demonstrates excellent delineation of UIAs using 7-T MRA within a clinical setting comparable to the gold standard, DSA. The combination of 7-T non-enhanced MPRAGE and TOF MRA for assessment of untreated UIAs is a promising clinical application of ultra-high-field MRA.
Key Points: • Non-enhanced 7-T MRA allowed excellent delineation of unruptured intracranial aneurysms (UIAs). • Image quality at 7-T was comparable with DSA considering both sequences. • Assessment of UIAs is a promising clinical application of ultra-high-field MRA.
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http://dx.doi.org/10.1007/s00330-016-4323-5 | DOI Listing |
Sci Rep
January 2025
Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611002, Tamil Nadu, India.
In response to the pressing need for the detection of Monkeypox caused by the Monkeypox virus (MPXV), this study introduces the Enhanced Spatial-Awareness Capsule Network (ESACN), a Capsule Network architecture designed for the precise multi-class classification of dermatological images. Addressing the shortcomings of traditional Machine Learning and Deep Learning models, our ESACN model utilizes the dynamic routing and spatial hierarchy capabilities of CapsNets to differentiate complex patterns such as those seen in monkeypox, chickenpox, measles, and normal skin presentations. CapsNets' inherent ability to recognize and process crucial spatial relationships within images outperforms conventional CNNs, particularly in tasks that require the distinction of visually similar classes.
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January 2025
Shandong Provincial Public Health Clinical Center, Shandong University, Jinan, 250013, Shandong, China.
Medical image annotation is scarce and costly. Few-shot segmentation has been widely used in medical image from only a few annotated examples. However, its research on lesion segmentation for lung diseases is still limited, especially for pulmonary aspergillosis.
View Article and Find Full Text PDFClin Oral Investig
January 2025
Department of Prosthetic Dentistry, LMU University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany.
Objective: Evaluation of the accuracy of direct digitization of maxillary scans depending on the scanning strategy.
Materials And Methods: A maxillary model with a metal bar as a reference structure fixed between the second molars was digitized using the CEREC Primescan AC scanner (N = 225 scans). Nine scanning strategies were selected (n = 25 scans per strategy), differing in scan area segmentation (F = full jaw, H = half jaw, S = sextant) and scan movement pattern (L = linear, Z = zig-zag, C = combined).
Nihon Hoshasen Gijutsu Gakkai Zasshi
January 2025
Department of Radiology, Nara Prefecture General Medical Center.
Purpose: There are attempts to assess tumor heterogeneity by texture analysis. However, the ordered subsets-expectation maximization (OSEM) reconstruction method has problems depicting heterogeneities. The aim of this study was to identify image reconstruction parameters that improve the ability to depict internal tumor necrosis using a self-made phantom that simulates internal necrosis.
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January 2025
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
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