A rigorous approach is proposed to improve the resolution of integral imaging (InI) by finding the appropriate form of irregularity in the arrangement of the InI lenslets. The improvement of the resolution is achieved through redistribution of the sampling points in a uniform manner. The optimization process for finding the optimum pattern of the lens-array irregularity is carried out by minimizing a cost function, whose mathematical closed-form expression is provided. The minimization of the proposed cost function ensures the uniform distribution of sampling points and thus improves the resolution within the desired depth of field (DOF) and field of view (FOV). A set of standard resolution charts is used to demonstrate the improvement of the quality of the three-dimensional (3D) images obtained by using the optimized irregular lens array. It is shown that the overall level of the lateral and depth resolutions is improved at the same time.
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http://dx.doi.org/10.1364/AO.51.006031 | DOI Listing |
J Cancer Res Clin Oncol
January 2025
Sarcoma Unit, Department of Surgery, University Medical Center and Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Purpose: The management of soft tissue sarcoma (STS) at reference centers with specialized multidisciplinary tumor boards (MTB) improves patient survival. The German Cancer Society (DKG) certifies sarcoma centers in German-speaking countries, promoting high standards of care. This study investigated the variability in treatment recommendations for localized STS across different German-speaking tertiary sarcoma centers.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
January 2025
Department of Medical Biophysics, University of Toronto, Toronto, Canada.
Purpose: During endovascular revascularization interventions for peripheral arterial disease, the standard modality of X-ray fluoroscopy (XRF) used for image guidance is limited in visualizing distal segments of infrapopliteal vessels. To enhance visualization of arteries, an image registration technique was developed to align pre-acquired computed tomography (CT) angiography images and to create fusion images highlighting arteries of interest.
Methods: X-ray image metadata capturing the position of the X-ray gantry initializes a multiscale iterative optimization process, which uses a local-variance masked normalized cross-correlation loss to rigidly align a digitally reconstructed radiograph (DRR) of the CT dataset with the target X-ray, using the edges of the fibula and tibia as the basis for alignment.
Sci Rep
January 2025
Cardiovascular Research Center, Rajaie Cardiovascular, Medical, and Research Center, University of Medical Sciences, Tehran, Iran.
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindications in patients with renal dysfunction and lengthy scan times. This study investigates the potential of non-contrast CMR techniques-feature tracking strain analysis and T1/T2 mapping-combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment.
View Article and Find Full Text PDFSci Rep
January 2025
School of Cyberspace Security, Hebei University of Engineering Science, Shijiazhuang, 050091, China.
Aerial images can cover a wide area and capture rich scene information. These images are often taken from a high altitude and contain many small objects. It is difficult to detect small objects accurately because their features are not obvious and are susceptible to background interference.
View Article and Find Full Text PDFSci Rep
January 2025
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.
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