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| http://dx.doi.org/10.1007/s00259-023-06199-z | DOI Listing |
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Deep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging.
Clin Nucl Med
January 2025
From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
Purpose: The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Moreover, low-dose CTAC images have poor quality, thus challenging the segmentation task.
View Article and Find Full Text PDFRetinal and Choroidal Circulation Impairments in Fanconi Anemia.
Am J Ophthalmol
January 2025
Hacettepe University School of Medicine, Department of Ophthalmology, Ankara, Turkey.
Objective: To evaluate the effects of Fanconi anemia (FA) on retinal and choroidal microvasculature using Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA).
Design: Cohort study with age-matched controls.
Subjects And Participants: This study included 11 eyes from 11 patients diagnosed with FA and 12 eyes from 12 age-matched healthy controls.
Intracameral voriconazole for severe fungal keratitis: a case series.
Arq Bras Oftalmol
January 2025
Department of Ophthalmology and Visual Sciences, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, SP, Brazil.
Purpose: This study aimed to report the use, efficacy, and safety of intracameral voriconazole as an adjuvant treatment for deep fungal keratitis.
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The neural networks offer iteration capability for low-density parity-check (LDPC) decoding with superior performance at transmission. However, to cope with increasing code length and rate, the complexity of the neural network increases significantly. This is due to the large amount of feature extraction required to maintain the error correction capability.
View Article and Find Full Text PDFDeep learning has been widely used in phase unwrapping. However, owing to the noise of the wrapped phase, errors in wrap count prediction and phase calculation can occur, making it challenging to achieve high measurement accuracy under high-noise conditions. To address this issue, a three-stage multi-task phase unwrapping method was proposed.
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