The aim of this study was to assess image quality and lesion detectability acquired with a digital Positron Emission Tomography/Computed Tomography (PET/CT) Siemens Biograph Vision 600 system. Consecutive patients who underwent a FDG PET/CT during the first week of use of a digital PET/CT (Siemens Biograph Vision 600) at the nuclear medicine department of the university hospital of Brest were analyzed. PET were realized using list mode acquisition. For all patients, 4 datasets were reconstructed. We determined, according to phantom measurements, an equivalent time acquisition/reconstruction parameters pair of the digital PET/CT corresponding to an analog PET/CT image quality ("analog-like") as reference dataset. We compared the reference dataset with 3 others digital PET/CT reconstruction parameters, allowing a decrease of emission duration: 60, 90, and 120 s per bed position. Three nuclear medicine physicians evaluated independently, for each dataset, overall image quality [Maximal Intensity Projection (MIP), noise, sharpness] using a 4-point scale. Physicians assessed also lesion detection capability by reporting new visible lesions on each digital datasets with their confidence level in comparison with analog-like dataset. Ninety-eight patients were analyzed. Image quality of MIP (IQ), sharpness (IQ), and noise (IQ) of all digital datasets (60, 90, and 120 s) were better than those evaluated with analog-like reconstruction. Moreover, digital PET/CT system improved IQ, IQ, and IQ whatever the BMI. Lesion detection capability and confidence level were higher for 60, 90, 120 s per bed position, respectively, than for analog-like images. Our study demonstrated an improvement of image quality and lesion detectability with a digital PET/CT system.
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http://dx.doi.org/10.3389/fmed.2021.629096 | DOI Listing |
Ophthalmic Physiol Opt
January 2025
Northeastern University College of Science, Boston, Massachusetts, USA.
Purpose: To assess longitudinal changes in optical quality across the periphery (horizontal meridian, 60°) in young children who are at high (HR) or low risk (LR) of developing myopia, as well as a small subgroup of children who developed myopia over a 3-year time frame.
Methods: Aberrations were measured every 6 months in 92 children with functional emmetropia at baseline. Children were classified into HR or LR based on baseline refractive error and parental myopia.
Ophthalmic Physiol Opt
January 2025
Vision and Hearing Sciences Research Centre, Anglia Ruskin University, Cambridge, UK.
Purpose: Wearable electronic low vision enhancement systems (wEVES) improve visual function but are not widely adopted by people with vision impairment. Here, qualitative research methods were used to investigate the usefulness of wEVES for people with age-related macular degeneration (AMD) after an extended home trial.
Methods: Following a 12-week non-masked randomised crossover trial, semi-structured interviews were completed with 34 participants with AMD, 64.
BMC Cardiovasc Disord
January 2025
School of Nursing and Midwifery, Griffith University, Southport, QLD 4215, Australia.
Background: Iliac vein compression syndrome (IVCS) impedes venous blood return from the lower extremities due to iliac vein compression, manifesting as leg swelling, varicose veins, and thrombosis. These symptoms significantly degrade quality of life. Although iliac vein stenting provides symptomatic relief, the recovery process is protracted and fraught with challenges such as in-stent restenosis and psychological distress.
View Article and Find Full Text PDFSci Rep
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 PDFUltrasound Med Biol
January 2025
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology - NTNU, Trondheim, Norway; Health Research, SINTEF, Trondheim, Norway.
Objective: To develop and compare methods to automatically estimate regional ultrasound image quality for echocardiography separate from view correctness.
Methods: Three methods for estimating image quality were developed: (i) classic pixel-based metric: the generalized contrast-to-noise ratio (gCNR), computed on myocardial segments (region of interest) and left ventricle lumen (background), extracted by a U-Net segmentation model; (ii) local image coherence: the average local coherence as predicted by a U-Net model that predicts image coherence from B-mode ultrasound images at the pixel level; (iii) deep convolutional network: an end-to-end deep-learning model that predicts the quality of each region in the image directly. These methods were evaluated against manual regional quality annotations provided by three experienced cardiologists.
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