I-131 is a frequently used isotope for radionuclide therapy. This technique for cancer treatment requires a pre-therapeutic dosimetric study. The latter is usually performed (for this radionuclide) by directly imaging the uptake of the therapeutic radionuclide in the body or by replacing it by one of its isotopes, which are more suitable for imaging. This study aimed to compare the image quality that can be achieved by three iodine isotopes: I-131 and I-123 for single-photon emission computed tomography imaging, and I-124 for positron emission tomography imaging. The imaging characteristics of each isotope were investigated by simulated data. Their spectrums, point-spread functions, and contrast-recovery curves were drawn and compared. I-131 was imaged with a high-energy all-purpose (HEAP) collimator, whereas two collimators were compared for I-123: low-energy high-resolution (LEHR) and medium energy (ME). No mechanical collimation was used for I-124. The influence of small high-energy peaks (>0.1%) on the main energy window contamination were evaluated. Furthermore, the effect of a scattering medium was investigated and the triple energy window (TEW) correction was used for spectral-based scatter correction. Results showed that I-123 gave the best results with a LEHR collimator when the scatter correction was applied. Without correction, the ME collimator reduced the effects of high-energy contamination. I-131 offered the worst results. This can be explained by the large amount of septal penetration from the photopeak and by the collimator, which gave a low spatial resolution. I-124 gave the best imaging properties owing to its electronic collimation (high sensitivity) and a short coincidence time window.
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http://dx.doi.org/10.1089/cbr.2006.323 | DOI Listing |
JAMA Cardiol
January 2025
Department of Emergency Medicine, Rush University Medical Center, Chicago, Illinois.
Importance: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires technical proficiency for image acquisition. Previous research has demonstrated the effectiveness of artificial intelligence (AI) in guiding novice users to acquire high-quality cardiac ultrasound images, suggesting its potential for broader use in LUS.
Objective: To evaluate the ability of AI to guide acquisition of diagnostic-quality LUS images by trained health care professionals (THCPs).
Insights Imaging
January 2025
Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Objectives: Renal cell carcinoma (RCC) with extrarenal fat (perinephric or renal sinus fat) invasion is the main evidence for the T3a stage. Currently, computed tomography (CT) is still the primary modality for staging RCC. This study aims to determine the diagnostic performance of CT in RCC patients with extrarenal fat invasion.
View Article and Find Full Text PDFEur J Pediatr
January 2025
Pediatric Emergency Department, St. Christopher's Hospital for Children, Drexel University College of Medicine, Philadelphia, PA, USA.
Background: Computed tomography (CT) scans are widely used for evaluating children with acute atraumatic altered mental status (AMS) despite concerns about radiation exposure and limited diagnostic yield. This study aims to assess the efficacy of CT scans in this population and provide evidence-based recommendations.
Methods: A systematic review was conducted according to PRISMA guidelines.
Rev Esp Enferm Dig
September 2024
Gastroenterology, Hospital Clínico Universitario de Santiago.
Background: diagnosis of early chronic pancreatitis (CP) is a challenge due to the lack of accurate methods. The ability of endoscopic ultrasound (EUS) guided biopsy to obtain pancreatic core tissue samples in patients with minimal changes of CP and its potential use for the histological diagnosis of early CP are unknown. The aim of the study was to evaluate the ability of different EUS-guided biopsy core needles to obtain histological samples of healthy pig pancreas.
View Article and Find Full Text PDFRadiol Artif Intell
January 2025
From the Department of Radiology, University Hospital, LMU Munich, Marchioninistr 15,81377 Munich, Germany (T.W., J.D., M.I.); Department of Statistics, LMU Munich, Munich, Germany (T.W., D.R.); and Munich Center for Machine Learning, Munich, Germany (T.W., J.D., D.R., M.I.).
Purpose To investigate whether the computational effort of 3D CT-based multiorgan segmentation with TotalSegmentator can be reduced via Tucker decomposition-based network compression. Materials and Methods In this retrospective study, Tucker decomposition was applied to the convolutional kernels of the TotalSegmentator model, an nnU-Net model trained on a comprehensive CT dataset for automatic segmentation of 117 anatomic structures. The proposed approach reduced the floating-point operations (FLOPs) and memory required during inference, offering an adjustable trade-off between computational efficiency and segmentation quality.
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