Objectives: To compare image noise, image quality and diagnostic accuracy of coronary CT angiography (cCTA) using a novel iterative reconstruction algorithm versus traditional filtered back projection (FBP) and to estimate the potential for radiation dose savings.
Methods: Sixty five consecutive patients (48 men; 59.3 ± 7.7 years) prospectively underwent cCTA and coronary catheter angiography (CCA). Full radiation dose data, using all projections, were reconstructed with FBP. To simulate image acquisition at half the radiation dose, 50% of the projections were discarded from the raw data. The resulting half-dose data were reconstructed with sinogram-affirmed iterative reconstruction (SAFIRE). Full-dose FBP and half-dose iterative reconstructions were compared with regard to image noise and image quality, and their respective accuracy for stenosis detection was compared against CCA.
Results: Compared with full-dose FBP, half-dose iterative reconstructions showed significantly (p = 0.001 - p = 0.025) lower image noise and slightly higher image quality. Iterative reconstruction improved the accuracy of stenosis detection compared with FBP (per-patient: accuracy 96.9% vs. 93.8%, sensitivity 100% vs. 100%, specificity 94.6% vs. 89.2%, NPV 100% vs. 100%, PPV 93.3% vs. 87.5%).
Conclusions: Iterative reconstruction significantly reduces image noise without loss of diagnostic information and holds the potential for substantial radiation dose reduction from cCTA.
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http://dx.doi.org/10.1007/s00330-011-2164-9 | DOI Listing |
J Cancer Res Clin Oncol
January 2025
Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, 256600, P.R. China.
Purpose: Immune checkpoint blockades (ICBs) are promising, however they do not fit all types of tumor, such as those lack of tumor antigens. Induction of potent anti-tumor T cell immunity is critical for cancer therapy. In this study, we investigated the efficacy of immunotherapy via the immunogenic cell death (ICD) dying tumor cells in mouse models of lung metastasis and tumorigenesis.
View Article and Find Full Text PDFJ Med Imaging Radiat Sci
January 2025
Instituto Politécnico de Coimbra, ESTESC - Coimbra Health School, Medical Imaging and Radiotherapy, Rua 5 de Outubro, S. Martinho do Bispo, Coimbra 3046-854, Portugal. Electronic address:
Background: Currently, there is an increase in procedures across various clinical specialties involving the use of ionising radiation.
Objective: The primary objective of this systematic review is to analyse and compare the existing literature regarding the effectiveness of leaded glasses for healthcare professionals.
Methods: Comprehensive literature searches were conducted for relevant studies published between 2018 and 2023 using the Scopus, PubMed, and Web of Science databases according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology.
Phys Med
January 2025
Medical Physics Dept IRCCS San Raffaele Scientific Institution Milano Italy.
Purpose: To train and validate KB prediction models by merging a large multi-institutional cohort of whole breast irradiation (WBI) plans using tangential fields.
Methods: Ten institutions (INST1-INST10, 1481 patients) developed their KB-institutional models for left/right WBI (ten models for right and eight models for left). The transferability of models among centers was assessed based on the overlap of the geometric Principal Component (PC1) of each model when applied to other institutions and/or on the presence of significantly different optimization policies.
Appl Radiat Isot
January 2025
Experimental Nuclear Physics Department, Nuclear Research Centre, Egyptian Atomic Energy Authority, Egypt; Cyclotron Facility, Egyptian Atomic Energy Authority, Egypt.
Neutron and gamma-ray shielding design for a 30Ci (1.11TBq) Am-Be irradiation facility is studied using MCNP5 Monte Carlo simulation code. The study focuses on the optimization of the shielding layers of the previously planned neutron irradiation facility.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Radiological Sciences, University of California Los Angeles, 924 Westwood Blvd, Los Angeles, California, 90095, UNITED STATES.
Objective: The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations.
Approach: A retrospective in-house sinogram dataset of 100 low-dose chest CT scans was reconstructed by varying radiation dose (100%, 25%, 10%) and reconstruction kernels (smooth, medium, sharp). A set of image processing, convolutional neural network (CNNs), and generative adversarial network-based (GANs) methods were trained to harmonize all image conditions to a reference condition (100% dose, medium kernel).
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