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Purpose: Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables.
Methods: Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision.
Results: Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A.
Conclusions: The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of accuracy on reconstruction algorithms, such that volumes quantified from scans of different reconstruction algorithms can be compared. The little difference found between the precision of FBP and iterative reconstructions could be a result of both iterative reconstruction's diminished noise reduction at the edge of the nodules as well as the loss of resolution at high noise levels with iterative reconstruction. The findings do not rule out potential advantage of IR that might be evident in a study that uses a larger number of nodules or repeated scans.
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http://dx.doi.org/10.1118/1.4823463 | DOI Listing |
Ultrason Imaging
September 2025
Pontificia Universidad Católica del Perú, San Miguel, Lima, Peru.
The acoustic nonlinearity parameter (B/A) could enhance the diagnostic capabilities of conventional ultrasonography and quantitative ultrasound in tissues and diseases. Nonlinear acoustic propagation theory of plane waves has been used to develop a dual-energy model of the depletion of the fundamental related to the Gol'dberg number and subsequently to the B/A of media (a reference phantom is used as a baseline). The depletion method, however, needs a priori information of the attenuation coefficient (AC) of the assessed media.
View Article and Find Full Text PDFJpn J Radiol
September 2025
Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Background: Stroke, frequently associated with carotid artery disease, is evaluated using carotid computed tomography angiography (CTA). Dual-energy CTA (DE-CTA) enhances imaging quality but presents challenges in maintaining high image clarity with low-dose scans.
Objectives: To compare the image quality of 50 keV virtual monoenergetic images (VMI) generated using Deep Learning Image Reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-V (ASIR-V) algorithms under a triple-low scanning protocol in carotid CTA.
Interv Neuroradiol
September 2025
Department of Neuroradiology, University Hospital RWTH Aachen, Aachen, Germany.
PurposeTo evaluate the potential of Photon-Counting Detector CT Angiography (PCD-CTA) for the assessment of carotid and subclavian artery stents compared to digital subtraction angiography (DSA) and Duplex ultrasound (DUS).MethodsThis study is a single-center, retrospective analysis of consecutive patients treated with a stent for high grade stenosis of the extra-cranial carotid and the subclavian artery between April 2023 and May 2024. Polyenergetic images (PE), iodine and virtual monoenergetic images were performed at different keV levels (40 and 80) and with two body vascular reconstruction kernels (Bv56 and 72) with and without iterative metal artifact reduction.
View Article and Find Full Text PDFNihon Hoshasen Gijutsu Gakkai Zasshi
September 2025
Division of Neonatology, Maternal and Perinatal Center, Toyama University Hospital.
Purpose: This study aimed to evaluate whether low-dose CT imaging using an Sn filter can provide image quality sufficient for the differential diagnosis of cranial deformities in infants while maintaining an effective dose comparable to that of conventional radiography.
Methods: We calculated the effective dose for both head X-ray imaging and low-dose CT with an Sn filter. Phantom images acquired using a CT scanner equipped with an Sn filter were evaluated for bone suture visibility at various conditions (from 10 mAs to 50 mAs, every 10 mAs) using a 4-point visual grading scale.
Int J Radiat Oncol Biol Phys
September 2025
Radiation Oncology, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143. Electronic address:
Purpose: Accelerating MR acquisition is essential for image guided therapeutic applications. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is computationally complex and difficult to generalize. Convolutional neural networks (CNNs)/Transformers-based deep learning (DL) methods emerged as a faster alternative but face challenges in modeling continuous k-space, a problem amplified with non-Cartesian sampling commonly used in accelerated acquisition.
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