Iterative reconstructions (IR) might alter radiomic features extraction. We aim to evaluate the influence of Adaptive Statistical Iterative Reconstruction-V (ASIR-V) on CT radiomic features. Patients who underwent unenhanced abdominal CT (Revolution Evo, GE Healthcare, USA) were retrospectively enrolled. Raw data of filtered-back projection (FBP) were reconstructed with 10 levels of ASIR-V (10-100%). CT texture analysis (CTTA) of liver, kidney, spleen and paravertebral muscle for all datasets was performed. Six radiomic features (mean intensity, standard deviation (SD), entropy, mean of positive pixel (MPP), skewness, kurtosis) were extracted and compared between FBP and all ASIR-V levels, with and without altering the spatial scale filter (SSF). CTTA of all organs revealed significant differences between FBP and all ASIR-V reconstructions for mean intensity, SD, entropy and MPP (all < 0.0001), while no significant differences were observed for skewness and kurtosis between FBP and all ASIR-V reconstructions (all > 0.05). A per-filter analysis was also performed comparing FBP with all ASIR-V reconstructions for all six SSF separately (SSF0-SSF6). Results showed significant differences between FBP and all ASIR-V reconstruction levels for mean intensity, SD, and MPP (all filters < 0.0315). Skewness and kurtosis showed no differences for all comparisons performed (all > 0.05). The application of incremental ASIR-V levels affects CTTA across various filters. Skewness and kurtosis are not affected by IR and may be reliable quantitative parameters for radiomic analysis.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8229560 | PMC |
http://dx.doi.org/10.3390/diagnostics11061000 | DOI Listing |
Bioengineering (Basel)
December 2024
Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany.
Deep learning image reconstruction (DLIR) has shown potential to enhance computed tomography (CT) image quality, but its impact on tumor visibility and adoption among radiologists with varying experience levels remains unclear. This study compared the performance of two deep learning-based image reconstruction methods, DLIR and Pixelshine, an adaptive statistical iterative reconstruction-volume (ASIR-V) method, and filtered back projection (FBP) across 33 contrast-enhanced CT staging examinations, evaluated by 20-24 radiologists. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured for tumor and surrounding organ tissues across DLIR (Low, Medium, High), Pixelshine (Soft, Ultrasoft), ASIR-V (30-100%), and FBP.
View Article and Find Full Text PDFAcad Radiol
December 2024
Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (H.Z., Q.C., W.M., J.Y., S.W., G.T., J.X., H.J., H.Y., L.Z.). Electronic address:
Rationale And Objectives: To comprehensively assess the feasibility of low-dose computed tomography (LDCT) using deep learning image reconstruction (DLIR) for evaluating pulmonary subsolid nodules, which are challenging due to their susceptibility to noise.
Materials And Methods: Patients undergoing both standard-dose CT (SDCT) and LDCT between March and June 2023 were prospectively enrolled. LDCT images were reconstructed with high-strength DLIR (DLIR-H), medium-strength DLIR (DLIR-M), adaptive statistical iterative reconstruction-V level 50% (ASIR-V-50%), and filtered back projection (FBP); SDCT with FBP as the reference standard.
Cancer Imaging
September 2024
Department of Radiology, the second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China.
Eur J Radiol Open
June 2024
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Purpose: This study aimed to investigate differences in cervical lymph node image quality on dual-energy computed tomography (CT) scan with datasets reconstructed using filter back projection (FBP), hybrid iterative reconstruction (IR), and deep learning-based image reconstruction (DLIR) in patients with head and neck cancer.
Method: Seventy patients with head and neck cancer underwent follow-up contrast-enhanced dual-energy CT examinations. All datasets were reconstructed using FBP, hybrid IR with 30 % adaptive statistical IR (ASiR-V), and DLIR with three selectable levels (low, medium, and high) at 2.
Acad Radiol
June 2024
Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Schillingallee 36, 18057 Rostock, Germany. Electronic address:
Rationale And Objectives: To assess the impact of deep learning-based imaging reconstruction (DLIR) on quantitative results of coronary artery calcium scoring (CACS) and to evaluate the potential of DLIR for radiation dose reduction in CACS.
Methods: For a retrospective cohort of 100 consecutive patients (mean age 62 ±10 years, 40% female), CACS scans were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASiR-V in 30%, 60% and 90% strength) and DLIR in low, medium and high strength. CACS was quantified semi-automatically and compared between image reconstructions.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!