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Chandran M O, Pendem S, P S P, Chacko C, - P, Kadavigere R. Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review. F1000Res 2024; 13:274. [PMID: 38725640 PMCID: PMC11079581 DOI: 10.12688/f1000research.147345.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/26/2024] [Indexed: 05/12/2024] Open
Abstract
Background The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations. Methods We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations. Conclusions DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.
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Affiliation(s)
- Obhuli Chandran M
- Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Saikiran Pendem
- Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Priya P S
- Department of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Cijo Chacko
- Philips Research and Development, Philips Innovation Campus, Yelahanka, Karnataka, 560064, India
| | - Priyanka -
- Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Rajagopal Kadavigere
- Department of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
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Varghese BA, Cen SY, Jensen K, Levy J, Andersen HK, Schulz A, Lei X, Duddalwar VA, Goodenough DJ. Investigating the role of imaging factors in the variability of CT-based texture analysis metrics. J Appl Clin Med Phys 2024; 25:e14192. [PMID: 37962032 PMCID: PMC11005980 DOI: 10.1002/acm2.14192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 08/02/2023] [Accepted: 10/12/2023] [Indexed: 11/15/2023] Open
Abstract
OBJECTIVE This study assesses the robustness of first-order radiomic texture features namely interquartile range (IQR), coefficient of variation (CV) and standard deviation (SD) derived from computed tomography (CT) images by varying dose, reconstruction algorithms and slice thickness using scans of a uniform water phantom, a commercial anthropomorphic liver phantom, and a human liver in-vivo. MATERIALS AND METHODS Scans were acquired on a 16 cm detector GE Revolution Apex Edition CT scanner with variations across three different nominal slice thicknesses: 0.625, 1.25, and 2.5 mm, three different dose levels: CTDIvol of 13.86 mGy for the standard dose, 40% reduced dose and 60% reduced dose and two different reconstruction algorithms: a deep learning image reconstruction (DLIR-high) algorithm and a hybrid iterative reconstruction (IR) algorithm ASiR-V50% (AV50) were explored, varying one at a time. To assess the effect of non-linear modifications of images by AV50 and DLIR-high, images of the water phantom were also reconstructed using filtered back projection (FBP). Quantitative measures of IQR, CV and SD were extracted from twelve pre-selected, circular (1 cm diameter) regions of interest (ROIs) capturing different texture patterns across all scans. RESULTS Across all scans, imaging, and reconstruction settings, CV, IQR and SD were observed to increase with reduction in dose and slice thickness. An exception to this observation was found when using FBP reconstruction. Lower values of CV, IQR and SD were observed in DLIR-high reconstructions compared to AV50 and FBP. The Poisson statistics were more stringently noted in FBP than DLIR-high and AV50, due to the non-linear nature of the latter two algorithms. CONCLUSION Variation in image noise due to dose reduction algorithms, tube current, and slice thickness show a consistent trend across phantom and patient scans. Prospective evaluation across multiple centers, scanners and imaging protocols is needed for establishing quality assurance standards of radiomics.
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Affiliation(s)
- Bino Abel Varghese
- Keck Medical CenterDepartment of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Steven Yong Cen
- Keck Medical CenterDepartment of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Kristin Jensen
- Department of Physics and Computational RadiologyOsloNorway
| | | | | | - Anselm Schulz
- Department of Radiology and Nuclear MedicineOslo University HospitalOsloNorway
| | - Xiaomeng Lei
- Keck Medical CenterDepartment of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Vinay Anant Duddalwar
- Keck Medical CenterDepartment of RadiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - David John Goodenough
- Department of RadiologyGeorge Washington UniversityWashingtonDistrict of ColumbiaUSA
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Catapano F, Lisi C, Savini G, Olivieri M, Figliozzi S, Caracciolo A, Monti L, Francone M. Deep Learning Image Reconstruction Algorithm for CCTA: Image Quality Assessment and Clinical Application. J Comput Assist Tomogr 2024; 48:217-221. [PMID: 37621087 DOI: 10.1097/rct.0000000000001537] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
OBJECTIVE The increasing number of coronary computed tomography angiography (CCTA) requests raised concerns about dose exposure. New dose reduction strategies based on artificial intelligence have been proposed to overcome limitations of iterative reconstruction (IR) algorithms. Our prospective study sought to explore the added value of deep-learning image reconstruction (DLIR) in comparison with a hybrid IR algorithm (adaptive statistical iterative reconstruction-veo [ASiR-V]) in CCTA, even in clinical challenging scenarios, as obesity, heavily calcified vessels and coronary stents. METHODS We prospectively included 103 consecutive patients who underwent CCTA. Data sets were reconstructed with ASiR-V and DLIR. For each reconstruction signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) was calculated, and qualitative assessment was made with a four-point Likert scale by two independent and blinded radiologists with different expertise. RESULTS Both SNR and CNR were significantly higher in DLIR (SNR-DLIR median value [interquartile range] of 13.89 [11.06-16.35] and SNR-ASiR-V 25.42 [22.46-32.22], P < 0.001; CNR-DLIR 16.84 [9.83-27.08] vs CNR-ASiR-V 10.09 [5.69-13.5], P < 0.001).Median qualitative score was 4 for DLIR images versus 3 for ASiR-V ( P < 0.001), with a good interreader reliability [intraclass correlation coefficient(2,1)e intraclass correlation coefficient(3,1) 0.60 for DLIR and 0.62 and 0.73 for ASiR-V].In the obese and in the "calcifications and stents" groups, DLIR showed significantly higher values of SNR (24.23 vs 11.11, P < 0.001 and 24.55 vs 14.09, P < 0.001, respectively) and CNR (16.08 vs 8.04, P = 0.008 and 17.31 vs 10.14, P = 0.003) and image quality. CONCLUSIONS Deep-learning image reconstruction in CCTA allows better SNR, CNR, and qualitative assessment than ASiR-V, with an added value in the most challenging clinical scenarios.
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Affiliation(s)
| | - Costanza Lisi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Giovanni Savini
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Marzia Olivieri
- Department of neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Stefano Figliozzi
- From the Department of Radiology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Alessandra Caracciolo
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
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Mese I, Altintas Mese C, Demirsoy U, Anik Y. Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond. Pediatr Radiol 2024; 54:1-11. [PMID: 38041712 DOI: 10.1007/s00247-023-05823-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/17/2023] [Accepted: 11/20/2023] [Indexed: 12/03/2023]
Abstract
In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans. Photon-counting detector CT has emerged as another groundbreaking technology, allowing for high-resolution images while substantially reducing radiation doses, proving highly beneficial for pediatric patients requiring frequent imaging. Furthermore, cloud-based dose tracking software focuses on monitoring radiation exposure, ensuring adherence to safety standards. However, the deployment of these technologies presents challenges, including the need for large datasets, computational demands, and potential data privacy issues. This article provides a comprehensive exploration of these technological advancements, their clinical implications, and the ongoing efforts to enhance pediatric radiology's safety and effectiveness.
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Affiliation(s)
- Ismail Mese
- Department of Radiology, Health Sciences University, Erenkoy Mental Health and Neurology Training and Research Hospital, 19 Mayis, Sinan Ercan Cd. No:23, Kadikoy, Istanbul, 34736, Turkey.
| | - Ceren Altintas Mese
- Department of Pediatrics, Haydarpasa Numune Training and Research Hospital, Istanbul, Turkey
| | - Ugur Demirsoy
- Department of Pediatric Oncology, Faculty of Medicine, Kocaeli University, Kocaeli, Turkey
| | - Yonca Anik
- Department of Pediatric Radiology, Faculty of Medicine, Kocaeli University, Kocaeli, Turkey
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Lei L, Zhou Y, Guo X, Wang L, Zhao X, Wang H, Ma J, Yue S. The value of a deep learning image reconstruction algorithm in whole-brain computed tomography perfusion in patients with acute ischemic stroke. Quant Imaging Med Surg 2023; 13:8173-8189. [PMID: 38106310 PMCID: PMC10722072 DOI: 10.21037/qims-23-547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 09/19/2023] [Indexed: 12/19/2023]
Abstract
Background Computed tomography perfusion (CTP) and computed tomography angiography (CTA) are valuable tools for diagnosing acute ischemic stroke (AIS). It is essential to obtain high-quality CTP and CTA images in short time. This study aimed to evaluate the image quality and diagnostic performance of brain CTP and CTA images generated from CTP reconstructed by a deep learning image reconstruction (DLIR) algorithm on patients with AIS. Methods The study prospectively enrolled 54 patients with suspected AIS undergoing non-contrast CT and CTP within 24 hours. CTP datasets were reconstructed with three levels of adaptive statistical iterative reconstruction-Veo algorithm [ASIR-V 0% with filtered back projection (FBP), ASIR-V 40%, and ASIR-V 80%] and three levels of DLIR, including low (DLIR-L), medium (DLIR-M), and high (DLIR-H). CTA images were generated using the CTP datasets at the peak arterial phase. Objective parameters including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and noise reduction rate. Subjective evaluation was assessed according to Abels scoring system. Perfusion parameters and detection accuracy for infarction core lesions were evaluated. The objective and subjective image quality of CTA images were also evaluated. Results All reconstructions produced similar CT values (P>0.05). With the increase of ASIR-V and DLIR reconstruction strength, image noise decreased, while SNR and CNR increased for CTP images, especially in white matter. DLIR-H, DLIR-M, and ASIR-V80% yielded higher subjective scores than did ASIR-V40% and FBP. DLIR-H provided the highest noise reduction rate and detection accuracy. No significant difference was found in conventional parameters, the volume of infarct core, or ischemic penumbra among the 6 groups (P>0.05). The objective evaluation of reconstructed CTA images was comparable in DLIR-H, DLIR-M, and ASIR-V80% (P>0.05). The subjective scores of the DLIR-H and DLIR-M images were higher than those of the other groups, especially ASIR-V40% and FBP (P<0.05). Conclusions Compared with FBP and ASIR-V40%, DLIR-H, DLIR-M, and ASIR-V80% improved the overall image quality of CTP and CTA images to varying degrees. Furthermore, DLIR-H and DLIR-M showed the best performance. DLIR-H is the best choice in diagnosing AIS with improved detection accuracy for cerebral infarction. Reconstructing CTA images using CTP datasets could reduce contrast agent and radiation dose.
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Affiliation(s)
- Limin Lei
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuhan Zhou
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoxu Guo
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | | | - Xitong Zhao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jinping Ma
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Songwei Yue
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Cozzi A, Cè M, De Padova G, Libri D, Caldarelli N, Zucconi F, Oliva G, Cellina M. Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality. Tomography 2023; 9:1629-1637. [PMID: 37736983 PMCID: PMC10514884 DOI: 10.3390/tomography9050130] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/23/2023] [Accepted: 08/30/2023] [Indexed: 09/23/2023] Open
Abstract
This exploratory retrospective study aims to quantitatively compare the image quality of unenhanced brain computed tomography (CT) reconstructed with an iterative (AIDR-3D) and a deep learning-based (AiCE) reconstruction algorithm. After a preliminary phantom study, AIDR-3D and AiCE reconstructions (0.5 mm thickness) of 100 consecutive brain CTs acquired in the emergency setting on the same 320-detector row CT scanner were retrospectively analyzed, calculating image noise reduction attributable to the AiCE algorithm, artifact indexes in the posterior cranial fossa, and contrast-to-noise ratios (CNRs) at the cortical and thalamic levels. In the phantom study, the spatial resolution of the two datasets proved to be comparable; conversely, AIDR-3D reconstructions showed a broader noise pattern. In the human study, median image noise was lower with AiCE compared to AIDR-3D (4.7 vs. 5.3, p < 0.001, median 19.6% noise reduction), whereas AIDR-3D yielded a lower artifact index than AiCE (7.5 vs. 8.4, p < 0.001). AiCE also showed higher median CNRs at the cortical (2.5 vs. 1.8, p < 0.001) and thalamic levels (2.8 vs. 1.7, p < 0.001). These results highlight how image quality improvements granted by deep learning-based (AiCE) and iterative (AIDR-3D) image reconstruction algorithms vary according to different brain areas.
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Affiliation(s)
- Andrea Cozzi
- Service of Radiology, Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900 Lugano, Switzerland;
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milano, Italy (G.D.P.); (D.L.); (N.C.)
| | - Giuseppe De Padova
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milano, Italy (G.D.P.); (D.L.); (N.C.)
| | - Dario Libri
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milano, Italy (G.D.P.); (D.L.); (N.C.)
| | - Nazarena Caldarelli
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milano, Italy (G.D.P.); (D.L.); (N.C.)
| | - Fabio Zucconi
- Department of Radioprotection, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milano, Italy;
| | - Giancarlo Oliva
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milano, Italy;
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milano, Italy;
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Ng CKC. Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1372. [PMID: 37628371 PMCID: PMC10453402 DOI: 10.3390/children10081372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023]
Abstract
Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1-158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.
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Affiliation(s)
- Curtise K. C. Ng
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia; or ; Tel.: +61-8-9266-7314; Fax: +61-8-9266-2377
- Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
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Pula M, Kucharczyk E, Zdanowicz A, Guzinski M. Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination. Tomography 2023; 9:1485-1493. [PMID: 37624111 PMCID: PMC10459011 DOI: 10.3390/tomography9040118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/14/2023] [Accepted: 07/19/2023] [Indexed: 08/26/2023] Open
Abstract
In this study, we assess image quality in computed tomography scans reconstructed via DLIR (Deep Learning Image Reconstruction) and compare it with iterative reconstruction ASIR-V (Adaptive Statistical Iterative Reconstruction) in CT (computed tomography) scans of the head. The CT scans of 109 patients were subjected to both objective and subjective evaluation of image quality. The objective evaluation was based on the SNR (signal-to-noise ratio) and CNR (contrast-to-noise ratio) of the brain's gray and white matter. The regions of interest for our study were set in the BGA (basal ganglia area) and PCF (posterior cranial fossa). Simultaneously, a subjective assessment of image quality, based on brain structure visibility, was conducted by experienced radiologists. In the assessed scans, we obtained up to a 54% increase in SNR for gray matter and a 60% increase for white matter using DLIR in comparison to ASIR-V. Moreover, we achieved a CNR increment of 58% in the BGA structures and 50% in the PCF. In the subjective assessment of the obtained images, DLIR had a mean rating score of 2.8, compared to the mean score of 2.6 for ASIR-V images. In conclusion, DLIR shows improved image quality compared to the standard iterative reconstruction of CT images of the head.
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Affiliation(s)
- Michal Pula
- Lower Silesian Oncology, Pulmonology and Hematology Center, Hirszfelda Square 12, 53-413 Wrocław, Poland;
| | - Emilia Kucharczyk
- Faculty of Medicine, Wroclaw Medical University, Ludwika Pasteura 1, 50-367 Wrocław, Poland;
| | - Agata Zdanowicz
- Department of General Radiology, Interventional Radiology and Neuroradiology, Wroclaw Medical University, Borowska 213, 50-556 Wrocław, Poland;
| | - Maciej Guzinski
- Department of General Radiology, Interventional Radiology and Neuroradiology, Wroclaw Medical University, Borowska 213, 50-556 Wrocław, Poland;
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Nagayama Y, Iwashita K, Maruyama N, Uetani H, Goto M, Sakabe D, Emoto T, Nakato K, Shigematsu S, Kato Y, Takada S, Kidoh M, Oda S, Nakaura T, Hatemura M, Ueda M, Mukasa A, Hirai T. Deep learning-based reconstruction can improve the image quality of low radiation dose head CT. Eur Radiol 2023; 33:3253-3265. [PMID: 36973431 DOI: 10.1007/s00330-023-09559-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 12/06/2022] [Accepted: 02/06/2023] [Indexed: 03/29/2023]
Abstract
OBJECTIVES To evaluate the image quality of deep learning-based reconstruction (DLR), model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose (LD) unenhanced head CT and compare it with those of standard-dose (STD) HIR images. METHODS This retrospective study included 114 patients who underwent unenhanced head CT using the STD (n = 57) or LD (n = 57) protocol on a 320-row CT. STD images were reconstructed with HIR; LD images were reconstructed with HIR (LD-HIR), MBIR (LD-MBIR), and DLR (LD-DLR). The image noise, gray and white matter (GM-WM) contrast, and contrast-to-noise ratio (CNR) at the basal ganglia and posterior fossa levels were quantified. The noise magnitude, noise texture, GM-WM contrast, image sharpness, streak artifact, and subjective acceptability were independently scored by three radiologists (1 = worst, 5 = best). The lesion conspicuity of LD-HIR, LD-MBIR, and LD-DLR was ranked through side-by-side assessments (1 = worst, 3 = best). Reconstruction times of three algorithms were measured. RESULTS The effective dose of LD was 25% lower than that of STD. Lower image noise, higher GM-WM contrast, and higher CNR were observed in LD-DLR and LD-MBIR than those in STD (all, p ≤ 0.035). Compared with STD, the noise texture, image sharpness, and subjective acceptability were inferior for LD-MBIR and superior for LD-DLR (all, p < 0.001). The lesion conspicuity of LD-DLR (2.9 ± 0.2) was higher than that of HIR (1.2 ± 0.3) and MBIR (1.8 ± 0.4) (all, p < 0.001). Reconstruction times of HIR, MBIR, and DLR were 11 ± 1, 319 ± 17, and 24 ± 1 s, respectively. CONCLUSION DLR can enhance the image quality of head CT while preserving low radiation dose level and short reconstruction time. KEY POINTS • For unenhanced head CT, DLR reduced the image noise and improved the GM-WM contrast and lesion delineation without sacrificing the natural noise texture and image sharpness relative to HIR. • The subjective and objective image quality of DLR was better than that of HIR even at 25% reduced dose without considerably increasing the image reconstruction times (24 s vs. 11 s). • Despite the strong noise reduction and improved GM-WM contrast performance, MBIR degraded the noise texture, sharpness, and subjective acceptance with prolonged reconstruction times relative to HIR, potentially hampering its feasibility.
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Affiliation(s)
- Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan.
| | - Koya Iwashita
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Natsuki Maruyama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Hiroyuki Uetani
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Makoto Goto
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Daisuke Sakabe
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Takafumi Emoto
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Kengo Nakato
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Shinsuke Shigematsu
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Yuki Kato
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Sentaro Takada
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Seitaro Oda
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Masahiro Hatemura
- Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Mitsuharu Ueda
- Department of Neurology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Akitake Mukasa
- Department of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
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10
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Xu JJ, Lönn L, Budtz-Jørgensen E, Jawad S, Ulriksen PS, Hansen KL. Evaluation of thin-slice abdominal DECT using deep-learning image reconstruction in 74 keV virtual monoenergetic images: an image quality comparison. Abdom Radiol (NY) 2023; 48:1536-1544. [PMID: 36810705 DOI: 10.1007/s00261-023-03845-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/23/2023]
Abstract
PURPOSE To compare noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and image quality using deep-learning image reconstruction (DLIR) vs. adaptive statistical iterative reconstruction (ASIR-V) in 0.625 and 2.5 mm slice thickness gray scale 74 keV virtual monoenergetic (VM) abdominal dual-energy CT (DECT). METHODS This retrospective study was approved by the institutional review board and regional ethics committee. We analysed 30 portal-venous phase abdominal fast kV-switching DECT (80/140kVp) scans. Data were reconstructed to ASIR-V 60% and DLIR-High at 74 keV in 0.625 and 2.5 mm slice thickness. Quantitative HU and noise assessment were measured within liver, aorta, adipose tissue and muscle. Two board-certified radiologists evaluated image noise, sharpness, texture and overall quality based on a five-point Likert scale. RESULTS DLIR significantly reduced image noise and increased CNR as well as SNR compared to ASIR-V, when slice thickness was maintained (p < 0.001). Slightly higher noise of 5.5-16.2% was measured (p < 0.01) in liver, aorta and muscle tissue at 0.625 mm DLIR compared to 2.5 mm ASIR-V, while noise in adipose tissue was 4.3% lower with 0.625 mm DLIR compared to 2.5 mm ASIR-V (p = 0.08). Qualitative assessments demonstrated significantly improved image quality for DLIR particularly in 0.625 mm images. CONCLUSIONS DLIR significantly reduced image noise, increased CNR and SNR and improved image quality in 0.625 mm slice images, when compared to ASIR-V. DLIR may facilitate thinner image slice reconstructions for routine contrast-enhanced abdominal DECT.
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Affiliation(s)
- Jack J Xu
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, 2100, Copenhagen, Denmark.
| | - Lars Lönn
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Esben Budtz-Jørgensen
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Samir Jawad
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark
| | - Peter S Ulriksen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark
| | - Kristoffer L Hansen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, 2100, Copenhagen, Denmark
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11
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Heinrich A, Yücel S, Böttcher B, Öner A, Manzke M, Klemenz AC, Weber MA, Meinel FG. Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction. Quant Imaging Med Surg 2023; 13:970-981. [PMID: 36819291 PMCID: PMC9929406 DOI: 10.21037/qims-22-639] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022]
Abstract
Background This study aims to evaluate the impact of a novel deep learning-based image reconstruction (DLIR) algorithm on the image quality in computed tomographic angiography (CTA) for pre-interventional planning of transcatheter aortic valve implantation (TAVI). Methods We analyzed 50 consecutive patients (median age 80 years, 25 men) who underwent TAVI planning CT on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) and DLIR. Intravascular image noise, edge sharpness, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were quantified for ascending aorta, descending aorta, abdominal aorta and iliac arteries. Two readers (one radiologist and one interventional cardiologist) scored task-specific subjective image quality on a five-point scale. Results DLIR significantly reduced median image noise by 29-57% at all anatomical locations (all P<0.001). Accordingly, median SNR improved by 44-133% (all P<0.001) and median CNR improved by 44-125% (all P<0.001). DLIR significantly improved subjective image quality for all four pre-specified TAVI-specific tasks (measuring the annulus, assessing valve morphology and calcifications, the coronary ostia, and the suitability of the aorto-iliac access route) for both the radiologist and the interventional cardiologist (P≤0.001). Measurements of the aortic annulus circumference, area and diameter did not differ between ASIR-V and DLIR reconstructions (all P>0.05). Conclusions DLIR significantly improves objective and subjective image quality in TAVI planning CT compared to a state-of-the-art iterative reconstruction without affecting measurements of the aortic annulus. This may provide an opportunity for further reductions in contrast medium volume in this population.
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Affiliation(s)
- Andra Heinrich
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Centre Rostock, Rostock, Germany
| | - Seyrani Yücel
- Department of Internal Medicine, Division of Cardiology, University Medical Centre Rostock, Rostock, Germany
| | - Benjamin Böttcher
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Centre Rostock, Rostock, Germany
| | - Alper Öner
- Department of Internal Medicine, Division of Cardiology, University Medical Centre Rostock, Rostock, Germany
| | - Mathias Manzke
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Centre Rostock, Rostock, Germany
| | - Ann-Christin Klemenz
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Centre Rostock, Rostock, Germany
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Centre Rostock, Rostock, Germany
| | - Felix G. Meinel
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Centre Rostock, Rostock, Germany
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12
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Xu JJ, Lönn L, Budtz-Jørgensen E, Hansen KL, Ulriksen PS. Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT. Eur Radiol 2022; 32:7098-7107. [PMID: 35895120 DOI: 10.1007/s00330-022-09018-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/01/2022] [Accepted: 07/04/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To evaluate a novel deep learning image reconstruction (DLIR) technique for dual-energy CT (DECT) derived virtual monoenergetic (VM) images compared to adaptive statistical iterative reconstruction (ASIR-V) in low kiloelectron volt (keV) images. METHODS We analyzed 30 venous phase acute abdominal DECT (80/140 kVp) scans. Data were reconstructed to ASIR-V and DLIR-High at four different keV levels (40, 50, 74, and 100) with 1- and 3-mm slice thickness. Quantitative Hounsfield unit (HU) and noise assessment were measured within the liver, aorta, fat, and muscle. Subjective assessment of image noise, sharpness, texture, and overall quality was performed by two board-certified radiologists. RESULTS DLIR reduced image noise by 19.9-35.5% (p < 0.001) compared to ASIR-V in all reconstructions at identical keV levels. Contrast-to-noise ratio (CNR) increased by 49.2-53.2% (p < 0.001) in DLIR 40-keV images compared to ASIR-V 50 keV, while no significant difference in noise was identified except for 1 and 3 mm in aorta and for 1-mm liver measurements, where ASIR-V 50 keV showed 5.5-6.8% (p < 0.002) lower noise levels. Qualitative assessment demonstrated significant improvement particularly in 1-mm reconstructions (p < 0.001). Lastly, DLIR 40 keV demonstrated comparable or improved image quality ratings when compared to ASIR-V 50 keV (p < 0.001 to 0.22). CONCLUSION DLIR significantly reduced image noise compared to ASIR-V. Qualitative assessment showed that DLIR significantly improved image quality particularly in thin sliced images. DLIR may facilitate 40 keV as a new standard for routine low-keV VM reconstruction in contrast-enhanced abdominal DECT. KEY POINTS • DLIR enables 40 keV as the routine low-keV VM reconstruction. • DLIR significantly reduced image noise compared to ASIR-V, across a wide range of keV levels in VM DECT images. • In low-keV VM reconstructions, improvements in image quality using DLIR were most evident and consistent in 1-mm sliced images.
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Affiliation(s)
- Jack Junchi Xu
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, 2100, Copenhagen, Denmark.
| | - Lars Lönn
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Esben Budtz-Jørgensen
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Kristoffer L Hansen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Peter S Ulriksen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
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13
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Ng CKC. Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review. CHILDREN 2022; 9:children9071044. [PMID: 35884028 PMCID: PMC9320231 DOI: 10.3390/children9071044] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/11/2022] [Accepted: 07/11/2022] [Indexed: 01/19/2023]
Abstract
Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question “What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?” Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36–70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.
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Affiliation(s)
- Curtise K. C. Ng
- Curtin Medical School, Curtin University, GPO Box U1987, Perth, WA 6845, Australia; or ; Tel.: +61-8-9266-7314; Fax: +61-8-9266-2377
- Curtin Health Innovation Research Institute (CHIRI), Faculty of Health Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia
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14
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Editorial Comment: Doing More With Less-Deep Learning Iterative Reconstruction Can Improve Diagnostic Image Quality in Pediatric Low-Dose Body CT. AJR Am J Roentgenol 2022; 219:325. [PMID: 35261283 DOI: 10.2214/ajr.22.27613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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15
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Deep learning versus iterative image reconstruction algorithm for head CT in trauma. Emerg Radiol 2022; 29:339-352. [PMID: 34984574 PMCID: PMC8917108 DOI: 10.1007/s10140-021-02012-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 12/19/2021] [Indexed: 10/27/2022]
Abstract
PURPOSE To compare the image quality between a deep learning-based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT. METHODS Head CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa. RESULTS DLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader. CONCLUSION The image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.
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