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Choi HU, Cho J, Hwang J, Lee S, Chang W, Park JH, Lee KH. Diagnostic performance and image quality of an image-based denoising algorithm applied to radiation dose-reduced CT in diagnosing acute appendicitis. Abdom Radiol (NY) 2024; 49:1839-1849. [PMID: 38411690 PMCID: PMC11213764 DOI: 10.1007/s00261-024-04246-3] [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: 11/10/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 02/28/2024]
Abstract
PURPOSE To evaluate diagnostic performance and image quality of ultralow-dose CT (ULDCT) in diagnosing acute appendicitis with an image-based deep-learning denoising algorithm (IDLDA). METHODS This retrospective multicenter study included 180 patients (mean ± standard deviation, 29 ± 9 years; 91 female) who underwent contrast-enhanced 2-mSv CT for suspected appendicitis from February 2014 to August 2016. We simulated ULDCT from 2-mSv CT, reducing the dose by at least 50%. Then we applied an IDLDA on ULDCT to produce denoised ULDCT (D-ULDCT). Six radiologists with different experience levels (three board-certified radiologists and three residents) independently reviewed the ULDCT and D-ULDCT. They rated the likelihood of appendicitis and subjective image qualities (subjective image noise, diagnostic acceptability, and artificial sensation). One radiologist measured image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). We used the receiver operating characteristic (ROC) analyses, Wilcoxon's signed-rank tests, and paired t-tests. RESULTS The area under the ROC curves (AUC) for diagnosing appendicitis ranged 0.90-0.97 for ULDCT and 0.94-0.97 for D-ULDCT. The AUCs of two residents were significantly higher on D-ULDCT (AUC difference = 0.06 [95% confidence interval, 0.01-0.11; p = .022] and 0.05 [0.00-0.10; p = .046], respectively). D-ULDCT provided better subjective image noise and diagnostic acceptability to all six readers. However, the response of board-certified radiologists and residents differed in artificial sensation (all p ≤ .003). D-ULDCT showed significantly lower image noise, higher SNR, and higher CNR (all p < .001). CONCLUSION An IDLDA can provide better ULDCT image quality and enhance diagnostic performance for less-experienced radiologists.
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Affiliation(s)
- Hyeon Ui Choi
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Jungheum Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea.
| | - Jinhee Hwang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
| | - Seungjae Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Institute of Health and Environment, Seoul National University, Seoul, Korea
| | - Won Chang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Ji Hoon Park
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Korea
| | - Kyoung Ho Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
- Department of Medical Device Development, Seoul National University College of Medicine, Seoul, Korea
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Zhou Z, Gong H, Hsieh S, McCollough CH, Yu L. Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution. Med Phys 2024. [PMID: 38555876 DOI: 10.1002/mp.17029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/19/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. PURPOSE In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. METHODS The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. RESULTS The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP > DCNN-Weak > IR > DCNN-Medium > DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. CONCLUSIONS A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.
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Affiliation(s)
- Zhongxing Zhou
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Hao Gong
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Scott Hsieh
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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You Y, Zhong S, Zhang G, Wen Y, Guo D, Li W, Li Z. Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01080-3. [PMID: 38502435 DOI: 10.1007/s10278-024-01080-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/06/2024] [Accepted: 03/07/2024] [Indexed: 03/21/2024]
Abstract
This study aims to investigate the maximum achievable dose reduction for applying a new deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in computed tomography (CT) for hepatic lesion detection. A total of 40 patients with 98 clinically confirmed hepatic lesions were retrospectively included. The mean volume CT dose index was 13.66 ± 1.73 mGy in routine-dose portal venous CT examinations, where the images were originally obtained with hybrid iterative reconstruction (HIR). Low-dose simulations were performed in projection domain for 40%-, 20%-, and 10%-dose levels, followed by reconstruction using both HIR and AIIR. Two radiologists were asked to detect hepatic lesion on each set of low-dose image in separate sessions. Qualitative metrics including lesion conspicuity, diagnostic confidence, and overall image quality were evaluated using a 5-point scale. The contrast-to-noise ratio (CNR) for lesion was also calculated for quantitative assessment. The lesion CNR on AIIR at reduced doses were significantly higher than that on routine-dose HIR (all p < 0.05). Lower qualitative image quality was observed as the radiation dose reduced, while there were no significant differences between 40%-dose AIIR and routine-dose HIR images. The lesion detection rate was 100%, 98% (96/98), and 73.5% (72/98) on 40%-, 20%-, and 10%-dose AIIR, respectively, whereas it was 98% (96/98), 73.5% (72/98), and 40% (39/98) on the corresponding low-dose HIR, respectively. AIIR outperformed HIR in simulated low-dose CT examinations of the liver. The use of AIIR allows up to 60% dose reduction for lesion detection while maintaining comparable image quality to routine-dose HIR.
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Affiliation(s)
- Yongchun You
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | | | | | - Yuting Wen
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Dian Guo
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Wanjiang Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
| | - Zhenlin Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
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Lee DH, Lee JM, Lee CH, Afat S, Othman A. Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT. Radiol Artif Intell 2024; 6:e230192. [PMID: 38231025 PMCID: PMC10982822 DOI: 10.1148/ryai.230192] [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: 05/31/2023] [Revised: 11/13/2023] [Accepted: 01/02/2024] [Indexed: 01/18/2024]
Abstract
Purpose To compare the image quality and diagnostic capability in detecting malignant liver tumors of low-dose CT (LDCT, 33% dose) with deep learning-based denoising (DLD) and standard-dose CT (SDCT, 100% dose) with model-based iterative reconstruction (MBIR). Materials and Methods In this prospective, multicenter, noninferiority study, individuals referred for liver CT scans were enrolled from three tertiary referral hospitals between February 2021 and August 2022. All liver CT scans were conducted using a dual-source scanner with the dose split into tubes A (67% dose) and B (33% dose). Blended images from tubes A and B were created using MBIR to produce SDCT images, whereas LDCT images used data from tube B and were reconstructed with DLD. The noise in liver images was measured and compared between imaging techniques. The diagnostic performance of each technique in detecting malignant liver tumors was evaluated by three independent radiologists using jackknife alternative free-response receiver operating characteristic analysis. Noninferiority of LDCT compared with SDCT was declared when the lower limit of the 95% CI for the difference in figure of merit (FOM) was greater than -0.10. Results A total of 296 participants (196 men, 100 women; mean age, 60.5 years ± 13.3 [SD]) were included. The mean noise level in the liver was significantly lower for LDCT (10.1) compared with SDCT (10.7) (P < .001). Diagnostic performance was assessed in 246 participants (108 malignant tumors in 90 participants). The reader-averaged FOM was 0.880 for SDCT and 0.875 for LDCT (P = .35). The difference fell within the noninferiority margin (difference, -0.005 [95% CI: -0.024, 0.012]). Conclusion Compared with SDCT with MBIR, LDCT using 33% of the standard radiation dose had reduced image noise and comparable diagnostic performance in detecting malignant liver tumors. Keywords: CT, Abdomen/GI, Liver, Comparative Studies, Diagnosis, Reconstruction Algorithms Clinical trial registration no. NCT05804799 © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Dong Ho Lee
- From the Departments of Radiology of Seoul National University
Hospital, Seoul, South Korea (D.H.L., J.M.L.); Seoul National University
Hospital, Seoul National University College of Medicine, 101 Daehak-ro,
Jongno-gu, Seoul 03080, South Korea (D.H.L., J.M.L.); Korea University Guro
Hospital, Korea University Medicine, Seoul, South Korea (C.H.L.); and
Tübingen University Hospital, Tübingen, Germany (S.A.,
A.O.)
| | - Jeong Min Lee
- From the Departments of Radiology of Seoul National University
Hospital, Seoul, South Korea (D.H.L., J.M.L.); Seoul National University
Hospital, Seoul National University College of Medicine, 101 Daehak-ro,
Jongno-gu, Seoul 03080, South Korea (D.H.L., J.M.L.); Korea University Guro
Hospital, Korea University Medicine, Seoul, South Korea (C.H.L.); and
Tübingen University Hospital, Tübingen, Germany (S.A.,
A.O.)
| | - Chang Hee Lee
- From the Departments of Radiology of Seoul National University
Hospital, Seoul, South Korea (D.H.L., J.M.L.); Seoul National University
Hospital, Seoul National University College of Medicine, 101 Daehak-ro,
Jongno-gu, Seoul 03080, South Korea (D.H.L., J.M.L.); Korea University Guro
Hospital, Korea University Medicine, Seoul, South Korea (C.H.L.); and
Tübingen University Hospital, Tübingen, Germany (S.A.,
A.O.)
| | - Saif Afat
- From the Departments of Radiology of Seoul National University
Hospital, Seoul, South Korea (D.H.L., J.M.L.); Seoul National University
Hospital, Seoul National University College of Medicine, 101 Daehak-ro,
Jongno-gu, Seoul 03080, South Korea (D.H.L., J.M.L.); Korea University Guro
Hospital, Korea University Medicine, Seoul, South Korea (C.H.L.); and
Tübingen University Hospital, Tübingen, Germany (S.A.,
A.O.)
| | - Ahmed Othman
- From the Departments of Radiology of Seoul National University
Hospital, Seoul, South Korea (D.H.L., J.M.L.); Seoul National University
Hospital, Seoul National University College of Medicine, 101 Daehak-ro,
Jongno-gu, Seoul 03080, South Korea (D.H.L., J.M.L.); Korea University Guro
Hospital, Korea University Medicine, Seoul, South Korea (C.H.L.); and
Tübingen University Hospital, Tübingen, Germany (S.A.,
A.O.)
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Sadia RT, Chen J, Zhang J. CT image denoising methods for image quality improvement and radiation dose reduction. J Appl Clin Med Phys 2024; 25:e14270. [PMID: 38240466 PMCID: PMC10860577 DOI: 10.1002/acm2.14270] [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: 09/18/2023] [Revised: 12/15/2023] [Accepted: 12/28/2023] [Indexed: 02/13/2024] Open
Abstract
With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low-dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)-based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL-based models.
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Affiliation(s)
- Rabeya Tus Sadia
- Department of Computer ScienceUniversity of KentuckyLexingtonKentuckyUSA
| | - Jin Chen
- Department of Medicine‐NephrologyUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Jie Zhang
- Department of RadiologyUniversity of KentuckyLexingtonKentuckyUSA
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Marcus RP, Nagy DA, Feuerriegel GC, Anhaus J, Nanz D, Sutter R. Photon-Counting Detector CT With Denoising for Imaging of the Osseous Pelvis at Low Radiation Doses: A Phantom Study. AJR Am J Roentgenol 2024; 222:e2329765. [PMID: 37646387 DOI: 10.2214/ajr.23.29765] [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] [Indexed: 09/01/2023]
Abstract
BACKGROUND. Photon-counting detector (PCD) CT may allow lower radiation doses than used for conventional energy-integrating detector (EID) CT, with preserved image quality. OBJECTIVE. The purpose of this study was to compare PCD CT and EID CT, reconstructed with and without a denoising tool, in terms of image quality of the osseous pelvis in a phantom, with attention to low radiation doses. METHODS. A pelvic phantom comprising human bones in acrylic material mimicking soft tissue underwent PCD CT and EID CT at various tube potentials and radiation doses ranging from 0.05 to 5.00 mGy. Additional denoised reconstructions were generated using a commercial tool. Noise was measured in the acrylic material. Two readers performed independent qualitative assessments that entailed determining the denoised EID CT reconstruction with the lowest acceptable dose and then comparing this reference reconstruction with PCD CT reconstructions without and with denoising, using subjective Likert scales. RESULTS. Noise was lower for PCD CT than for EID CT. For instance, at 0.05 mGy and 100 kV with tin filter, noise was 38.4 HU for PCD CT versus 48.8 HU for EID CT. Denoising further reduced noise; for example, for PCD CT at 100 kV with tin filter at 0.25 mGy, noise was 19.9 HU without denoising versus 9.7 HU with denoising. For both readers, lowest acceptable dose for EID CT was 0.10 mGy (total score, 11 of 15 for both readers). Both readers somewhat agreed that PCD CT without denoising at 0.10 mGy (reflecting reference reconstruction dose) was relatively better than the reference reconstruction in terms of osseous structures, artifacts, and image quality. Both readers also somewhat agreed that denoised PCD CT reconstructions at 0.10 mGy and 0.05 mGy (reflecting matched and lower doses, respectively, with respect to reference reconstruction dose) were relatively better than the reference reconstruction for the image quality measures. CONCLUSION. PCD CT showed better-quality images than EID CT when performed at the lowest acceptable radiation dose for EID CT. PCD CT with denoising yielded better-quality images at a dose lower than lowest acceptable dose for EID CT. CLINICAL IMPACT. PCD CT with denoising could facilitate lower radiation doses for pelvic imaging.
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Affiliation(s)
- Roy P Marcus
- Department of Radiology, Balgrist University Hospital Zurich, Forchstrasse 340, Zurich 8008, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Daniel A Nagy
- Department of Radiology, Balgrist University Hospital Zurich, Forchstrasse 340, Zurich 8008, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Georg C Feuerriegel
- Department of Radiology, Balgrist University Hospital Zurich, Forchstrasse 340, Zurich 8008, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | | | - Daniel Nanz
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Swiss Center for Musculoskeletal Imaging, Balgrist Campus, Zurich, Switzerland
| | - Reto Sutter
- Department of Radiology, Balgrist University Hospital Zurich, Forchstrasse 340, Zurich 8008, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
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Lamm R, Kumar SS, Collings AT, Haskins IN, Abou-Setta A, Narula N, Nepal P, Hanna NM, Athanasiadis DI, Scholz S, Bradley JF, Train AT, Pucher PH, Quinteros F, Slater B. Diagnosis and treatment of appendicitis: systematic review and meta-analysis. Surg Endosc 2023; 37:8933-8990. [PMID: 37914953 DOI: 10.1007/s00464-023-10456-5] [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: 08/18/2023] [Accepted: 09/07/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND The optimal diagnosis and treatment of appendicitis remains controversial. This systematic review details the evidence and current best practices for the evaluation and management of uncomplicated and complicated appendicitis in adults and children. METHODS Eight questions regarding the diagnosis and management of appendicitis were formulated. PubMed, Embase, CINAHL, Cochrane and clinicaltrials.gov/NLM were queried for articles published from 2010 to 2022 with key words related to at least one question. Randomized and non-randomized studies were included. Two reviewers screened each publication for eligibility and then extracted data from eligible studies. Random effects meta-analyses were performed on all quantitative data. The quality of randomized and non-randomized studies was assessed using the Cochrane Risk of Bias 2.0 or Newcastle Ottawa Scale, respectively. RESULTS 2792 studies were screened and 261 were included. Most had a high risk of bias. Computerized tomography scan yielded the highest sensitivity (> 80%) and specificity (> 93%) in the adult population, although high variability existed. In adults with uncomplicated appendicitis, non-operative management resulted in higher odds of readmission (OR 6.10) and need for operation (OR 20.09), but less time to return to work/school (SMD - 1.78). In pediatric patients with uncomplicated appendicitis, non-operative management also resulted in higher odds of need for operation (OR 38.31). In adult patients with complicated appendicitis, there were higher odds of need for operation following antibiotic treatment only (OR 29.00), while pediatric patients had higher odds of abscess formation (OR 2.23). In pediatric patients undergoing appendectomy for complicated appendicitis, higher risk of reoperation at any time point was observed in patients who had drains placed at the time of operation (RR 2.04). CONCLUSIONS This review demonstrates the diagnosis and treatment of appendicitis remains nuanced. A personalized approach and appropriate patient selection remain key to treatment success. Further research on controversies in treatment would be useful for optimal management.
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Affiliation(s)
- Ryan Lamm
- Department of Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Sunjay S Kumar
- Department of Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA.
- Thomas Jefferson University, 1015 Walnut Street, 613 Curtis, Philadelphia, PA, 19107, USA.
| | - Amelia T Collings
- Hiram C. Polk, Jr Department of Surgery, University of Louisville, Louisville, KY, USA
| | - Ivy N Haskins
- Department of Surgery, University of Nebraska Medical Center, Omaha, NE, USA
| | - Ahmed Abou-Setta
- Centre for Healthcare Innovation, University of Manitoba, Winnipeg, MB, Canada
| | - Nisha Narula
- Department of Surgery, Rutgers, New Jersey Medical School, Newark, NJ, USA
| | - Pramod Nepal
- Division of Colon and Rectal Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Nader M Hanna
- Department of Surgery, Queen's University, Kingston, ON, Canada
| | | | - Stefan Scholz
- Division of General and Thoracic Pediatric Surgery, Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joel F Bradley
- Division of General Surgery, Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Arianne T Train
- Department of Surgery, Penn Medicine Lancaster General Health, Lancaster, PA, USA
| | - Philip H Pucher
- Department of Surgery, Queen Alexandra Hospital, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Francisco Quinteros
- Division of Colorectal Surgery, Advocate Lutheran General Hospital, Park Ridge, IL, USA
| | - Bethany Slater
- Division of Pediatric Surgery, University of Chicago Medicine, Chicago, IL, USA
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Lyoo Y, Choi YH, Lee SB, Lee S, Cho YJ, Shin SM, Phi JH, Kim SK, Cheon JE. Ultra-low-dose computed tomography with deep learning reconstruction for craniosynostosis at radiation doses comparable to skull radiographs: a pilot study. Pediatr Radiol 2023; 53:2260-2268. [PMID: 37488451 DOI: 10.1007/s00247-023-05717-3] [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: 12/30/2022] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND Craniofacial computed tomography (CT) is the diagnostic investigation of choice for craniosynostosis, but high radiation dose remains a concern. OBJECTIVE To evaluate the image quality and diagnostic performance of an ultra-low-dose craniofacial CT protocol with deep learning reconstruction for diagnosis of craniosynostosis. MATERIALS AND METHODS All children who underwent initial craniofacial CT for suspected craniosynostosis between September 2021 and September 2022 were included in the study. The ultra-low-dose craniofacial CT protocol using 70 kVp, model-based iterative reconstruction and deep learning reconstruction techniques was compared with a routine-dose craniofacial CT protocol. Quantitative analysis of the signal-to-noise ratio and noise was performed. The 3-dimensional (D) volume-rendered images were independently evaluated by two radiologists with regard to surface coarseness, step-off artifacts and overall image quality on a 5-point scale. Sutural patency was assessed for each of six sutures. Radiation dose was compared between the two protocols. RESULTS Among 29 patients (15 routine-dose CT and 14 ultra-low-dose CT), 23 patients had craniosynostosis. The 3-D volume-rendered images of ultra-low-dose CT without deep learning showed decreased image quality compared to routine-dose CT. The 3-D volume-rendered images of ultra-low-dose CT with deep learning reconstruction showed higher noise level, higher surface coarseness but decreased step-off artifacts, comparable signal-to-noise ratio and overall similar image quality compared to the routine-dose CT images. Diagnostic performance for detecting craniosynostosis at the suture level showed no significant difference between ultra-low-dose CT without deep learning reconstruction, ultra-low-dose CT with deep learning reconstruction and routine-dose CT. The estimated effective radiation dose for the ultra-low-dose CT was 0.05 mSv (range, 0.03-0.06 mSv), a 95% reduction in dose over the routine-dose CT at 1.15 mSv (range, 0.54-1.74 mSv). This radiation dose is comparable to 4-view skull radiography (0.05-0.1 mSv) and lower than previously reported effective dose for craniosynostosis protocols (0.08-3.36 mSv). CONCLUSION In this pilot study, an ultra-low-dose CT protocol using radiation doses at a level similar to skull radiographs showed preserved diagnostic performance for craniosynostosis, but decreased image quality compared to the routine-dose CT protocol. However, by combining the ultra-low-dose CT protocol with deep learning reconstruction, image quality was improved to a level comparable to the routine-dose CT protocol, without sacrificing diagnostic performance for craniosynostosis.
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Affiliation(s)
- Youngwook Lyoo
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Seul Bi Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seunghyun Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yeon Jin Cho
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Su-Mi Shin
- Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Ji Hoon Phi
- Department of Pediatric Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Seung Ki Kim
- Department of Pediatric Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jung-Eun Cheon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
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Kim CH, Chung MJ, Cha YK, Oh S, Kim KG, Yoo H. The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease. PLoS One 2023; 18:e0291745. [PMID: 37756357 PMCID: PMC10529569 DOI: 10.1371/journal.pone.0291745] [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] [Received: 09/30/2022] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated. Also, CT patterns of usual interstitial pneumonia (UIP) were classified according to the 2022 idiopathic pulmonary fibrosis (IPF) diagnostic criteria. The differences between CT images subjected to FBP, ASiR-V 30%, and DLM were evaluated. The image noise and BRISQUE scores of DLM images was lower and SNR was higher than that of the ASiR-V and FBP images (ASiR-V vs. DLM, p < 0.001 and FBP vs. DLR-M, p < 0.001, respectively). The agreement of the diagnostic categorization of IPF between the three reconstruction methods was almost perfect (κ = 0.992, CI 0.990-0.994). Image quality was improved with DLM compared to ASiR-V and FBP.
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Affiliation(s)
- Chu hyun Kim
- Center for Health Promotion, Samsung Medical Center, Seoul, Republic of Korea
- Department of Radiology and AI Research Center, Samsung Medical Center, Sungkyunkwan University, Seoul, Korea
| | - Myung Jin Chung
- Department of Radiology and AI Research Center, Samsung Medical Center, Sungkyunkwan University, Seoul, Korea
- Department of Data Convergence and Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yoon Ki Cha
- Department of Radiology and AI Research Center, Samsung Medical Center, Sungkyunkwan University, Seoul, Korea
| | - Seok Oh
- Gil Medical Center, Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, Korea
| | - Kwang gi Kim
- Gil Medical Center, Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, Korea
| | - Hongseok Yoo
- Division of Pulmonary and Critical Care Medicine, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
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Hu Y, Zheng Z, Yu H, Wang J, Yang X, Shi H. Ultra-low-dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm (AIIR) in 18F-FDG total-body PET/CT examination: a preliminary study. EJNMMI Phys 2023; 10:1. [PMID: 36592256 PMCID: PMC9807709 DOI: 10.1186/s40658-022-00521-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/20/2022] [Indexed: 01/03/2023] Open
Abstract
PURPOSE To investigate the feasibility of ultra-low-dose CT (ULDCT) reconstructed with the artificial intelligence iterative reconstruction (AIIR) algorithm in total-body PET/CT imaging. METHODS The study included both the phantom and clinical parts. An anthropomorphic phantom underwent CT imaging with ULDCT (10mAs) and standard-dose CT (SDCT) (120mAs), respectively. ULDCT was reconstructed with AIIR and hybrid iterative reconstruction (HIR) (expressed as ULDCT-AIIRphantom and ULDCT-HIRphantom), respectively, and SDCT was reconstructed with HIR (SDCT-HIRphantom) as control. In the clinical part, 52 patients with malignant tumors underwent the total-body PET/CT scan. ULDCT with AIIR (ULDCT-AIIR) and HIR (ULDCT-HIR), respectively, was reconstructed for PET attenuation correction, followed by the SDCT reconstructed with HIR (SDCT-HIR) for anatomical location. PET/CT images' quality was qualitatively assessed by two readers. The CTmean, as well as the CT standard deviation (CTsd), SUVmax, SUVmean, and the SUV standard deviation (SUVsd), was recorded. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated and compared. RESULTS The image quality of ULDCT-HIRphantom was inferior to the SDCT-HIRphantom, but no significant difference was found between the ULDCT-AIIRphantom and SDCT-HIRphantom. The subjective score of ULDCT-AIIR in the neck, chest and lower limb was equivalent to that of SDCT-HIR. Besides the brain and lower limb, the change rates of CTmean in thyroid, neck muscle, lung, mediastinum, back muscle, liver, lumbar muscle, first lumbar spine and sigmoid colon were -2.15, -1.52, 0.66, 2.97, 0.23, 8.91, 0.06, -4.29 and 8.78%, respectively, while all CTsd of ULDCT-AIIR was lower than that of SDCT-HIR. Except for the brain, the CNR of ULDCT-AIIR was the same as the SDCT-HIR, but the SNR was higher. The change rates of SUVmax, SUVmean and SUVsd were within [Formula: see text] 3% in all ROIs. For the lesions, the SUVmax, SUVsd and TBR showed no significant difference between PET-AIIR and PET-HIR. CONCLUSION The SDCT-HIR could not be replaced by the ULDCT-AIIR at date, but the AIIR algorithm decreased the image noise and increased the SNR, which can be implemented under special circumstances in PET/CT examination.
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Affiliation(s)
- Yan Hu
- grid.8547.e0000 0001 0125 2443Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Nuclear Medicine Institute of Fudan University, Shanghai, 200032 China ,grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, Shanghai, 200032 China
| | - Zhe Zheng
- grid.8547.e0000 0001 0125 2443Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Nuclear Medicine Institute of Fudan University, Shanghai, 200032 China ,grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, Shanghai, 200032 China
| | - Haojun Yu
- grid.8547.e0000 0001 0125 2443Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Nuclear Medicine Institute of Fudan University, Shanghai, 200032 China ,grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, Shanghai, 200032 China
| | - Jingyi Wang
- grid.497849.fUnited Imaging Healthcare Co., Ltd., Shanghai, China
| | - Xinlan Yang
- grid.497849.fUnited Imaging Healthcare Co., Ltd., Shanghai, China
| | - Hongcheng Shi
- grid.8547.e0000 0001 0125 2443Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Rd, Shanghai, 200032 China ,grid.8547.e0000 0001 0125 2443Nuclear Medicine Institute of Fudan University, Shanghai, 200032 China ,grid.413087.90000 0004 1755 3939Shanghai Institute of Medical Imaging, Shanghai, 200032 China
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Kambadakone AR, Santillan CS, Kim DH, Fowler KJ, Birkholz JH, Camacho MA, Cash BD, Dane B, Felker RA, Grossman EJ, Korngold EK, Liu PS, Marin D, McCrary M, Pietryga JA, Weinstein S, Zukotynski K, Carucci LR. ACR Appropriateness Criteria® Right Lower Quadrant Pain: 2022 Update. J Am Coll Radiol 2022; 19:S445-S461. [PMID: 36436969 DOI: 10.1016/j.jacr.2022.09.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/01/2022] [Indexed: 11/27/2022]
Abstract
This document focuses on imaging in the adult and pregnant populations with right lower quadrant (RLQ) abdominal pain, including patients with fever and leukocytosis. Appendicitis remains the most common surgical pathology responsible for RLQ abdominal pain in the United States. Other causes of RLQ pain include right colonic diverticulitis, ureteral stone, and infectious enterocolitis. Appropriate imaging in the diagnosis of appendicitis has resulted in decreased negative appendectomy rate from as high as 25% to approximately 1% to 3%. Contrast-enhanced CT remains the primary and most appropriate imaging modality to evaluate this patient population. MRI is approaching CT in sensitivity and specificity as this technology becomes more widely available and utilization increases. Unenhanced MRI and ultrasound remain the diagnostic procedures of choice in the pregnant patient. MRI and ultrasound continue to perform best in the hands of the experts. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer-reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances in which peer-reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.
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Affiliation(s)
- Avinash R Kambadakone
- Division Chief, Abdominal Imaging, Massachusetts General Hospital, Boston, Massachusetts; Medical Director, Martha's Vineyard Hospital Imaging.
| | - Cynthia S Santillan
- Vice Chair of Clinical Operations, University of California, San Diego, San Diego, California
| | - David H Kim
- Panel Chair; Vice Chair of Education, Department of Radiology, University of Wisconsin Hospital & Clinics, Madison, Wisconsin
| | - Kathryn J Fowler
- Panel Vice-Chair, University of California, San Diego, San Diego, California. ACR LI-RADS; Division Chief, SAR Portfolio Director; RSNA Radiology Senior DE
| | - James H Birkholz
- Divisional Director, Quality and Safety (Abdominal Imaging), Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania. Radiology Representative to the Interdisciplinary Dysmotility (GIMIG) Conference
| | - Marc A Camacho
- The University of South Florida Morsani College of Medicine, Tampa, Florida; Committee on Emergency Radiology-GSER
| | - Brooks D Cash
- Chief of Gastroenterology, Hepatology, and Nutrition Division, University of Texas Health Science Center at Houston and McGovern Medical School, Houston, Texas; American Gastroenterological Association
| | - Bari Dane
- Director of Body CT, Abdominal Imaging; Director of Quality and Safety Outpatient Imaging, NYU Grossman School of Medicine, New York, New York
| | - Robin A Felker
- Associate Clerkship Director for Internal Medicine, Georgetown University; Primary care physician, Medstar Georgetown University Hospital, Washington, District of Columbia
| | - Eric J Grossman
- Medical Director, Multi-Specialty Clinic, Santa Barbara Cottage Hospital, Santa Barbara, California; American College of Surgeons
| | - Elena K Korngold
- Section Chief, Body Imaging, Chair, Department of Radiology Promotion and Tenure Committee, Oregon Health and Science University, Portland, Oregon
| | - Peter S Liu
- Section Head, Abdominal Imaging, Cleveland Clinic, Cleveland, Ohio
| | - Daniele Marin
- Duke University Medical Center, Durham, North Carolina
| | - Marion McCrary
- Associate Director of Duke GME Coaching, Duke Signature Care, Durham, North Carolina; American College of Physicians; Governor-Elect, American College of Physicians North Carolina Chapter
| | | | | | - Katherine Zukotynski
- Co-Associate Chair for Research, Department of Radiology, McMaster University, Hamilton, Ontario, Canada; Commission on Nuclear Medicine and Molecular Imaging
| | - Laura R Carucci
- Specialty Chair; Section Chief Abdominal Imaging, Director of MRI and CT, Virginia Commonwealth University Medical Center, Richmond, Virginia
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Diagnostic accuracy of computed tomography and ultrasound for the diagnosis of acute appendicitis: A systematic review and meta-analysis. Radiography (Lond) 2022; 28:1127-1141. [PMID: 36130469 DOI: 10.1016/j.radi.2022.08.012] [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: 07/05/2022] [Revised: 08/10/2022] [Accepted: 08/24/2022] [Indexed: 11/21/2022]
Abstract
INTRODUCTION The diagnosis of acute appendicitis remains challenging. This review determined the current diagnostic accuracy of CT and ultrasound for suspected acute appendicitis in adults. METHODS This systematic review adhered to the PRISMA for diagnostic test accuracy guidelines. A systematic search was undertaken in appropriate databases. Screening of potential titles and abstracts, full-text retrieval, methodological quality assessment using QUADAS, and data extraction was performed. Meta-analyses were performed for relevant subgroups, and sensitivity analysis was completed to account for outliers. GRADE was utilized to assess the certainty of findings. RESULTS 31 studies evaluating CT, 10 evaluating US, and six evaluating both were included. Pooled sensitivity and specificity for CT was 0.972 [0.958, 0.981] and 0.956 [0.941, 0.967] respectively, and 0.821 [0.738, 0.882] and 0.859 [0.727, 0.933] for US, respectively. When analyzing subgroups based on the use of contrast enhancement, sensitivity and specificity was highest for CT with intravenous and oral contrast (0.992 [0.965, 0.998], 0.974 [0.936, 0.99]), compared to CT with intravenous contrast (0.955 [0.922, 0.974], 0.942 [0.916, 0.960]). Low-Dose CT produced comparable values (0.934 [0.885,0.963], 0.937 [0.911, 0.955]) relative to these subgroups and standard dose non-contrast CT (0.877 [0.774,0.937], 0.914 [0.827, 0.959]). US studies which excluded equivocal findings demonstrated significantly greater values than the remainder of US studies (p < 0.0001). CONCLUSION The updated diagnostic test accuracies of CT, US and relevant subgroups should be implemented in light of factors such as dose, cost, and timing. IMPLICATIONS FOR PRACTICE For diagnosis of adult acute appendicitis: • CT with intravenous plus oral contrast enhancement yields statistically significantly greater diagnostic accuracy than CT with intravenous contrast alone. • Low-dose CT yields comparable sensitivity and specificity to standard-dose CT. • Ultrasound studies which exclude equivocal results may overinflate sensitivity and specificity.
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Balogh ZA, Janos Kis B. Comparison of CT noise reduction performances with deep learning-based, conventional, and combined denoising algorithms. Med Eng Phys 2022; 109:103897. [DOI: 10.1016/j.medengphy.2022.103897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 09/18/2022] [Accepted: 09/22/2022] [Indexed: 11/29/2022]
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Chun M, Choi JH, Kim S, Ahn C, Kim JH. Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study. PLoS One 2022; 17:e0271724. [PMID: 35857804 PMCID: PMC9299323 DOI: 10.1371/journal.pone.0271724] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 07/06/2022] [Indexed: 12/21/2022] Open
Abstract
While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.
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Affiliation(s)
- Minsoo Chun
- Department of Radiation Oncology, Chung-Ang University Gwang Myeong Hospital, Gyeonggi-do, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Jin Hwa Choi
- Department of Radiation Oncology, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| | - Sihwan Kim
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Chulkyun Ahn
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
- ClariPi Research, Seoul, Republic of Korea
| | - Jong Hyo Kim
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
- ClariPi Research, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon, Republic of Korea
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Brendlin AS, Schmid U, Plajer D, Chaika M, Mader M, Wrazidlo R, Männlin S, Spogis J, Estler A, Esser M, Schäfer J, Afat S, Tsiflikas I. AI Denoising Improves Image Quality and Radiological Workflows in Pediatric Ultra-Low-Dose Thorax Computed Tomography Scans. Tomography 2022; 8:1678-1689. [PMID: 35894005 PMCID: PMC9326759 DOI: 10.3390/tomography8040140] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/09/2022] [Accepted: 06/23/2022] [Indexed: 11/16/2022] Open
Abstract
(1) This study evaluates the impact of an AI denoising algorithm on image quality, diagnostic accuracy, and radiological workflows in pediatric chest ultra-low-dose CT (ULDCT). (2) Methods: 100 consecutive pediatric thorax ULDCT were included and reconstructed using weighted filtered back projection (wFBP), iterative reconstruction (ADMIRE 2), and AI denoising (PixelShine). Place-consistent noise measurements were used to compare objective image quality. Eight blinded readers independently rated the subjective image quality on a Likert scale (1 = worst to 5 = best). Each reader wrote a semiquantitative report to evaluate disease severity using a severity score with six common pathologies. The time to diagnosis was measured for each reader to compare the possible workflow benefits. Properly corrected mixed-effects analysis with post-hoc subgroup tests were used. Spearman’s correlation coefficient measured inter-reader agreement for the subjective image quality analysis and the severity score sheets. (3) Results: The highest noise was measured for wFBP, followed by ADMIRE 2, and PixelShine (76.9 ± 9.62 vs. 43.4 ± 4.45 vs. 34.8 ± 3.27 HU; each p < 0.001). The highest subjective image quality was measured for PixelShine, followed by ADMIRE 2, and wFBP (4 (4−5) vs. 3 (4−5) vs. 3 (2−4), each p < 0.001) with good inter-rater agreement (r ≥ 0.790; p ≤ 0.001). In diagnostic accuracy analysis, there was a good inter-rater agreement between the severity scores (r ≥ 0.764; p < 0.001) without significant differences between severity score items per reconstruction mode (F (5.71; 566) = 0.792; p = 0.570). The shortest time to diagnosis was measured for the PixelShine datasets, followed by ADMIRE 2, and wFBP (2.28 ± 1.56 vs. 2.45 ± 1.90 vs. 2.66 ± 2.31 min; F (1.000; 99.00) = 268.1; p < 0.001). (4) Conclusions: AI denoising significantly improves image quality in pediatric thorax ULDCT without compromising the diagnostic confidence and reduces the time to diagnosis substantially.
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Brendlin AS, Estler A, Plajer D, Lutz A, Grözinger G, Bongers MN, Tsiflikas I, Afat S, Artzner CP. AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography. Tomography 2022; 8:933-947. [PMID: 35448709 PMCID: PMC9031402 DOI: 10.3390/tomography8020075] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 11/16/2022] Open
Abstract
(1) To investigate whether interventional cone-beam computed tomography (cbCT) could benefit from AI denoising, particularly with respect to patient body mass index (BMI); (2) From 1 January 2016 to 1 January 2022, 100 patients with liver-directed interventions and peri-procedural cbCT were included. The unenhanced mask run and the contrast-enhanced fill run of the cbCT were reconstructed using weighted filtered back projection. Additionally, each dataset was post-processed using a novel denoising software solution. Place-consistent regions of interest measured signal-to-noise ratio (SNR) per dataset. Corrected mixed-effects analysis with BMI subgroup analyses compared objective image quality. Multiple linear regression measured the contribution of “Radiation Dose”, “Body-Mass-Index”, and “Mode” to SNR. Two radiologists independently rated diagnostic confidence. Inter-rater agreement was measured using Spearman correlation (r); (3) SNR was significantly higher in the denoised datasets than in the regular datasets (p < 0.001). Furthermore, BMI subgroup analysis showed significant SNR deteriorations in the regular datasets for higher patient BMI (p < 0.001), but stable results for denoising (p > 0.999). In regression, only denoising contributed positively towards SNR (0.6191; 95%CI 0.6096 to 0.6286; p < 0.001). The denoised datasets received overall significantly higher diagnostic confidence grades (p = 0.010), with good inter-rater agreement (r ≥ 0.795, p < 0.001). In a subgroup analysis, diagnostic confidence deteriorated significantly for higher patient BMI (p < 0.001) in the regular datasets but was stable in the denoised datasets (p ≥ 0.103).; (4) AI denoising can significantly enhance image quality in interventional cone-beam CT and effectively mitigate diagnostic confidence deterioration for rising patient BMI.
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Abstract
IMPORTANCE Acute appendicitis is the most common abdominal surgical emergency in the world, with an annual incidence of 96.5 to 100 cases per 100 000 adults. OBSERVATIONS The clinical diagnosis of acute appendicitis is based on history and physical, laboratory evaluation, and imaging. Classic symptoms of appendicitis include vague periumbilical pain, anorexia/nausea/intermittent vomiting, migration of pain to the right lower quadrant, and low-grade fever. The diagnosis of acute appendicitis is made in approximately 90% of patients presenting with these symptoms. Laparoscopic appendectomy remains the most common treatment. However, increasing evidence suggests that broad-spectrum antibiotics, such as piperacillin-tazobactam monotherapy or combination therapy with either cephalosporins or fluroquinolones with metronidazole, successfully treats uncomplicated acute appendicitis in approximately 70% of patients. Specific imaging findings on computed tomography (CT), such as appendiceal dilatation (appendiceal diameter ≥7 mm), or presence of appendicoliths, defined as the conglomeration of feces in the appendiceal lumen, identify patients for whom an antibiotics-first management strategy is more likely to fail. CT findings of appendicolith, mass effect, and a dilated appendix greater than 13 mm are associated with higher risk of treatment failure (≈40%) of an antibiotics-first approach. Therefore, surgical management should be recommended in patients with CT findings of appendicolith, mass effect, or a dilated appendix who are fit for surgery, defined as having relatively low risk of adverse outcomes or postoperative mortality and morbidity. In patients without high-risk CT findings, either appendectomy or antibiotics can be considered as first-line therapy. In unfit patients without these high-risk CT findings, the antibiotics-first approach is recommended, and surgery may be considered if antibiotic treatment fails. In unfit patients with high-risk CT findings, perioperative risk assessment as well as patient preferences should be considered. CONCLUSIONS AND RELEVANCE Acute appendicitis affects 96.5 to 100 people per 100 000 adults per year worldwide. Appendectomy remains first-line therapy for acute appendicitis, but treatment with antibiotics rather than surgery is appropriate in selected patients with uncomplicated appendicitis.
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Affiliation(s)
- Dimitrios Moris
- Department of Surgery, Duke University Medical Center, Durham, North Carolina
| | - Erik Karl Paulson
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
| | - Theodore N Pappas
- Department of Surgery, Duke University Medical Center, Durham, North Carolina
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Noda Y, Iritani Y, Kawai N, Miyoshi T, Ishihara T, Hyodo F, Matsuo M. Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction. Abdom Radiol (NY) 2021; 46:4238-4244. [PMID: 33973060 DOI: 10.1007/s00261-021-03111-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 04/12/2021] [Accepted: 04/27/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE To evaluate image quality, image noise, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic low-dose computed tomography (LDCT) reconstructed using deep learning image reconstruction (DLIR) and compare with those of images reconstructed using hybrid iterative reconstruction (IR). METHODS Our institutional review board approved this prospective study. Written informed consent was obtained from all patients. Twenty-eight consecutive patients with PDAC undergoing chemotherapy (14 men and 14 women; mean age, 68.4 years) underwent pancreatic LDCT for therapy evaluation. The LDCT images were reconstructed using 40% adaptive statistical iterative reconstruction-Veo (hybrid-IR) and DLIR at medium and high levels (DLIR-M and DLIR-H). The image noise, diagnostic acceptability, and conspicuity of PDAC were qualitatively assessed using a 5-point scale. CT numbers of the abdominal aorta, portal vein, pancreas, PDAC, background noise, signal-to-noise ratio (SNR) of the anatomical structures, and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. Qualitative and quantitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H images. RESULTS CT dose-index volumes and dose-length product in pancreatic LDCT were 2.3 ± 1.0 mGy and 74.9 ± 37.0 mGy•cm, respectively. The image noise, diagnostic acceptability, and conspicuity of PDAC were significantly better in DLIR-H than those in hybrid-IR and DLIR-M (all P < 0.001). The background noise was significantly lower in the DLIR-H images (P < 0.001) and resulted in improved SNRs (P < 0.001) and CNR (P < 0.001) compared with those in the hybrid-IR and DLIR-M images. CONCLUSION DLIR significantly reduced image noise and improved image quality in pancreatic LDCT images compared with hybrid-IR.
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Yeoh H, Hong SH, Ahn C, Choi JY, Chae HD, Yoo HJ, Kim JH. Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT. Korean J Radiol 2021; 22:1850-1857. [PMID: 34431248 PMCID: PMC8546130 DOI: 10.3348/kjr.2021.0140] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/27/2021] [Accepted: 06/01/2021] [Indexed: 11/16/2022] Open
Abstract
Objective The purpose of this study was to assess whether a deep learning (DL) algorithm could enable simultaneous noise reduction and edge sharpening in low-dose lumbar spine CT. Materials and Methods This retrospective study included 52 patients (26 male and 26 female; median age, 60.5 years) who had undergone CT-guided lumbar bone biopsy between October 2015 and April 2020. Initial 100-mAs survey images and 50-mAs intraprocedural images were reconstructed by filtered back projection. Denoising was performed using a vendor-agnostic DL model (ClariCT.AI™, ClariPI) for the 50-mAS images, and the 50-mAs, denoised 50-mAs, and 100-mAs CT images were compared. Noise, signal-to-noise ratio (SNR), and edge rise distance (ERD) for image sharpness were measured. The data were summarized as the mean ± standard deviation for these parameters. Two musculoskeletal radiologists assessed the visibility of the normal anatomical structures. Results Noise was lower in the denoised 50-mAs images (36.38 ± 7.03 Hounsfield unit [HU]) than the 50-mAs (93.33 ± 25.36 HU) and 100-mAs (63.33 ± 16.09 HU) images (p < 0.001). The SNRs for the images in descending order were as follows: denoised 50-mAs (1.46 ± 0.54), 100-mAs (0.99 ± 0.34), and 50-mAs (0.58 ± 0.18) images (p < 0.001). The denoised 50-mAs images had better edge sharpness than the 100-mAs images at the vertebral body (ERD; 0.94 ± 0.2 mm vs. 1.05 ± 0.24 mm, p = 0.036) and the psoas (ERD; 0.42 ± 0.09 mm vs. 0.50 ± 0.12 mm, p = 0.002). The denoised 50-mAs images significantly improved the visualization of the normal anatomical structures (p < 0.001). Conclusion DL-based reconstruction may enable simultaneous noise reduction and improvement in image quality with the preservation of edge sharpness on low-dose lumbar spine CT. Investigations on further radiation dose reduction and the clinical applicability of this technique are warranted.
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Affiliation(s)
- Hyunjung Yeoh
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Sung Hwan Hong
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
| | - Chulkyun Ahn
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
| | - Ja-Young Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Hee-Dong Chae
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Hye Jin Yoo
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Jong Hyo Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.,Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.,Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon, Korea
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Choi H, Chang W, Kim JH, Ahn C, Lee H, Kim HY, Cho J, Lee YJ, Kim YH. Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning-based image reconstruction algorithm on CT: a phantom study. Eur Radiol 2021; 32:1247-1255. [PMID: 34390372 PMCID: PMC8364308 DOI: 10.1007/s00330-021-08199-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/11/2021] [Accepted: 07/02/2021] [Indexed: 12/25/2022]
Abstract
Objectives To compare the dose reduction potential (DRP) of a vendor-agnostic deep learning model (DLM, ClariCT.AI) with that of a vendor-specific deep learning–based image reconstruction algorithm (DLR, TrueFidelity™). Methods Computed tomography (CT) images of a multi-sized image quality phantom (Mercury v4.0) were acquired under six radiation dose levels (0.48/0.97/1.93/3.87/7.74/15.47 mGy) and were reconstructed using filtered back projection (FBP) and three strength levels of the DLR (low/medium/high). The FBP images were denoised using the DLM. For all DLM and DLR images, the detectability index (d′) (a task-based detection performance metric) was obtained, under various combinations of three target sizes (10/5/1 mm), five inlets (CT value difference with the background; −895/50/90/335/1000 HU), five phantom diameters (36/31/26/21/16 cm), and six radiation dose levels. Dose reduction potential (DRP) measures the dose reduction made by using DLM or DLR, while yielding d′ equivalent to that of FBP at full dose. Results The DRPs of the DLM, DLR-low, DLR-medium, and DLR-high were 86% (81–88%), 60% (46–67%), 76% (60–81%), and 87% (78–92%), respectively. For 10-mm targets, the DRP of the DLM (87%) was higher than that of all DLR algorithms (58–86%). However, for smaller targets (5 mm/1 mm), the DRPs of the DLR-high (89/88%) were greater than those of the DLM (87/84%). Conclusion The dose reduction potential of the vendor-agnostic DLM was shown to be comparable to that of the vendor-specific DLR at high strength and superior to those of the DLRs at medium and low strengths. Key Points • DRP of the vendor-agnostic model was comparable to that of high-strength vendor-specific model and superior to those of medium- and low-strength models. • Under various radiation dose levels, the deep learning model shows higher detectability indexes compared to FBP. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08199-9.
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Affiliation(s)
- Hyunsu Choi
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea
| | - Won Chang
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea.
| | - Jong Hyo Kim
- Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Chulkyun Ahn
- Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Heejin Lee
- Department of Applied bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Hae Young Kim
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea
| | - Jungheum Cho
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea
| | - Yoon Jin Lee
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea
| | - Young Hoon Kim
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea
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Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques. Eur Radiol 2021; 31:5139-5147. [PMID: 33415436 DOI: 10.1007/s00330-020-07537-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 10/30/2020] [Accepted: 11/17/2020] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT. METHODS Vendor-agnostic deep learning post-processing model (DLM), vendor-specific deep learning image reconstruction (DLIR, high level), and adaptive statistical iterative reconstruction (ASiR, 70%) algorithms were employed. One hundred consecutive ultralow-dose noncontrast CT scans (CTDIvol; mean, 0.33 ± 0.056 mGy) were reconstructed with five algorithms: DLM-stnd (standard kernel), DLM-shrp (sharp kernel), DLIR, ASiR-stnd, and ASiR-shrp. Three thoracic radiologists blinded to the reconstruction algorithms reviewed five sets of 100 images and assessed subjective noise, spatial resolution, distortion artifact, and overall image quality. They selected the most preferred algorithm among five image sets for each case. Image noise and signal-to-noise ratio were measured. Edge-rise-distance was measured at a pulmonary vessel, i.e., the distance between two points where attenuation was 10% and 90% of maximal intravascular intensity. The skewness of attenuation was calculated in homogeneous areas. RESULTS DLM-stnd, followed by DLIR, showed the best subjective noise on both lung and mediastinal windows, while DLIR yielded the least measured noise (ps < .0001). Compared to DLM-stnd, DLIR showed inferior subjective spatial resolution on lung window and higher edge-rise-distance (ps < .0001). Additionally, DLIR showed the most frequent distortion artifacts and deviated skewness (ps < .0001). DLM-stnd scored the best overall image quality, followed by DLM-shrp and DLIR (mean score 3.89 ± 0.19, 3.68 ± 0.24, and 3.53 ± 0.33; ps < .001). Two among three readers preferred DLM-stnd on both windows. CONCLUSION Although DLIR provided the best quantitative noise profile, DLM-stnd showed the best overall image quality with fewer artifacts and was preferred by two among three readers. KEY POINTS • A vendor-agnostic deep learning post-processing algorithm applied to ultralow-dose chest CT exhibited the best image quality compared to vendor-specific deep learning algorithm and ASiR techniques. • Two out of three readers preferred a vendor-agnostic deep learning post-processing algorithm in comparison to vendor-specific deep learning algorithm and ASiR techniques. • A vendor-specific deep learning reconstruction algorithm yielded the least image noise, but showed significantly more frequent specific distortion artifacts and increased skewness of attenuation compared to a vendor-agnostic algorithm.
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Lee S, Choi YH, Cho YJ, Lee SB, Cheon JE, Kim WS, Ahn CK, Kim JH. Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique. Eur Radiol 2020; 31:2218-2226. [PMID: 33030573 DOI: 10.1007/s00330-020-07349-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 08/15/2020] [Accepted: 09/24/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVES To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning-based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT). METHODS From December 2016 to May 2017, DECT with 300 mg•I/mL contrast medium was performed in 29 pediatric patients (17 boys, 12 girls; age, 2-19 years). The DECT images were reconstructed using a noise-optimized virtual monoenergetic reconstruction image (VMI) with and without a deep learning method. SECT images with 350 mg•I/mL contrast medium, performed within the last 3 months before the DECT, served as reference images. The quantitative and qualitative parameters were compared using paired t tests and Wilcoxon signed-rank tests, and the differences in radiation dose and total iodine administration were assessed. RESULTS The linearly blended DECT showed lower attenuation and higher noise than SECT. The 60-keV VMI showed an increase in attenuation and higher noise than SECT. The combined 60-keV VMI plus deep learning images showed low noise, no difference in contrast-to-noise ratios, and overall image quality or diagnostic image quality, but showed a higher signal-to-noise ratio in the liver and lower enhancement of lesions than SECT. The overall image and diagnostic quality of lesions were maintained on the combined noise reduction approach. The CT dose index volume and total iodine administration in DECT were respectively 19.6% and 14.3% lower than those in SECT. CONCLUSION Low iodine concentration DECT, combined with deep learning in pediatric abdominal CT, can maintain image quality while reducing the radiation dose and iodine load, compared with standard SECT. KEY POINTS • An image noise reduction approach combining deep learning and noise-optimized virtual monoenergetic image reconstruction can maintain image quality while reducing radiation dose and iodine load. • The 60-keV virtual monoenergetic image reconstruction plus deep learning images showed low noise, no difference in contrast-to-noise ratio, and overall image quality, but showed a higher signal-to-noise ratio in the liver and a lower enhancement of lesion than single-energy polychromatic CT. • This combination could offer a 19.6% reduction in radiation dose and a 14.3% reduction in iodine load, in comparison with a control group that underwent single-energy polychromatic CT with the standard protocol.
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Affiliation(s)
- Seunghyun Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Young Hun Choi
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. .,Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Yeon Jin Cho
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Seul Bi Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Jung-Eun Cheon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Woo Sun Kim
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Chul Kyun Ahn
- Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Jong Hyo Kim
- Department of Radiology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Advanced Institutes of Convergence Technology, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
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