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Tonkopi E, Tetteh MA, Gunn C, Ashraf H, Rusten SL, Safi P, Tinsoe NS, Colford K, Ouellet O, Naimi S, Johansen S. A multi-institutional assessment of low-dose protocols in chest computed tomography: Dose and image quality. Acta Radiol Open 2024; 13:20584601241228220. [PMID: 38304118 PMCID: PMC10829498 DOI: 10.1177/20584601241228220] [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: 09/08/2023] [Accepted: 01/09/2024] [Indexed: 02/03/2024] Open
Abstract
Background Low-dose CT (LDCT) chest protocols have widespread clinical applications for many indications; as a result, there is a need for protocol assessment prior to standardization. Dalhousie University and Oslo Metropolitan University have a formally established cooperative relationship. Purpose The purpose is to assess radiation dose and image quality for LDCT chest protocols in seven different hospital locations in Norway and Canada. Material and methods Retrospective dosimetry data, volumetric CT dose index (CTDIvol), and dose length product (DLP) from 240 average-sized patients as well as CT protocol parameters were included in the survey. Effective dose (ED) and size-specific dose estimate (SSDE) were calculated for each examination. For a quantitative image quality analysis, noise, CT number, and signal-to-noise ratio (SNR) were determined for three regions in the chest. The contrast-to-noise ratio (CNR) was calculated for lung parenchyma in comparison to the subcutaneous fat. Differences in dose and image quality were evaluated by a single-factor ANOVA test. A two-sample t-test was performed to determine differences in means between individual scanners. Results The ANOVA test revealed significant differences (p < .05) in dose values for all scanners, including identical scanner models. Statistically significant differences (p < .05) were determined in mean values of the SNR distributions between the scanners in all three measured regions in the chest, as well as the CNR values. Conclusion The observed variations in dose and image quality measurements, even within the same hospitals and between identical scanner models, indicate a potential for protocol optimization in the involved hospitals in both countries.
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Affiliation(s)
- Elena Tonkopi
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
- Department of Radiation Oncology, Dalhousie University, Halifax, NS, Canada
- Department of Diagnostic Imaging, Nova Scotia Health Authority, Halifax, NS, Canada
| | - Mercy Afadzi Tetteh
- Department of Diagnostic Imaging, Akershus University Hospital, Loerenskog, Norway
| | - Catherine Gunn
- Department of Radiation Oncology, Dalhousie University, Halifax, NS, Canada
- School of Health Sciences, Dalhousie University, Halifax, NS, Canada
| | - Haseem Ashraf
- Department of Diagnostic Imaging, Akershus University Hospital, Loerenskog, Norway
- Medicine Faculty, University of Oslo, Oslo Norway
| | - Sigrid Lia Rusten
- Health Faculty, Department of Life Sciences and Health, Oslo Metropolitan University Oslo, Norway
| | - Perkhah Safi
- Health Faculty, Department of Life Sciences and Health, Oslo Metropolitan University Oslo, Norway
| | - Nora Suu Tinsoe
- Health Faculty, Department of Life Sciences and Health, Oslo Metropolitan University Oslo, Norway
| | - Kylie Colford
- School of Health Sciences, Dalhousie University, Halifax, NS, Canada
| | - Olivia Ouellet
- School of Health Sciences, Dalhousie University, Halifax, NS, Canada
| | - Salma Naimi
- Department of Diagnostic Imaging, Akershus University Hospital, Loerenskog, Norway
| | - Safora Johansen
- Health Faculty, Department of Life Sciences and Health, Oslo Metropolitan University Oslo, Norway
- Department of Cancer Treatment, Oslo University Hospital, Oslo, Norway
- Health and Social Science Cluster, Singapore Institute of Technology, Singapore
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Altmann S, Abello Mercado MA, Ucar FA, Kronfeld A, Al-Nawas B, Mukhopadhyay A, Booz C, Brockmann MA, Othman AE. Ultra-High-Resolution CT of the Head and Neck with Deep Learning Reconstruction-Assessment of Image Quality and Radiation Exposure and Intraindividual Comparison with Normal-Resolution CT. Diagnostics (Basel) 2023; 13:diagnostics13091534. [PMID: 37174926 PMCID: PMC10177822 DOI: 10.3390/diagnostics13091534] [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: 03/23/2023] [Revised: 04/16/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
OBJECTIVES To assess the benefits of ultra-high-resolution CT (UHR-CT) with deep learning-based image reconstruction engine (AiCE) regarding image quality and radiation dose and intraindividually compare it to normal-resolution CT (NR-CT). METHODS Forty consecutive patients with head and neck UHR-CT with AiCE for diagnosed head and neck malignancies and available prior NR-CT of a different scanner were retrospectively evaluated. Two readers evaluated subjective image quality using a 5-point Likert scale regarding image noise, image sharpness, artifacts, diagnostic acceptability, and assessability of various anatomic regions. For reproducibility, inter-reader agreement was analyzed. Furthermore, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and slope of the gray-value transition between different tissues were calculated. Radiation dose was evaluated by comparing CTDIvol, DLP, and mean effective dose values. RESULTS UHR-CT with AiCE reconstruction led to significant improvement in subjective (image noise and diagnostic acceptability: p < 0.000; ICC ≥ 0.91) and objective image quality (SNR: p < 0.000; CNR: p < 0.025) at significantly lower radiation doses (NR-CT 2.03 ± 0.14 mSv; UHR-CT 1.45 ± 0.11 mSv; p < 0.0001) compared to NR-CT. CONCLUSIONS Compared to NR-CT, UHR-CT combined with AiCE provides superior image quality at a markedly lower radiation dose. With improved soft tissue assessment and potentially improved tumor detection, UHR-CT may add further value to the role of CT in the assessment of head and neck pathologies.
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Affiliation(s)
- Sebastian Altmann
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany
| | - Mario A Abello Mercado
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany
| | - Felix A Ucar
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany
| | - Andrea Kronfeld
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany
| | - Bilal Al-Nawas
- Department of Oral and Maxillofacial Surgery, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany
| | - Anirban Mukhopadhyay
- Department of Computer Science, Technical University of Darmstadt, Fraunhoferst. 5, 64283 Darmstadt, Germany
| | - Christian Booz
- Department of Diagnostic and Interventional Radiology, University Clinic Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt, Germany
| | - Marc A Brockmann
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany
| | - Ahmed E Othman
- Department of Neuroradiology, University Medical Center Mainz, Johannes Gutenberg University, Langenbeckst. 1, 55131 Mainz, Germany
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Kawashima H, Ichikawa K, Takata T, Seto I. Comparative Assessment of Noise Properties for Two Deep Learning CT Image Reconstruction Techniques and Filtered Back Projection. Med Phys 2022; 49:6359-6367. [PMID: 36047991 DOI: 10.1002/mp.15918] [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: 03/23/2022] [Revised: 07/25/2022] [Accepted: 08/03/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Two deep-learning image reconstruction (DLIR) techniques from two different CT vendors have recently been introduced into clinical practice. PURPOSE To characterize the noise properties of two DLIR techniques with different training methods, using a phantom containing a simple uniform and a complex non-uniform region. METHODS A water-bath phantom with a diameter of 300 mm was used as a base phantom. A textured phantom with a diameter of 128 mm, which was made of two materials, one equivalent to water and the other being 12 mg/mL diluted iodine, irregularly mixed to create a complex texture (non-uniform region), was placed in the base phantom. Thirty repeated phantom scans were performed using two CT scanners (GE, Revolution CT with Apex Edition; Canon, Aquilion One PRISM Edition) at two dose levels (CTDI: 5 and 15 mGy). Images were reconstructed with each CT system's filtered back projection (FBP) and DLIR [GE, TrueFidelity (TF); Canon, Advanced intelligent Clear-IQ Engine Body Sharp (AC)] for three process strengths. For basic characteristics of noise, the standard deviation (SD) and noise power spectrum (NPS) were measured for the uniform (water) region. A noise magnitude map was generated by calculating the inter-image SD at each pixel position across the 30 images. Then, a noise reduction map (NRM), which visualizes the relative differences in noise magnitude between FBP and DLIR, was calculated. The NRM values ranged from 0.0 to 1.0. A low NRM value represents a less aggressive noise reduction. The histograms of the NRM value were analyzed for the uniform and non-uniform regions. RESULTS The reduction in noise magnitude compared with FBP tended to be greater with AC (45%-85%) than with TF (32%-65%). The average NPS frequencies of TF and AC were almost comparable to those of FBP, except for the low-dose condition and the high noise reduction strength for AC. The NRM values of TF and AC were higher in the uniform region than in the non-uniform region. In the non-uniform region, TF's average NRM values (0.21-0.48) tended to be lower than AC's (0.39-0.78). The histograms for TF showed a small overlap between the uniform and the non-uniform regions; in contrast, those for AC showed a greater overlap. This difference seems to indicate that TF processes the uniform and non-uniform regions more differently than AC does. CONCLUSION This study has revealed a distinct difference in characteristics between the two DLIR techniques: TF tends to offer less aggressive noise reduction in non-uniform regions and preserve the original signals, whereas AC tends to prioritize noise filtering over edge-preservation, especially at the low-dose condition and with the high noise reduction strength. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Hiroki Kawashima
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University
| | - Katsuhiro Ichikawa
- Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University
| | | | - Issei Seto
- Department of Radiological Technology, Mitsubishi Kyoto Hospital
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Sartoretti T, Landsmann A, Nakhostin D, Eberhard M, Röeren C, Mergen V, Higashigaito K, Raupach R, Alkadhi H, Euler A. Quantum Iterative Reconstruction for Abdominal Photon-counting Detector CT Improves Image Quality. Radiology 2022; 303:339-348. [PMID: 35103540 DOI: 10.1148/radiol.211931] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background An iterative reconstruction (IR) algorithm was introduced for clinical photon-counting detector (PCD) CT. Purpose To investigate the image quality and the optimal strength level of a quantum IR algorithm (QIR; Siemens Healthcare) for virtual monoenergetic images and polychromatic images (T3D) in a phantom and in patients undergoing portal venous abdominal PCD CT. Materials and Methods In this retrospective study, noise power spectrum (NPS) was measured in a water-filled phantom. Consecutive oncologic patients who underwent portal venous abdominal PCD CT between March and April 2021 were included. Virtual monoenergetic images at 60 keV and T3D were reconstructed without QIR (QIR-off; reference standard) and with QIR at four levels (QIR 1-4; index tests). Global noise index, contrast-to-noise ratio (CNR), and voxel-wise CT attenuation differences were measured. Noise and texture, artifacts, diagnostic confidence, and overall quality were assessed qualitatively. Conspicuity of hypodense liver lesions was rated by four readers. Parametric (analyses of variance, paired t tests) and nonparametric tests (Friedman, post hoc Wilcoxon signed-rank tests) were used to compare quantitative and qualitative image quality among reconstructions. Results In the phantom, NPS showed unchanged noise texture across reconstructions with maximum spatial frequency differences of 0.01 per millimeter. Fifty patients (mean age, 59 years ± 16 [standard deviation]; 31 women) were included. Global noise index was reduced from QIR-off to QIR-4 by 45% for 60 keV and by 44% for T3D (both, P < .001). CNR of the liver improved from QIR-off to QIR-4 by 74% for 60 keV and by 69% for T3D (both, P < .001). No evidence of difference was found in mean attenuation of fat and liver (P = .79-.84) and on a voxel-wise basis among reconstructions. Qualitatively, QIR-4 outperformed all reconstructions in every category for 60 keV and T3D (P value range, <.001 to .01). All four readers rated QIR-4 superior to other strengths for lesion conspicuity (P value range, <.001 to .04). Conclusion In portal venous abdominal photon-counting detector CT, an iterative reconstruction algorithm (QIR; Siemens Healthcare) at high strength levels improved image quality by reducing noise and improving contrast-to-noise ratio and lesion conspicuity without compromising image texture or CT attenuation values. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Sinitsyn in this issue.
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Affiliation(s)
- Thomas Sartoretti
- From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland (T.S., A.L., D.N., M.E., C.R., V.M., K.H., H.A., A.E.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands (T.S.); and Siemens Healthcare, Forchheim, Germany (R.R.)
| | - Anna Landsmann
- From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland (T.S., A.L., D.N., M.E., C.R., V.M., K.H., H.A., A.E.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands (T.S.); and Siemens Healthcare, Forchheim, Germany (R.R.)
| | - Dominik Nakhostin
- From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland (T.S., A.L., D.N., M.E., C.R., V.M., K.H., H.A., A.E.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands (T.S.); and Siemens Healthcare, Forchheim, Germany (R.R.)
| | - Matthias Eberhard
- From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland (T.S., A.L., D.N., M.E., C.R., V.M., K.H., H.A., A.E.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands (T.S.); and Siemens Healthcare, Forchheim, Germany (R.R.)
| | - Christian Röeren
- From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland (T.S., A.L., D.N., M.E., C.R., V.M., K.H., H.A., A.E.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands (T.S.); and Siemens Healthcare, Forchheim, Germany (R.R.)
| | - Victor Mergen
- From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland (T.S., A.L., D.N., M.E., C.R., V.M., K.H., H.A., A.E.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands (T.S.); and Siemens Healthcare, Forchheim, Germany (R.R.)
| | - Kai Higashigaito
- From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland (T.S., A.L., D.N., M.E., C.R., V.M., K.H., H.A., A.E.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands (T.S.); and Siemens Healthcare, Forchheim, Germany (R.R.)
| | - Rainer Raupach
- From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland (T.S., A.L., D.N., M.E., C.R., V.M., K.H., H.A., A.E.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands (T.S.); and Siemens Healthcare, Forchheim, Germany (R.R.)
| | - Hatem Alkadhi
- From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland (T.S., A.L., D.N., M.E., C.R., V.M., K.H., H.A., A.E.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands (T.S.); and Siemens Healthcare, Forchheim, Germany (R.R.)
| | - André Euler
- From the Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland (T.S., A.L., D.N., M.E., C.R., V.M., K.H., H.A., A.E.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht University, Maastricht, the Netherlands (T.S.); and Siemens Healthcare, Forchheim, Germany (R.R.)
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