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Pace E, Caruana CJ, Bosmans H, Cortis K, D'Anastasi M, Valentino G. An inventory of patient-image based risk/dose, image quality and body habitus/size metrics for adult abdomino-pelvic CT protocol optimisation. Phys Med 2024; 125:103434. [PMID: 39096718 DOI: 10.1016/j.ejmp.2024.103434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 07/04/2024] [Accepted: 07/17/2024] [Indexed: 08/05/2024] Open
Abstract
PURPOSE Patient-specific protocol optimisation in abdomino-pelvic Computed Tomography (CT) requires measurement of body habitus/size (BH), sensitivity-specificity (surrogates image quality (IQ) metrics) and risk (surrogates often dose quantities) (RD). This work provides an updated inventory of metrics available for each of these three categories of optimisation variables derivable directly from patient measurements or images. We consider objective IQ metrics mostly in the spatial domain (i.e., those related directly to sharpness, contrast, noise quantity/texture and perceived detectability as these are used by radiologists to assess the acceptability or otherwise of patient images in practice). MATERIALS AND METHODS The search engine used was PubMed with the search period being 2010-2024. The key words used were: 'comput* tomography', 'CT', 'abdom*', 'dose', 'risk', 'SSDE', 'image quality', 'water equivalent diameter', 'size', 'body composition', 'habit*', 'BMI', 'obes*', 'overweight'. Since BH is critical for patient specific optimisation, articles correlating RD vs BH, and IQ vs BH were reviewed. RESULTS The inventory includes 11 BH, 12 IQ and 6 RD metrics. 25 RD vs BH correlation studies and 9 IQ vs BH correlation studies were identified. 7 articles in the latter group correlated metrics from all three categories concurrently. CONCLUSIONS Protocol optimisation should be fine-tuned to the level of the individual patient and particular clinical query. This would require a judicious choice of metrics from each of the three categories. It is suggested that, for increased utility in clinical practice, more future optimisation studies be clinical task based and involve the three categories of metrics concurrently.
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Affiliation(s)
- Eric Pace
- Medical Physics, Faculty of Health Science, University of Malta, Msida MSD2080, Malta.
| | - Carmel J Caruana
- Medical Physics, Faculty of Health Science, University of Malta, Msida MSD2080, Malta
| | - Hilde Bosmans
- Medical Physics & Quality Assessment, Department of Imaging & Pathology, KU Leuven, Leuven, Belgium
| | - Kelvin Cortis
- Medical Imaging Department, Mater Dei Hospital, Msida MSD2090, Malta
| | - Melvin D'Anastasi
- Medical Imaging Department, Mater Dei Hospital, Msida MSD2090, Malta
| | - Gianluca Valentino
- Communications & Computer Engineering Department, Faculty of Information and Communication Technology, University of Malta, Msida MSD2080, Malta
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Kuo HC, Mahmood U, Kirov AS, Mechalakos J, Della Biancia C, Cerviño LI, Lim SB. An automated technique for global noise level measurement in CT image with a conjunction of image gradient. Phys Med Biol 2024; 69:09NT01. [PMID: 38537310 DOI: 10.1088/1361-6560/ad3883] [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: 10/08/2023] [Accepted: 03/27/2024] [Indexed: 04/16/2024]
Abstract
Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson's tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.
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Affiliation(s)
- Hsiang-Chi Kuo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - Usman Mahmood
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - Assen S Kirov
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - James Mechalakos
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - Cesar Della Biancia
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - Laura I Cerviño
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - Seng Boh Lim
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
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Kronfeld A, Rose P, Baumgart J, Brockmann C, Othman AE, Schweizer B, Brockmann MA. Quantitative multi-energy micro-CT: A simulation and phantom study for simultaneous imaging of four different contrast materials using an energy integrating detector. Heliyon 2024; 10:e23013. [PMID: 38148814 PMCID: PMC10750148 DOI: 10.1016/j.heliyon.2023.e23013] [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: 03/30/2023] [Revised: 11/23/2023] [Accepted: 11/23/2023] [Indexed: 12/28/2023] Open
Abstract
Emerging from the development of single-energy Computed Tomography (CT) and Dual-Energy Computed Tomography, Multi-Energy Computed Tomography (MECT) is a promising tool allowing advanced material and tissue decomposition and thereby enabling the use of multiple contrast materials in preclinical research. The scope of this work was to evaluate whether a usual preclinical micro-CT system is applicable for the decomposition of different materials using MECT together with a matrix-inversion method and how different changes of the measurement-environment affect the results. A matrix-inversion based algorithm to differentiate up to five materials (iodine, iron, barium, gadolinium, residual material) by applying four different acceleration voltages/energy levels was established. We carried out simulations using different ratios and concentrations (given in fractions of volume units, VU) of the four different materials (plus residual material) at different noise-levels for 30 keV, 40 keV, 50 keV, 60 keV, 80 keV and 100 keV (monochromatic). Our simulation results were then confirmed by using region of interest-based measurements in a phantom-study at corresponding acceleration voltages. Therefore, different mixtures of contrast materials were scanned using a micro-CT. Voxel wise evaluation of the phantom imaging data was conducted to confirm its usability for future imaging applications and to estimate the influence of varying noise-levels, scattering, artifacts and concentrations. The analysis of our simulations showed the smallest deviation of 0.01 (0.003-0.15) VU between given and calculated concentrations of the different contrast materials when using an energy-combination of 30 keV, 40 keV, 50 keV and 100 keV for MECT. Subsequent MECT phantom measurements, however, revealed a combination of acceleration voltages of 30 kV, 40 kV, 60 kV and 100 kV as most effective for performing material decomposition with a deviation of 0.28 (0-1.07) mg/ml. The feasibility of our voxelwise analyses using the proposed algorithm was then confirmed by the generation of phantom parameter-maps that matched the known contrast material concentrations. The results were mostly influenced by the noise-level and the concentrations used in the phantoms. MECT using a standard micro-CT combined with a matrix inversion method is feasible at four different imaging energies and allows the differentiation of mixtures of up to four contrast materials plus an additional residual material.
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Affiliation(s)
- Andrea Kronfeld
- University Medical Center of the Johannes Gutenberg University Mainz, Department of Neuroradiology, Langenbeck 1, 55131, Mainz, Germany
| | - Patrick Rose
- University Medical Center of the Johannes Gutenberg University Mainz, Department of Neuroradiology, Langenbeck 1, 55131, Mainz, Germany
- RheinMain University of Applied Sciences, Faculty of Engineering, Am Brückweg 26, 65428, Rüsselsheim am Main, Germany
| | - Jan Baumgart
- University Medical Center of the Johannes Gutenberg University Mainz, Translational Animal Research Center, Hanns-Dieter-Hüsch-Weg 19, 55128, Mainz, Germany
| | - Carolin Brockmann
- University Medical Center of the Johannes Gutenberg University Mainz, Department of Neuroradiology, Langenbeck 1, 55131, Mainz, Germany
| | - Ahmed E. Othman
- University Medical Center of the Johannes Gutenberg University Mainz, Department of Neuroradiology, Langenbeck 1, 55131, Mainz, Germany
| | - Bernd Schweizer
- RheinMain University of Applied Sciences, Faculty of Engineering, Am Brückweg 26, 65428, Rüsselsheim am Main, Germany
| | - Marc Alexander Brockmann
- University Medical Center of the Johannes Gutenberg University Mainz, Department of Neuroradiology, Langenbeck 1, 55131, Mainz, Germany
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Vegas Sánchez-Ferrero G, Díaz AA, Ash SY, Baraghoshi D, Strand M, Crapo JD, Silverman EK, Humphries SM, Washko GR, Lynch DA, San José Estépar R. Quantification of Emphysema Progression at CT Using Simultaneous Volume, Noise, and Bias Lung Density Correction. Radiology 2024; 310:e231632. [PMID: 38165244 PMCID: PMC10831481 DOI: 10.1148/radiol.231632] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/19/2023] [Accepted: 10/30/2023] [Indexed: 01/03/2024]
Abstract
Background CT attenuation is affected by lung volume, dosage, and scanner bias, leading to inaccurate emphysema progression measurements in multicenter studies. Purpose To develop and validate a method that simultaneously corrects volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT in a longitudinal multicenter study. Materials and Methods In this secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, lung function data were obtained from participants who completed baseline and 5-year follow-up visits from January 2008 to August 2017. CT emphysema progression was measured with volume-adjusted lung density (VALD) and compared with the joint volume-noise-bias-adjusted lung density (VNB-ALD). Reproducibility was studied under change of dosage protocol and scanner model with repeated acquisitions. Emphysema progression was visually scored in 102 randomly selected participants. A stratified analysis of clinical characteristics was performed that considered groups based on their combined lung density change measured by VALD and VNB-ALD. Results A total of 4954 COPDGene participants (mean age, 60 years ± 9 [SD]; 2511 male, 2443 female) were analyzed (1329 with repeated reduced-dose acquisition in the follow-up visit). Mean repeatability coefficients were 30 g/L ± 0.46 for VALD and 14 g/L ± 0.34 for VNB-ALD. VALD measurements showed no evidence of differences between nonprogressors and progressors (mean, -5.5 g/L ± 9.5 vs -8.6 g/L ± 9.6; P = .11), while VNB-ALD agreed with visual readings and showed a difference (mean, -0.67 g/L ± 4.8 vs -4.2 g/L ± 5.5; P < .001). Analysis of progression showed that VNB-ALD progressors had a greater decline in forced expiratory volume in 1 second (-42 mL per year vs -32 mL per year; Tukey-adjusted P = .002). Conclusion Simultaneously correcting volume, noise, and interscanner bias for lung density change estimation in emphysema progression at CT improved repeatability analyses and agreed with visual readings. It distinguished between progressors and nonprogressors and was associated with a greater decline in lung function metrics. Clinical trial registration no. NCT00608764 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Goo in this issue.
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Affiliation(s)
- Gonzalo Vegas Sánchez-Ferrero
- From the Applied Chest Imaging Laboratory, Department of Radiology
(G.V.S.F., R.S.J.E.), Applied Chest Imaging Laboratory, Division of Pulmonary
and Critical Care Medicine, Department of Medicine (A.A.D., S.Y.A., G.R.W.), and
Channing Division of Network Medicine and Division of Pulmonary and Critical
Care Medicine, Department of Medicine (E.K.S.), Brigham and Women's
Hospital, 75 Francis St, Boston, MA 02115; and Division of Biostatistics and
Bioinformatics (D.B., M.S.), Division of Pulmonary and Critical Care Medicine,
Department of Medicine (J.D.C.), and Department of Radiology (S.M.H., D.A.L.),
National Jewish Health, Denver, Colo
| | - Alejandro A. Díaz
- From the Applied Chest Imaging Laboratory, Department of Radiology
(G.V.S.F., R.S.J.E.), Applied Chest Imaging Laboratory, Division of Pulmonary
and Critical Care Medicine, Department of Medicine (A.A.D., S.Y.A., G.R.W.), and
Channing Division of Network Medicine and Division of Pulmonary and Critical
Care Medicine, Department of Medicine (E.K.S.), Brigham and Women's
Hospital, 75 Francis St, Boston, MA 02115; and Division of Biostatistics and
Bioinformatics (D.B., M.S.), Division of Pulmonary and Critical Care Medicine,
Department of Medicine (J.D.C.), and Department of Radiology (S.M.H., D.A.L.),
National Jewish Health, Denver, Colo
| | - Samuel Y. Ash
- From the Applied Chest Imaging Laboratory, Department of Radiology
(G.V.S.F., R.S.J.E.), Applied Chest Imaging Laboratory, Division of Pulmonary
and Critical Care Medicine, Department of Medicine (A.A.D., S.Y.A., G.R.W.), and
Channing Division of Network Medicine and Division of Pulmonary and Critical
Care Medicine, Department of Medicine (E.K.S.), Brigham and Women's
Hospital, 75 Francis St, Boston, MA 02115; and Division of Biostatistics and
Bioinformatics (D.B., M.S.), Division of Pulmonary and Critical Care Medicine,
Department of Medicine (J.D.C.), and Department of Radiology (S.M.H., D.A.L.),
National Jewish Health, Denver, Colo
| | - David Baraghoshi
- From the Applied Chest Imaging Laboratory, Department of Radiology
(G.V.S.F., R.S.J.E.), Applied Chest Imaging Laboratory, Division of Pulmonary
and Critical Care Medicine, Department of Medicine (A.A.D., S.Y.A., G.R.W.), and
Channing Division of Network Medicine and Division of Pulmonary and Critical
Care Medicine, Department of Medicine (E.K.S.), Brigham and Women's
Hospital, 75 Francis St, Boston, MA 02115; and Division of Biostatistics and
Bioinformatics (D.B., M.S.), Division of Pulmonary and Critical Care Medicine,
Department of Medicine (J.D.C.), and Department of Radiology (S.M.H., D.A.L.),
National Jewish Health, Denver, Colo
| | - Matthew Strand
- From the Applied Chest Imaging Laboratory, Department of Radiology
(G.V.S.F., R.S.J.E.), Applied Chest Imaging Laboratory, Division of Pulmonary
and Critical Care Medicine, Department of Medicine (A.A.D., S.Y.A., G.R.W.), and
Channing Division of Network Medicine and Division of Pulmonary and Critical
Care Medicine, Department of Medicine (E.K.S.), Brigham and Women's
Hospital, 75 Francis St, Boston, MA 02115; and Division of Biostatistics and
Bioinformatics (D.B., M.S.), Division of Pulmonary and Critical Care Medicine,
Department of Medicine (J.D.C.), and Department of Radiology (S.M.H., D.A.L.),
National Jewish Health, Denver, Colo
| | - James D. Crapo
- From the Applied Chest Imaging Laboratory, Department of Radiology
(G.V.S.F., R.S.J.E.), Applied Chest Imaging Laboratory, Division of Pulmonary
and Critical Care Medicine, Department of Medicine (A.A.D., S.Y.A., G.R.W.), and
Channing Division of Network Medicine and Division of Pulmonary and Critical
Care Medicine, Department of Medicine (E.K.S.), Brigham and Women's
Hospital, 75 Francis St, Boston, MA 02115; and Division of Biostatistics and
Bioinformatics (D.B., M.S.), Division of Pulmonary and Critical Care Medicine,
Department of Medicine (J.D.C.), and Department of Radiology (S.M.H., D.A.L.),
National Jewish Health, Denver, Colo
| | - Edwin K. Silverman
- From the Applied Chest Imaging Laboratory, Department of Radiology
(G.V.S.F., R.S.J.E.), Applied Chest Imaging Laboratory, Division of Pulmonary
and Critical Care Medicine, Department of Medicine (A.A.D., S.Y.A., G.R.W.), and
Channing Division of Network Medicine and Division of Pulmonary and Critical
Care Medicine, Department of Medicine (E.K.S.), Brigham and Women's
Hospital, 75 Francis St, Boston, MA 02115; and Division of Biostatistics and
Bioinformatics (D.B., M.S.), Division of Pulmonary and Critical Care Medicine,
Department of Medicine (J.D.C.), and Department of Radiology (S.M.H., D.A.L.),
National Jewish Health, Denver, Colo
| | - Stephen M. Humphries
- From the Applied Chest Imaging Laboratory, Department of Radiology
(G.V.S.F., R.S.J.E.), Applied Chest Imaging Laboratory, Division of Pulmonary
and Critical Care Medicine, Department of Medicine (A.A.D., S.Y.A., G.R.W.), and
Channing Division of Network Medicine and Division of Pulmonary and Critical
Care Medicine, Department of Medicine (E.K.S.), Brigham and Women's
Hospital, 75 Francis St, Boston, MA 02115; and Division of Biostatistics and
Bioinformatics (D.B., M.S.), Division of Pulmonary and Critical Care Medicine,
Department of Medicine (J.D.C.), and Department of Radiology (S.M.H., D.A.L.),
National Jewish Health, Denver, Colo
| | - George R. Washko
- From the Applied Chest Imaging Laboratory, Department of Radiology
(G.V.S.F., R.S.J.E.), Applied Chest Imaging Laboratory, Division of Pulmonary
and Critical Care Medicine, Department of Medicine (A.A.D., S.Y.A., G.R.W.), and
Channing Division of Network Medicine and Division of Pulmonary and Critical
Care Medicine, Department of Medicine (E.K.S.), Brigham and Women's
Hospital, 75 Francis St, Boston, MA 02115; and Division of Biostatistics and
Bioinformatics (D.B., M.S.), Division of Pulmonary and Critical Care Medicine,
Department of Medicine (J.D.C.), and Department of Radiology (S.M.H., D.A.L.),
National Jewish Health, Denver, Colo
| | - David A. Lynch
- From the Applied Chest Imaging Laboratory, Department of Radiology
(G.V.S.F., R.S.J.E.), Applied Chest Imaging Laboratory, Division of Pulmonary
and Critical Care Medicine, Department of Medicine (A.A.D., S.Y.A., G.R.W.), and
Channing Division of Network Medicine and Division of Pulmonary and Critical
Care Medicine, Department of Medicine (E.K.S.), Brigham and Women's
Hospital, 75 Francis St, Boston, MA 02115; and Division of Biostatistics and
Bioinformatics (D.B., M.S.), Division of Pulmonary and Critical Care Medicine,
Department of Medicine (J.D.C.), and Department of Radiology (S.M.H., D.A.L.),
National Jewish Health, Denver, Colo
| | - Raúl San José Estépar
- From the Applied Chest Imaging Laboratory, Department of Radiology
(G.V.S.F., R.S.J.E.), Applied Chest Imaging Laboratory, Division of Pulmonary
and Critical Care Medicine, Department of Medicine (A.A.D., S.Y.A., G.R.W.), and
Channing Division of Network Medicine and Division of Pulmonary and Critical
Care Medicine, Department of Medicine (E.K.S.), Brigham and Women's
Hospital, 75 Francis St, Boston, MA 02115; and Division of Biostatistics and
Bioinformatics (D.B., M.S.), Division of Pulmonary and Critical Care Medicine,
Department of Medicine (J.D.C.), and Department of Radiology (S.M.H., D.A.L.),
National Jewish Health, Denver, Colo
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5
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Baraghoshi D, Strand M, Humphries SM, San José Estépar R, Vegas Sanchez-Ferrero G, Charbonnier JP, Latisenko R, Silverman EK, Crapo JD, Lynch DA. Quantitative CT Evaluation of Emphysema Progression over 10 Years in the COPDGene Study. Radiology 2023; 307:e222786. [PMID: 37039685 PMCID: PMC10286952 DOI: 10.1148/radiol.222786] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/02/2023] [Accepted: 02/16/2023] [Indexed: 04/12/2023]
Abstract
Background Long-term studies of chronic obstructive pulmonary disease (COPD) can evaluate emphysema progression. Adjustment for differences in equipment and scanning protocols of individual CT examinations have not been studied extensively. Purpose To evaluate emphysema progression in current and former smokers in the COPDGene cohort over three imaging points obtained at 5-year intervals accounting for individual CT parameters. Materials and Methods Current and former cigarette smokers enrolled between 2008 and 2011 from the COPDGene study were prospectively followed for 10 years between 2008 and 2020. Extent of emphysema as adjusted lung density (ALD) from quantitative CT was measured at baseline and at 5- and 10-year follow-up. Linear mixed models adjusted for CT technical characteristics were constructed to evaluate emphysema progression. Mean annual changes in ALD over consecutive 5-year study periods were estimated by smoking status and baseline emphysema. Results Of 8431 participants at baseline (mean age, 60 years ± 9 [SD]; 3905 female participants), 4913 were at 5-year follow-up and 1544 participants were at 10-year follow-up. There were 4134 (49%) participants who were current smokers, and 4449 (53%) participants had more than trace emphysema at baseline. Current smokers with more than trace emphysema showed the largest decline in ALD, with mean annual decreases of 1.4 g/L (95% CI: 1.2, 1.5) in the first 5 years and 0.9 g/L (95% CI: 0.7, 1.2) in the second 5 years. Accounting for CT noise, field of view, and scanner model improved model fit for estimation of emphysema progression (P < .001 by likelihood ratio test). Conclusion Evaluation at CT of emphysema progression in the COPDGene study showed that, during the span of 10 years, participants with pre-existing emphysema who continued smoking had the largest decline in ALD. Adjusting for CT equipment and protocol factors improved these longitudinal estimates. Clinical trial registration no. NCT00608764 © RSNA, 2023 Supplemental material is available for this article. See the editorial by Parraga and Kirby in this issue.
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Affiliation(s)
- David Baraghoshi
- From the Division of Biostatistics, Environment and Health (D.B.,
M.S.), Department of Radiology (S.M.H., D.A.L.), and Division of Pulmonary and
Critical Care Medicine, Department of Medicine (J.D.C.), National Jewish Health,
1400 Jackson St, Denver, CO 80206; Applied Chest Imaging Laboratory (R.S.J.E.,
G.V.S.F.), Department of Radiology (R.S.J.E., G.V.S.F.), Channing Division of
Network Medicine (E.K.S.), and Division of Pulmonary and Critical Care Medicine,
Department of Medicine (E.K.S.), Brigham and Women’s Hospital, Boston,
Mass; and Thirona, Nijmegen, the Netherlands (J.P.C., R.L.)
| | - Matthew Strand
- From the Division of Biostatistics, Environment and Health (D.B.,
M.S.), Department of Radiology (S.M.H., D.A.L.), and Division of Pulmonary and
Critical Care Medicine, Department of Medicine (J.D.C.), National Jewish Health,
1400 Jackson St, Denver, CO 80206; Applied Chest Imaging Laboratory (R.S.J.E.,
G.V.S.F.), Department of Radiology (R.S.J.E., G.V.S.F.), Channing Division of
Network Medicine (E.K.S.), and Division of Pulmonary and Critical Care Medicine,
Department of Medicine (E.K.S.), Brigham and Women’s Hospital, Boston,
Mass; and Thirona, Nijmegen, the Netherlands (J.P.C., R.L.)
| | - Stephen M. Humphries
- From the Division of Biostatistics, Environment and Health (D.B.,
M.S.), Department of Radiology (S.M.H., D.A.L.), and Division of Pulmonary and
Critical Care Medicine, Department of Medicine (J.D.C.), National Jewish Health,
1400 Jackson St, Denver, CO 80206; Applied Chest Imaging Laboratory (R.S.J.E.,
G.V.S.F.), Department of Radiology (R.S.J.E., G.V.S.F.), Channing Division of
Network Medicine (E.K.S.), and Division of Pulmonary and Critical Care Medicine,
Department of Medicine (E.K.S.), Brigham and Women’s Hospital, Boston,
Mass; and Thirona, Nijmegen, the Netherlands (J.P.C., R.L.)
| | - Raúl San José Estépar
- From the Division of Biostatistics, Environment and Health (D.B.,
M.S.), Department of Radiology (S.M.H., D.A.L.), and Division of Pulmonary and
Critical Care Medicine, Department of Medicine (J.D.C.), National Jewish Health,
1400 Jackson St, Denver, CO 80206; Applied Chest Imaging Laboratory (R.S.J.E.,
G.V.S.F.), Department of Radiology (R.S.J.E., G.V.S.F.), Channing Division of
Network Medicine (E.K.S.), and Division of Pulmonary and Critical Care Medicine,
Department of Medicine (E.K.S.), Brigham and Women’s Hospital, Boston,
Mass; and Thirona, Nijmegen, the Netherlands (J.P.C., R.L.)
| | - Gonzalo Vegas Sanchez-Ferrero
- From the Division of Biostatistics, Environment and Health (D.B.,
M.S.), Department of Radiology (S.M.H., D.A.L.), and Division of Pulmonary and
Critical Care Medicine, Department of Medicine (J.D.C.), National Jewish Health,
1400 Jackson St, Denver, CO 80206; Applied Chest Imaging Laboratory (R.S.J.E.,
G.V.S.F.), Department of Radiology (R.S.J.E., G.V.S.F.), Channing Division of
Network Medicine (E.K.S.), and Division of Pulmonary and Critical Care Medicine,
Department of Medicine (E.K.S.), Brigham and Women’s Hospital, Boston,
Mass; and Thirona, Nijmegen, the Netherlands (J.P.C., R.L.)
| | - Jean-Paul Charbonnier
- From the Division of Biostatistics, Environment and Health (D.B.,
M.S.), Department of Radiology (S.M.H., D.A.L.), and Division of Pulmonary and
Critical Care Medicine, Department of Medicine (J.D.C.), National Jewish Health,
1400 Jackson St, Denver, CO 80206; Applied Chest Imaging Laboratory (R.S.J.E.,
G.V.S.F.), Department of Radiology (R.S.J.E., G.V.S.F.), Channing Division of
Network Medicine (E.K.S.), and Division of Pulmonary and Critical Care Medicine,
Department of Medicine (E.K.S.), Brigham and Women’s Hospital, Boston,
Mass; and Thirona, Nijmegen, the Netherlands (J.P.C., R.L.)
| | - Rudolfs Latisenko
- From the Division of Biostatistics, Environment and Health (D.B.,
M.S.), Department of Radiology (S.M.H., D.A.L.), and Division of Pulmonary and
Critical Care Medicine, Department of Medicine (J.D.C.), National Jewish Health,
1400 Jackson St, Denver, CO 80206; Applied Chest Imaging Laboratory (R.S.J.E.,
G.V.S.F.), Department of Radiology (R.S.J.E., G.V.S.F.), Channing Division of
Network Medicine (E.K.S.), and Division of Pulmonary and Critical Care Medicine,
Department of Medicine (E.K.S.), Brigham and Women’s Hospital, Boston,
Mass; and Thirona, Nijmegen, the Netherlands (J.P.C., R.L.)
| | - Edwin K. Silverman
- From the Division of Biostatistics, Environment and Health (D.B.,
M.S.), Department of Radiology (S.M.H., D.A.L.), and Division of Pulmonary and
Critical Care Medicine, Department of Medicine (J.D.C.), National Jewish Health,
1400 Jackson St, Denver, CO 80206; Applied Chest Imaging Laboratory (R.S.J.E.,
G.V.S.F.), Department of Radiology (R.S.J.E., G.V.S.F.), Channing Division of
Network Medicine (E.K.S.), and Division of Pulmonary and Critical Care Medicine,
Department of Medicine (E.K.S.), Brigham and Women’s Hospital, Boston,
Mass; and Thirona, Nijmegen, the Netherlands (J.P.C., R.L.)
| | - James D. Crapo
- From the Division of Biostatistics, Environment and Health (D.B.,
M.S.), Department of Radiology (S.M.H., D.A.L.), and Division of Pulmonary and
Critical Care Medicine, Department of Medicine (J.D.C.), National Jewish Health,
1400 Jackson St, Denver, CO 80206; Applied Chest Imaging Laboratory (R.S.J.E.,
G.V.S.F.), Department of Radiology (R.S.J.E., G.V.S.F.), Channing Division of
Network Medicine (E.K.S.), and Division of Pulmonary and Critical Care Medicine,
Department of Medicine (E.K.S.), Brigham and Women’s Hospital, Boston,
Mass; and Thirona, Nijmegen, the Netherlands (J.P.C., R.L.)
| | - David A. Lynch
- From the Division of Biostatistics, Environment and Health (D.B.,
M.S.), Department of Radiology (S.M.H., D.A.L.), and Division of Pulmonary and
Critical Care Medicine, Department of Medicine (J.D.C.), National Jewish Health,
1400 Jackson St, Denver, CO 80206; Applied Chest Imaging Laboratory (R.S.J.E.,
G.V.S.F.), Department of Radiology (R.S.J.E., G.V.S.F.), Channing Division of
Network Medicine (E.K.S.), and Division of Pulmonary and Critical Care Medicine,
Department of Medicine (E.K.S.), Brigham and Women’s Hospital, Boston,
Mass; and Thirona, Nijmegen, the Netherlands (J.P.C., R.L.)
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6
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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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7
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Yamada K, Kawata Y, Amano M, Suzuki H, Tominaga M, Sasaki M, Nishiyama H, Harada M, Niki N. Influence of Pitch on Surface Dose Distribution and Image Noise of Computed Tomography Scans. SENSORS (BASEL, SWITZERLAND) 2023; 23:3472. [PMID: 37050532 PMCID: PMC10098581 DOI: 10.3390/s23073472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/17/2023] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Abstract
This study evaluated the effect of pitch on 256-slice helical computed tomography (CT) scans. Cylindrical water phantoms (CWP) were measured using axial and helical scans with various pitch values. The surface dose distributions of CWP were measured, and reconstructed images were obtained using filtered back-projection (FBP) and iterative model reconstruction (IMR). The image noise in each reconstructed image was decomposed into a baseline component and another component that varied along the z-axis. The baseline component of the image noise was highest at the center of the reconstructed image and decreased toward the edges. The normalized 2D power spectra for each pitch were almost identically distributed. Furthermore, the ratios of the 2D power spectra for IMR and FBP at different pitch values were obtained. The magnitudes of the components varying along the z-axis were smallest at the center of the reconstructed image and increased toward the edge. The ratios of the 3D power spectra on the fx axis for IMR and FBP at different pitch values were obtained. The results showed that the effect of the pitch was related to the component that varied along the z-axis. Furthermore, the pitch had a smaller effect on IMR than on FBP.
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Affiliation(s)
- Kenji Yamada
- Division of Clinical Technology, Tokushima University Hospital, Tokushima 7708503, Japan
| | - Yoshiki Kawata
- Institute of Post-LED Photonics, Tokushima University, Tokushima 7708506, Japan
| | - Masafumi Amano
- Division of Clinical Technology, Tokushima University Hospital, Tokushima 7708503, Japan
| | - Hidenobu Suzuki
- Institute of Post-LED Photonics, Tokushima University, Tokushima 7708506, Japan
| | - Masahide Tominaga
- Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Tokushima University, Tokushima 7708503, Japan
| | - Motoharu Sasaki
- Department of Therapeutic Radiology, Graduate School of Biomedical Sciences, Tokushima University, Tokushima 7708503, Japan
| | - Hikaru Nishiyama
- Department of Radiological Technology, Ehime University Hospital, Toon 7910295, Japan
| | - Masafumi Harada
- Department of Radiology and Radiation Oncology, Tokushima University, Tokushima 7708503, Japan
| | - Noboru Niki
- Faculty of Science and Technology, Tokushima University, Tokushima 7708506, Japan
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8
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Rasul RB, Avedisian CT, Xu Y, Hicks MC, Reeves AP. Dynamic Differential Image Circle Diameter Measurement Precision Assessment: Application to Burning Droplets. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:1668-1681. [PMID: 35503825 DOI: 10.1109/tpami.2022.3170926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Dynamic measurement precision assessment has been achieved for a differential circle measurement application. Differential circle diameter measurement, in image analysis, typically requires fitting a circle model that optimizes for image distortions, defects or occlusions. The differential task occurs when precise measurements of diameter change are required given object size variation with time. An automated system was designed to provide diameter measurements and associated measurement precision of images of a fuel droplet undergoing combustion in zero gravity for the FLEX-2 dataset. An image gradient-based, least-squares boundary point fitting method to a circle or ellipse model is used for diameter measurement. The presence of soot aggregates poses significant challenges for diameter measurements when it occludes part of the droplet boundary. The precision of the diameter measurements depends upon the image quality. Using synthetic image simulations that model the soot behavior, we developed a model based on image quality measures that assesses the measurement precision for each individual diameter measurement. Thus, diameter measurements with precision assessments were made available for follow-up scientific analysis. The algorithm's success rate for measurable runs was 98%. In cases of limited occlusion, a measurement precision of ±0.2 pixels for the FLEX-2 dataset was achieved.
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9
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Bhatt SP, Bodduluri S, Dransfield MT, Reinhardt JM, Crapo JD, Silverman EK, Humphries S, Lynch DA, Strand MJ. Acute Exacerbations Are Associated with Progression of Emphysema. Ann Am Thorac Soc 2022; 19:2108-2111. [PMID: 35914221 PMCID: PMC9743469 DOI: 10.1513/annalsats.202112-1385rl] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
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10
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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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11
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Inkinen SI, Mäkelä T, Kaasalainen T, Peltonen J, Kangasniemi M, Kortesniemi M. Automatic head computed tomography image noise quantification with deep learning. Phys Med 2022; 99:102-112. [PMID: 35671678 DOI: 10.1016/j.ejmp.2022.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/02/2022] [Accepted: 05/25/2022] [Indexed: 10/18/2022] Open
Abstract
PURPOSE Computed tomography (CT) image noise is usually determined by standard deviation (SD) of pixel values from uniform image regions. This study investigates how deep learning (DL) could be applied in head CT image noise estimation. METHODS Two approaches were investigated for noise image estimation of a single acquisition image: direct noise image estimation using supervised DnCNN convolutional neural network (CNN) architecture, and subtraction of a denoised image estimated with denoising UNet-CNN experimented with supervised and unsupervised noise2noise training approaches. Noise was assessed with local SD maps using 3D- and 2D-CNN architectures. Anthropomorphic phantom CT image dataset (N = 9 scans, 3 repetitions) was used for DL-model comparisons. Mean square error (MSE) and mean absolute percentage errors (MAPE) of SD values were determined using the SD values of subtraction images as ground truth. Open-source clinical head CT low-dose dataset (Ntrain = 37, Ntest = 10 subjects) were used to demonstrate DL applicability in noise estimation from manually labeled uniform regions and in automated noise and contrast assessment. RESULTS The direct SD estimation using 3D-CNN was the most accurate assessment method when comparing in phantom dataset (MAPE = 15.5%, MSE = 6.3HU). Unsupervised noise2noise approach provided only slightly inferior results (MAPE = 20.2%, MSE = 13.7HU). 2DCNN and unsupervised UNet models provided the smallest MSE on clinical labeled uniform regions. CONCLUSIONS DL-based clinical image assessment is feasible and provides acceptable accuracy as compared to true image noise. Noise2noise approach may be feasible in clinical use where no ground truth data is available. Noise estimation combined with tissue segmentation may enable more comprehensive image quality characterization.
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Affiliation(s)
- Satu I Inkinen
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland.
| | - Teemu Mäkelä
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland; Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland
| | - Touko Kaasalainen
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland
| | - Juha Peltonen
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland
| | - Marko Kangasniemi
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland
| | - Mika Kortesniemi
- HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland
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12
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Guo X, Zhang L, Xing Y. Analytical covariance estimation for iterative CT reconstruction methods. Biomed Phys Eng Express 2022; 8. [PMID: 35213850 DOI: 10.1088/2057-1976/ac58bf] [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: 01/06/2022] [Accepted: 02/25/2022] [Indexed: 11/11/2022]
Abstract
Covariance of reconstruction images are useful to analyze the magnitude and correlation of noise in the evaluation of systems and reconstruction algorithms. The covariance estimation requires a big number of image samples that are hard to acquire in reality. A covariance propagation method from projection by a few noisy realizations is studied in this work. Based on the property of convergent points of cost funtions, the proposed method is composed of three steps, (1) construct a relationship between the covariance of projection and corresponding reconstruction from cost functions at its convergent point, (2) simplify the covariance relationship constructed in (1) by introducing an approximate gradient of penalties, and (3) obtain an analytical covariance estimation according to the simplified relationship in (2). Three approximation methods for step (2) are studied: the linear approximation of the gradient of penalties (LAM), the Taylor apprximation (TAM), and the mixture of LAM and TAM (MAM). TV and qGGMRF penalized weighted least square methods are experimented on. Results from statistical methods are used as reference. Under the condition of unstable 2nd derivative of penalties such as TV, the covariance image estimated by LAM accords to reference well but of smaller values, while the covarianc estimation by TAM is quite off. Under the conditon of relatively stable 2nd derivative of penalties such as qGGMRF, TAM performs well and LAM is again with a negative bias in magnitude. MAM gives a best performance under both conditions by combining LAM and TAM. Results also show that only one noise realization is enough to obtain reasonable covariance estimation analytically, which is important for practical usage. This work suggests the necessity and a new way to estimate the covariance for non-quadratically penalized reconstructions. Currently, the proposed method is computationally expensive for large size reconstructions.Computational efficiency is our future work to focus.
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Affiliation(s)
- Xiaoyue Guo
- Department of Engineering Physics, Tsinghua University, Beijing, People's Republic of China.,Key Laboratory of Particle & Radiation Imaging, Tsinghua University, Beijing, People's Republic of China
| | - Li Zhang
- Department of Engineering Physics, Tsinghua University, Beijing, People's Republic of China.,Key Laboratory of Particle & Radiation Imaging, Tsinghua University, Beijing, People's Republic of China
| | - Yuxiang Xing
- Department of Engineering Physics, Tsinghua University, Beijing, People's Republic of China.,Key Laboratory of Particle & Radiation Imaging, Tsinghua University, Beijing, People's Republic of China
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13
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Ahmad M, Tan D, Marisetty S. Assessment of the global noise algorithm for automatic noise measurement in head CT examinations. Med Phys 2021; 48:5702-5711. [PMID: 34314528 PMCID: PMC9291315 DOI: 10.1002/mp.15133] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/19/2021] [Accepted: 07/19/2021] [Indexed: 12/18/2022] Open
Abstract
PURPOSE The global noise (GN) algorithm has been previously introduced as a method for automatic noise measurement in clinical CT images. The accuracy of the GN algorithm has been assessed in abdomen CT examinations, but not in any other body part until now. This work assesses the GN algorithm accuracy in automatic noise measurement in head CT examinations. METHODS A publicly available image dataset of 99 head CT examinations was used to evaluate the accuracy of the GN algorithm in comparison to reference noise values. Reference noise values were acquired using a manual noise measurement procedure. The procedure used a consistent instruction protocol and multiple observers to mitigate the influence of intra- and interobserver variation, resulting in precise reference values. Optimal GN algorithm parameter values were determined. The GN algorithm accuracy and the corresponding statistical confidence interval were determined. The GN measurements were compared across the six different scan protocols used in this dataset. The correlation of GN to patient head size was also assessed using a linear regression model, and the CT scanner's X-ray beam quality was inferred from the model fit parameters. RESULTS Across all head CT examinations in the dataset, the range of reference noise was 2.9-10.2 HU. A precision of ±0.33 HU was achieved in the reference noise measurements. After optimization, the GN algorithm had a RMS error 0.34 HU corresponding to a percent RMS error of 6.6%. The GN algorithm had a bias of +3.9%. Statistically significant differences in GN were detected in 11 out of the 15 different pairs of scan protocols. The GN measurements were correlated with head size with a statistically significant regression slope parameter (p < 10-7 ). The CT scanner X-ray beam quality estimated from the slope parameter was 3.5 cm water HVL (2.8-4.8 cm 95% CI). CONCLUSION The GN algorithm was validated for application in head CT examinations. The GN algorithm was accurate in comparison to reference manual measurement, with errors comparable to interobserver variation in manual measurement. The GN algorithm can detect noise differences in examinations performed on different scanner models or using different scan protocols. The trend in GN across patients of different head sizes closely follows that predicted by a physical model of X-ray attenuation.
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Affiliation(s)
- Moiz Ahmad
- Department of Imaging Physics ‐ Unit 1472The University of Texas MD Anderson Cancer CenterHoustonTexasUSA
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14
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Improved precision of noise estimation in CT with a volume-based approach. Eur Radiol Exp 2021; 5:39. [PMID: 34505172 PMCID: PMC8429536 DOI: 10.1186/s41747-021-00237-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 08/03/2021] [Indexed: 11/10/2022] Open
Abstract
Assessment of image noise is a relevant issue in computed tomography (CT). Noise is routinely measured by the standard deviation of density values (Hounsfield units, HU) within a circular region of interest (ROI). We explored the effect of a spherical volume of interest (VOI) on noise measurements. Forty-nine chronic obstructive pulmonary disease patients underwent CT with clinical protocol (regular dose [RD], volumetric CT dose index [CTDIvol] 3.04 mGy, 64-slice unit), and ultra-low dose (ULD) protocol (median CTDIvol 0.38 mGy, dual-source unit). Noise was measured in 27 1-cm2 ROIs and 27 0.75-cm3 VOIs inside the trachea. Median true noise was 21 HU (range 17-29) for RD-CT and 33 HU (26-39) for ULD-CT. The VOI approach resulted in a lower mean distance between limits of agreement compared to ROI: 5.9 versus 10.0 HU for RD-CT (-40%); 4.7 versus 9.9 HU for ULD-CT (-53%). Mean systematic bias barely changed: -1.6 versus -0.9HU for RD-CT; 0.0 to 0.4HU for ULD-CT. The average measurement time was 6.8 s (ROI) versus 9.7 (VOI), independent of dose level. For chest CT, measuring noise with a VOI-based instead of a ROI-based approach reduces variability by 40-53%, without a relevant effect on systematic bias and measurement time.
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15
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Mahmood U, Shrestha R, Bates DDB, Mannelli L, Corrias G, Erdi YE, Kanan C. Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems. Front Digit Health 2021; 3:671015. [PMID: 34713144 PMCID: PMC8521929 DOI: 10.3389/fdgth.2021.671015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/29/2021] [Indexed: 11/23/2022] Open
Abstract
Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset from one or more institutions, followed by a clinical validation study of the system's efficacy during deployment. Clinical validation studies are time-consuming, and best practices dictate limited re-use of analytical validation data, so it is ideal to know ahead of time if a system is likely to fail analytical or clinical validation. In this paper, we describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons. We illustrate the sanity tests' value by designing a deep learning system to classify pancreatic cancer seen in computed tomography scans.
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Affiliation(s)
- Usman Mahmood
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Robik Shrestha
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States
| | - David D. B. Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Lorenzo Mannelli
- Institute of Research and Medical Care (IRCCS) SDN, Institute of Diagnostic and Nuclear Research, Naples, Italy
| | - Giuseppe Corrias
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Yusuf Emre Erdi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Christopher Kanan
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States
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16
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Anam C, Arif I, Haryanto F, Lestari FP, Widita R, Budi WS, Sutanto H, Adi K, Fujibuchi T, Dougherty G. An Improved Method of Automated Noise Measurement System in CT Images. J Biomed Phys Eng 2021; 11:163-174. [PMID: 33937124 PMCID: PMC8064134 DOI: 10.31661/jbpe.v0i0.1198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 08/04/2019] [Indexed: 12/12/2022]
Abstract
Background: It is necessary to have an automated noise measurement system working accurately to optimize dose in computerized tomography (CT) examinations. Objective: This study aims to develop an algorithm to automate noise measurement that can be implemented in CT images of all body regions. Materials and Methods:
In this retrospective study, our automated noise measurement method consists of three steps as follows: the first is segmenting the image of the patient. The second is developing a standard deviation (SD) map by calculating the SD value for each pixel with a sliding window operation. The third step is estimating the noise as the smallest SD from the SD map. The proposed method was applied to the images of a homogenous phantom and a full body adult anthropomorphic phantom, and retrospectively applied to 27 abdominal images of patients.
Results: For a homogeneous phantom, the noises calculated using our proposed and previous algorithms have a linear correlation with R2 = 0.997.
It is found that the noise magnitude closely follows the magnitude of the water equivalent diameter (Dw) in all body regions. The proposed algorithm is able to distinguish the noise magnitude due to variations in tube currents and different noise suppression techniques such as strong, standard, mild, and weak ones in a reconstructed image using the AIDR 3D algorithm. Conclusion: An automated noise calculation has been proposed and successfully implemented in all body regions. It is not only accurate and easy to implement but also not influenced by the subjectivity of user.
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Affiliation(s)
- Choirul Anam
- PhD, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
| | - Idam Arif
- PhD, Department of Physics, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Ganesha 10, Bandung 40132, West Java, Indonesia
| | - Freddy Haryanto
- PhD, Department of Physics, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Ganesha 10, Bandung 40132, West Java, Indonesia
| | - Fauzia P Lestari
- MSc, Department of Physics, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Ganesha 10, Bandung 40132, West Java, Indonesia
| | - Rena Widita
- PhD, Department of Physics, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Ganesha 10, Bandung 40132, West Java, Indonesia
| | - Wahyu S Budi
- PhD, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
| | - Heri Sutanto
- PhD, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
| | - Kusworo Adi
- PhD, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
| | - Toshioh Fujibuchi
- PhD, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Geoff Dougherty
- PhD, Department of Applied Physics and Medical Imaging, California State University Channel Islands, Camarillo, CA 93012, USA
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Ahmad M, Jacobsen MC, Thomas MA, Chen HS, Layman RR, Jones AK. A Benchmark for automatic noise measurement in clinical computed tomography. Med Phys 2020; 48:640-647. [PMID: 33283284 DOI: 10.1002/mp.14635] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 11/15/2020] [Accepted: 11/24/2020] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Assessment of image quality directly in clinical image data is an important quality control objective as phantom-based testing does not fully represent image quality across patient variation. Computer algorithms for automatically measuring noise in clinical computed tomography (CT) images have been introduced, but the accuracy of these algorithms is unclear. This work benchmarks the accuracy of the global noise (GN) algorithm for automatic noise measurement in contrast-enhanced abdomen CT exams in comparison to precise reference noise measurements. The GN algorithm was further optimized compared to the previous report in the literature. METHODS Reference values of noise were established in a public image dataset of 82 contrast-enhanced abdomen CT exams. The reference noise values were obtained by manual regions-of-interest measurements of pixel standard deviation in the liver parenchyma according to an instruction protocol. Noise measurements taken by six observers were averaged together to improve reference noise statistical precision. The GN algorithm was used to automatically measure noise in each image set. The accuracy of the GN algorithm was determined in terms of RMS error compared to reference noise. The GN algorithm was optimized by conducting 1000 trials with random algorithm parameter values. The trial with the lowest RMS error was used to select optimum algorithm parameters. RESULTS The range of noise across CT image sets was 8.8-28.8 HU. Reference noise measurements were made with a precision of ±0.78 HU (95% confidence interval). The RMS error of automatic noise measurement was 0.93 HU (0.77-1.19 HU 95% confidence interval). The automatic noise measurements were equally accurate across image sets of varying noise magnitude. Optimum GN algorithm parameter values were: a kernel size of 7 pixels, and soft tissue lower and upper thresholds of 0 and 170 HU, respectively. CONCLUSIONS The performance of automatic noise measurement was benchmarked in a large clinical CT dataset. The study provides a framework for thorough validation of automatic clinical image quality measurement methods. The GN algorithm was optimized and validated for automatic measurement of soft-tissue noise in abdomen CT exams.
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Affiliation(s)
- Moiz Ahmad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Megan C Jacobsen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - M Allan Thomas
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Henry S Chen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Rick R Layman
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - A Kyle Jones
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
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Anam C, Sutanto H, Adi K, Budi WS, Muhlisin Z, Haryanto F, Matsubara K, Fujibuchi T, Dougherty G. Development of a computational phantom for validation of automated noise measurement in CT images. Biomed Phys Eng Express 2020; 6. [PMID: 35135906 DOI: 10.1088/2057-1976/abb2f8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 08/26/2020] [Indexed: 11/11/2022]
Abstract
The purpose of this study was to develop a computational phantom for validation of automatic noise calculations applied to all parts of the body, to investigate kernel size in determining noise, and to validate the accuracy of automatic noise calculation for several noise levels. The phantom consisted of objects with a very wide range of HU values, from -1000 to +950. The incremental value for each object was 10 HU. Each object had a size of 15 × 15 pixels separated by a distance of 5 pixels. There was no dominant homogeneous part in the phantom. The image of the phantom was then degraded to mimic the real image quality of CT by convolving it with a point spread function (PSF) and by addition of Gaussian noise. The magnitude of the Gaussian noises was varied (5, 10, 25, 50, 75 and 100 HUs), and they were considered as the ground truth noise (NG). We also used a computational phantom with added actual noise from a CT scanner. The phantom was used to validate the automated noise measurement based on the average of the ten smallest standard deviations (SD) from the standard deviation map (SDM). Kernel sizes from 3 × 3 up to 27 × 27 pixels were examined in this study. A computational phantom for automated noise calculations validation has been successfully developed. It was found that the measured noise (NM) was influenced by the kernel size. For kernels of 15 × 15 pixels or smaller, the NMvalue was much smaller than the NG. For kernel sizes from 17 × 17 to 21 × 21 pixels, the NMvalue was about 90% of NG. And for kernel sizes of 23 × 23 pixels and above, NMis greater than NG. It was also found that even with small kernel sizes the relationship between NMand NGis linear with R2more than 0.995. Thus accurate noise levels can be automatically obtained even with small kernel sizes without any concern regarding the inhomogeneity of the object.
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Affiliation(s)
- Choirul Anam
- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
| | - Heri Sutanto
- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
| | - Kusworo Adi
- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
| | - Wahyu Setia Budi
- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
| | - Zaenul Muhlisin
- Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia
| | - Freddy Haryanto
- Department of Physics, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Bandung, West Java, Indonesia
| | - Kosuke Matsubara
- Department of Quantum Medical Technology, Faculty of Health Sciences, Institute of Medical Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan
| | - Toshioh Fujibuchi
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Geoff Dougherty
- Department of Applied Physics and Medical Imaging, California State University Channel Islands, Camarillo, CA 93012, United States of America
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Zhou Y. Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low-contrast detectability in abdomen CT. J Appl Clin Med Phys 2020; 21:128-135. [PMID: 31898865 PMCID: PMC7021010 DOI: 10.1002/acm2.12813] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 11/20/2019] [Accepted: 12/02/2019] [Indexed: 11/30/2022] Open
Abstract
Purpose The utilization of iterative reconstruction makes it difficult to identify the dose‐noise relationship, resulting in empirical design of scan protocols and inconsistent conclusions on dose reduction for consistent image quality. This study was to quantitatively determine the dose and the blending fraction of adaptive statistical iterative reconstruction (ASIR) based on the specified low‐contrast detectability (LCD). Methods A tissue equivalent abdomen phantom and a GE discovery 750 HD computed tomography (CT) were utilized. The normality of the noise distribution was tested at various spatial scales (2.1–9.8 mm) in the presence of ASIR (10–100%) with a wide range of doses (2.24–38 mGy). The statically defined minimum detectable contrast (MDC) was used as the image quality metric. The parametric model decomposed the MDC into two terms: one with and the other without ASIR, each was related to the dose in the form of power law with factors and indices dependent on spatial scales. The parameters were identified by least‐square fitting to the experimental data. By considering the constraint of the blending fraction in the range of [0, 1], the dose and ASIR blending fraction were determined for any specified low‐contrast detectability (LCD), quantified by the MDC at the concerned lesion size. Results It was verified that noise distribution is normal in the presence of ASIR. It was also found that the noises obtained from the subtractions of adjacent slices had an underestimate of 20% as compared to the ground truth noises, regardless of the spatial scale, pitch, or ASIR blending fraction. The least‐square fitting for the parametric model resulted in correlation coefficients from 0.905 to 0.996. The root‐mean‐square errors ranged from 1.27% to 7.15%. Conclusion The parametric model can be used to form a look‐up‐table for dose and ASIR blending fraction. The dose choices may be substantially limited in some cases depending on the required LCD.
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Affiliation(s)
- Yifang Zhou
- Department of ImagingImaging Physics DivisionS. Mark Taper Foundation Imaging CenterCedars‐Sinai Medical Center8700 Beverly Blvd.Los AngelesCalifornia90048USA
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20
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Clinically Acceptable Optimized Dose Reduction in Computed Tomographic Imaging of Necrotizing Pancreatitis Using a Noise Addition Software Tool. J Comput Assist Tomogr 2018; 42:197-203. [DOI: 10.1097/rct.0000000000000684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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21
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Fu W, Marin D, Ramirez-Giraldo JC, Choudhury KR, Solomon J, Schabel C, Patel BN, Samei E. Optimizing window settings for improved presentation of virtual monoenergetic images in dual-energy computed tomography. Med Phys 2017; 44:5686-5696. [PMID: 28777467 DOI: 10.1002/mp.12501] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 06/06/2017] [Accepted: 07/20/2017] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Dual-energy computed tomography virtual monoenergetic imaging (VMI) at 40 keV exhibits superior contrast-to-noise ratio (CNR), although practicing radiologists do not consistently prefer it over VMI at 70 keV due to high perceivable noise. We hypothesize that the presentation of 40 keV VMI may be compromised using window settings (i.e., window-and-level values [W-L values]) designed for conventional single-energy CT. This study aimed to devise optimum window settings that reduce the apparent noise and utilize the high CNR of 40 keV VMI, in order to improve the conspicuity of hypervascular liver lesions. MATERIALS AND METHODS Three W-L value adjustment methods were investigated to alter the presentation of 40 keV VMI. To harness the high CNR of 40 keV VMI, the methods were designed to achieve (a) liver histogram distribution, (b) lesion-to-liver contrast, or (c) liver background noise comparable to those perceived in 70 keV VMI. This IRB-approved study included 18 patient abdominal datasets reconstructed at 40 and 70 keV. For each patient, the W-L values were determined using the three methods. For each of the images with default or adjusted W-L values, the noise, contrast, and CNR were calculated in terms of both display space and native CT number (referred to as HU) space. An observer study was performed to compare the 40 keV images with the three adjusted W-L values, and 40 and 70 keV images with default W-L values in terms of noise, contrast, and diagnostic preference. A comparison was also made in terms of the applicability of using patient-specific or patient-averaged W-L values. RESULTS Using the default W-L values, 40 keV VMI exhibited higher HU CNR than 70 keV VMI by 24.6 ± 14.9% (P < 0.001) but lower display CNR by 38.0 ± 16.4% (P < 0.001). Using adjusted W-L values, 40 keV images showed increased display CNR as compared to 70 keV images, by 21.2 ± 13.1%, 17.4 ± 13.6%, and 24.2 ± 15.9% (P < 0.001) for histogram-, noise-, and contrast equalization methods, respectively. The 40 keV images with all three W-L value adjustment methods showed improved perceived conspicuity (CNR) of liver presentation by 103-120% (P < 0.001), as compared to default W-L values. The qualitative observer study revealed that 40 keV images with noise- and histogram-equalized W-L values were the most preferred, followed by 40 keV images with contrast-equalized W-L values and 70 keV images with default W-L values. The 40 keV images with default W-L values were the least preferred. Patient-specific W-L values offered similar results to those of patient-averaged W-L values. CONCLUSION The adjusted W-L values can significantly improve the perception of VMI dataset image quality by improving the actual display CNR.
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Affiliation(s)
- Wanyi Fu
- Department of Electrical and Computer Engineering, and Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, 27705, USA
| | - Daniele Marin
- Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA
| | | | - Kingshuk Roy Choudhury
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University, Durham, NC, 27705, USA
| | - Justin Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA
| | - Christoph Schabel
- Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA
| | - Bhavik N Patel
- Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, and Departments of Physics, Biomedical Engineering, and Electrical and Computer Engineering, Duke University, Durham, NC, 27705, USA
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Abadi E, Sanders J, Samei E. Patient-specific quantification of image quality: An automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images. Med Phys 2017; 44:4736-4746. [DOI: 10.1002/mp.12438] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2016] [Revised: 06/14/2017] [Accepted: 06/18/2017] [Indexed: 12/25/2022] Open
Affiliation(s)
- Ehsan Abadi
- Department of Electrical and Computer Engineering; Carl E. Ravin Advanced Imaging Laboratories; Clinical Imaging Physics Group; Duke University; 2424 Erwin Rd Suite 302 Durham NC 27705 USA
| | - Jeremiah Sanders
- Clinical Imaging Physics Group; Medical Physics Graduate Program; Carl E. Ravin Advanced Imaging Laboratories; Duke University; 2424 Erwin Rd Suite 302 Durham NC 27705 USA
| | - Ehsan Samei
- Clinical Imaging Physics Group; Medical Physics Graduate Program; Carl E. Ravin Advanced Imaging Laboratories; Departments of Radiology, Physics, Biomedical Engineering, and Electrical and Computer Engineering; Duke University; 2424 Erwin Rd Suite 302 Durham NC 27705 USA
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Malkus A, Szczykutowicz TP. A method to extract image noise level from patient images in CT. Med Phys 2017; 44:2173-2184. [DOI: 10.1002/mp.12240] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Revised: 01/20/2017] [Accepted: 03/16/2017] [Indexed: 12/14/2022] Open
Affiliation(s)
- Annelise Malkus
- Department of Medical Physics; University of Wisconsin-Madison; Madison WI 53705 USA
| | - Timothy P. Szczykutowicz
- Department of Medical Physics; University of Wisconsin-Madison; Madison WI 53705 USA
- Department of Radiology; University of Wisconsin-Madison; Madison WI 53705 USA
- Department of Biomedical Engineering; University of Wisconsin-Madison; WI 53706 USA
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Evaluation of automatic image quality assessment in chest CT - A human cadaver study. Phys Med 2017; 36:32-37. [PMID: 28410683 DOI: 10.1016/j.ejmp.2017.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Revised: 03/06/2017] [Accepted: 03/07/2017] [Indexed: 11/20/2022] Open
Abstract
PURPOSE The evaluation of clinical image quality (IQ) is important to optimize CT protocols and to keep patient doses as low as reasonably achievable. Considering the significant amount of effort needed for human observer studies, automatic IQ tools are a promising alternative. The purpose of this study was to evaluate automatic IQ assessment in chest CT using Thiel embalmed cadavers. METHODS Chest CT's of Thiel embalmed cadavers were acquired at different exposures. Clinical IQ was determined by performing a visual grading analysis. Physical-technical IQ (noise, contrast-to-noise and contrast-detail) was assessed in a Catphan phantom. Soft and sharp reconstructions were made with filtered back projection and two strengths of iterative reconstruction. In addition to the classical IQ metrics, an automatic algorithm was used to calculate image quality scores (IQs). To be able to compare datasets reconstructed with different kernels, the IQs values were normalized. RESULTS Good correlations were found between IQs and the measured physical-technical image quality: noise (ρ=-1.00), contrast-to-noise (ρ=1.00) and contrast-detail (ρ=0.96). The correlation coefficients between IQs and the observed clinical image quality of soft and sharp reconstructions were 0.88 and 0.93, respectively. CONCLUSIONS The automatic scoring algorithm is a promising tool for the evaluation of thoracic CT scans in daily clinical practice. It allows monitoring of the image quality of a chest protocol over time, without human intervention. Different reconstruction kernels can be compared after normalization of the IQs.
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Sanders J, Hurwitz L, Samei E. Patient-specific quantification of image quality: An automated method for measuring spatial resolution in clinical CT images. Med Phys 2016; 43:5330. [DOI: 10.1118/1.4961984] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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