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Greffier J, Barbotteau Y, Gardavaud F. iQMetrix-CT: New software for task-based image quality assessment of phantom CT images. Diagn Interv Imaging 2022; 103:555-562. [DOI: 10.1016/j.diii.2022.05.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 05/27/2022] [Indexed: 01/09/2023]
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Monnin P, Viry A, Verdun FR, Racine D. Slice NEQ and system DQE to assess CT imaging performance. ACTA ACUST UNITED AC 2020; 65:105009. [DOI: 10.1088/1361-6560/ab807a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Lee D, Kim H, Choi B, Kim HJ. Development of a deep neural network for generating synthetic dual-energy chest x-ray images with single x-ray exposure. Phys Med Biol 2019; 64:115017. [PMID: 31026841 DOI: 10.1088/1361-6560/ab1cee] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Dual-energy chest radiography (DECR) is a medical imaging technology that can improve diagnostic accuracy. This technique can decompose single-energy chest radiography (SECR) images into separate bone- and soft tissue-only images. This can, however, double the radiation exposure to the patient. To address this limitation, we developed an algorithm for the synthesis of DECR from a SECR through deep learning. To predict high resolution images, we developed a novel deep learning architecture by modifying a conventional U-net to take advantage of the high frequency-dominant information that propagates from the encoding part to the decoding part. In addition, we used the anticorrelated relationship (ACR) of DECR for improving the quality of the predicted images. For training data, 300 pairs of SECR and their corresponding DECR images were used. To test the trained model, 50 DECR images from Yonsei University Severance Hospital and 662 publicly accessible SECRs were used. To evaluate the performance of the proposed method, we compared DECR and predicted images using a structural similarity approach (SSIM). In addition, we quantitatively evaluated image quality calculating the modulation transfer function and coefficient of variation. The proposed model selectively predicted the bone- and soft tissue-only CR images from an SECR image. The strategy for improving the spatial resolution by ACR was effective. Quantitative evaluation showed that the proposed method with ACR showed relatively high SSIM (over 0.85). In addition, predicted images with the proposed ACR model achieved better image quality measures than those of U-net. In conclusion, the proposed method can obtain high-quality bone- and soft tissue-only CR images without the need for additional hardware for double x-ray exposures in clinical practice.
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Affiliation(s)
- Donghoon Lee
- Department of Radiation Convergence Engineering, Research Institute of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon, Republic of Korea
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Robins M, Solomon J, Richards T, Samei E. 3D task-transfer function representation of the signal transfer properties of low-contrast lesions in FBP- and iterative-reconstructed CT. Med Phys 2018; 45:4977-4985. [PMID: 30231193 DOI: 10.1002/mp.13205] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 08/21/2018] [Accepted: 09/13/2018] [Indexed: 01/23/2023] Open
Abstract
PURPOSE The purpose of this study was to investigate how accurately the task-transfer function (TTF) models the signal transfer properties of low-contrast features in a non-linear commercial CT system. METHODS A cylindrical phantom containing 24 anthropomorphic "physical" lesions was 3D printed. Lesions had two sizes (523, 2145 mm3 ), and two nominal radio-densities (80 and 100 HU at 120 kV). CT images were acquired on a commercial CT system (Siemens Flash scanner) at four dose levels (CTDIvol , 32 cm phantom:1.5, 3.0, 6.0, 22.0 mGy) and reconstructed using FBP and IR kernels (B31f, B45f, I31f\2, I44f\2). Low-contrast rod inserts (in-plane) and a slanted edge (z-direction) were used to estimate 3D-TTFs. CAD versions of lesions were blurred by the 3D-TTFs, virtually superimposed into corresponding phantom images, and compared to the physical lesions in terms of (a) a 4AFC visual assessment, (b) edge gradient, (c) size, and (d) shape similarity. Assessments 2 and 3 were based on an equivalence criterion D ¯ ≥ COV ¯ to determine if the natural variability COV ¯ in the physical lesions was greater or equal to the difference D ¯ between physical and simulated. Shape similarity was quantified via Sorensen-Dice coefficient (SDC). Comparisons were done for each lesion and for all imaging conditions. RESULTS The readers detected simulated lesions at a rate of 37.9 ± 3.1% (25% implies random guessing). Lesion edge blur and volume differences D ¯ were on average less than physical lesions' natural variability COV ¯ . The SDC (average ± SD) was 0.80 ± 0.13 (max of 1 possible). CONCLUSIONS The visual appearance, edge blur, size, and shape of simulated lesions were similar to the physical lesions, which suggests 3D-TTF models the low-contrast signal transfer properties of this non-linear CT system reasonably well.
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Affiliation(s)
- Marthony Robins
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA
| | - Justin Solomon
- Clinical Imaging Physics Group, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA
| | - Taylor Richards
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA
| | - Ehsan Samei
- Clinical Imaging Physics Group, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Departments of Physics, Biomedical Engineering, and Electrical and Computer Engineering, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA
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Zhang L, Zhao H, Ma W, Jiang J, Zhang L, Li J, Gao F, Zhou Z. Resolution and noise performance of sparse view X-ray CT reconstruction via Lp-norm regularization. Phys Med 2018; 52:72-80. [DOI: 10.1016/j.ejmp.2018.04.396] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 03/28/2018] [Accepted: 04/25/2018] [Indexed: 10/28/2022] Open
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A Third-Generation Adaptive Statistical Iterative Reconstruction Technique: Phantom Study of Image Noise, Spatial Resolution, Lesion Detectability, and Dose Reduction Potential. AJR Am J Roentgenol 2018; 210:1301-1308. [DOI: 10.2214/ajr.17.19102] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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Ba A, Abbey CK, Baek J, Han M, Bouwman RW, Balta C, Brankov J, Massanes F, Gifford HC, Hernandez-Giron I, Veldkamp WJH, Petrov D, Marshall N, Samuelson FW, Zeng R, Solomon JB, Samei E, Timberg P, Förnvik H, Reiser I, Yu L, Gong H, Bochud FO. Inter-laboratory comparison of channelized hotelling observer computation. Med Phys 2018; 45:3019-3030. [PMID: 29704868 DOI: 10.1002/mp.12940] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 04/11/2018] [Accepted: 04/15/2018] [Indexed: 01/14/2023] Open
Abstract
PURPOSE The task-based assessment of image quality using model observers is increasingly used for the assessment of different imaging modalities. However, the performance computation of model observers needs standardization as well as a well-established trust in its implementation methodology and uncertainty estimation. The purpose of this work was to determine the degree of equivalence of the channelized Hotelling observer performance and uncertainty estimation using an intercomparison exercise. MATERIALS AND METHODS Image samples to estimate model observer performance for detection tasks were generated from two-dimensional CT image slices of a uniform water phantom. A common set of images was sent to participating laboratories to perform and document the following tasks: (a) estimate the detectability index of a well-defined CHO and its uncertainty in three conditions involving different sized targets all at the same dose, and (b) apply this CHO to an image set where ground truth was unknown to participants (lower image dose). In addition, and on an optional basis, we asked the participating laboratories to (c) estimate the performance of real human observers from a psychophysical experiment of their choice. Each of the 13 participating laboratories was confidentially assigned a participant number and image sets could be downloaded through a secure server. Results were distributed with each participant recognizable by its number and then each laboratory was able to modify their results with justification as model observer calculation are not yet a routine and potentially error prone. RESULTS Detectability index increased with signal size for all participants and was very consistent for 6 mm sized target while showing higher variability for 8 and 10 mm sized target. There was one order of magnitude between the lowest and the largest uncertainty estimation. CONCLUSIONS This intercomparison helped define the state of the art of model observer performance computation and with thirteen participants, reflects openness and trust within the medical imaging community. The performance of a CHO with explicitly defined channels and a relatively large number of test images was consistently estimated by all participants. In contrast, the paper demonstrates that there is no agreement on estimating the variance of detectability in the training and testing setting.
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Affiliation(s)
- Alexandre Ba
- Institute of Radiation Physics, Lausanne University Hospital, Lausanne, Switzerland
| | - Craig K Abbey
- Department of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Jongduk Baek
- School of Integrated Technology, Yonsei University, 406-840, Incheon, Korea
| | - Minah Han
- School of Integrated Technology, Yonsei University, 406-840, Incheon, Korea
| | - Ramona W Bouwman
- Dutch Expert Centre for Screening, Radboud University Nijmegen Medical Centre (LRCB), P.O. Box 6873, 6503 GJ, Nijmegen, The Netherlands
| | - Christiana Balta
- Dutch Expert Centre for Screening, Radboud University Nijmegen Medical Centre (LRCB), P.O. Box 6873, 6503 GJ, Nijmegen, The Netherlands
| | - Jovan Brankov
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, 3301 South Dearborn Street, Chicago, IL, 60616, USA
| | - Francesc Massanes
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, 3301 South Dearborn Street, Chicago, IL, 60616, USA
| | - Howard C Gifford
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Irene Hernandez-Giron
- Radiology Department, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333ZA, Leiden, The Netherlands
| | - Wouter J H Veldkamp
- Radiology Department, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333ZA, Leiden, The Netherlands
| | - Dimitar Petrov
- Department of Medical Physics and Quality Assessment, KU Leuven, Leuven, Belgium
| | - Nicholas Marshall
- Department of Medical Physics and Quality Assessment, KU Leuven, Leuven, Belgium.,Department of Radiology, UZ Leuven, Leuven, Belgium
| | - Frank W Samuelson
- Division of Imaging, Diagnostics and Software Reliability, US Food and Drug Administration, 10903 New Hampshire Ave Building 62, Room 3102, Silver Spring, MD, 20903-1058, USA
| | - Rongping Zeng
- Division of Imaging, Diagnostics and Software Reliability, US Food and Drug Administration, 10903 New Hampshire Ave Building 62, Room 3102, Silver Spring, MD, 20903-1058, USA
| | - Justin B Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Departments of Radiology, Electrical and Computer Engineering, Biomedical Engineering, and Physics, Clinical Imaging Physics Group, Medical Physics Graduate Program, Duke University, Durham, NC, 27705, USA
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Departments of Radiology, Electrical and Computer Engineering, Biomedical Engineering, and Physics, Clinical Imaging Physics Group, Medical Physics Graduate Program, Duke University, Durham, NC, 27705, USA
| | - Pontus Timberg
- Department of Medical Radiation Physics, Translational Medicine Malmö, Lund University, Malmö, Sweden
| | - Hannie Förnvik
- Department of Medical Radiation Physics, Translational Medicine Malmö, Lund University, Malmö, Sweden
| | - Ingrid Reiser
- Department of Radiology, University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL, 60637, USA
| | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Hao Gong
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - François O Bochud
- Institute of Radiation Physics, Lausanne University Hospital, Lausanne, Switzerland
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Barca P, Giannelli M, Fantacci ME, Caramella D. Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm: dependence of image quality on the blending level of reconstruction. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2018; 41:463-473. [DOI: 10.1007/s13246-018-0645-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Accepted: 04/26/2018] [Indexed: 12/16/2022]
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Abadi E, Segars WP, Sturgeon GM, Roos JE, Ravin CE, Samei E. Modeling Lung Architecture in the XCAT Series of Phantoms: Physiologically Based Airways, Arteries and Veins. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:693-702. [PMID: 29533891 PMCID: PMC6434530 DOI: 10.1109/tmi.2017.2769640] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The purpose of this paper was to extend the extended cardiac-torso (XCAT) series of computational phantoms to include a detailed lung architecture including airways and pulmonary vasculature. Eleven XCAT phantoms of varying anatomy were used in this paper. The lung lobes and initial branches of the airways, pulmonary arteries, and veins were previously defined in each XCAT model. These models were extended from the initial branches of the airways and vessels to the level of terminal branches using an anatomically-based volume-filling branching algorithm. This algorithm grew the airway and vasculature branches separately and iteratively without intersecting each other using cylindrical models with diameters estimated by order-based anatomical measurements. Geometrical features of the extended branches were compared with the literature anatomy values to quantitatively evaluate the models. These features include branching angle, length to diameter ratio, daughter to parent diameter ratio, asymmetrical branching pattern, diameter, and length ratios. The XCAT phantoms were then used to simulate CT images to qualitatively compare them with the original phantom images. The proposed growth model produced 46369 ± 12521 airways, 44737 ± 11773 arteries, and 39819 ± 9988 veins to the XCAT phantoms. Furthermore, the growth model was shown to produce asymmetrical airway, artery, and vein networks with geometrical attributes close to morphometry and model based studies. The simulated CT images of the phantoms were judged to be more realistic, including more airways and pulmonary vessels compared with the original phantoms. Future work will seek to add a heterogeneous parenchymal background into the XCAT lungs to make the phantoms even more representative of human anatomy, paving the way towards the use of XCAT models as a tool to virtually evaluate the current and emerging medical imaging technologies.
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Winslow J, Zhang Y, Samei E. A method for characterizing and matching CT image quality across CT scanners from different manufacturers. Med Phys 2017; 44:5705-5717. [DOI: 10.1002/mp.12554] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 08/03/2017] [Accepted: 08/07/2017] [Indexed: 11/09/2022] Open
Affiliation(s)
- James Winslow
- Clinical Imaging Physics Group; Department of Radiology; Duke University Medical Center; Durham NC 27705 USA
| | - Yakun Zhang
- Clinical Imaging Physics Group; Department of Radiology; Duke University Medical Center; Durham NC 27705 USA
| | - Ehsan Samei
- Clinical Imaging Physics Group; Carl E. Ravin Advanced Imaging Laboratories; Department of Radiology; Duke University Medical Center; Durham NC 27705 USA
- Departments of Physics, Electrical and Computer Engineering, and Biomedical Engineering; Medical Physics Graduate Program; Duke University; Durham NC 27705 USA
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Ott JG, Ba A, Racine D, Viry A, Bochud FO, Verdun FR. Assessment of low contrast detection in CT using model observers: Developing a clinically-relevant tool for characterising adaptive statistical and model-based iterative reconstruction. Z Med Phys 2017; 27:86-97. [DOI: 10.1016/j.zemedi.2016.04.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Revised: 02/15/2016] [Accepted: 04/08/2016] [Indexed: 10/21/2022]
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Solomon J, Ba A, Bochud F, Samei E. Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms. Med Phys 2017; 43:6497. [PMID: 27908164 DOI: 10.1118/1.4967478] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To use novel voxel-based 3D printed textured phantoms in order to compare low-contrast detectability between two reconstruction algorithms, FBP (filtered-backprojection) and SAFIRE (sinogram affirmed iterative reconstruction) and determine what impact background texture (i.e., anatomical noise) has on estimating the dose reduction potential of SAFIRE. METHODS Liver volumes were segmented from 23 abdominal CT cases. The volumes were characterized in terms of texture features from gray-level co-occurrence and run-length matrices. Using a 3D clustered lumpy background (CLB) model, a fitting technique based on a genetic optimization algorithm was used to find CLB textures that were reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, four cylindrical phantoms (Textures A-C and uniform, 165 mm in diameter, and 30 mm height) were designed, each containing 20 low-contrast spherical signals (6 mm diameter at nominal contrast levels of ∼3.2, 5.2, 7.2, 10, and 14 HU with four repeats per signal). The phantoms were voxelized and input into a commercial multimaterial 3D printer (Object Connex 350), with custom software for voxel-based printing (using principles of digital dithering). Images of the textured phantoms and a corresponding uniform phantom were acquired at six radiation dose levels (SOMATOM Flash, Siemens Healthcare) and observer model detection performance (detectability index of a multislice channelized Hotelling observer) was estimated for each condition (5 contrasts × 6 doses × 2 reconstructions × 4 backgrounds = 240 total conditions). A multivariate generalized regression analysis was performed (linear terms, no interactions, random error term, log link function) to assess whether dose, reconstruction algorithm, signal contrast, and background type have statistically significant effects on detectability. Also, fitted curves of detectability (averaged across contrast levels) as a function of dose were constructed for each reconstruction algorithm and background texture. FBP and SAFIRE were compared for each background type to determine the improvement in detectability at a given dose, and the reduced dose at which SAFIRE had equivalent performance compared to FBP at 100% dose. RESULTS Detectability increased with increasing radiation dose (P = 2.7 × 10-59) and contrast level (P = 2.2 × 10-86) and was higher in the uniform phantom compared to the textured phantoms (P = 6.9 × 10-51). Overall, SAFIRE had higher d' compared to FBP (P = 0.02). The estimated dose reduction potential of SAFIRE was found to be 8%, 10%, 27%, and 8% for Texture-A, Texture-B, Texture-C and uniform phantoms. CONCLUSIONS In all background types, detectability was higher with SAFIRE compared to FBP. However, the relative improvement observed from SAFIRE was highly dependent on the complexity of the background texture. Iterative algorithms such as SAFIRE should be assessed in the most realistic context possible.
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Affiliation(s)
- Justin Solomon
- Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Clinical Imaging Physics Group, Duke University Medical Center, Durham, North Carolina 27705
| | - Alexandre Ba
- Institute of Radiation Physics, Lausanne University Hospital, Lausanne 1007, Switzerland
| | - François Bochud
- Institute of Radiation Physics, Lausanne University Hospital, Lausanne 1007, Switzerland
| | - Ehsan Samei
- Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Clinical Imaging Physics Group, Duke University Medical Center, Durham, North Carolina 27705 and Departments of Biomedical Engineering, Physics, and Electrical and Computer Engineering, Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705
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Solomon J, Wilson J, Samei E. Characteristic image quality of a third generation dual-source MDCT scanner: Noise, resolution, and detectability. Med Phys 2016; 42:4941-53. [PMID: 26233220 DOI: 10.1118/1.4923172] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
PURPOSE The purpose of this work was to assess the inherent image quality characteristics of a new multidetector computed tomography system in terms of noise, resolution, and detectability index as a function of image acquisition and reconstruction for a range of clinically relevant settings. METHODS A multisized image quality phantom (37, 30, 23, 18.5, and 12 cm physical diameter) was imaged on a SOMATOM Force scanner (Siemens Medical Solutions) under variable dose, kVp, and tube current modulation settings. Images were reconstructed with filtered back projection (FBP) and with advanced modeled iterative reconstruction (ADMIRE) with iterative strengths of 3, 4, and 5. Image quality was assessed in terms of the noise power spectrum (NPS), task transfer function (TTF), and detectability index for a range of detection tasks (contrasts of approximately 45, 90, 300, -900, and 1000 HU, and 2-20 mm diameter) based on a non-prewhitening matched filter model observer with eye filter. RESULTS Image noise magnitude decreased with decreasing phantom size, increasing dose, and increasing ADMIRE strength, offering up to 64% noise reduction relative to FBP. Noise texture in terms of the NPS was similar between FBP and ADMIRE (<5% shift in peak frequency). The resolution, based on the TTF, improved with increased ADMIRE strength by an average of 15% in the TTF 50% frequency for ADMIRE-5. The detectability index increased with increasing dose and ADMIRE strength by an average of 55%, 90%, and 163% for ADMIRE 3, 4, and 5, respectively. Assessing the impact of mA modulation for a fixed average dose over the length of the phantom, detectability was up to 49% lower in smaller phantom sections and up to 26% higher in larger phantom sections for the modulated scan compared to a fixed tube current scan. Overall, the detectability exhibited less variability with phantom size for modulated scans compared to fixed tube current scans. CONCLUSIONS Image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose. The use of tube current modulation resulted in more consistent image quality with changing phantom size.
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Affiliation(s)
- Justin Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Health System, Durham, North Carolina 27705
| | - Joshua Wilson
- Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Health System, Durham, North Carolina 27705; Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705; and Departments of Biomedical Engineering and Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina 27705
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Ott JG, Becce F, Monnin P, Schmidt S, Bochud FO, Verdun FR. Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms. Phys Med Biol 2014; 59:4047-64. [DOI: 10.1088/0031-9155/59/4/4047] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Chen B, Christianson O, Wilson JM, Samei E. Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods. Med Phys 2014; 41:071909. [DOI: 10.1118/1.4881519] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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