1
|
Ahmad M, Liu X, Morani AC, Ganeshan D, Anderson MR, Samei E, Jensen CT. Oncology-specific radiation dose and image noise reference levels in adult abdominal-pelvic CT. Clin Imaging 2023; 93:52-59. [PMID: 36375364 PMCID: PMC9712239 DOI: 10.1016/j.clinimag.2022.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 10/23/2022] [Accepted: 10/25/2022] [Indexed: 11/09/2022]
Abstract
OBJECTIVES To provide our oncology-specific adult abdominal-pelvic CT reference levels for image noise and radiation dose from a high-volume, oncologic, tertiary referral center. METHODS The portal venous phase abdomen-pelvis acquisition was assessed for image noise and radiation dose in 13,320 contrast-enhanced CT examinations. Patient size (effective diameter) and radiation dose (CTDIvol) were recorded using a commercial software system, and image noise (Global Noise metric) was quantified using a custom processing system. The reference level and range for dose and noise were calculated for the full dataset, and for examinations grouped by CT scanner model. Dose and noise reference levels were also calculated for exams grouped by five different patient size categories. RESULTS The noise reference level was 11.25 HU with a reference range of 10.25-12.25 HU. The dose reference level at a median effective diameter of 30.7 cm was 26.7 mGy with a reference range of 19.6-37.0 mGy. Dose increased with patient size; however, image noise remained approximately constant within the noise reference range. The doses were 2.1-2.5 times than the doses in the ACR DIR registry for corresponding patient sizes. The image noise was 0.63-0.75 times the previously published reference level in abdominal-pelvic CT examinations. CONCLUSIONS Our oncology-specific abdominal-pelvic CT dose reference levels are higher than in the ACR dose index registry and our oncology-specific image noise reference levels are lower than previously proposed image noise reference levels. ADVANCES IN KNOWLEDGE This study reports reference image noise and radiation dose levels appropriate for the indication of abdomen-pelvis CT examination for cancer diagnosis and staging. The difference in these reference levels from non-oncology-specific CT examinations highlight a need for indication-specific, dose index and image quality reference registries.
Collapse
Affiliation(s)
- Moiz Ahmad
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, United States of America.
| | - Xinming Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, United States of America.
| | - Ajaykumar C Morani
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, United States of America.
| | - Dhakshinamoorthy Ganeshan
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, United States of America.
| | - Marcus R Anderson
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, United States of America.
| | - Ehsan Samei
- Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and Electrical and Computer Engineering, Duke University Medical Center, Durham, NC, United States of America.
| | - Corey T Jensen
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX 77030-4009, United States of America.
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Jensen CT, Gupta S, Saleh MM, Liu X, Wong VK, Salem U, Qiao W, Samei E, Wagner-Bartak NA. Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases. Radiology 2022; 303:90-98. [PMID: 35014900 PMCID: PMC8962777 DOI: 10.1148/radiol.211838] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/19/2021] [Accepted: 10/28/2021] [Indexed: 12/22/2022]
Abstract
Background Assessment of liver lesions is constrained as CT radiation doses are lowered; evidence suggests deep learning reconstructions mitigate such effects. Purpose To evaluate liver metastases and image quality between reduced-dose deep learning image reconstruction (DLIR) and standard-dose filtered back projection (FBP) contrast-enhanced abdominal CT. Materials and Methods In this prospective Health Insurance Portability and Accountability Act-compliant study (September 2019 through April 2021), participants with biopsy-proven colorectal cancer and liver metastases at baseline CT underwent standard-dose and reduced-dose portal venous abdominal CT in the same breath hold. Three radiologists detected and characterized lesions at standard-dose FBP and reduced-dose DLIR, reported confidence, and scored image quality. Contrast-to-noise ratios for liver metastases were recorded. Summary statistics were reported, and a generalized linear mixed model was used. Results Fifty-one participants (mean age ± standard deviation, 57 years ± 13; 31 men) were evaluated. The mean volume CT dose index was 65.1% lower with reduced-dose CT (12.2 mGy) than with standard-dose CT (34.9 mGy). A total of 161 lesions (127 metastases, 34 benign lesions) with a mean size of 0.7 cm ± 0.3 were identified. Subjective image quality of reduced-dose DLIR was superior to that of standard-dose FBP (P < .001). The mean contrast-to-noise ratio for liver metastases of reduced-dose DLIR (3.9 ± 1.7) was higher than that of standard-dose FBP (3.5 ± 1.4) (P < .001). Differences in detection were identified only for lesions 0.5 cm or smaller: 63 of 65 lesions detected with standard-dose FBP (96.9%; 95% CI: 89.3, 99.6) and 47 lesions with reduced-dose DLIR (72.3%; 95% CI: 59.8, 82.7). Lesion accuracy with standard-dose FBP and reduced-dose DLIR was 80.1% (95% CI: 73.1, 86.0; 129 of 161 lesions) and 67.1% (95% CI: 59.3, 74.3; 108 of 161 lesions), respectively (P = .01). Lower lesion confidence was reported with a reduced dose (P < .001). Conclusion Deep learning image reconstruction (DLIR) improved CT image quality at 65% radiation dose reduction while preserving detection of liver lesions larger than 0.5 cm. Reduced-dose DLIR demonstrated overall inferior characterization of liver lesions and reader confidence. Clinical trial registration no. NCT03151564 © RSNA, 2022 Online supplemental material is available for this article.
Collapse
Affiliation(s)
- Corey T. Jensen
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Shiva Gupta
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Mohammed M. Saleh
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Xinming Liu
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Vincenzo K. Wong
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Usama Salem
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Wei Qiao
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Ehsan Samei
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| | - Nicolaus A. Wagner-Bartak
- From the Departments of Abdominal Imaging (C.T.J., S.G., M.M.S.,
V.K.W., U.S., N.A.W.B.), Physics (X.L.), and Biostatistics (W.Q.), the
University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1473,
Houston, TX 77030-4009; and Center for Virtual Imaging Trials, Carl E. Ravin
Advanced Imaging Laboratories, Clinical Imaging Physics Group, Medical Physics
Graduate Program, Departments of Radiology, Physics, Biomedical Engineering, and
Electrical and Computer Engineering, Duke University Medical Center, Durham, NC
(E.S.)
| |
Collapse
|
4
|
Mohammadinejad P, Mileto A, Yu L, Leng S, Guimaraes LS, Missert AD, Jensen CT, Gong H, McCollough CH, Fletcher JG. CT Noise-Reduction Methods for Lower-Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction Algorithms and New Techniques. Radiographics 2021; 41:1493-1508. [PMID: 34469209 DOI: 10.1148/rg.2021200196] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Iterative reconstruction (IR) algorithms are the most widely used CT noise-reduction method to improve image quality and have greatly facilitated radiation dose reduction within the radiology community. Various IR methods have different strengths and limitations. Because IR algorithms are typically nonlinear, they can modify spatial resolution and image noise texture in different regions of the CT image; hence traditional image-quality metrics are not appropriate to assess the ability of IR to preserve diagnostic accuracy, especially for low-contrast diagnostic tasks. In this review, the authors highlight emerging IR algorithms and CT noise-reduction techniques and summarize how these techniques can be evaluated to help determine the appropriate radiation dose levels for different diagnostic tasks in CT. In addition to advanced IR techniques, we describe novel CT noise-reduction methods based on convolutional neural networks (CNNs). CNN-based noise-reduction techniques may offer the ability to reduce image noise while maintaining high levels of image detail but may have unique drawbacks. Other novel CT noise-reduction methods are being developed to leverage spatial and/or spectral redundancy in multiphase or multienergy CT. Radiologists and medical physicists should be familiar with these different alternatives to adapt available CT technology for different diagnostic tasks. The scope of this article is (a) to review the clinical applications of IR algorithms as well as their strengths, weaknesses, and methods of assessment and (b) to explore new CT image reconstruction and noise-reduction techniques that promise to facilitate radiation dose reduction. ©RSNA, 2021.
Collapse
Affiliation(s)
- Payam Mohammadinejad
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Achille Mileto
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Lifeng Yu
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Shuai Leng
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Luis S Guimaraes
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Andrew D Missert
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Corey T Jensen
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Hao Gong
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Cynthia H McCollough
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| | - Joel G Fletcher
- From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (P.M., L.Y., S.L., A.D.M., H.G., C.H.M., J.G.F.); Department of Radiology, Harborview Medical Center, Seattle, Wash (A.M.); Department of Medical Imaging, Toronto General Hospital, Toronto, ON, Canada (L.S.G.); and Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.T.J.)
| |
Collapse
|