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Kim H, Lee H, Lee S, Choi YW, Choi YJ, Kim KH, Seo W, Shin CW, Cho S. A feasibility study on deep-neural-network-based dose-neutral dual-energy digital breast tomosynthesis. Med Phys 2023; 50:791-807. [PMID: 36273397 DOI: 10.1002/mp.16071] [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: 12/28/2021] [Revised: 08/01/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Diagnostic performance based on x-ray breast imaging is subject to breast density. Although digital breast tomosynthesis (DBT) is reported to outperform conventional mammography in denser breasts, mass detection and malignancy characterization are often considered challenging yet. PURPOSE As an improved diagnostic solution to the dense breast cases, we propose a dual-energy DBT imaging technique that enables breast compositional imaging at comparable scanning time and patient dose compared to the conventional single-energy DBT. METHODS The proposed dual-energy DBT acquires projection data by alternating two different energy spectra. Then, we synthesize unmeasured projection data using a deep neural network that exploits the measured projection data and adjacent projection data obtained under the other x-ray energy spectrum. For material decomposition, we estimate partial path lengths of an x-ray through water, lipid, and protein from the measured and the synthesized projection data with the object thickness information. After material decomposition in the projection domain, we reconstruct material-selective DBT images. The deep neural network is trained with the numerical breast phantoms. A pork meat phantom is scanned with a prototype dual-energy DBT system to demonstrate the feasibility of the proposed imaging method. RESULTS The developed deep neural network successfully synthesized missing projections. Material-selective images reconstructed from the synthesized data present comparable compositional contrast of the cancerous masses compared with those from the fully measured data. CONCLUSIONS The proposed dual-energy DBT scheme is expected to substantially contribute to enhancing mass malignancy detection accuracy particularly in dense breasts.
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Affiliation(s)
- Hyeongseok Kim
- KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Hoyeon Lee
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Seoyoung Lee
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Young-Wook Choi
- Korea Electrotechnology Research Institute (KERI), Ansan, South Korea
| | - Young Jin Choi
- Korea Electrotechnology Research Institute (KERI), Ansan, South Korea
| | - Kee Hyun Kim
- Korea Electrotechnology Research Institute (KERI), Ansan, South Korea
| | | | | | - Seungryong Cho
- KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.,Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.,KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.,KAIST Institute for IT Convergence, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
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Habibalahi A, Bala C, Allende A, Anwer AG, Goldys EM. Novel automated non invasive detection of ocular surface squamous neoplasia using multispectral autofluorescence imaging. Ocul Surf 2019; 17:540-550. [PMID: 30904597 DOI: 10.1016/j.jtos.2019.03.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/28/2019] [Accepted: 03/12/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE Diagnosing Ocular surface squamous neoplasia (OSSN) using newly designed multispectral imaging technique. METHODS Eighteen patients with histopathological diagnosis of Ocular Surface Squamous Neoplasia (OSSN) were recruited. Their previously collected biopsy specimens of OSSN were reprocessed without staining to obtain auto fluorescence multispectral microscopy images. This technique involved a custom-built spectral imaging system with 38 spectral channels. Inter and intra-patient frameworks were deployed to automatically detect and delineate OSSN using machine learning methods. Different machine learning methods were evaluated, with K nearest neighbor and Support Vector Machine chosen as preferred classifiers for intra- and inter-patient frameworks, respectively. The performance of the technique was evaluated against a pathological assessment. RESULTS Quantitative analysis of the spectral images provided a strong multispectral signature of a relative difference between neoplastic and normal tissue both within each patient (at p < 0.0005) and between patients (at p < 0.001). Our fully automated diagnostic method based on machine learning produces maps of the relatively well circumscribed neoplastic-non neoplastic interface. Such maps can be rapidly generated in quasi-real time and used for intraoperative assessment. Generally, OSSN could be detected using multispectral analysis in all patients investigated here. The cancer margins detected by multispectral analysis were in close and reasonable agreement with the margins observed in the H&E sections in intra- and inter-patient classification, respectively. CONCLUSIONS This study shows the feasibility of using multispectral auto-fluorescence imaging to detect and find the boundary of human OSSN. Fully automated analysis of multispectral images based on machine learning methods provides a promising diagnostic tool for OSSN which can be translated to future clinical applications.
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Affiliation(s)
- Abbas Habibalahi
- ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, North Ryde, NSW, 2109, Australia; School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, 2109, Australia; Graduate School of Biomedical Engineering, University of New South Wales, Sydney, 2032, NSW, Australia.
| | - Chandra Bala
- Department of Ophthalmology, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Alexandra Allende
- Douglass Hanly Moir Pathology, Macquarie Park, Sydney, NSW, 2113, Australia; Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Ayad G Anwer
- ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, North Ryde, NSW, 2109, Australia; Graduate School of Biomedical Engineering, University of New South Wales, Sydney, 2032, NSW, Australia
| | - Ewa M Goldys
- ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, North Ryde, NSW, 2109, Australia; Graduate School of Biomedical Engineering, University of New South Wales, Sydney, 2032, NSW, Australia.
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Drukker K, Giger ML, Joe BN, Kerlikowske K, Greenwood H, Drukteinis JS, Niell B, Fan B, Malkov S, Avila J, Kazemi L, Shepherd J. Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set. Radiology 2018; 290:621-628. [PMID: 30526359 DOI: 10.1148/radiol.2018180608] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Purpose To investigate the combination of mammography radiomics and quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnecessary benign breast biopsies. Materials and Methods For this prospective study, dual-energy craniocaudal and mediolateral oblique mammograms were obtained immediately before biopsy in 109 women (mean age, 51 years; range, 31-85 years) with Breast Imaging Reporting and Data System category 4 or 5 breast masses (35 invasive cancers, 74 benign) from 2013 through 2017. The three quantitative compartments of water, lipid, and protein thickness at each pixel were calculated from the attenuation at high and low energy by using a within-image phantom. Masses were automatically segmented and features were extracted from the low-energy mammograms and the quantitative compartment images. Tenfold cross-validations using a linear discriminant classifier with predefined feature signatures helped differentiate between malignant and benign masses by means of (a) water-lipid-protein composition images alone, (b) mammography radiomics alone, and (c) a combined image analysis of both. Positive predictive value of biopsy performed (PPV3) at maximum sensitivity was the primary performance metric, and results were compared with those for conventional diagnostic digital mammography. Results The PPV3 for conventional diagnostic digital mammography in our data set was 32.1% (35 of 109; 95% confidence interval [CI]: 23.9%, 41.3%), with a sensitivity of 100%. In comparison, combined mammography radiomics plus quantitative 3CB image analysis had PPV3 of 49% (34 of 70; 95% CI: 36.5%, 58.9%; P < .001), with a sensitivity of 97% (34 of 35; 95% CI: 90.3%, 100%; P < .001) and 35.8% (39 of 109) fewer total biopsies (P < .001). Conclusion Quantitative three-compartment breast image analysis of breast masses combined with mammography radiomics has the potential to reduce unnecessary breast biopsies. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Karen Drukker
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Maryellen L Giger
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Bonnie N Joe
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Karla Kerlikowske
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Heather Greenwood
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Jennifer S Drukteinis
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Bethany Niell
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Bo Fan
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Serghei Malkov
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Jesus Avila
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - Leila Kazemi
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
| | - John Shepherd
- From the Department of Radiology, University of Chicago, 5481 S Maryland Ave, MC2026, Chicago, IL 60637 (K.D., M.L.G.); Department of Radiology and Biomedical Imaging (B.N.J., H.G., B.F., S.M., J.A., L.K., J.S.) and Department of Medicine and Epidemiology (K.K.), University of California, San Francisco, San Francisco, Calif; and Department of Diagnostic Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Fla (J.S.D., B.N.)
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