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Porter EM, Myziuk NK, Quinn TJ, Lozano D, Peterson AB, Quach DM, Siddiqui ZA, Guerrero TM. Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning. Phys Med Biol 2021; 66. [PMID: 34293726 DOI: 10.1088/1361-6560/ac16ec] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 07/22/2021] [Indexed: 01/14/2023]
Abstract
Purpose.To develop and evaluate the performance of a deep learning model to generate synthetic pulmonary perfusion images from clinical 4DCT images for patients undergoing radiotherapy for lung cancer.Methods. A clinical data set of 58 pre- and post-radiotherapy99mTc-labeled MAA-SPECT perfusion studies (32 patients) each with contemporaneous 4DCT studies was collected. Using the inhale and exhale phases of the 4DCT, a 3D-residual network was trained to create synthetic perfusion images utilizing the MAA-SPECT as ground truth. The training process was repeated for a 50-imaging study, five-fold validation with twenty model instances trained per fold. The highest performing model instance from each fold was selected for inference upon the eight-study test set. A manual lung segmentation was used to compute correlation metrics constrained to the voxels within the lungs. From the pre-treatment test cases (N = 5), 50th percentile contours of well-perfused lung were generated from both the clinical and synthetic perfusion images and the agreement was quantified.Results. Across the hold-out test set, our deep learning model predicted perfusion with a Spearman correlation coefficient of 0.70 (IQR: 0.61-0.76) and a Pearson correlation coefficient of 0.66 (IQR: 0.49-0.73). The agreement of the functional avoidance contour pairs was Dice of 0.803 (IQR: 0.750-0.810) and average surface distance of 5.92 mm (IQR: 5.68-7.55).Conclusion. We demonstrate that from 4DCT alone, a deep learning model can generate synthetic perfusion images with potential application in functional avoidance treatment planning.
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Affiliation(s)
- Evan M Porter
- Department of Medical Physics, Wayne State University, Detroit, MI, United States of America.,Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Department of Radiation Oncology, UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
| | - Nicholas K Myziuk
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, United States of America
| | - Thomas J Quinn
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, United States of America
| | - Daniela Lozano
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Oakland University William Beaumont School of Medicine, Oakland University, Rochester, MI, United States of America
| | - Avery B Peterson
- Department of Medical Physics, Wayne State University, Detroit, MI, United States of America.,Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America
| | - Duyen M Quach
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Oakland University William Beaumont School of Medicine, Oakland University, Rochester, MI, United States of America
| | - Zaid A Siddiqui
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Department of Radiation Oncology, UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
| | - Thomas M Guerrero
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, United States of America.,Oakland University William Beaumont School of Medicine, Oakland University, Rochester, MI, United States of America
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Arabi H, Zaidi H. Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy. Eur J Hybrid Imaging 2020; 4:17. [PMID: 34191161 PMCID: PMC8218135 DOI: 10.1186/s41824-020-00086-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 08/10/2020] [Indexed: 12/22/2022] Open
Abstract
This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and radiation therapy research. To this end, the applications of artificial intelligence in five generic fields of molecular imaging and radiation therapy, including PET instrumentation design, PET image reconstruction quantification and segmentation, image denoising (low-dose imaging), radiation dosimetry and computer-aided diagnosis, and outcome prediction are discussed. This review sets out to cover briefly the fundamental concepts of AI and deep learning followed by a presentation of seminal achievements and the challenges facing their adoption in clinical setting.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
- Geneva University Neurocenter, Geneva University, CH-1205, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700, Groningen, RB, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, 500, Odense, Denmark.
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Gong K, Berg E, Cherry SR, Qi J. Machine Learning in PET: from Photon Detection to Quantitative Image Reconstruction. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:51-68. [PMID: 38045770 PMCID: PMC10691821 DOI: 10.1109/jproc.2019.2936809] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Machine learning has found unique applications in nuclear medicine from photon detection to quantitative image reconstruction. While there have been impressive strides in detector development for time-of-flight positron emission tomography, most detectors still make use of simple signal processing methods to extract the time and position information from the detector signals. Now with the availability of fast waveform digitizers, machine learning techniques have been applied to estimate the position and arrival time of high-energy photons. In quantitative image reconstruction, machine learning has been used to estimate various corrections factors, including scattered events and attenuation images, as well as to reduce statistical noise in reconstructed images. Here machine learning either provides a faster alternative to an existing time-consuming computation, such as in the case of scatter estimation, or creates a data-driven approach to map an implicitly defined function, such as in the case of estimating the attenuation map for PET/MR scans. In this article, we will review the abovementioned applications of machine learning in nuclear medicine.
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Affiliation(s)
- Kuang Gong
- Department of Biomedical Engineering, University of California, Davis, CA, USA and is now with Massachusetts General Hospital, Boston, MA, USA
| | - Eric Berg
- Department of Biomedical Engineering, University of California, Davis, CA, USA
| | - Simon R. Cherry
- Department of Biomedical Engineering and Department of Radiology, University of California, Davis, CA, USA
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California, Davis, CA, USA
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