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Gao Y, Xie H, Chang CW, Peng J, Pan S, Qiu RLJ, Wang T, Ghavidel B, Roper J, Zhou J, Yang X. CT-based synthetic iodine map generation using conditional denoising diffusion probabilistic model. Med Phys 2024. [PMID: 38889368 DOI: 10.1002/mp.17258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 04/17/2024] [Accepted: 06/03/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Iodine maps, derived from image-processing of contrast-enhanced dual-energy computed tomography (DECT) scans, highlight the differences in tissue iodine intake. It finds multiple applications in radiology, including vascular imaging, pulmonary evaluation, kidney assessment, and cancer diagnosis. In radiation oncology, it can contribute to designing more accurate and personalized treatment plans. However, DECT scanners are not commonly available in radiation therapy centers. Additionally, the use of iodine contrast agents is not suitable for all patients, especially those allergic to iodine agents, posing further limitations to the accessibility of this technology. PURPOSE The purpose of this work is to generate synthetic iodine map images from non-contrast single-energy CT (SECT) images using conditional denoising diffusion probabilistic model (DDPM). METHODS One-hundered twenty-six head-and-neck patients' images were retrospectively investigated in this work. Each patient underwent non-contrast SECT and contrast DECT scans. Ground truth iodine maps were generated from contrast DECT scans using commercial software syngo.via installed in the clinic. A conditional DDPM was implemented in this work to synthesize iodine maps. Three-fold cross-validation was conducted, with each iteration selecting the data from 42 patients as the test dataset and the remainder as the training dataset. Pixel-to-pixel generative adversarial network (GAN) and CycleGAN served as reference methods for evaluating the proposed DDPM method. RESULTS The accuracy of the proposed DDPM was evaluated using three quantitative metrics: mean absolute error (MAE) (1.039 ± 0.345 mg/mL), structural similarity index measure (SSIM) (0.89 ± 0.10) and peak signal-to-noise ratio (PSNR) (25.4 ± 3.5 db) respectively. Compared to the reference methods, the proposed technique showcased superior performance across the evaluated metrics, further validated by the paired two-tailed t-tests. CONCLUSION The proposed conditional DDPM framework has demonstrated the feasibility of generating synthetic iodine map images from non-contrast SECT images. This method presents a potential clinical application, which is providing accurate iodine contrast map in instances where only non-contrast SECT is accessible.
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Affiliation(s)
- Yuan Gao
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Huiqiao Xie
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Chih-Wei Chang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Junbo Peng
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Shaoyan Pan
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Richard L J Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Tonghe Wang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Beth Ghavidel
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
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Pinnock MA, Hu Y, Bandula S, Barratt DC. Time conditioning for arbitrary contrast phase generation in interventional computed tomography. Phys Med Biol 2024; 69:115010. [PMID: 38697200 PMCID: PMC11103281 DOI: 10.1088/1361-6560/ad46dd] [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: 12/19/2023] [Revised: 04/16/2024] [Accepted: 05/01/2024] [Indexed: 05/04/2024]
Abstract
Minimally invasive ablation techniques for renal cancer are becoming more popular due to their low complication rate and rapid recovery period. Despite excellent visualisation, one drawback of the use of computed tomography (CT) in these procedures is the requirement for iodine-based contrast agents, which are associated with adverse reactions and require a higher x-ray dose. The purpose of this work is to examine the use of time information to generate synthetic contrast enhanced images at arbitrary points after contrast agent injection from non-contrast CT images acquired during renal cryoablation cases. To achieve this, we propose a new method of conditioning generative adversarial networks with normalised time stamps and demonstrate that the use of a HyperNetwork is feasible for this task, generating images of competitive quality compared to standard generative modelling techniques. We also show that reducing the receptive field can help tackle challenges in interventional CT data, offering significantly better image quality as well as better performance when generating images for a downstream segmentation task. Lastly, we show that all proposed models are robust enough to perform inference on unseen intra-procedural data, while also improving needle artefacts and generalising contrast enhancement to other clinically relevant regions and features.
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Affiliation(s)
- Mark A Pinnock
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Yipeng Hu
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Steve Bandula
- Centre for Medical Imaging, Division of Medicine, University College London, London, United Kingdom
- Department of Interventional Radiology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Dean C Barratt
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
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Glielmo P, Fusco S, Gitto S, Zantonelli G, Albano D, Messina C, Sconfienza LM, Mauri G. Artificial intelligence in interventional radiology: state of the art. Eur Radiol Exp 2024; 8:62. [PMID: 38693468 PMCID: PMC11063019 DOI: 10.1186/s41747-024-00452-2] [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: 09/28/2023] [Accepted: 02/26/2024] [Indexed: 05/03/2024] Open
Abstract
Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.
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Affiliation(s)
- Pierluigi Glielmo
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy.
| | - Stefano Fusco
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
| | - Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Giulia Zantonelli
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
- Dipartimento di Scienze Biomediche, Chirurgiche ed Odontoiatriche, Università degli Studi di Milano, Via della Commenda, 10, 20122, Milan, Italy
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Mangiagalli, 31, 20133, Milan, Italy
- IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso, 173, 20157, Milan, Italy
| | - Giovanni Mauri
- Divisione di Radiologia Interventistica, IEO, IRCCS Istituto Europeo di Oncologia, Milan, Italy
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Azarfar G, Ko SB, Adams SJ, Babyn PS. Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review. Int J Comput Assist Radiol Surg 2023; 18:1903-1914. [PMID: 36947337 DOI: 10.1007/s11548-023-02862-w] [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: 09/16/2022] [Accepted: 02/27/2023] [Indexed: 03/23/2023]
Abstract
PURPOSE The usage of iodinated contrast media (ICM) can improve the sensitivity and specificity of computed tomography (CT) for many clinical indications. However, the adverse effects of ICM administration can include renal injury, life-threatening allergic-like reactions, and environmental contamination. Deep learning (DL) models can generate full-dose ICM CT images from non-contrast or low-dose ICM administration or generate non-contrast CT from full-dose ICM CT. Eliminating the need for both contrast-enhanced and non-enhanced imaging or reducing the amount of required contrast while maintaining diagnostic capability may reduce overall patient risk, improve efficiency and minimize costs. We reviewed the current capabilities of DL to reduce the need for contrast administration in CT. METHODS We conducted a systematic review of articles utilizing DL to reduce the amount of ICM required in CT, searching MEDLINE, Embase, Compendex, Inspec, and Scopus to identify papers published from 2016 to 2022. We classified the articles based on the DL model and ICM reduction. RESULTS Eighteen papers met the inclusion criteria for analysis. Of these, ten generated synthetic full-dose (100%) ICM from real non-contrast CT, while four augmented low-dose to full-dose ICM CT. Three used DL to create synthetic non-contrast CT from real 100% ICM CT, while one paper used DL to translate the 100% ICM to non-contrast CT and vice versa. DL models commonly used generative adversarial networks trained and tested by paired contrast-enhanced and non-contrast or low ICM CTs. Image quality metrics such as peak signal-to-noise ratio and structural similarity index were frequently used for comparing synthetic versus real CT image quality. CONCLUSION DL-generated contrast-enhanced or non-contrast CT may assist in diagnosis and radiation therapy planning; however, further work to optimize protocols to reduce or eliminate ICM for specific pathology is still needed along with a dedicated assessment of the clinical utility of these synthetic images.
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Affiliation(s)
- Ghazal Azarfar
- Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK, Canada.
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada.
| | - Seok-Bum Ko
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Scott J Adams
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Paul S Babyn
- Department of Medical Imaging, University of Saskatchewan, Saskatoon, SK, Canada
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Potočnik J, Foley S, Thomas E. Current and potential applications of artificial intelligence in medical imaging practice: A narrative review. J Med Imaging Radiat Sci 2023; 54:376-385. [PMID: 37062603 DOI: 10.1016/j.jmir.2023.03.033] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/25/2023] [Accepted: 03/28/2023] [Indexed: 04/18/2023]
Abstract
BACKGROUND AND PURPOSE Artificial intelligence (AI) is present in many areas of our lives. Much of the digital data generated in health care can be used for building automated systems to bring improvements to existing workflows and create a more personalised healthcare experience for patients. This review outlines select current and potential AI applications in medical imaging practice and provides a view of how diagnostic imaging suites will operate in the future. Challenges associated with potential applications will be discussed and healthcare staff considerations necessary to benefit from AI-enabled solutions will be outlined. METHODS Several electronic databases, including PubMed, ScienceDirect, Google Scholar, and University College Dublin Library Database, were used to identify relevant articles with a Boolean search strategy. Textbooks, government sources and vendor websites were also considered. RESULTS/DISCUSSION Many AI-enabled solutions in radiographic practice are available with more automation on the horizon. Traditional workflow will become faster, more effective, and more user friendly. AI can handle administrative or technical types of work, meaning it is applicable across all aspects of medical imaging practice. CONCLUSION AI offers significant potential to automate most of the manual tasks, ensure service consistency, and improve patient care. Radiographers, radiation therapists, and clinicians should ensure they have adequate understanding of the technology to enable ethical oversight of its implementation.
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Affiliation(s)
- Jaka Potočnik
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland.
| | - Shane Foley
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland
| | - Edel Thomas
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland
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Pasquini L, Napolitano A, Pignatelli M, Tagliente E, Parrillo C, Nasta F, Romano A, Bozzao A, Di Napoli A. Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media. Pharmaceutics 2022; 14:pharmaceutics14112378. [PMID: 36365197 PMCID: PMC9695136 DOI: 10.3390/pharmaceutics14112378] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022] Open
Abstract
Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of 'virtual' and 'augmented' contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media.
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Affiliation(s)
- Luca Pasquini
- Neuroradiology Unit, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy
- Correspondence:
| | - Matteo Pignatelli
- Radiology Department, Castelli Hospital, Via Nettunense Km 11.5, 00040 Ariccia, Italy
| | - Emanuela Tagliente
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy
| | - Chiara Parrillo
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy
| | - Francesco Nasta
- Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, Piazza di Sant’Onofrio, 4, 00165 Rome, Italy
| | - Andrea Romano
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Alessandro Bozzao
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy
| | - Alberto Di Napoli
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Via di Grottarossa 1035, 00189 Rome, Italy
- Neuroimaging Lab, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
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