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Chavan R, Hyman G, Qureshi Z, Jayakumar N, Terrell W, Wardius M, Berr S, Schiff D, Fountain N, Eluvathingal Muttikkal T, Quigg M, Zhang M, K Kundu B. An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping. Biomed Phys Eng Express 2024; 10:055028. [PMID: 39094595 PMCID: PMC11333809 DOI: 10.1088/2057-1976/ad6a64] [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: 04/15/2024] [Revised: 07/13/2024] [Accepted: 08/02/2024] [Indexed: 08/04/2024]
Abstract
Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This research introduces a novel approach employing non-invasive deep learning model-based computations from the internal carotid arteries (ICA) with partial volume (PV) corrections, thereby eliminating the need for invasive arterial sampling. We present an end-to-end pipeline incorporating a 3D U-Net based ICA-net for ICA segmentation, alongside a Recurrent Neural Network (RNN) based MCIF-net for the derivation of a model-corrected blood input function (MCIF) with PV corrections. The developed 3D U-Net and RNN was trained and validated using a 5-fold cross-validation approach on 50 human brain FDG PET scans. The ICA-net achieved an average Dice score of 82.18% and an Intersection over Union of 68.54% across all tested scans. Furthermore, the MCIF-net exhibited a minimal root mean squared error of 0.0052. The application of this pipeline to ground truth data for dFDG-PET brain scans resulted in the precise localization of seizure onset regions, which contributed to a successful clinical outcome, with the patient achieving a seizure-free state after treatment. These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure's distribution and automating MCIF computation with PV corrections. This advancement marks a significant leap in non-invasive neuroimaging.
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Affiliation(s)
- Rugved Chavan
- Department of Computer Science and Engineering, University of Virginia, Charlottesville, VA, United States of America
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
| | - Gabriel Hyman
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America
| | - Zoraiz Qureshi
- Department of Computer Science and Engineering, University of Virginia, Charlottesville, VA, United States of America
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
| | - Nivetha Jayakumar
- Department of Computer Science and Engineering, University of Virginia, Charlottesville, VA, United States of America
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
| | - William Terrell
- Department of Computer Science and Engineering, University of Virginia, Charlottesville, VA, United States of America
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
| | - Megan Wardius
- Brain Institute, University of Virginia, Charlottesville, VA, United States of America
| | - Stuart Berr
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America
| | - David Schiff
- Department of Neurology, University of Virginia, Charlottesville, VA, United States of America
| | - Nathan Fountain
- Department of Neurology, University of Virginia, Charlottesville, VA, United States of America
| | | | - Mark Quigg
- Department of Neurology, University of Virginia, Charlottesville, VA, United States of America
| | - Miaomiao Zhang
- Department of Computer Science and Engineering, University of Virginia, Charlottesville, VA, United States of America
| | - Bijoy K Kundu
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America
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Eidex Z, Ding Y, Wang J, Abouei E, Qiu RL, Liu T, Wang T, Yang X. Deep Learning in MRI-guided Radiation Therapy: A Systematic Review. ARXIV 2023:arXiv:2303.11378v2. [PMID: 36994167 PMCID: PMC10055493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed with emphasis placed on underlying methods. Studies are further categorized into the areas of segmentation, synthesis, radiomics, and real time MRI. Finally, clinical implications, current challenges, and future directions are discussed.
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Affiliation(s)
- Zach Eidex
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
| | - Yifu Ding
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Jing Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Elham Abouei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Richard L.J. Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
| | - Tian Liu
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Tonghe Wang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA
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Mackay K, Bernstein D, Glocker B, Kamnitsas K, Taylor A. A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy. Clin Oncol (R Coll Radiol) 2023; 35:354-369. [PMID: 36803407 DOI: 10.1016/j.clon.2023.01.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 12/05/2022] [Accepted: 01/23/2023] [Indexed: 02/01/2023]
Abstract
Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of consensus on how to assess and validate auto-contouring systems currently limits clinical use. This review formally quantifies the assessment metrics used in studies published during one calendar year and assesses the need for standardised practice. A PubMed literature search was undertaken for papers evaluating radiotherapy auto-contouring published during 2021. Papers were assessed for types of metric and the methodology used to generate ground-truth comparators. Our PubMed search identified 212 studies, of which 117 met the criteria for clinical review. Geometric assessment metrics were used in 116 of 117 studies (99.1%). This includes the Dice Similarity Coefficient used in 113 (96.6%) studies. Clinically relevant metrics, such as qualitative, dosimetric and time-saving metrics, were less frequently used in 22 (18.8%), 27 (23.1%) and 18 (15.4%) of 117 studies, respectively. There was heterogeneity within each category of metric. Over 90 different names for geometric measures were used. Methods for qualitative assessment were different in all but two papers. Variation existed in the methods used to generate radiotherapy plans for dosimetric assessment. Consideration of editing time was only given in 11 (9.4%) papers. A single manual contour as a ground-truth comparator was used in 65 (55.6%) studies. Only 31 (26.5%) studies compared auto-contours to usual inter- and/or intra-observer variation. In conclusion, significant variation exists in how research papers currently assess the accuracy of automatically generated contours. Geometric measures are the most popular, however their clinical utility is unknown. There is heterogeneity in the methods used to perform clinical assessment. Considering the different stages of system implementation may provide a framework to decide the most appropriate metrics. This analysis supports the need for a consensus on the clinical implementation of auto-contouring.
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Affiliation(s)
- K Mackay
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK.
| | - D Bernstein
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
| | - B Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - K Kamnitsas
- Department of Computing, Imperial College London, South Kensington Campus, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK
| | - A Taylor
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
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Dang K, Vo T, Ngo L, Ha H. A deep learning framework integrating MRI image preprocessing methods for brain tumor segmentation and classification. IBRO Neurosci Rep 2022; 13:523-532. [PMID: 36590099 PMCID: PMC9795279 DOI: 10.1016/j.ibneur.2022.10.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022] Open
Abstract
Glioma grading is critical in treatment planning and prognosis. This study aims to address this issue through MRI-based classification to develop an accurate model for glioma diagnosis. Here, we employed a deep learning pipeline with three essential steps: (1) MRI images were segmented using preprocessing approaches and UNet architecture, (2) brain tumor regions were extracted using segmentation, then (3) high-grade gliomas and low-grade gliomas were classified using the VGG and GoogleNet implementations. Among the additional preprocessing techniques used in conjunction with the segmentation task, the combination of data augmentation and Window Setting Optimization was found to be the most effective tool, resulting in the Dice coefficient of 0.82, 0.91, and 0.72 for enhancing tumor, whole tumor, and tumor core, respectively. While most of the proposed models achieve comparable accuracies of about 93 % on the testing dataset, the pipeline of VGG combined with UNet segmentation obtains the highest accuracy of 97.44 %. In conclusion, the presented architecture illustrates a realistic model for detecting gliomas; moreover, it emphasizes the significance of data augmentation and segmentation in improving model performance.
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Affiliation(s)
- Khiet Dang
- School of Biomedical Engineering, International University, Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, Viet Nam
- Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, Viet Nam
| | - Toi Vo
- School of Biomedical Engineering, International University, Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, Viet Nam
- Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, Viet Nam
| | - Lua Ngo
- School of Biomedical Engineering, International University, Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, Viet Nam
- Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, Viet Nam
- Corresponding authors at: School of Biomedical Engineering, International University, Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, Viet Nam.
| | - Huong Ha
- School of Biomedical Engineering, International University, Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, Viet Nam
- Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, Viet Nam
- Corresponding authors at: School of Biomedical Engineering, International University, Vietnam National University – Ho Chi Minh City, Ho Chi Minh City, Viet Nam.
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