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Bugler H, Berto R, Souza R, Harris AD. Frequency and phase correction of GABA-edited magnetic resonance spectroscopy using complex-valued convolutional neural networks. Magn Reson Imaging 2024; 111:186-195. [PMID: 38744351 DOI: 10.1016/j.mri.2024.05.008] [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: 10/18/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/16/2024]
Abstract
PURPOSE To determine the significance of complex-valued inputs and complex-valued convolutions compared to real-valued inputs and real-valued convolutions in convolutional neural networks (CNNs) for frequency and phase correction (FPC) of GABA-edited magnetic resonance spectroscopy (MRS) data. METHODS An ablation study using simulated data was performed to determine the most effective input (real or complex) and convolution type (real or complex) to predict frequency and phase shifts in GABA-edited MEGA-PRESS data using CNNs. The best CNN model was subsequently compared using both simulated and in vivo data to two recently proposed deep learning (DL) methods for FPC of GABA-edited MRS. All methods were trained using the same experimental setup and evaluated using the signal-to-noise ratio (SNR) and linewidth of the GABA peak, choline artifact, and by visually assessing the reconstructed final difference spectrum. Statistical significance was assessed using the Wilcoxon signed rank test. RESULTS The ablation study showed that using complex values for the input represented by real and imaginary channels in our model input tensor, with complex convolutions was most effective for FPC. Overall, in the comparative study using simulated data, our CC-CNN model (that received complex-valued inputs with complex convolutions) outperformed the other models as evaluated by the mean absolute error. CONCLUSION Our results indicate that the optimal CNN configuration for GABA-edited MRS FPC uses a complex-valued input and complex convolutions. Overall, this model outperformed existing DL models.
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Affiliation(s)
- Hanna Bugler
- Department of Biomedical Engineering, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary,Canada; Alberta Children's Hospital Research Institute, University of Calgary, Canada.
| | - Rodrigo Berto
- Department of Biomedical Engineering, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary,Canada; Alberta Children's Hospital Research Institute, University of Calgary, Canada
| | - Roberto Souza
- Hotchkiss Brain Institute, University of Calgary,Canada; Department of Electrical and Software Engineering, University of Calgary, Canada
| | - Ashley D Harris
- Department of Radiology, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary,Canada; Alberta Children's Hospital Research Institute, University of Calgary, Canada
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2
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Safari M, Eidex Z, Chang CW, Qiu RL, Yang X. Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review. ARXIV 2024:arXiv:2405.00241v1. [PMID: 38745700 PMCID: PMC11092677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Magnetic resonance imaging (MRI) has revolutionized medical imaging, providing a non-invasive and highly detailed look into the human body. However, the long acquisition times of MRI present challenges, causing patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, researchers are exploring various techniques to reduce acquisition time and improve the overall efficiency of MRI. One such technique is compressed sensing (CS), which reduces data acquisition by leveraging image sparsity in transformed spaces. In recent years, deep learning (DL) has been integrated with CS-MRI, leading to a new framework that has seen remarkable growth. DL-based CS-MRI approaches are proving to be highly effective in accelerating MR imaging without compromising image quality. This review comprehensively examines DL-based CS-MRI techniques, focusing on their role in increasing MR imaging speed. We provide a detailed analysis of each category of DL-based CS-MRI including end-to-end, unroll optimization, self-supervised, and federated learning. Our systematic review highlights significant contributions and underscores the exciting potential of DL in CS-MRI. Additionally, our systematic review efficiently summarizes key results and trends in DL-based CS-MRI including quantitative metrics, the dataset used, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based CS-MRI in the advancement of medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based CS-MRI publications and publicly available datasets - https://github.com/mosaf/Awesome-DL-based-CS-MRI.
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Affiliation(s)
- Mojtaba Safari
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Zach Eidex
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Chih-Wei Chang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Richard L.J. Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America
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Dubljevic N, Moore S, Lauzon ML, Souza R, Frayne R. Effect of MR head coil geometry on deep-learning-based MR image reconstruction. Magn Reson Med 2024. [PMID: 38647191 DOI: 10.1002/mrm.30130] [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: 09/30/2023] [Revised: 04/02/2024] [Accepted: 04/07/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE To investigate whether parallel imaging-imposed geometric coil constraints can be relaxed when using a deep learning (DL)-based image reconstruction method as opposed to a traditional non-DL method. THEORY AND METHODS Traditional and DL-based MR image reconstruction approaches operate in fundamentally different ways: Traditional methods solve a system of equations derived from the image data whereas DL methods use data/target pairs to learn a generalizable reconstruction model. Two sets of head coil profiles were evaluated: (1) 8-channel and (2) 32-channel geometries. A DL model was compared to conjugate gradient SENSE (CG-SENSE) and L1-wavelet compressed sensing (CS) through quantitative metrics and visual assessment as coil overlap was increased. RESULTS Results were generally consistent between experiments. As coil overlap increased, there was a significant (p < 0.001) decrease in performance in most cases for all methods. The decrease was most pronounced for CG-SENSE, and the DL models significantly outperformed (p < 0.001) their non-DL counterparts in all scenarios. CS showed improved robustness to coil overlap and signal-to-noise ratio (SNR) versus CG-SENSE, but had quantitatively and visually poorer reconstructions characterized by blurriness as compared to DL. DL showed virtually no change in performance across SNR and very small changes across coil overlap. CONCLUSION The DL image reconstruction method produced images that were robust to coil overlap and of higher quality than CG-SENSE and CS. This suggests that geometric coil design constraints can be relaxed when using DL reconstruction methods.
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Affiliation(s)
- Natalia Dubljevic
- Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Stephen Moore
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- O'Brien Centre for the Health Sciences, Cumming School of Medicine, Calgary, Alberta, Canada
| | - Michel Louis Lauzon
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Radiology and Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
| | - Roberto Souza
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Richard Frayne
- Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Radiology and Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada
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4
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Berto RP, Bugler H, Dias G, Oliveira M, Ueda L, Dertkigil S, Costa PDP, Rittner L, Merkofer JP, van de Sande DMJ, Amirrajab S, Drenthen GS, Veta M, Jansen JFA, Breeuwer M, van Sloun RJG, Qayyum A, Rodero C, Niederer S, Souza R, Harris AD. Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01156-9. [PMID: 38613715 DOI: 10.1007/s10334-024-01156-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 02/16/2024] [Accepted: 03/11/2024] [Indexed: 04/15/2024]
Abstract
PURPOSE Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan. METHODS There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data. RESULTS Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics. CONCLUSION DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.
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Affiliation(s)
- Rodrigo Pommot Berto
- Department of Biomedical Engineering, University of Calgary, Calgary, Canada
- Department of Radiology, University of Calgary, Calgary, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
| | - Hanna Bugler
- Department of Biomedical Engineering, University of Calgary, Calgary, Canada.
- Department of Radiology, University of Calgary, Calgary, Canada.
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada.
| | - Gabriel Dias
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Mateus Oliveira
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Lucas Ueda
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
- Research and Development Center in Telecommunications, CPQD, Campinas, Brazil
| | - Sergio Dertkigil
- School of Medical Sciences, University of Campinas, Campinas, Brazil
| | - Paula D P Costa
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
- Artificial Intelligence Lab., Recod.Ai, University of Campinas, Campinas, Brazil
| | - Leticia Rittner
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Julian P Merkofer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Dennis M J van de Sande
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Sina Amirrajab
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Gerhard S Drenthen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands
| | - Mitko Veta
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Marcel Breeuwer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- MR R&D-Clinical Science, Philips Healthcare, Best, Netherlands
| | - Ruud J G van Sloun
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Abdul Qayyum
- National Heart & Lung Institute, Imperial College London, London, UK
| | - Cristobal Rodero
- National Heart & Lung Institute, Imperial College London, London, UK
| | - Steven Niederer
- National Heart & Lung Institute, Imperial College London, London, UK
- The Alan Turing Institute, London, UK
| | - Roberto Souza
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
- Department of Electrical and Software Engineering, University of Calgary, Calgary, Canada
| | - Ashley D Harris
- Department of Radiology, University of Calgary, Calgary, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
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Wang B, Lian Y, Xiong X, Zhou H, Liu Z, Zhou X. DCT-net: Dual-domain cross-fusion transformer network for MRI reconstruction. Magn Reson Imaging 2024; 107:69-79. [PMID: 38237693 DOI: 10.1016/j.mri.2024.01.007] [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: 09/20/2023] [Revised: 12/26/2023] [Accepted: 01/14/2024] [Indexed: 01/22/2024]
Abstract
Current challenges in Magnetic Resonance Imaging (MRI) include long acquisition times and motion artifacts. To address these issues, under-sampled k-space acquisition has gained popularity as a fast imaging method. However, recovering fine details from under-sampled data remains challenging. In this study, we introduce a pioneering deep learning approach, namely DCT-Net, designed for dual-domain MRI reconstruction. DCT-Net seamlessly integrates information from the image domain (IRM) and frequency domain (FRM), utilizing a novel Cross Attention Block (CAB) and Fusion Attention Block (FAB). These innovative blocks enable precise feature extraction and adaptive fusion across both domains, resulting in a significant enhancement of the reconstructed image quality. The adaptive interaction and fusion mechanisms of CAB and FAB contribute to the method's effectiveness in capturing distinctive features and optimizing image reconstruction. Comprehensive ablation studies have been conducted to assess the contributions of these modules to reconstruction quality and accuracy. Experimental results on the FastMRI (2023) and Calgary-Campinas datasets (2021) demonstrate the superiority of our MRI reconstruction framework over other typical methods (most are illustrated in 2023 or 2022) in both qualitative and quantitative evaluations. This holds for knee and brain datasets under 4× and 8× accelerated imaging scenarios.
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Affiliation(s)
- Bin Wang
- National Institute of Metrology, Beijing 100029, China; Key Laboratory of Metrology Digitalization and Digital Metrology for State Market Regulation, Beijing 100029, China; School of Printing and Packaging Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
| | - Yusheng Lian
- School of Printing and Packaging Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
| | - Xingchuang Xiong
- National Institute of Metrology, Beijing 100029, China; Key Laboratory of Metrology Digitalization and Digital Metrology for State Market Regulation, Beijing 100029, China.
| | - Han Zhou
- School of Printing and Packaging Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
| | - Zilong Liu
- National Institute of Metrology, Beijing 100029, China; Key Laboratory of Metrology Digitalization and Digital Metrology for State Market Regulation, Beijing 100029, China.
| | - Xiaohao Zhou
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China.
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6
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Moriakov N, Sonke JJ, Teuwen J. End-to-end memory-efficient reconstruction for cone beam CT. Med Phys 2023; 50:7579-7593. [PMID: 37846969 DOI: 10.1002/mp.16779] [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: 02/07/2023] [Revised: 07/28/2023] [Accepted: 08/08/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND Cone beam computed tomography (CBCT) plays an important role in many medical fields nowadays. Unfortunately, the potential of this imaging modality is hampered by lower image quality compared to the conventional CT, and producing accurate reconstructions remains challenging. A lot of recent research has been directed towards reconstruction methods relying on deep learning, which have shown great promise for various imaging modalities. However, practical application of deep learning to CBCT reconstruction is complicated by several issues, such as exceedingly high memory costs of deep learning methods when working with fully 3D data. Additionally, deep learning methods proposed in the literature are often trained and evaluated only on data from a specific region of interest, thus raising concerns about possible lack of generalization to other regions. PURPOSE In this work, we aim to address these limitations and propose LIRE: a learned invertible primal-dual iterative scheme for CBCT reconstruction. METHODS LIRE is a learned invertible primal-dual iterative scheme for CBCT reconstruction, wherein we employ a U-Net architecture in each primal block and a residual convolutional neural network (CNN) architecture in each dual block. Memory requirements of the network are substantially reduced while preserving its expressive power through a combination of invertible residual primal-dual blocks and patch-wise computations inside each of the blocks during both forward and backward pass. These techniques enable us to train on data with isotropic 2 mm voxel spacing, clinically-relevant projection count and detector panel resolution on current hardware with 24 GB video random access memory (VRAM). RESULTS Two LIRE models for small and for large field-of-view (FoV) setting were trained and validated on a set of 260 + 22 thorax CT scans and tested using a set of 142 thorax CT scans plus an out-of-distribution dataset of 79 head and neck CT scans. For both settings, our method surpasses the classical methods and the deep learning baselines on both test sets. On the thorax CT set, our method achieves peak signal-to-noise ratio (PSNR) of 33.84 ± 2.28 for the small FoV setting and 35.14 ± 2.69 for the large FoV setting; U-Net baseline achieves PSNR of 33.08 ± 1.75 and 34.29 ± 2.71 respectively. On the head and neck CT set, our method achieves PSNR of 39.35 ± 1.75 for the small FoV setting and 41.21 ± 1.41 for the large FoV setting; U-Net baseline achieves PSNR of 33.08 ± 1.75 and 34.29 ± 2.71 respectively. Additionally, we demonstrate that LIRE can be finetuned to reconstruct high-resolution CBCT data with the same geometry but 1 mm voxel spacing and higher detector panel resolution, where it outperforms the U-Net baseline as well. CONCLUSIONS Learned invertible primal-dual schemes with additional memory optimizations can be trained to reconstruct CBCT volumes directly from the projection data with clinically-relevant geometry and resolution. Such methods can offer better reconstruction quality and generalization compared to classical deep learning baselines.
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Affiliation(s)
- Nikita Moriakov
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Jonas Teuwen
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands
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7
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Zhao H, Liu Z, Tang J, Gao B, Qin Q, Li J, Zhou Y, Yao P, Xi Y, Lin Y, Qian H, Wu H. Energy-efficient high-fidelity image reconstruction with memristor arrays for medical diagnosis. Nat Commun 2023; 14:2276. [PMID: 37081008 PMCID: PMC10119144 DOI: 10.1038/s41467-023-38021-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 04/06/2023] [Indexed: 04/22/2023] Open
Abstract
Medical imaging is an important tool for accurate medical diagnosis, while state-of-the-art image reconstruction algorithms raise critical challenges in massive data processing for high-speed and high-quality imaging. Here, we present a memristive image reconstructor (MIR) to greatly accelerate image reconstruction with discrete Fourier transformation (DFT) by computing-in-memory (CIM) with memristor arrays. A high-accuracy quasi-analogue mapping (QAM) method and generic complex matrix transfer (CMT) scheme was proposed to improve the mapping precision and transfer efficiency, respectively. High-fidelity magnetic resonance imaging (MRI) and computed tomography (CT) image reconstructions were demonstrated, achieving software-equivalent qualities and DICE scores after segmentation with nnU-Net algorithm. Remarkably, our MIR exhibited 153× and 79× improvements in energy efficiency and normalized image reconstruction speed, respectively, compared to graphics processing unit (GPU). This work demonstrates MIR as a promising high-fidelity image reconstruction platform for future medical diagnosis, and also largely extends the application of memristor-based CIM beyond artificial neural networks.
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Affiliation(s)
- Han Zhao
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Zhengwu Liu
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Jianshi Tang
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China.
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China.
| | - Bin Gao
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Qi Qin
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Jiaming Li
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Ying Zhou
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Peng Yao
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Yue Xi
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - Yudeng Lin
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
| | - He Qian
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
| | - Huaqiang Wu
- School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China
- Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China
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Saat P, Nogovitsyn N, Hassan MY, Ganaie MA, Souza R, Hemmati H. A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation. Front Neuroinform 2022; 16:919779. [PMID: 36213544 PMCID: PMC9538795 DOI: 10.3389/fninf.2022.919779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/29/2022] [Indexed: 01/18/2023] Open
Abstract
Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques for this task. Nevertheless, the segmentations produced by machine learning models often degrade in the presence of expected domain shifts between the test and train sets data distributions. These domain shifts are expected due to several factors, such as scanner hardware and software differences, technology updates, and differences in MRI acquisition parameters. Domain adaptation (DA) methods can make machine learning models more resilient to these domain shifts. This paper proposes a benchmark for investigating DA techniques for brain MR image segmentation using data collected across sites with scanners from different vendors (Philips, Siemens, and General Electric). Our work provides labeled data, publicly available source code for a set of baseline and DA models, and a benchmark for assessing different brain MR image segmentation techniques. We applied the proposed benchmark to evaluate two segmentation tasks: skull-stripping; and white-matter, gray-matter, and cerebrospinal fluid segmentation, but the benchmark can be extended to other brain structures. Our main findings during the development of this benchmark are that there is not a single DA technique that consistently outperforms others, and hyperparameter tuning and computational times for these methods still pose a challenge before broader adoption of these methods in the clinical practice.
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Affiliation(s)
- Parisa Saat
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- *Correspondence: Parisa Saat
| | - Nikita Nogovitsyn
- Centre for Depression and Suicide Studies, St. Michael's Hospital, Toronto, ON, Canada
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Muhammad Yusuf Hassan
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Electrical Engineering, Indian Institute of Technology, Gandhinagar, Gujarat, India
| | - Muhammad Athar Ganaie
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Chemical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India
| | - Roberto Souza
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Hadi Hemmati
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada
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