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Xiang H, Fessler JA, Noll DC. Model-based reconstruction for looping-star MRI. Magn Reson Med 2024; 91:2104-2113. [PMID: 38282253 PMCID: PMC10950512 DOI: 10.1002/mrm.29927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 01/30/2024]
Abstract
PURPOSE The aim of this study was to develop a reconstruction method that more fully models the signals and reconstructs gradient echo (GRE) images without sacrificing the signal to noise ratio and spatial resolution, compared to conventional gridding and model-based image reconstruction method. METHODS By modeling the trajectories for every spoke and simplifying the scenario to only echo-in and echo-out mixture, the approach explicitly models the overlapping echoes. After modeling the overlapping echoes with two system matrices, we use the conjugate gradient algorithm (CG-SENSE) with the nonuniform FFT (NUFFT) to optimize the image reconstruction cost function. RESULTS The proposed method is demonstrated in phantoms and in-vivo volunteer experiments for three-dimensional, high-resolution T2*-weighted imaging and functional MRI tasks. Compared to the gridding method, the high resolution protocol exhibits improved spatial resolution and reduced signal loss as a result of less intra-voxel dephasing. The fMRI task shows that the proposed model-based method produced images with reduced artifacts and blurring as well as more stable and prominent time courses. CONCLUSION The proposed model-based reconstruction results shows improved spatial resolution and reduced artifacts. The fMRI task shows improved time series and activation map due to the reduced overlapping echoes and under-sampling artifacts.
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Affiliation(s)
| | - Jeffrey A. Fessler
- EECS, University of Michigan, Michigan, USA
- Biomedical Engineering, University of Michigan, Michigan, USA
| | - Douglas C. Noll
- Biomedical Engineering, University of Michigan, Michigan, USA
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Saju GA, Li Z, Chang Y. Improving deep PROPELLER MRI via synthetic blade augmentation and enhanced generalization. Magn Reson Imaging 2024; 108:1-10. [PMID: 38295910 DOI: 10.1016/j.mri.2024.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/15/2024] [Accepted: 01/27/2024] [Indexed: 02/03/2024]
Abstract
In PROPELLER MRI, obtaining sufficient high-quality blade data remains a challenge, so the efficiency and generalization of deep learning-based reconstruction models are deteriorated. Due to narrow rotated and translated blades acquired in PROPELLER, the technique of data augmentation that is used for deep learning-based Cartesian MRI reconstruction cannot be directly applied. To address the issue, this paper introduces a novel approach for the generation of synthetic PROPELLER blades, and it is subsequently employed in data augmentation for undersampled blades reconstruction. The principal aim of this study is to address the challenges of reconstructing undersampled blades to enhance both image quality and computational efficiency. Evaluation metrics including PSNR, NMSE, and SSIM indicate superior performance of the model trained with augmented data compared to non-augmented counterparts. The synthetic blade augmentation significantly enhances the model's generalization capability and enables robust performance across varying imaging conditions. Furthermore, the study demonstrates the feasibility of utilizing synthetic blades exclusively in the training phase, suggesting a reduced dependency on real PROPELLER blades. This innovation in synthetic blade generation and data augmentation technique contributes to enhanced image quality and improved generalization capability of the associated deep learning model for PROPELLER MRI reconstruction.
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Affiliation(s)
- Gulfam Ahmed Saju
- Department of Computer and Information Science Department, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA.
| | - Zhiqiang Li
- Barrow Neurological Institute, Phoenix, AZ 85013, USA.
| | - Yuchou Chang
- Department of Computer and Information Science Department, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA.
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Qiu Z, Hu S, Zhao W, Sakaie K, Sun JE, Griswold MA, Jones DK, Ma D. Self-calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and diffusion quantification. Magn Reson Med 2024; 91:1978-1993. [PMID: 38102776 PMCID: PMC10950540 DOI: 10.1002/mrm.29969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 11/22/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023]
Abstract
PURPOSE To propose a new reconstruction method for multidimensional MR fingerprinting (mdMRF) to address shading artifacts caused by physiological motion-induced measurement errors without navigating or gating. METHODS The proposed method comprises two procedures: self-calibration and subspace reconstruction. The first procedure (self-calibration) applies temporally local matrix completion to reconstruct low-resolution images from a subset of under-sampled data extracted from the k-space center. The second procedure (subspace reconstruction) utilizes temporally global subspace reconstruction with pre-estimated temporal subspace from low-resolution images to reconstruct aliasing-free, high-resolution, and time-resolved images. After reconstruction, a customized outlier detection algorithm was employed to automatically detect and remove images corrupted by measurement errors. Feasibility, robustness, and scan efficiency were evaluated through in vivo human brain imaging experiments. RESULTS The proposed method successfully reconstructed aliasing-free, high-resolution, and time-resolved images, where the measurement errors were accurately represented. The corrupted images were automatically and robustly detected and removed. Artifact-free T1, T2, and ADC maps were generated simultaneously. The proposed reconstruction method demonstrated robustness across different scanners, parameter settings, and subjects. A high scan efficiency of less than 20 s per slice has been achieved. CONCLUSION The proposed reconstruction method can effectively alleviate shading artifacts caused by physiological motion-induced measurement errors. It enables simultaneous and artifact-free quantification of T1, T2, and ADC using mdMRF scans without prospective gating, with robustness and high scan efficiency.
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Affiliation(s)
- Zhilang Qiu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
| | - Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
| | - Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
| | - Ken Sakaie
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, United States
| | - Jessie E.P. Sun
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, United States
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, United States
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
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Donners R, Vosshenrich J, Gutzeit A, Bach M, Schlicht F, Obmann MM, Harder D, Breit HC. New-Generation 0.55 T MRI of the Knee-Initial Clinical Experience and Comparison With 3 T MRI. Invest Radiol 2024; 59:298-305. [PMID: 37747455 DOI: 10.1097/rli.0000000000001016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
OBJECTIVES The aim of this study was to compare the detection rate of and reader confidence in 0.55 T knee magnetic resonance imaging (MRI) findings with 3 T knee MRI in patients with acute trauma and knee pain. MATERIALS AND METHODS In this prospective study, 0.55 T and 3 T knee MRI of 25 symptomatic patients (11 women; median age, 38 years) with suspected internal derangement of the knee was obtained in 1 setting. On the 0.55 T system, a commercially available deep learning image reconstruction algorithm was used (Deep Resolve Gain and Deep Resolve Sharp; Siemens Healthineers), which was not available on the 3 T system. Two board-certified radiologists reviewed all images independently and graded image quality parameters, noted MRI findings and their respective reporting confidence level for the presence or absence, as well as graded the bone, cartilage, meniscus, ligament, and tendon lesions. Image quality and reader confidence levels were compared ( P < 0.05 = significant), and clinical findings were correlated between 0.55 T and 3 T MRI by calculation of the intraclass correlation coefficient (ICC). RESULTS Image quality was rated higher at 3 T compared with 0.55 T studies (each P ≤ 0.017). Agreement between 0.55 T and 3 T MRI for the detection and grading of bone marrow edema and fractures, ligament and tendon lesions, high-grade meniscus and cartilage lesions, Baker cysts, and joint effusions was perfect for both readers. Overall identification and grading of cartilage and meniscal lesions showed good agreement between high- and low-field MRI (each ICC > 0.76), with lower agreement for low-grade cartilage (ICC = 0.77) and meniscus lesions (ICC = 0.49). There was no difference in readers' confidence levels for reporting lesions of bone, ligaments, tendons, Baker cysts, and joint effusions between 0.55 T and 3 T (each P > 0.157). Reader reporting confidence was higher for cartilage and meniscal lesions at 3 T (each P < 0.041). CONCLUSIONS New-generation 0.55 T knee MRI, with deep learning-aided image reconstruction, allows for reliable detection and grading of joint lesions in symptomatic patients, but it showed limited accuracy and reader confidence for low-grade cartilage and meniscal lesions in comparison with 3 T MRI.
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Affiliation(s)
- Ricardo Donners
- From the Department of Radiology, University Hospital Basel (R.D., J.V., M.B., F.S., M.O., D.H., H.-C.B.), Basel, Switzerland; and Institute of Radiology and Nuclear Medicine and Breast Center St. Anna (A.G.), Lucerne, Switzerland
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Lee S, Jung JY, Chung H, Lee HS, Nickel D, Lee J, Lee SY. Comparative analysis of image quality and interchangeability between standard and deep learning-reconstructed T2-weighted spine MRI. Magn Reson Imaging 2024; 109:211-220. [PMID: 38513791 DOI: 10.1016/j.mri.2024.03.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/28/2024] [Accepted: 03/16/2024] [Indexed: 03/23/2024]
Abstract
RATIONALE AND OBJECTIVES MRI reconstruction of undersampled data using a deep learning (DL) network has been recently performed as part of accelerated imaging. Herein, we compared DL-reconstructed T2-weighted image (T2-WI) to conventional T2-WI regarding image quality and degenerative lesion detection. MATERIALS AND METHODS Sixty-two patients underwent C-spine (n = 27) or L-spine (n = 35) MRIs, including conventional and DL-reconstructed T2-WI. Image quality was assessed with non-uniformity measurement and 4-scale grading of structural visibility. Three readers (R1, R2, R3) independently assessed the presence and types of degenerative lesions. Student t-test was used to compare non-uniformity measurements. Interprotocol and interobserver agreement of structural visibility was analyzed with Wilcoxon signed-rank test and weighted-κ values, respectively. The diagnostic equivalence of degenerative lesion detection between two protocols was assessed with interchangeability test. RESULTS The acquisition time of DL-reconstructed images was reduced to about 21-58% compared to conventional images. Non-uniformity measurement was insignificantly different between the two images (p-value = 0.17). All readers rated DL-reconstructed images as showing the same or superior structural visibility compared to conventional images. Significantly improved visibility was observed at disk margin of C-spine (R1, p < 0.001; R2, p = 0.04) and dorsal root ganglia (R1, p = 0.03; R3, p = 0.02) and facet joint (R1, p = 0.04; R2, p < 0.001; R3, p = 0.03) of L-spine. Interobserver agreements of image quality were variable in each structure. Clinical interchangeability between two protocols for degenerative lesion detection was verified showing <5% in the upper bounds of 95% confidence intervals of agreement rate differences. CONCLUSIONS DL-reconstructed T2-WI demonstrates comparable image quality and diagnostic performance with conventional T2-WI in spine imaging, with reduced acquisition time.
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Affiliation(s)
- Seungeun Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Joon-Yong Jung
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
| | - Heeyoung Chung
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Hyun-Soo Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; Siemens Healthineers, Seoul 06620, Republic of Korea.
| | - Dominik Nickel
- Siemens Healthcare GmbH, Allee am Roethelheimpark, Erlangen 91052, Germany.
| | - Jooyeon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX 77030, USA.
| | - So-Yeon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Wei H, Yoon JH, Jeon SK, Choi JW, Lee J, Kim JH, Nickel MD, Song B, Duan T, Lee JM. Enhancing gadoxetic acid-enhanced liver MRI: a synergistic approach with deep learning CAIPIRINHA-VIBE and optimized fat suppression techniques. Eur Radiol 2024:10.1007/s00330-024-10693-9. [PMID: 38492004 DOI: 10.1007/s00330-024-10693-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 02/02/2024] [Accepted: 02/18/2024] [Indexed: 03/18/2024]
Abstract
OBJECTIVE To investigate whether a deep learning (DL) controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-volumetric interpolated breath-hold examination (VIBE) technique can improve image quality, lesion conspicuity, and lesion detection compared to a standard CAIPIRINHA-VIBE technique in gadoxetic acid-enhanced liver MRI. METHODS This retrospective single-center study included 168 patients who underwent gadoxetic acid-enhanced liver MRI at 3 T using both standard CAIPIRINHA-VIBE and DL CAIPIRINHA-VIBE techniques on pre-contrast and hepatobiliary phase (HBP) images. Additionally, high-resolution (HR) DL CAIPIRINHA-VIBE was obtained with 1-mm slice thickness on the HBP. Three abdominal radiologists independently assessed the image quality and lesion conspicuity of pre-contrast and HBP images. Statistical analyses involved the Wilcoxon signed-rank test for image quality assessment and the generalized estimation equation for lesion conspicuity and detection evaluation. RESULTS DL and HR-DL CAIPIRINHA-VIBE demonstrated significantly improved overall image quality and reduced artifacts on pre-contrast and HBP images compared to standard CAIPIRINHA-VIBE (p < 0.001), with a shorter acquisition time (DL vs standard, 11 s vs 17 s). However, the former presented a more synthetic appearance (both p < 0.05). HR-DL CAIPIRINHA-VIBE showed superior lesion conspicuity to standard and DL CAIPIRINHA-VIBE on HBP images (p < 0.001). Moreover, HR-DL CAIPIRINHA-VIBE exhibited a significantly higher detection rate of small (< 2 cm) solid focal liver lesions (FLLs) on HBP images compared to standard CAIPIRINHA-VIBE (92.5% vs 87.4%; odds ratio = 1.83; p = 0.036). CONCLUSION DL and HR-DL CAIPIRINHA-VIBE achieved superior image quality compared to standard CAIPIRINHA-VIBE. Additionally, HR-DL CAIPIRINHA-VIBE improved the lesion conspicuity and detection of small solid FLLs. DL and HR-DL CAIPIRINHA-VIBE hold the potential clinical utility for gadoxetic acid-enhanced liver MRI. CLINICAL RELEVANCE STATEMENT DL and HR-DL CAIPIRINHA-VIBE hold promise as potential alternatives to standard CAIPIRINHA-VIBE in routine clinical liver MRI, improving the image quality and lesion conspicuity, enhancing the detection of small (< 2 cm) solid focal liver lesions, and reducing the acquisition time. KEY POINTS • DL and HR-DL CAIPIRINHA-VIBE demonstrated improved overall image quality and reduced artifacts on pre-contrast and HBP images compared to standard CAIPIRINHA-VIBE, in addition to a shorter acquisition time. • DL and HR-DL CAIPIRINHA-VIBE yielded a more synthetic appearance than standard CAIPIRINHA-VIBE. • HR-DL CAIPIRINHA-VIBE showed improved lesion conspicuity than standard CAIPIRINHA-VIBE on HBP images, with a higher detection of small (< 2 cm) solid focal liver lesions.
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Affiliation(s)
- Hong Wei
- Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Sun Kyung Jeon
- Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Jae Won Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
- Department of Radiology, Armed Forces Yangju Hospital, Yangju, 482863, Republic of Korea
| | - Jihyuk Lee
- Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Jae Hyun Kim
- Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Marcel Dominik Nickel
- MR Application Predevelopment, Siemens Healthcare GmbH, Henkestr. 127, 91052, Erlangen, Germany
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Radiology, Sanya People's Hospital, Sanya, 572000, Hainan, China
| | - Ting Duan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
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Roy CW, Milani B, Yerly J, Si-Mohamed S, Romanin L, Bustin A, Tenisch E, Rutz T, Prsa M, Stuber M. Intra-bin correction and inter-bin compensation of respiratory motion in free-running 5D whole-heart MRI. J Cardiovasc Magn Reson 2024:101037. [PMID: 38499269 DOI: 10.1016/j.jocmr.2024.101037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 03/20/2024] Open
Abstract
BACKGROUND Free-running cardiac and respiratory motion-resolved whole-heart 5D MRI can reduce scan planning and provide a means of evaluating respiratory-driven changes in clinical parameters of interest. However, respiratory-resolved imaging can be limited by user-defined parameters which create trade-offs between residual artifact and motion blur. In this work, we develop and validate strategies for both correction of intra-bin and compensation of inter-bin respiratory motion to improve the quality of 5D MRI. METHODS Each component of the reconstruction framework was systematically validated and compared to the previously established 5D approach using simulated free-running data (N=50) and a cohort of 32 patients with congenital heart disease. The impact of intra-bin respiratory motion correction was evaluated in terms of image sharpness while inter-bin respiratory motion compensation was evaluated in terms of reconstruction error, compression of respiratory motion, and image sharpness. The full reconstruction framework (IIMC 5D) was evaluated in terms of image sharpness and scoring of image quality by expert reviewers. RESULTS Intra-bin motion correction provides significantly (p < 10-3) sharper images for both simulated and patient data. Inter-bin motion compensation results in significant (p < 10-3) lower reconstruction error, lower motion compression, and higher sharpness in both simulated (10/11) and patient (9/11) data. The combined framework resulted in significantly (p < 10-3) sharper IIMC 5D reconstructions (End-Exp: 0.45±0.09, End-Ins: 0.46±0.10) relative to the previously established 5D implementation (End-Exp: 0.43±0.08, End-Ins: 0.39±0.09). Similarly, image scoring by three expert reviewers was significantly (p < 10-3) higher using IIMC 5D (End-Exp: 3.39±0.44, End-Ins: 3.32±0.45) relative to 5D images (End-Exp: 3.02±0.54, End-Ins: 2.45±0.52). CONCLUSION The proposed IIMC reconstruction significantly improves the quality of 5D whole-heart MRI. This may be exploited for higher resolution or abbreviated scan. Further investigation of the diagnostic impact of this framework and comparison to gold-standards is needed to understand its full clinical utility including exploration of respiratory-driven changes in physiological measurements of interest.
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Affiliation(s)
- Christopher W Roy
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
| | - Bastien Milani
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jérôme Yerly
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Salim Si-Mohamed
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; University Lyon, INSA-Lyon, University Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F-69621, 7 Avenue Jean Capelle O, 69100 Villeurbanne, France; Department of Radiology, Louis Pradel Hospital, Hospices Civils de Lyon, 59 Boulevard Pinel, 69500 Bron, France
| | - Ludovica Romanin
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Aurélien Bustin
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; IHU LIRYC, Electrophysiology and Heart Modeling Institute, Université de Bordeaux - INSERM U1045, Avenue du Haut Lévêque, 33604, Pessac, France; Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Avenue de Magellan, 33604 Pessac, France
| | - Estelle Tenisch
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tobias Rutz
- Service of Cardiology, Heart and Vessel Department, Lausanne University Hospital and University of Lausanne
| | - Milan Prsa
- Division of Pediatric Cardiology, Woman-Mother-Child Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Matthias Stuber
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
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Meyer NK, In MH, Black DF, Campeau NG, Welker KM, Huston J, Halverson MA, Bernstein MA, Trzasko JD. Model-based iterative reconstruction for direct imaging with point spread function encoded echo planar MRI. Magn Reson Imaging 2024; 109:189-202. [PMID: 38490504 DOI: 10.1016/j.mri.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/08/2024] [Accepted: 03/08/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND Echo planar imaging (EPI) is a fast measurement technique commonly used in magnetic resonance imaging (MRI), but is highly sensitive to measurement non-idealities in reconstruction. Point spread function (PSF)-encoded EPI is a multi-shot strategy which alleviates distortion, but acquisition of encodings suitable for direct distortion-free imaging prolongs scan time. In this work, a model-based iterative reconstruction (MBIR) framework is introduced for direct imaging with PSF-EPI to improve image quality and acceleration potential. METHODS An MBIR platform was developed for accelerated PSF-EPI. The reconstruction utilizes a subspace representation, is regularized to promote local low-rankedness (LLR), and uses variable splitting for efficient iteration. Comparisons were made against standard reconstructions from prospectively accelerated PSF-EPI data and with retrospective subsampling. Exploring aggressive partial Fourier acceleration of the PSF-encoding dimension, additional comparisons were made against an extension of Homodyne to direct PSF-EPI in numerical experiments. A neuroradiologists' assessment was completed comparing images reconstructed with MBIR from retrospectively truncated data directly against images obtained with standard reconstructions from non-truncated datasets. RESULTS Image quality results were consistently superior for MBIR relative to standard and Homodyne reconstructions. As the MBIR signal model and reconstruction allow for arbitrary sampling of the PSF space, random sampling of the PSF-encoding dimension was also demonstrated, with quantitative assessments indicating best performance achieved through nonuniform PSF sampling combined with partial Fourier. With retrospective subsampling, MBIR reconstructs high-quality images from sub-minute scan datasets. MBIR was shown to be superior in a neuroradiologists' assessment with respect to three of five performance criteria, with equivalence for the remaining two. CONCLUSIONS A novel image reconstruction framework is introduced for direct imaging with PSF-EPI, enabling arbitrary PSF space sampling and reconstruction of diagnostic-quality images from highly accelerated PSF-encoded EPI data.
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Affiliation(s)
- Nolan K Meyer
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Myung-Ho In
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - David F Black
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Norbert G Campeau
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Kirk M Welker
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - John Huston
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Maria A Halverson
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Matt A Bernstein
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Joshua D Trzasko
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
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Cheng J, Cui ZX, Zhu Q, Wang H, Zhu Y, Liang D. Integrating data distribution prior via Langevin dynamics for end-to-end MR reconstruction. Magn Reson Med 2024. [PMID: 38469985 DOI: 10.1002/mrm.30065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 01/24/2024] [Accepted: 02/08/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE To develop a novel deep learning-based method inheriting the advantages of data distribution prior and end-to-end training for accelerating MRI. METHODS Langevin dynamics is used to formulate image reconstruction with data distribution before facilitate image reconstruction. The data distribution prior is learned implicitly through the end-to-end adversarial training to mitigate the hyper-parameter selection and shorten the testing time compared to traditional probabilistic reconstruction. By seamlessly integrating the deep equilibrium model, the iteration of Langevin dynamics culminates in convergence to a fix-point, ensuring the stability of the learned distribution. RESULTS The feasibility of the proposed method is evaluated on the brain and knee datasets. Retrospective results with uniform and random masks show that the proposed method demonstrates superior performance both quantitatively and qualitatively than the state-of-the-art. CONCLUSION The proposed method incorporating Langevin dynamics with end-to-end adversarial training facilitates efficient and robust reconstruction for MRI. Empirical evaluations conducted on brain and knee datasets compellingly demonstrate the superior performance of the proposed method in terms of artifact removing and detail preserving.
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Affiliation(s)
- Jing Cheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China
| | - Zhuo-Xu Cui
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qingyong Zhu
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Haifeng Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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10
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Vergara J, Miles A, Lopes de Moraes J, Chone CT. Contribution of Wireless Wi-Fi Intraoral Cameras to the Assessment of Swallowing Safety and Efficiency. J Speech Lang Hear Res 2024; 67:821-836. [PMID: 38437030 DOI: 10.1044/2023_jslhr-23-00375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
BACKGROUND Clinical evaluation of swallowing provides important clinical information but is limited in detecting penetration, aspiration, and pharyngeal residue in patients with suspected dysphagia. Although this is an old problem, there remains limited access to low-cost methods to evaluate swallowing safety and efficiency. PURPOSE The purpose of this technical report is to describe the experience of a single center that recently began using a wireless Wi-Fi intraoral camera for transoral endoscopic procedures as an adjunct to clinical swallowing evaluation. We describe the theoretical structure of this new clinical evaluation proposal. We present descriptive findings on its diagnostic performance in relation to videofluoroscopic swallowing study as the gold standard in a cohort of seven patients with dysphagia following head and neck cancer. We provide quantitative data on intra- and interrater reliability. Furthermore, this report discusses how this technology can be applied in the clinical practice of professionals who treat patients with dysphagia and provides directions for future research. CONCLUSIONS This preliminary retrospective study suggests that intraoral cameras can reveal the accumulated oropharyngeal secretions and postswallow pharyngolaryngeal residue in patients with suspected dysphagia. Future large-scale studies focusing on validating and exploring this contemporary low-cost technology as part of a clinical swallowing evaluation are warranted.
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Affiliation(s)
- José Vergara
- Department of Surgery, Head and Neck Surgery, University of Campinas, São Paulo, Brazil
| | - Anna Miles
- Department of Speech Science, School of Psychology, University of Auckland, New Zealand
| | - Juliana Lopes de Moraes
- Department of Otorhinolaryngology and Head and Neck Surgery, University of Campinas, São Paolo, Brazil
| | - Carlos Takahiro Chone
- Department of Otorhinolaryngology and Head and Neck Surgery, University of Campinas, São Paolo, Brazil
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11
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Wang Z, Luo G, Li Y, Cao P. Using a deep learning prior for accelerating hyperpolarized 13 C MRSI on synthetic cancer datasets. Magn Reson Med 2024. [PMID: 38440832 DOI: 10.1002/mrm.30053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 03/06/2024]
Abstract
PURPOSE We aimed to incorporate a deep learning prior with k-space data fidelity for accelerating hyperpolarized carbon-13 MRSI, demonstrated on synthetic cancer datasets. METHODS A two-site exchange model, derived from the Bloch equation of MR signal evolution, was firstly used in simulating training and testing data, that is, synthetic phantom datasets. Five singular maps generated from each simulated dataset were used to train a deep learning prior, which was then employed with the fidelity term to reconstruct the undersampled MRI k-space data. The proposed method was assessed on synthetic human brain tumor images (N = 33), prostate cancer images (N = 72), and mouse tumor images (N = 58) for three undersampling factors and 2.5% additive Gaussian noise. Furthermore, varied levels of Gaussian noise with SDs of 2.5%, 5%, and 10% were added on synthetic prostate cancer data, and corresponding reconstruction results were evaluated. RESULTS For quantitative evaluation, peak SNRs were approximately 32 dB, and the accuracy was generally improved for 5 to 8 dB compared with those from compressed sensing with L1-norm regularization or total variation regularization. Reasonable normalized RMS error were obtained. Our method also worked robustly against noise, even on a data with noise SD of 10%. CONCLUSION The proposed singular value decomposition + iterative deep learning model could be considered as a general framework that extended the application of deep learning MRI reconstruction to metabolic imaging. The morphology of tumors and metabolic images could be measured robustly in six times acceleration using our method.
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Affiliation(s)
- Zuojun Wang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Guanxiong Luo
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany
| | - Ye Li
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology of Chinese Academy of Sciences, Shenzhen, People's Republic of China
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, People's Republic of China
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12
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Li L, Yan S, Bakker BM, Hoefsloot H, Chawes B, Horner D, Rasmussen MA, Smilde AK, Acar E. Analyzing postprandial metabolomics data using multiway models: a simulation study. BMC Bioinformatics 2024; 25:94. [PMID: 38438850 PMCID: PMC10913623 DOI: 10.1186/s12859-024-05686-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/31/2024] [Indexed: 03/06/2024] Open
Abstract
BACKGROUND Analysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data. RESULTS We introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postprandial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using principal component analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased versus healthy groups. CONCLUSIONS Our experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.
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Affiliation(s)
- Lu Li
- Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
| | - Shi Yan
- Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | - Barbara M Bakker
- Laboratory of Pediatrics, Section Systems Medicine and Metabolic Signalling, Center for Liver, Digestive and Metabolic Disease, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Huub Hoefsloot
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Bo Chawes
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - David Horner
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Morten A Rasmussen
- Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Food Science, University of Copenhagen, Copenhagen, Denmark
| | - Age K Smilde
- Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Evrim Acar
- Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
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13
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Shahidi LK, Collins LM, Mainsah BO. Objective intelligibility measurement of reverberant vocoded speech for normal-hearing listeners: Towards facilitating the development of speech enhancement algorithms for cochlear implants. J Acoust Soc Am 2024; 155:2151-2168. [PMID: 38501923 PMCID: PMC10959555 DOI: 10.1121/10.0025285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 02/24/2024] [Indexed: 03/20/2024]
Abstract
Cochlear implant (CI) recipients often struggle to understand speech in reverberant environments. Speech enhancement algorithms could restore speech perception for CI listeners by removing reverberant artifacts from the CI stimulation pattern. Listening studies, either with cochlear-implant recipients or normal-hearing (NH) listeners using a CI acoustic model, provide a benchmark for speech intelligibility improvements conferred by the enhancement algorithm but are costly and time consuming. To reduce the associated costs during algorithm development, speech intelligibility could be estimated offline using objective intelligibility measures. Previous evaluations of objective measures that considered CIs primarily assessed the combined impact of noise and reverberation and employed highly accurate enhancement algorithms. To facilitate the development of enhancement algorithms, we evaluate twelve objective measures in reverberant-only conditions characterized by a gradual reduction of reverberant artifacts, simulating the performance of an enhancement algorithm during development. Measures are validated against the performance of NH listeners using a CI acoustic model. To enhance compatibility with reverberant CI-processed signals, measure performance was assessed after modifying the reference signal and spectral filterbank. Measures leveraging the speech-to-reverberant ratio, cepstral distance and, after modifying the reference or filterbank, envelope correlation are strong predictors of intelligibility for reverberant CI-processed speech.
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Affiliation(s)
- Lidea K Shahidi
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina 27701, USA
| | - Leslie M Collins
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina 27701, USA
| | - Boyla O Mainsah
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina 27701, USA
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14
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Kopp M, Wiesmueller M, Buchbender M, Kesting M, Nagel AM, May MS, Uder M, Roemer FW, Heiss R. MRI of Temporomandibular Joint Disorders: A Comparative Study of 0.55 T and 1.5 T MRI. Invest Radiol 2024; 59:223-229. [PMID: 37493286 DOI: 10.1097/rli.0000000000001008] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
OBJECTIVES Temporomandibular disorders (TMDs) are common and may cause persistent functional limitations and pain. Magnetic resonance imaging (MRI) at 1.5 and 3 T is commonly applied for the evaluation of the temporomandibular joint (TMJ). No evidence is available regarding the feasibility of modern low-field MRI for the assessment of TMDs. The objective of this prospective study was to evaluate the image quality (IQ) of 0.55 T MRI in direct comparison with 1.5 T MRI. MATERIALS AND METHODS Seventeen patients (34 TMJs) with suspected intraarticular TMDs were enrolled, and both 0.55 and 1.5 T MRI were performed on the same day. Two senior readers independently evaluated the IQ focusing on the conspicuity of disc morphology (DM), disc position (DP), and osseous joint morphology (OJM) for each joint. We analyzed the IQ and degree of artifacts using a 4-point Likert scale (LS) at both field strengths. A fully sufficient IQ was defined as an LS score of ≥3. Nonparametric Wilcoxon test for related samples was used for statistical comparison. RESULTS The median IQ for the DM and OJM at 0.55 T was inferior to that at 1.5 T (DM: 3 [interquartile range {IQR}, 3-4] vs 4 [IQR, 4-4]; OJM: 3 [IQR, 3-4] vs 4 [IQR 4-4]; each P < 0.001). For DP, the IQ was comparable (4 [IQR 3-4] vs 4 [IQR 4-4]; P > 0.05). A sufficient diagnostic IQ was maintained for the DM, DP, and OJM in 92% of the cases at 0.55 T and 100% at 1.5 T. Minor image artifacts (LS score of ≥3) were more prevalent at 0.55 T (29%) than at 1.5 T (12%). CONCLUSIONS Magnetic resonance imaging of the TMJ at 0.55 T yields a lower IQ than does MRI at 1.5 T but maintains sufficient diagnostic confidence in the majority of patients. Further improvements are needed for reliable clinical application.
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Affiliation(s)
- Markus Kopp
- From the Department of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany (M.K., M.W., A.M.N., M.S.M., M.U., F.W.R., R.H.); Department of Oral and Maxillofacial Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany (M.B., M.K.); and Department of Radiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA (F.W.R.)
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Aminizadeh S, Heidari A, Dehghan M, Toumaj S, Rezaei M, Jafari Navimipour N, Stroppa F, Unal M. Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artif Intell Med 2024; 149:102779. [PMID: 38462281 DOI: 10.1016/j.artmed.2024.102779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 12/30/2023] [Accepted: 01/14/2024] [Indexed: 03/12/2024]
Abstract
The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.2 %). The studies under examination employ a diverse range of ML and DL methods, along with distributed systems, with Convolutional Neural Networks (CNNs) being the most commonly used (16.7 %), followed by Long Short-Term Memory (LSTM) networks (14.6 %) and shallow learning networks (12.5 %). In evaluating QoS, the predominant emphasis revolves around the accuracy parameter (80 %). This study highlights how ML, DL, and distributed systems reshape healthcare. It contributes to advancing healthcare quality, bridging the gap between technology and medical adoption, and benefiting practitioners and patients.
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Affiliation(s)
- Sarina Aminizadeh
- Medical Faculty, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Arash Heidari
- Department of Software Engineering, Haliç University, Istanbul 34060, Turkiye.
| | - Mahshid Dehghan
- Tabriz University of Medical Sciences, Faculty of Medicine, Tabriz, Iran
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Mahsa Rezaei
- Tabriz University of Medical Sciences, Faculty of Surgery, Tabriz, Iran
| | - Nima Jafari Navimipour
- Future Technology Research Center, National Yunlin University of Science and Technology, Douliou 64002, Taiwan; Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Türkiye.
| | - Fabio Stroppa
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Türkiye
| | - Mehmet Unal
- Department of Mathematics, School of Engineering and Natural Sciences, Bahçeşehir University, Istanbul, Turkiye
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Subramanian V, Syeda-Mahmood T, Do MN. Modelling-based joint embedding of histology and genomics using canonical correlation analysis for breast cancer survival prediction. Artif Intell Med 2024; 149:102787. [PMID: 38462287 DOI: 10.1016/j.artmed.2024.102787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 03/12/2024]
Abstract
Traditional approaches to predicting breast cancer patients' survival outcomes were based on clinical subgroups, the PAM50 genes, or the histological tissue's evaluation. With the growth of multi-modality datasets capturing diverse information (such as genomics, histology, radiology and clinical data) about the same cancer, information can be integrated using advanced tools and have improved survival prediction. These methods implicitly exploit the key observation that different modalities originate from the same cancer source and jointly provide a complete picture of the cancer. In this work, we investigate the benefits of explicitly modelling multi-modality data as originating from the same cancer under a probabilistic framework. Specifically, we consider histology and genomics as two modalities originating from the same breast cancer under a probabilistic graphical model (PGM). We construct maximum likelihood estimates of the PGM parameters based on canonical correlation analysis (CCA) and then infer the underlying properties of the cancer patient, such as survival. Equivalently, we construct CCA-based joint embeddings of the two modalities and input them to a learnable predictor. Real-world properties of sparsity and graph-structures are captured in the penalized variants of CCA (pCCA) and are better suited for cancer applications. For generating richer multi-dimensional embeddings with pCCA, we introduce two novel embedding schemes that encourage orthogonality to generate more informative embeddings. The efficacy of our proposed prediction pipeline is first demonstrated via low prediction errors of the hidden variable and the generation of informative embeddings on simulated data. When applied to breast cancer histology and RNA-sequencing expression data from The Cancer Genome Atlas (TCGA), our model can provide survival predictions with average concordance-indices of up to 68.32% along with interpretability. We also illustrate how the pCCA embeddings can be used for survival analysis through Kaplan-Meier curves.
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Affiliation(s)
- Vaishnavi Subramanian
- Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801, IL, USA.
| | | | - Minh N Do
- Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801, IL, USA
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17
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Licht C, Reichert S, Guye M, Schad LR, Rapacchi S. Multidimensional compressed sensing to advance 23 Na multi-quantum coherences MRI. Magn Reson Med 2024; 91:926-941. [PMID: 37881829 DOI: 10.1002/mrm.29902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 09/13/2023] [Accepted: 10/09/2023] [Indexed: 10/27/2023]
Abstract
PURPOSE Sodium (23 Na) multi-quantum coherences (MQC) MRI was accelerated using three-dimensional (3D) and a dedicated five-dimensional (5D) compressed sensing (CS) framework for simultaneous Cartesian single (SQ) and triple quantum (TQ) sodium imaging of in vivo human brain at 3.0 and 7.0 T. THEORY AND METHODS 3D 23 Na MQC MRI requires multi-echo paired with phase-cycling and exhibits thus a multidimensional space. A joint reconstruction framework to exploit the sparsity in all imaging dimensions by extending the conventional 3D CS framework to 5D was developed. 3D MQC images of simulated brain, phantom and healthy brain volunteers obtained from 3.0 T and 7.0 T were retrospectively and prospectively undersampled. Performance of the CS models were analyzed by means of structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR) and signal quantification of tissue sodium concentration and TQ/SQ ratio. RESULTS It was shown that an acceleration of three-fold, leading to less than2 × 10 $$ 2\times 10 $$ min of scan time with a resolution of8 × 8 × 20 mm 3 $$ 8\times 8\times 20\;{\mathrm{mm}}^3 $$ at 3.0 T, are possible. 5D CS improved SSIM by 3%, 5%, 1% and reduced RMSE by 50%, 30%, 8% for in vivo SQ, TQ, and TQ/SQ ratio maps, respectively. Furthermore, for the first time prospective undersampling enabled unprecedented high resolution from8 × 8 × 20 mm 3 $$ 8\times 8\times 20\;{\mathrm{mm}}^3 $$ to6 × 6 × 10 mm 3 $$ 6\times 6\times 10\;{\mathrm{mm}}^3 $$ MQC images of in vivo human brain at 7.0 T without extending acquisition time. CONCLUSION 5D CS proved to allow up to three-fold acceleration retrospectively on 3.0 T data. 2-fold acceleration was demonstrated prospectively at 7.0 T to reach higher spatial resolution of 23 Na MQC MRI.
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Affiliation(s)
- Christian Licht
- Computer Assisted Clinical Medicine, Medical Faculty Mannhein, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent System in Medicine, Medical Faculty Mannheim, Mannheim, Germany
| | - Simon Reichert
- Computer Assisted Clinical Medicine, Medical Faculty Mannhein, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent System in Medicine, Medical Faculty Mannheim, Mannheim, Germany
| | - Maxime Guye
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Lothar R Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannhein, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent System in Medicine, Medical Faculty Mannheim, Mannheim, Germany
| | - Stanislas Rapacchi
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
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Cao C, Cui ZX, Zhu Q, Liu C, Liang D, Zhu Y. Annihilation-Net: Learned annihilation relation for dynamic MR imaging. Med Phys 2024; 51:1883-1898. [PMID: 37665786 DOI: 10.1002/mp.16723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 07/17/2023] [Accepted: 08/13/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Deep learning methods driven by the low-rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. The effectiveness of existing methods lies mainly in their ability to capture interframe relationships using network modules, which are lack interpretability. PURPOSE This study aims to design an interpretable methodology for modeling interframe relationships using convolutiona networks, namely Annihilation-Net and use it for accelerating dynamic MRI. METHODS Based on the equivalence between Hankel matrix product and convolution, we utilize convolutional networks to learn the null space transform for characterizing low-rankness. We employ low-rankness to represent interframe correlations in dynamic MR imaging, while combining with sparse constraints in the compressed sensing framework. The corresponding optimization problem is solved in an iterative form with the semi-quadratic splitting method (HQS). The iterative steps are unrolled into a network, dubbed Annihilation-Net. All the regularization parameters and null space transforms are set as learnable in the Annihilation-Net. RESULTS Experiments on the cardiac cine dataset show that the proposed model outperforms other competing methods both quantitatively and qualitatively. The training set and test set have 800 and 118 images, respectively. CONCLUSIONS The proposed Annihilation-Net improves the reconstruction quality of accelerated dynamic MRI with better interpretability.
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Affiliation(s)
- Chentao Cao
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhuo-Xu Cui
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qingyong Zhu
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Congcong Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Dong Liang
- Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Bermudez C, Kerley CI, Ramadass K, Farber-Eger EH, Lin YC, Kang H, Taylor WD, Wells QS, Landman BA. Volumetric brain MRI signatures of heart failure with preserved ejection fraction in the setting of dementia. Magn Reson Imaging 2024; 109:49-55. [PMID: 38430976 DOI: 10.1016/j.mri.2024.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy.
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Affiliation(s)
- Camilo Bermudez
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Cailey I Kerley
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Eric H Farber-Eger
- Department of Cardiology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ya-Chen Lin
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Warren D Taylor
- Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quinn S Wells
- Department of Cardiology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Bennett A Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, USA.
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Tavakkoli M, Svenningsen S, Friedlander Y, Konyer NB, Nair P, Noseworthy MD. Sampling pattern discrepancy in the application of compressed sensing hyperpolarized xenon-129 lung MRI. NMR Biomed 2024:e5121. [PMID: 38423986 DOI: 10.1002/nbm.5121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 01/14/2024] [Accepted: 01/23/2024] [Indexed: 03/02/2024]
Abstract
Although hyperpolarized (HP) 129 Xe ventilation MRI can be carried out within a breath hold, it is still challenging for many sick patients. Compressed sensing (CS) is a viable alternative to accelerate this approach. However, undersampled images with identical sampling ratios differ from one another. Twenty subjects (n = 10 healthy and n = 10 patients with asthma) were scanned using a GE MR750 3 T scanner, acquiring fully sampled 2D multi-slice HP 129 Xe lung ventilation images (10 s breath hold, 128 × 80 (FE × PE-frequency encoding × phase encoding) and 16 slices). Using fully sampled data, 500 variable-density Cartesian random undersampling patterns were generated, each at eight different sampling ratios from 10% to 80%. The parallel imaging and compressed sensing (PICS) command from BART was employed to reconstruct undersampled data. The signal to noise ratio (SNR), structural similarity index measurement (SSIM) and sidelobe to peak ratio of each were subsequently compared. There was a high degree of variation in both SNR and SSIM results from each of the 500 masks of each sampling rate. As the undersampling increases, there is more variation in the quantifying metrics, for both healthy and asthmatic individuals. Our study shows that random undersampling poses a significant challenge when applied at sampling ratios less than 60%, despite fulfilling CS's incoherency criteria. Such low sampling ratios will result in a large variety of undersampling patterns. Therefore, skipped segments of k-space cannot be allowed to happen randomly at low sampling rates. By optimizing the sampling pattern, CS will reach its full potential and be able to be applied to a highly undersampled 129 Xe lung dataset.
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Affiliation(s)
- Mitra Tavakkoli
- Imaging Research Centre, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
- Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada
| | - Sarah Svenningsen
- Firestone Institute for Respiratory Health, St Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
- Department of Medicine, Division of Respirology, McMaster University, Hamilton, Ontario, Canada
- School of Biomedical Engineering, McMaster University, 1280 Main St W, Hamilton, Ontario, Canada
| | - Yonni Friedlander
- Firestone Institute for Respiratory Health, St Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Norman B Konyer
- Imaging Research Centre, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
| | - Parameswaran Nair
- Firestone Institute for Respiratory Health, St Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
- Department of Medicine, Division of Respirology, McMaster University, Hamilton, Ontario, Canada
| | - Michael D Noseworthy
- Imaging Research Centre, St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
- Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada
- School of Biomedical Engineering, McMaster University, 1280 Main St W, Hamilton, Ontario, Canada
- Department of Radiology, McMaster University, Hamilton, Ontario, Canada
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21
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Chen Y, Zekelman LR, Zhang C, Xue T, Song Y, Makris N, Rathi Y, Golby AJ, Cai W, Zhang F, O'Donnell LJ. TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance. Med Image Anal 2024; 94:103120. [PMID: 38458095 DOI: 10.1016/j.media.2024.103120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 11/30/2023] [Accepted: 02/21/2024] [Indexed: 03/10/2024]
Abstract
We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.
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Affiliation(s)
- Yuqian Chen
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Leo R Zekelman
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, MA, USA
| | - Chaoyi Zhang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Tengfei Xue
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Yang Song
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
| | - Nikos Makris
- Departments of Psychiatry and Neurology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Weidong Cai
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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22
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Nigam S, Gjelaj E, Wang R, Wei GW, Wang P. Machine Learning and Deep Learning Applications in Magnetic Particle Imaging. J Magn Reson Imaging 2024. [PMID: 38358090 DOI: 10.1002/jmri.29294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non-ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X-space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence-based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Saumya Nigam
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, Michigan, USA
| | - Elvira Gjelaj
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Lyman Briggs College, Michigan State University, East Lansing, Michigan, USA
| | - Rui Wang
- Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
| | - Guo-Wei Wei
- Department of Mathematics, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
- Department of Electrical and Computer Engineering, College of Engineering, Michigan State University, East Lansing, Michigan, USA
- Department of Biochemistry and Molecular Biology, College of Natural Science, Michigan State University, East Lansing, Michigan, USA
| | - Ping Wang
- Precision Health Program, Michigan State University, East Lansing, Michigan, USA
- Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, Michigan, USA
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23
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Kumar N, Krause L, Wondrak T, Eckert S, Eckert K, Gumhold S. Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks. Sensors (Basel) 2024; 24:1213. [PMID: 38400371 PMCID: PMC10893175 DOI: 10.3390/s24041213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024]
Abstract
Electrolysis stands as a pivotal method for environmentally sustainable hydrogen production. However, the formation of gas bubbles during the electrolysis process poses significant challenges by impeding the electrochemical reactions, diminishing cell efficiency, and dramatically increasing energy consumption. Furthermore, the inherent difficulty in detecting these bubbles arises from the non-transparency of the wall of electrolysis cells. Additionally, these gas bubbles induce alterations in the conductivity of the electrolyte, leading to corresponding fluctuations in the magnetic flux density outside of the electrolysis cell, which can be measured by externally placed magnetic sensors. By solving the inverse problem of the Biot-Savart Law, we can estimate the conductivity distribution as well as the void fraction within the cell. In this work, we study different approaches to solve the inverse problem including Invertible Neural Networks (INNs) and Tikhonov regularization. Our experiments demonstrate that INNs are much more robust to solving the inverse problem than Tikhonov regularization when the level of noise in the magnetic flux density measurements is not known or changes over space and time.
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Affiliation(s)
- Nishant Kumar
- Institute of Software and Multimedia Technology, Technische Universität Dresden, 01187 Dresden, Germany;
| | - Lukas Krause
- Institute of Process Engineering and Environmental Technology, Technische Universität Dresden, 01069 Dresden, Germany; (L.K.); (K.E.)
- Institute of Fluid Dynamics, Helmholtz-Zentrum Dresden-Rossendorf, 01328 Dresden, Germany; (T.W.); (S.E.)
| | - Thomas Wondrak
- Institute of Fluid Dynamics, Helmholtz-Zentrum Dresden-Rossendorf, 01328 Dresden, Germany; (T.W.); (S.E.)
| | - Sven Eckert
- Institute of Fluid Dynamics, Helmholtz-Zentrum Dresden-Rossendorf, 01328 Dresden, Germany; (T.W.); (S.E.)
| | - Kerstin Eckert
- Institute of Process Engineering and Environmental Technology, Technische Universität Dresden, 01069 Dresden, Germany; (L.K.); (K.E.)
- Institute of Fluid Dynamics, Helmholtz-Zentrum Dresden-Rossendorf, 01328 Dresden, Germany; (T.W.); (S.E.)
| | - Stefan Gumhold
- Institute of Software and Multimedia Technology, Technische Universität Dresden, 01187 Dresden, Germany;
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24
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Lee JH, Kim JY, Ryu K, Al-Masni MA, Kim TH, Han D, Kim HG, Kim DH. JUST-Net: Jointly unrolled cross-domain optimization based spatio-temporal reconstruction network for accelerated 3D myelin water imaging. Magn Reson Med 2024. [PMID: 38342983 DOI: 10.1002/mrm.30021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/08/2024] [Accepted: 01/08/2024] [Indexed: 02/13/2024]
Abstract
PURPOSE We introduced a novel reconstruction network, jointly unrolled cross-domain optimization-based spatio-temporal reconstruction network (JUST-Net), aimed at accelerating 3D multi-echo gradient-echo (mGRE) data acquisition and improving the quality of resulting myelin water imaging (MWI) maps. METHOD An unrolled cross-domain spatio-temporal reconstruction network was designed. The main idea is to combine frequency and spatio-temporal image feature representations and to sequentially implement convolution layers in both domains. The k-space subnetwork utilizes shared information from adjacent frames, whereas the image subnetwork applies separate convolutions in both spatial and temporal dimensions. The proposed reconstruction network was evaluated for both retrospectively and prospectively accelerated acquisition. Furthermore, it was assessed in simulation studies and real-world cases with k-space corruptions to evaluate its potential for motion artifact reduction. RESULTS The proposed JUST-Net enabled highly reproducible and accelerated 3D mGRE acquisition for whole-brain MWI, reducing the acquisition time from fully sampled 15:23 to 2:22 min within a 3-min reconstruction time. The normalized root mean squared error of the reconstructed mGRE images increased by less than 4.0%, and the correlation coefficients for MWI showed a value of over 0.68 when compared to the fully sampled reference. Additionally, the proposed method demonstrated a mitigating effect on both simulated and clinical motion-corrupted cases. CONCLUSION The proposed JUST-Net has demonstrated the capability to achieve high acceleration factors for 3D mGRE-based MWI, which is expected to facilitate widespread clinical applications of MWI.
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Affiliation(s)
- Jae-Hun Lee
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
- Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Jae-Yoon Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Kanghyun Ryu
- Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Mohammed A Al-Masni
- Department of Artificial Intelligence, Sejong University, Seoul, Republic of Korea
| | - Tae Hyung Kim
- Department of Computer Engineering, Hongik University, Seoul, Republic of Korea
| | - Dongyeob Han
- Siemens Healthineers Ltd, Seoul, Republic of Korea
| | - Hyun Gi Kim
- Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
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25
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Bian W, Jang A, Liu F. Improving quantitative MRI using self-supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping. Magn Reson Med 2024. [PMID: 38342980 DOI: 10.1002/mrm.30045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 12/21/2023] [Accepted: 01/23/2024] [Indexed: 02/13/2024]
Abstract
PURPOSE This paper proposes a novel self-supervised learning framework that uses model reinforcement, REference-free LAtent map eXtraction with MOdel REinforcement (RELAX-MORE), for accelerated quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll an iterative model-based qMRI reconstruction into a deep learning framework, enabling accelerated MR parameter maps that are highly accurate and robust. METHODS Unlike conventional deep learning methods which require large amounts of training data, RELAX-MORE is a subject-specific method that can be trained on single-subject data through self-supervised learning, making it accessible and practically applicable to many qMRI studies. Using quantitativeT 1 $$ {\mathrm{T}}_1 $$ mapping as an example, the proposed method was applied to the brain, knee and phantom data. RESULTS The proposed method generates high-quality MR parameter maps that correct for image artifacts, removes noise, and recovers image features in regions of imperfect image conditions. Compared with other state-of-the-art conventional and deep learning methods, RELAX-MORE significantly improves efficiency, accuracy, robustness, and generalizability for rapid MR parameter mapping. CONCLUSION This work demonstrates the feasibility of a new self-supervised learning method for rapid MR parameter mapping, that is readily adaptable to the clinical translation of qMRI.
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Affiliation(s)
- Wanyu Bian
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Albert Jang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Fang Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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26
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Shiri I, Salimi Y, Sirjani N, Razeghi B, Bagherieh S, Pakbin M, Mansouri Z, Hajianfar G, Avval AH, Askari D, Ghasemian M, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khosravi B, Bijari S, Sayfollahi S, Atashzar MR, Hasanian M, Shahhamzeh A, Teimouri A, Goharpey N, Shirzad-Aski H, Karimi J, Radmard AR, Rezaei-Kalantari K, Oghli MG, Oveisi M, Vafaei Sadr A, Voloshynovskiy S, Zaidi H. Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset. Med Phys 2024. [PMID: 38335175 DOI: 10.1002/mp.16964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/10/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model. PURPOSE This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images. METHODS After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold-out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. RESULTS The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79-0.85) and (95% CI: 0.77-0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p-value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. CONCLUSION The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi-institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Nasim Sirjani
- Research and Development Department, Med Fanavarn Plus Co, Karaj, Iran
| | - Behrooz Razeghi
- Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Sara Bagherieh
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Masoumeh Pakbin
- Imaging Department, Qom University of Medical Sciences, Qom, Iran
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | | | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Ghasemian
- Department of Radiology, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qom, Iran
| | - Saleh Sandoughdaran
- Department of Clinical Oncology, Royal Surrey County Hospital, Guildford, UK
| | - Ahmad Sohrabi
- Radin Makian Azma Mehr Ltd., Radinmehr Veterinary Laboratory, Iran University of Medical Sciences, Gorgan, Iran
| | - Elham Sadati
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Somayeh Livani
- Clinical Research Development Unit (CRDU), Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran
| | - Pooya Iranpour
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahriar Kolahi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Bardia Khosravi
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Salar Bijari
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Sahar Sayfollahi
- Department of Neurosurgery, Faculty of Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Atashzar
- Department of Immunology, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Mohammad Hasanian
- Department of Radiology, Arak University of Medical Sciences, Arak, Iran
| | - Alireza Shahhamzeh
- Clinical research development center, Qom University of Medical Sciences, Qom, Iran
| | - Arash Teimouri
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Neda Goharpey
- Department of radiation oncology, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Jalal Karimi
- Department of Infectious Disease, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Kiara Rezaei-Kalantari
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | | | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alireza Vafaei Sadr
- Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, Pennsylvania, USA
| | | | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
- University Research and Innovation Center, Óbuda University, Budapest, Hungary
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Lee S, Song E, Zhu M, Appel-Cresswell S, McKeown MJ. Apathy scores in Parkinson's disease relate to EEG components in an incentivized motor task. Brain Commun 2024; 6:fcae025. [PMID: 38370450 PMCID: PMC10873141 DOI: 10.1093/braincomms/fcae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 11/12/2023] [Accepted: 02/07/2024] [Indexed: 02/20/2024] Open
Abstract
Apathy is one of the most prevalent non-motor symptoms of Parkinson's disease and is characterized by decreased goal-directed behaviour due to a lack of motivation and/or impaired emotional reactivity. Despite its high prevalence, the neurophysiological mechanisms underlying apathy in Parkinson's disease, which may guide neuromodulation interventions, are poorly understood. Here, we investigated the neural oscillatory characteristics of apathy in Parkinson's disease using EEG data recorded during an incentivized motor task. Thirteen Parkinson's disease patients with apathy and 13 Parkinson's disease patients without apathy as well as 12 healthy controls were instructed to squeeze a hand grip device to earn a monetary reward proportional to the grip force they used. Event-related spectral perturbations during the presentation of a reward cue and squeezing were analysed using multiset canonical correlation analysis to detect different orthogonal components of temporally consistent event-related spectral perturbations across trials and participants. The first component, predominantly located over parietal regions, demonstrated suppression of low-beta (12-20 Hz) power (i.e. beta desynchronization) during reward cue presentation that was significantly smaller in Parkinson's disease patients with apathy compared with healthy controls. Unlike traditional event-related spectral perturbation analysis, the beta desynchronization in this component was significantly correlated with clinical apathy scores. Higher monetary rewards resulted in larger beta desynchronization in healthy controls but not Parkinson's disease patients. The second component contained gamma and theta frequencies and demonstrated exaggerated theta (4-8 Hz) power in Parkinson's disease patients with apathy during the reward cue and squeezing compared with healthy controls (HCs), and this was positively correlated with Montreal Cognitive Assessment scores. The third component, over central regions, demonstrated significantly different beta power across groups, with apathetic groups having the lowest beta power. Our results emphasize that altered low-beta and low-theta oscillations are critical for reward processing and motor planning in Parkinson's disease patients with apathy and these may provide a target for non-invasive neuromodulation.
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Affiliation(s)
- Soojin Lee
- Pacific Parkinson’s Research Centre, The University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Esther Song
- Pacific Parkinson’s Research Centre, The University of British Columbia, Vancouver, BC V6T 2B5, Canada
- Department of Psychiatry, The University of British Columbia, Vancouver, BC V6T 2A1, Canada
| | - Maria Zhu
- Pacific Parkinson’s Research Centre, The University of British Columbia, Vancouver, BC V6T 2B5, Canada
- Department of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Silke Appel-Cresswell
- Pacific Parkinson’s Research Centre, The University of British Columbia, Vancouver, BC V6T 2B5, Canada
- Division of Neurology, Department of Medicine, The University of British Columbia, Vancouver, BC V6T 2B5, Canada
| | - Martin J McKeown
- Pacific Parkinson’s Research Centre, The University of British Columbia, Vancouver, BC V6T 2B5, Canada
- Division of Neurology, Department of Medicine, The University of British Columbia, Vancouver, BC V6T 2B5, Canada
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28
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Huang S, Zhong L, Shi Y. Automated Mapping of Residual Distortion Severity in Diffusion MRI. Comput Diffus MRI 2024; 14328:58-69. [PMID: 38500569 PMCID: PMC10948104 DOI: 10.1007/978-3-031-47292-3_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Susceptibility-induced distortion is a common artifact in diffusion MRI (dMRI), which deforms the dMRI locally and poses significant challenges in connectivity analysis. While various methods were proposed to correct the distortion, residual distortions often persist at varying degrees across brain regions and subjects. Generating a voxel-level residual distortion severity map can thus be a valuable tool to better inform downstream connectivity analysis. To fill this current gap in dMRI analysis, we propose a supervised deep-learning network to predict a severity map of residual distortion. The training process is supervised using the structural similarity index measure (SSIM) of the fiber orientation distribution (FOD) in two opposite phase encoding (PE) directions. Only b0 images and related outputs from the distortion correction methods are needed as inputs in the testing process. The proposed method is applicable in large-scale datasets such as the UK Biobank, Adolescent Brain Cognitive Development (ABCD), and other emerging studies that only have complete dMRI data in one PE direction but acquires b0 images in both PEs. In our experiments, we trained the proposed model using the Lifespan Human Connectome Project Aging (HCP-Aging) dataset ( n = 662 ) and apply the trained model to data ( n = 1330 ) from UK Biobank. Our results show low training, validation, and test errors, and the severity map correlates excellently with an FOD integrity measure in both HCP-Aging and UK Biobank data. The proposed method is also highly efficient and can generate the severity map in around 1 second for each subject.
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Affiliation(s)
- Shuo Huang
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
| | - Lujia Zhong
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
| | - Yonggang Shi
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA
- Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
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Aparicio L, Crowley L, Christin JR, Laplaca CJ, Hibshoosh H, Rabadan R, Shen MM. Meta-analyses of mouse and human prostate single-cell transcriptomes reveal widespread epithelial plasticity in tissue regression, regeneration, and cancer. bioRxiv 2024:2024.01.30.578066. [PMID: 38352515 PMCID: PMC10862785 DOI: 10.1101/2024.01.30.578066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Recent advances in single-cell RNA-sequencing (scRNA-seq) technology have facilitated studies of cell states and plasticity in tissue maintenance and cancer, including in the prostate. Here we present meta-analyses of multiple new and published scRNA-seq datasets to establish reference cell type classifications for the normal mouse and human prostate. Our analyses demonstrate transcriptomic similarities between epithelial cell states in the normal prostate, in the regressed prostate after androgen-deprivation, and in primary prostate tumors. During regression in the mouse prostate, all epithelial cells shift their expression profiles towards a proximal periurethral (PrU) state, demonstrating an androgen-dependent plasticity that is restored to normal during androgen restoration and regeneration. In the human prostate, we find progressive rewiring of transcriptional programs across epithelial cell types in benign prostate hyperplasia and treatment-naïve prostate cancer. Notably, we detect copy number variants predominantly within Luminal Acinar cells in prostate tumors, suggesting a bias in their cell type of origin, as well as a larger field of transcriptomic alterations in non-tumor cells. Finally, we observe that Luminal Acinar tumor cells in treatment-naïve prostate cancer display heterogeneous androgen receptor (AR) signaling activity, including a split between high-AR and low-AR profiles with similarity to PrU-like states. Taken together, our analyses of cellular heterogeneity and plasticity provide important translational insights into the origin and treatment response of prostate cancer.
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Affiliation(s)
- Luis Aparicio
- Program for Mathematical Genomics, Columbia University Irving Medical Center, New York, NY
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - Laura Crowley
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY
- Department of Urology, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - John R. Christin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY
- Department of Urology, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - Caroline J. Laplaca
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY
- Department of Urology, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - Hanina Hibshoosh
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - Raul Rabadan
- Program for Mathematical Genomics, Columbia University Irving Medical Center, New York, NY
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - Michael M. Shen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY
- Department of Urology, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
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Wu W. Dynamic field mapping and distortion correction using single-shot blip-rewound EPI and joint multi-echo reconstruction. Magn Reson Med 2024. [PMID: 38308081 DOI: 10.1002/mrm.30038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 01/02/2024] [Accepted: 01/16/2024] [Indexed: 02/04/2024]
Abstract
PURPOSE To develop a method for dynamic∆ B 0 $$ \Delta {B}_0 $$ mapping and distortion correction. METHODS A blip-rewound EPI trajectory was developed to acquire multiple 2D EPI images in a single readout with an interleaved order, which allows a short TE difference. A joint multi-echo reconstruction was utilized to exploit the shared information between EPI images. The reconstructed images from each readout are combined to produce a final magnitude image. A∆ B 0 $$ \Delta {B}_0 $$ map is calculated from the phase of these images for distortion correction. The efficacy of the proposed method is assessed with phantom and in vivo experiments. The performance of the proposed method in the presence of subject motion is also investigated. RESULTS Compared to conventional multi-echo EPI, the proposed method allows dynamic∆ B 0 $$ \Delta {B}_0 $$ mapping at matched resolution with a much shorter TR. Phantom and in vivo results show that the proposed method can provide a comparable magnitude image as conventional single-shot EPI. The∆ B 0 $$ \Delta {B}_0 $$ maps calculated from the proposed method are consistent with conventional multi-echo EPI in the phantom experiment. For in vivo experiments, the proposed method provides a more accurate estimation of∆ B 0 $$ \Delta {B}_0 $$ than conventional multi-echo EPI, which is prone to phase wrapping problems due to the long TE difference. In-vivo scan with subject motion shows the proposed dynamic field mapping method can improve the temporal stability of EPI time series compared to gradient echo (GRE) based static field mapping. CONCLUSION The proposed method allows accurate dynamic∆ B 0 $$ \Delta {B}_0 $$ mapping for robust distortion correction without compromising spatial or temporal resolution.
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Affiliation(s)
- Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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Kofler A, Kerkering KM, Goschel L, Fillmer A, Kolbitsch C. Quantitative MR Image Reconstruction Using Parameter-Specific Dictionary Learning With Adaptive Dictionary-Size and Sparsity-Level Choice. IEEE Trans Biomed Eng 2024; 71:388-399. [PMID: 37540614 DOI: 10.1109/tbme.2023.3300090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2023]
Abstract
OBJECTIVE We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI). METHODS Because different quantitative parameter-maps differ from each other in terms of local features, we propose a method where the employed dictionary learning (DL) and sparse coding (SC) algorithms automatically estimate the optimal dictionary-size and sparsity level separately for each parameter-map. We evaluated the method on a T1-mapping QMRI problem in the brain using the BrainWeb data as well as in-vivo brain images acquired on an ultra-high field 7 T scanner. We compared it to a model-based acceleration for parameter mapping (MAP) approach, other sparsity-based methods using total variation (TV), Wavelets (Wl), and Shearlets (Sh) to a method which uses DL and SC to reconstruct qualitative images, followed by a non-linear (DL+Fit). RESULTS Our algorithm surpasses MAP, TV, Wl, and Sh in terms of RMSE and PSNR. It yields better or comparable results to DL+Fit by additionally significantly accelerating the reconstruction by a factor of approximately seven. CONCLUSION The proposed method outperforms the reported methods of comparison and yields accurate T1-maps. Although presented for T1-mapping in the brain, our method's structure is general and thus most probably also applicable for the the reconstruction of other quantitative parameters in other organs. SIGNIFICANCE From a clinical perspective, the obtained T1-maps could be utilized to differentiate between healthy subjects and patients with Alzheimer's disease. From a technical perspective, the proposed unsupervised method could be employed to obtain ground-truth data for the development of data-driven methods based on supervised learning.
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Avidan N, Freiman M. MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation. Comput Methods Programs Biomed 2024; 244:107942. [PMID: 38039921 DOI: 10.1016/j.cmpb.2023.107942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/11/2023] [Accepted: 11/21/2023] [Indexed: 12/03/2023]
Abstract
BACKGROUND AND OBJECTIVE High-quality reconstruction of MRI images from under-sampled 'k-space' data, which is in the Fourier domain, is crucial for shortening MRI acquisition times and ensuring superior temporal resolution. Over recent years, a wealth of deep neural network (DNN) methods have emerged, aiming to tackle the complex, ill-posed inverse problem linked to this process. However, their instability against variations in the acquisition process and anatomical distribution exposes a deficiency in the generalization of relevant physical models within these DNN architectures. The goal of our work is to enhance the generalization capabilities of DNN methods for k-space interpolation by introducing 'MA-RECON', an innovative mask-aware DNN architecture and associated training method. METHODS Unlike preceding approaches, our 'MA-RECON' architecture encodes not only the observed data but also the under-sampling mask within the model structure. It implements a tailored training approach that leverages data generated with a variety of under-sampling masks to stimulate the model's generalization of the under-sampled MRI reconstruction problem. Therefore, effectively represents the associated inverse problem, akin to the classical compressed sensing approach. RESULTS The benefits of our MA-RECON approach were affirmed through rigorous testing with the widely accessible fastMRI dataset. Compared to standard DNN methods and DNNs trained with under-sampling mask augmentation, our approach demonstrated superior generalization capabilities. This resulted in a considerable improvement in robustness against variations in both the acquisition process and anatomical distribution, especially in regions with pathology. CONCLUSION In conclusion, our mask-aware strategy holds promise for enhancing the generalization capacity and robustness of DNN-based methodologies for MRI reconstruction from undersampled k-space data. Code is available in the following link: https://github.com/nitzanavidan/PD_Recon.
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Affiliation(s)
- Nitzan Avidan
- Faculty of Biomedical Engineering, Technion IIT, Haifa, Israel.
| | - Moti Freiman
- Faculty of Biomedical Engineering, Technion IIT, Haifa, Israel.
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Bousse A, Kandarpa VSS, Rit S, Perelli A, Li M, Wang G, Zhou J, Wang G. Systematic Review on Learning-based Spectral CT. IEEE Trans Radiat Plasma Med Sci 2024; 8:113-137. [PMID: 38476981 PMCID: PMC10927029 DOI: 10.1109/trpms.2023.3314131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.
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Affiliation(s)
- Alexandre Bousse
- LaTIM, Inserm UMR 1101, Université de Bretagne Occidentale, 29238 Brest, France
| | | | - Simon Rit
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69373, Lyon, France
| | - Alessandro Perelli
- Department of Biomedical Engineering, School of Science and Engineering, University of Dundee, DD1 4HN, UK
| | - Mengzhou Li
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Guobao Wang
- Department of Radiology, University of California Davis Health, Sacramento, USA
| | - Jian Zhou
- CTIQ, Canon Medical Research USA, Inc., Vernon Hills, 60061, USA
| | - Ge Wang
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA
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Jiao H, Mao Q, Razzaq N, Ankri R, Cui J. Ultrasound technology assisted colloidal nanocrystal synthesis and biomedical applications. Ultrason Sonochem 2024; 103:106798. [PMID: 38330546 PMCID: PMC10865478 DOI: 10.1016/j.ultsonch.2024.106798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 12/08/2023] [Accepted: 02/01/2024] [Indexed: 02/10/2024]
Abstract
Non-invasive and high spatiotemporal resolution mythologies for the diagnosis and treatment of disease in clinical medicine promote the development of modern medicine. Ultrasound (US) technology provides a non-invasive, real-time, and cost-effective clinical imaging modality, which plays a significant role in chemical synthesis and clinical translation, especially in in vivo imaging and cancer therapy. On the one hand, the US treatment is usually accompanied by cavitation, leading to high temperature and pressure, so-called "hot spot", playing a significant role in sonochemical-based colloidal synthesis. Compared with the classical nucleation synthetic method, the sonochemical synthesis strategy presents high efficiency for the fabrication of colloidal nanocrystals due to its fast nucleation and growth procedure. On the other hand, the US is attractive for in vivo and medical treatment, with applications increasing with the development of novel contrast agents, such as the micro and nano bubbles, which are widely used in neuromodulation, with which the US can breach the blood-brain barrier temporarily and safely, opening a new door to neuromodulation and therapy. In terms of cancer treatment, sonodynamic therapy and US-assisted synergetic therapy show great effects against cancer and sonodynamic immunotherapy present unparalleled potentiality compared with other synergetic therapies. Further development of ultrasound technology can revolutionize both chemical synthesis and clinical translation by improving efficiency, precision, and accessibility while reducing environmental impact and enhancing patient care. In this paper, we review the US-assisted sonochemical synthesis and biological applications, to promote the next generation US technology-assisted applications.
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Affiliation(s)
- Haorong Jiao
- The Center for Molecular Imaging and Nuclear Medicine, State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, 199 Renai Road, Industrial Park, Suzhou 215123, Jiangsu, China
| | - Qiulian Mao
- The Center for Molecular Imaging and Nuclear Medicine, State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, 199 Renai Road, Industrial Park, Suzhou 215123, Jiangsu, China
| | - Noman Razzaq
- The Center for Molecular Imaging and Nuclear Medicine, State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, 199 Renai Road, Industrial Park, Suzhou 215123, Jiangsu, China
| | - Rinat Ankri
- The Biomolecular and Nanophotonics Lab, Ariel University, 407000, P.O.B. 3, Ariel, Israel.
| | - Jiabin Cui
- The Center for Molecular Imaging and Nuclear Medicine, State Key Laboratory of Radiation Medicine and Protection, School for Radiological and Interdisciplinary Sciences (RAD-X) and Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, 199 Renai Road, Industrial Park, Suzhou 215123, Jiangsu, China.
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Ramos-Llordén G, Park DJ, Kirsch JE, Scholz A, Keil B, Maffei C, Lee HH, Bilgic B, Edlow BL, Mekkaoui C, Yendiki A, Witzel T, Huang SY. Eddy current-induced artifact correction in high b-value ex vivo human brain diffusion MRI with dynamic field monitoring. Magn Reson Med 2024; 91:541-557. [PMID: 37753621 PMCID: PMC10842131 DOI: 10.1002/mrm.29873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 08/30/2023] [Accepted: 09/02/2023] [Indexed: 09/28/2023]
Abstract
PURPOSE To investigate whether spatiotemporal magnetic field monitoring can correct pronounced eddy current-induced artifacts incurred by strong diffusion-sensitizing gradients up to 300 mT/m used in high b-value diffusion-weighted (DW) EPI. METHODS A dynamic field camera equipped with 16 1 H NMR field probes was first used to characterize field perturbations caused by residual eddy currents from diffusion gradients waveforms in a 3D multi-shot EPI sequence on a 3T Connectom scanner for different gradient strengths (up to 300 mT/m), diffusion directions, and shots. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-gradient strength, submillimeter resolution whole-brain ex vivo diffusion MRI. A 3D multi-shot image reconstruction framework was developed that incorporated the nonlinear phase evolution measured with the dynamic field camera. RESULTS Phase perturbations in the readout induced by residual eddy currents from strong diffusion gradients are highly nonlinear in space and time, vary among diffusion directions, and interfere significantly with the image encoding gradients, changing the k-space trajectory. During the readout, phase modulations between odd and even EPI echoes become non-static and diffusion encoding direction-dependent. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting reduction approaches such as navigator- and structured low-rank-based methods or MUSE followed by image-based distortion correction with the FSL tool "eddy." CONCLUSION Strong eddy current artifacts characteristic of high-gradient strength DW-EPI can be well corrected with dynamic field monitoring-based image reconstruction.
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Affiliation(s)
- Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Daniel J Park
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - John E Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Alina Scholz
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps University of Marburg, Marburg, Germany
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Brian L Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Choukri Mekkaoui
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | | | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Perkov S, Vorobev V, Kurochkin MA, Gorodkov S, Gorin D. Rapid low-cost hyperspectral imaging system for quantitative assessment of infantile hemangioma. J Biophotonics 2024; 17:e202300375. [PMID: 38009761 DOI: 10.1002/jbio.202300375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/29/2023] [Accepted: 11/05/2023] [Indexed: 11/29/2023]
Abstract
Hemangioma, the predominant benign tumor occurring in infancy, exhibits a wide range of prognoses and associated outcomes. The accurate determination of prognosis through noninvasive imaging modalities holds essential importance in enabling effective personalized treatment strategies and minimizing unnecessary surgical interventions for individual patients. The present study focuses on advancing the personalized prognosis of hemangioma by leveraging noninvasive optical sensing technologies by the development of a novel rapid hyperspectral sensor (image collection in 5 s, lateral resolution of 10 μm) that is capable of quantifying hemoglobin oxygenation and vascularization dynamics during the course of tumor evolution. We have developed a quantitative parameter for hemangioma assessment, that demonstrated agreement with the clinician's conclusion in 90% among all cases during clinical studies on six patients, who visited clinician from two to four times. The presented methodology has potential to be implemented as a supportive tool for accurate hemangioma diagnostics in clinics.
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Affiliation(s)
- Sergei Perkov
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Viktor Vorobev
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Maxim A Kurochkin
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Sergey Gorodkov
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russia
- Faculty of Pediatrics, Saratov State Medical University, Saratov, Russia
| | - Dmitry Gorin
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow, Russia
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Xu J, Zu T, Hsu YC, Wang X, Chan KWY, Zhang Y. Accelerating CEST imaging using a model-based deep neural network with synthetic training data. Magn Reson Med 2024; 91:583-599. [PMID: 37867413 DOI: 10.1002/mrm.29889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/31/2023] [Accepted: 09/25/2023] [Indexed: 10/24/2023]
Abstract
PURPOSE To develop a model-based deep neural network for high-quality image reconstruction of undersampled multi-coil CEST data. THEORY AND METHODS Inspired by the variational network (VN), the CEST image reconstruction equation is unrolled into a deep neural network (CEST-VN) with a k-space data-sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial-frequential convolution kernels that exploit correlations in the x-ω domain. Additionally, a new pipeline based on multiple-pool Bloch-McConnell simulations is devised to synthesize multi-coil CEST data from publicly available anatomical MRI data. The proposed network is trained on simulated data with a CEST-specific loss function that jointly measures the structural and CEST contrast. The performance of CEST-VN was evaluated on four healthy volunteers and five brain tumor patients using retrospectively or prospectively undersampled data with various acceleration factors, and then compared with other conventional and state-of-the-art reconstruction methods. RESULTS The proposed CEST-VN method generated high-quality CEST source images and amide proton transfer-weighted maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original VN. With the acceleration factors increasing from 3 to 6, CEST-VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the CEST-specific loss function and data-sharing block used. CONCLUSIONS The proposed CEST-VN method can offer high-quality CEST source images and amide proton transfer-weighted maps from highly undersampled multi-coil data by integrating the deep learning prior and multi-coil sensitivity encoding model.
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Affiliation(s)
- Jianping Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Tao Zu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
| | - Yi-Cheng Hsu
- MR Collaboration, Siemens Healthcare Ltd., Shanghai, People's Republic of China
| | - Xiaoli Wang
- School of Medical Imaging, Weifang Medical University, Weifang, People's Republic of China
| | - Kannie W Y Chan
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, People's Republic of China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, People's Republic of China
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Jun Y, Arefeen Y, Cho J, Fujita S, Wang X, Grant PE, Gagoski B, Jaimes C, Gee MS, Bilgic B. Zero-DeepSub: Zero-shot deep subspace reconstruction for rapid multiparametric quantitative MRI using 3D-QALAS. Magn Reson Med 2024. [PMID: 38282270 DOI: 10.1002/mrm.30018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 12/15/2023] [Accepted: 01/06/2024] [Indexed: 01/30/2024]
Abstract
PURPOSE To develop and evaluate methods for (1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T1 and T2 mapping, and (2) improving the fidelity of subspace QALAS by combining scan-specific deep-learning-based reconstruction and subspace modeling. THEORY AND METHODS A low-rank subspace method for 3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method (i.e., Zero-DeepSub) were proposed for rapid and high fidelity T1 and T2 mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T1 and T2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques. The reconstruction performance of the proposed subspace QALAS using Zero-DeepSub was evaluated in vivo and compared with conventional QALAS at high reduction factors of up to nine-fold. RESULTS Phantom experiments showed that subspace QALAS had good linearity with respect to the reference methods while reducing biases and improving precision compared to conventional QALAS, especially for T2 maps. Moreover, in vivo results demonstrated that subspace QALAS had better g-factor maps and could reduce voxel blurring, noise, and artifacts compared to conventional QALAS and showed robust performance at up to nine-fold acceleration with Zero-DeepSub, which enabled whole-brain T1 , T2 , and PD mapping at 1 mm isotropic resolution within 2 min of scan time. CONCLUSION The proposed subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid whole-brain multiparametric quantification and time-resolved imaging.
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Affiliation(s)
- Yohan Jun
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Yamin Arefeen
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas, Austin, Texas, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jaejin Cho
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Shohei Fujita
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Xiaoqing Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - P Ellen Grant
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Camilo Jaimes
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michael S Gee
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard/MIT Health Sciences and Technology, Cambridge, Massachusetts, USA
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Dong Z, Wald LL, Polimeni JR, Wang F. Single-shot Echo Planar Time-resolved Imaging for multi-echo functional MRI and distortion-free diffusion imaging. bioRxiv 2024:2024.01.24.577002. [PMID: 38328081 PMCID: PMC10849706 DOI: 10.1101/2024.01.24.577002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Purpose To develop EPTI, a multi-shot distortion-free multi-echo imaging technique, into a single-shot acquisition to achieve improved robustness to motion and physiological noise, increased temporal resolution, and high SNR efficiency for dynamic imaging applications. Methods A new spatiotemporal encoding was developed to achieve single-shot EPTI by enhancing spatiotemporal correlation in k-t space. The proposed single-shot encoding improves reconstruction conditioning and sampling efficiency, with additional optimization under various accelerations to achieve optimized performance. To achieve high SNR efficiency, continuous readout with minimized deadtime was employed that begins immediately after excitation and extends for an SNR-optimized length. Moreover, k-t partial Fourier and simultaneous multi-slice acquisition were integrated to further accelerate the acquisition and achieve high spatial and temporal resolution. Results We demonstrated that ss-EPTI achieves higher tSNR efficiency than multi-shot EPTI, and provides distortion-free imaging with densely-sampled multi-echo images at resolutions ~1.25-3 mm at 3T and 7T-with high SNR efficiency and with comparable temporal resolutions to ss-EPI. The ability of ss-EPTI to eliminate dynamic distortions common in EPI also further improves temporal stability. For fMRI, ss-EPTI also provides early-TE images (e.g., 2.9ms) to recover signal-intensity and functional-sensitivity dropout in challenging regions. The multi-echo images provide TE-dependent information about functional fluctuations, successfully distinguishing noise-components from BOLD signals and further improving tSNR. For diffusion MRI, ss-EPTI provides high-quality distortion-free diffusion images and multi-echo diffusion metrics. Conclusion ss-EPTI provides distortion-free imaging with high image quality, rich multi-echo information, and enhanced efficiency within comparable temporal resolution to ss-EPI, offering a robust and efficient acquisition for dynamic imaging.
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Affiliation(s)
- Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Jonathan R. Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
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Hossain MB, Shinde RK, Oh S, Kwon KC, Kim N. A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction. Sensors (Basel) 2024; 24:753. [PMID: 38339469 PMCID: PMC10856856 DOI: 10.3390/s24030753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/05/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024]
Abstract
Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI reconstruction is essential for ensuring accurate diagnosis, supporting clinical decision-making, enhancing patient safety, facilitating efficient workflows, and contributing to the validity of research studies and clinical trials. Recently, deep learning has demonstrated several advantages over conventional MRI reconstruction methods. Conventional methods rely on manual feature engineering to capture complex patterns and are usually computationally demanding due to their iterative nature. Conversely, DL methods use neural networks with hundreds of thousands of parameters and automatically learn relevant features and representations directly from the data. Nevertheless, there are some limitations to DL-based techniques concerning MRI reconstruction tasks, such as the need for large, labeled datasets, the possibility of overfitting, and the complexity of model training. Researchers are striving to develop DL models that are more efficient, adaptable, and capable of providing valuable information for medical practitioners. We provide a comprehensive overview of the current developments and clinical uses by focusing on state-of-the-art DL architectures and tools used in MRI reconstruction. This study has three objectives. Our main objective is to describe how various DL designs have changed over time and talk about cutting-edge tactics, including their advantages and disadvantages. Hence, data pre- and post-processing approaches are assessed using publicly available MRI datasets and source codes. Secondly, this work aims to provide an extensive overview of the ongoing research on transformers and deep convolutional neural networks for rapid MRI reconstruction. Thirdly, we discuss several network training strategies, like supervised, unsupervised, transfer learning, and federated learning for rapid and efficient MRI reconstruction. Consequently, this article provides significant resources for future improvement of MRI data pre-processing and fast image reconstruction.
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Affiliation(s)
- Md. Biddut Hossain
- School of Information and Communication Engineering, Chungbuk National University, Cheongju-si 28644, Chungcheongbuk-do, Republic of Korea; (M.B.H.); (R.K.S.)
| | - Rupali Kiran Shinde
- School of Information and Communication Engineering, Chungbuk National University, Cheongju-si 28644, Chungcheongbuk-do, Republic of Korea; (M.B.H.); (R.K.S.)
| | - Sukhoon Oh
- Research Equipment Operation Department, Korea Basic Science Institute, Cheongju-si 28119, Chungcheongbuk-do, Republic of Korea;
| | - Ki-Chul Kwon
- School of Information and Communication Engineering, Chungbuk National University, Cheongju-si 28644, Chungcheongbuk-do, Republic of Korea; (M.B.H.); (R.K.S.)
| | - Nam Kim
- School of Information and Communication Engineering, Chungbuk National University, Cheongju-si 28644, Chungcheongbuk-do, Republic of Korea; (M.B.H.); (R.K.S.)
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Adkinson BD, Rosenblatt M, Dadashkarimi J, Tejavibulya L, Jiang R, Noble S, Scheinost D. Brain-phenotype predictions can survive across diverse real-world data. bioRxiv 2024:2024.01.23.576916. [PMID: 38328100 PMCID: PMC10849571 DOI: 10.1101/2024.01.23.576916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.
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Affiliation(s)
- Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Javid Dadashkarimi
- Department of Radiology, Athinoula. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02129, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Bioengineering, Northeastern University, Boston, MA, 02120, USA
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06510, USA
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Kilic T, Liebig P, Demirel OB, Herrler J, Nagel AM, Ugurbil K, Akçakaya M. Unsupervised deep learning with convolutional neural networks for static parallel transmit design: A retrospective study. Magn Reson Med 2024. [PMID: 38247050 DOI: 10.1002/mrm.30014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024]
Abstract
PURPOSE To mitigateB 1 + $$ {B}_1^{+} $$ inhomogeneity at 7T for multi-channel transmit arrays using unsupervised deep learning with convolutional neural networks (CNNs). METHODS Deep learning parallel transmit (pTx) pulse design has received attention, but such methods have relied on supervised training and did not use CNNs for multi-channelB 1 + $$ {B}_1^{+} $$ maps. In this work, we introduce an alternative approach that facilitates the use of CNNs with multi-channelB 1 + $$ {B}_1^{+} $$ maps while performing unsupervised training. The multi-channelB 1 + $$ {B}_1^{+} $$ maps are concatenated along the spatial dimension to enable shift-equivariant processing amenable to CNNs. Training is performed in an unsupervised manner using a physics-driven loss function that minimizes the discrepancy of the Bloch simulation with the target magnetization, which eliminates the calculation of reference transmit RF weights. The training database comprises 3824 2D sagittal, multi-channelB 1 + $$ {B}_1^{+} $$ maps of the healthy human brain from 143 subjects.B 1 + $$ {B}_1^{+} $$ data were acquired at 7T using an 8Tx/32Rx head coil. The proposed method is compared to the unregularized magnitude least-squares (MLS) solution for the target magnetization in static pTx design. RESULTS The proposed method outperformed the unregularized MLS solution for RMS error and coefficient-of-variation and had comparable energy consumption. Additionally, the proposed method did not show local phase singularities leading to distinct holes in the resulting magnetization unlike the unregularized MLS solution. CONCLUSION Proposed unsupervised deep learning with CNNs performs better than unregularized MLS in static pTx for speed and robustness.
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Affiliation(s)
- Toygan Kilic
- Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Omer Burak Demirel
- Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Armin M Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Division of Medical Physics in Radiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Mehmet Akçakaya
- Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
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Yang G, Xia J, Dai X, Zhao H, Gao W, Ding W, Tao X, Zhu L. A Targeted Multi-Crystalline Manganese Oxide as a Tumor-Selective Nano-Sized MRI Contrast Agent for Early and Accurate Diagnosis of Tumors. Int J Nanomedicine 2024; 19:527-540. [PMID: 38260241 PMCID: PMC10802178 DOI: 10.2147/ijn.s444061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
Introduction Magnetic resonance imaging (MRI) is an important tool for the accurate diagnosis of malignant tumors in clinical settings. However, the lack of tumor-specific MRI contrast agents limits diagnostic accuracy. Methods Herein, we developed αv integrin receptor-targeting multi-crystalline manganese oxide (MCMO) as a novel MRI contrast agent for accurate diagnosis of tumors by coupling iRGD cyclopeptide PEGylation polymer onto the surface of MCMO (iRGD-pMCMO). Results The MCMO consisted of numerous small crystals and exhibited an oval structure of 200 nm in size. The iRGD-pMCMO actively recognizes tumor cells and effectively accumulates at the tumor site, consequently releasing abundant Mn2+ ions in a weakly acidic and high-GSH-expressing tumor microenvironment. Subsequently, Mn2+ ions interact with cellular GSH to form Mn-GSH chelates, enabling efficient T1-weighted MR contrast imaging. In vivo experiments indicated that iRGD-pMCMO significantly improved T1-weighted images, achieving an accurate diagnosis of subcutaneous and orthotopic tumors. The results verified that the T1 contrast effect of iRGD-pMCMO was closely associated with the expression of GSH in tumor cells. Conclusion Altogether, the novel tumor-targeting, highly sensitive MRI contrast agent developed in this study can improve the accuracy of MRI for tumor diagnosis.
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Affiliation(s)
- Gongxin Yang
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200001, People’s Republic of China
| | - Jikai Xia
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, 264100, People’s Republic of China
| | - Xiaoqing Dai
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200001, People’s Republic of China
| | - Hongbo Zhao
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200001, People’s Republic of China
| | - Weiqing Gao
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200001, People’s Republic of China
| | - Weilong Ding
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200001, People’s Republic of China
| | - Xiaofeng Tao
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200001, People’s Republic of China
| | - Ling Zhu
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, 200001, People’s Republic of China
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Verbeke EJ, Gilles MA, Bendory T, Singer A. Self Fourier shell correlation: properties and application to cryo-ET. Commun Biol 2024; 7:101. [PMID: 38228756 PMCID: PMC10791666 DOI: 10.1038/s42003-023-05724-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 12/19/2023] [Indexed: 01/18/2024] Open
Abstract
The Fourier shell correlation (FSC) is a measure of the similarity between two signals computed over corresponding shells in the frequency domain and has broad applications in microscopy. In structural biology, the FSC is ubiquitous in methods for validation, resolution determination, and signal enhancement. Computing the FSC usually requires two independent measurements of the same underlying signal, which can be limiting for some applications. Here, we analyze and extend on an approach to estimate the FSC from a single measurement. In particular, we derive the necessary conditions required to estimate the FSC from downsampled versions of a single noisy measurement. These conditions reveal additional corrections which we implement to increase the applicability of the method. We then illustrate two applications of our approach, first as an estimate of the global resolution from a single 3-D structure and second as a data-driven method for denoising tomographic reconstructions in electron cryo-tomography. These results provide general guidelines for computing the FSC from a single measurement and suggest new applications of the FSC in microscopy.
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Affiliation(s)
- Eric J Verbeke
- Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA.
| | - Marc Aurèle Gilles
- Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA
| | - Tamir Bendory
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Amit Singer
- Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA
- Department of Mathematics, Princeton University, Princeton, NJ, USA
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Artemenko NV, Genaev MA, Epifanov RU, Komyshev EG, Kruchinina YV, Koval VS, Goncharov NP, Afonnikov DA. Image-based classification of wheat spikes by glume pubescence using convolutional neural networks. Front Plant Sci 2024; 14:1336192. [PMID: 38283969 PMCID: PMC10811101 DOI: 10.3389/fpls.2023.1336192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 12/20/2023] [Indexed: 01/30/2024]
Abstract
Introduction Pubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat. In wheat, pubescence is visible on leaves, leaf sheath, glumes and nodes. Regarding glumes, the presence of pubescence plays a pivotal role in its classification. It supplements other spike characteristics, aiding in distinguishing between different varieties within the wheat species. The determination of pubescence typically involves visual analysis by an expert. However, methods without the use of binocular loupe tend to be subjective, while employing additional equipment is labor-intensive. This paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks. Methods Initially, image segmentation is conducted to extract the contour of the spike body, followed by cropping of the spike images to an equal size. These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). The networks were trained and tested on a dataset comprising 9,719 spike images. Results For segmentation, the U-Net model with EfficientNet-B1 encoder was chosen, achieving the segmentation accuracy IoU = 0.947 for the spike body and 0.777 for awns. The classification model for glume pubescence with the highest performance utilized the EfficientNet-B1 architecture. On the test sample, the model exhibited prediction accuracy parameters of F1 = 0.85 and AUC = 0.96, while on the holdout sample it showed F1 = 0.84 and AUC = 0.89. Additionally, the study investigated the relationship between image scale, artificial distortions, and model prediction performance, revealing that higher magnification and smaller distortions yielded a more accurate prediction of glume pubescence.
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Affiliation(s)
- Nikita V Artemenko
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Department of Mathematics and Mechanics, Novosibirsk State University, Novosibirsk, Russia
| | - Mikhail A Genaev
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Kurchatov Center for Genome Research, Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Rostislav Ui Epifanov
- Department of Mathematics and Mechanics, Novosibirsk State University, Novosibirsk, Russia
| | - Evgeny G Komyshev
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Yulia V Kruchinina
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Kurchatov Center for Genome Research, Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Vasiliy S Koval
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Kurchatov Center for Genome Research, Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Nikolay P Goncharov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Dmitry A Afonnikov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
- Department of Mathematics and Mechanics, Novosibirsk State University, Novosibirsk, Russia
- Kurchatov Center for Genome Research, Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
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Hu P, Tong X, Lin L, Wang LV. Data-driven system matrix manipulation enabling fast functional imaging and intra-image nonrigid motion correction in tomography. bioRxiv 2024:2024.01.07.574504. [PMID: 38260429 PMCID: PMC10802502 DOI: 10.1101/2024.01.07.574504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Tomographic imaging modalities are described by large system matrices. Sparse sampling and tissue motion degrade system matrix and image quality. Various existing techniques improve the image quality without correcting the system matrices. Here, we compress the system matrices to improve computational efficiency (e.g., 42 times) using singular value decomposition and fast Fourier transform. Enabled by the efficiency, we propose (1) fast sparsely sampling functional imaging by incorporating a densely sampled prior image into the system matrix, which maintains the critical linearity while mitigating artifacts and (2) intra-image nonrigid motion correction by incorporating the motion as subdomain translations into the system matrix and reconstructing the translations together with the image iteratively. We demonstrate the methods in 3D photoacoustic computed tomography with significantly improved image qualities and clarify their applicability to X-ray CT and MRI or other types of imperfections due to the similarities in system matrices.
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Affiliation(s)
- Peng Hu
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Xin Tong
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Li Lin
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
- Present address: College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Lihong V Wang
- Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
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Ekanayake M, Pawar K, Harandi M, Egan G, Chen Z. McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction. Comput Biol Med 2024; 168:107775. [PMID: 38061154 DOI: 10.1016/j.compbiomed.2023.107775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024]
Abstract
Deep learning MRI reconstruction methods are often based on Convolutional neural network (CNN) models; however, they are limited in capturing global correlations among image features due to the intrinsic locality of the convolution operation. Conversely, the recent vision transformer models (ViT) are capable of capturing global correlations by applying self-attention operations on image patches. Nevertheless, the existing transformer models for MRI reconstruction rarely leverage the physics of MRI. In this paper, we propose a novel physics-based transformer model titled, the Multi-branch Cascaded Swin Transformers (McSTRA) for robust MRI reconstruction. McSTRA combines several interconnected MRI physics-related concepts with the Swin transformers: it exploits global MRI features via the shifted window self-attention mechanism; it extracts MRI features belonging to different spectral components via a multi-branch setup; it iterates between intermediate de-aliasing and data consistency via a cascaded network with intermediate loss computations; furthermore, we propose a point spread function-guided positional embedding generation mechanism for the Swin transformers which exploit the spread of the aliasing artifacts for effective reconstruction. With the combination of all these components, McSTRA outperforms the state-of-the-art methods while demonstrating robustness in adversarial conditions such as higher accelerations, noisy data, different undersampling protocols, out-of-distribution data, and abnormalities in anatomy.
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Affiliation(s)
- Mevan Ekanayake
- Monash Biomedical Imaging, Monash University, Australia; Department of Electrical and Computer Systems Engineering, Monash University, Australia.
| | - Kamlesh Pawar
- Monash Biomedical Imaging, Monash University, Australia
| | - Mehrtash Harandi
- Department of Electrical and Computer Systems Engineering, Monash University, Australia
| | - Gary Egan
- Monash Biomedical Imaging, Monash University, Australia; School of Psychological Sciences, Monash University, Australia
| | - Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Australia; Department of Data Science and AI, Monash University, Australia
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Sakoda K, Baba S. Technical Note: Novel imaging method to obtain gray matter-attenuated inversion recovery image using low-field magnetic resonance imaging systems. Radiography (Lond) 2024; 30:231-236. [PMID: 38035438 DOI: 10.1016/j.radi.2023.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/16/2023] [Accepted: 11/11/2023] [Indexed: 12/02/2023]
Abstract
INTRODUCTION The double inversion recovery (DIR) technique suppresses two types of tissue signals with different T1 values by applying two inversion recovery (IR) pulses with different inversion times (TI). In contrast, the double tissue suppression with multi-echo acquisition and single TI combining HIRE (DOMUST-HIRE) method, is a technique enabling the white-matter-attenuated inversion recovery (WAIR) images by setting one inversion time (TI) in a sequence based on the multi-echo method and subtracting the second echo image from the first echo image. Here, we propose a new sequence that can provide the gray-matter-attenuated inversion recovery image based on the DOMUST-HIRE method. METHODS In this small clinical study, we performed determination of optimal TI and physical evaluation by imaging a subject's head with T1WI and our proposed method for GAIR images. RESULTS Our proposed method could increase the contrast ratio and the contrast-to-noise ratio between white matter (WM) and gray matter (GM), whereas the signal-to-noise ratio WM and GM decreased than with T1WI method. CONCLUSIONS Our proposed method can be used to suppress GM and CSF signals. IMPLICATIONS FOR PRACTICE The use of our proposed method in low-field MRI systems could provide GAIR image.
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Affiliation(s)
- K Sakoda
- Department of Radiological Technology, Kagoshima Medical Technology College, Japan.
| | - S Baba
- Department of Radiological Technology, Kagoshima Medical Technology College, Japan
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Lobos RA, Chan CC, Haldar JP. New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI. IEEE Trans Med Imaging 2024; 43:286-296. [PMID: 37478037 PMCID: PMC10848144 DOI: 10.1109/tmi.2023.3297851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Sensitivity map estimation is important in many multichannel MRI applications. Subspace-based sensitivity map estimation methods like ESPIRiT are popular and perform well, though can be computationally expensive and their theoretical principles can be nontrivial to understand. In the first part of this work, we present a novel theoretical derivation of subspace-based sensitivity map estimation based on a linear-predictability/structured low-rank modeling perspective. This results in an estimation approach that is equivalent to ESPIRiT, but with distinct theory that may be more intuitive for some readers. In the second part of this work, we propose and evaluate a set of computational acceleration approaches (collectively known as PISCO) that can enable substantial improvements in computation time (up to ∼ 100× in the examples we show) and memory for subspace-based sensitivity map estimation.
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Lucas A, Campbell Arnold T, Okar SV, Vadali C, Kawatra KD, Ren Z, Cao Q, Shinohara RT, Schindler MK, Davis KA, Litt B, Reich DS, Stein JM. Multi-contrast high-field quality image synthesis for portable low-field MRI using generative adversarial networks and paired data. medRxiv 2023:2023.12.28.23300409. [PMID: 38234785 PMCID: PMC10793526 DOI: 10.1101/2023.12.28.23300409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Introduction Portable low-field strength (64mT) MRI scanners promise to increase access to neuroimaging for clinical and research purposes, however these devices produce lower quality images compared to high-field scanners. In this study, we developed and evaluated a deep learning architecture to generate high-field quality brain images from low-field inputs using a paired dataset of multiple sclerosis (MS) patients scanned at 64mT and 3T. Methods A total of 49 MS patients were scanned on portable 64mT and standard 3T scanners at Penn (n=25) or the National Institutes of Health (NIH, n=24) with T1-weighted, T2-weighted and FLAIR acquisitions. Using this paired data, we developed a generative adversarial network (GAN) architecture for low- to high-field image translation (LowGAN). We then evaluated synthesized images with respect to image quality, brain morphometry, and white matter lesions. Results Synthetic high-field images demonstrated visually superior quality compared to low-field inputs and significantly higher normalized cross-correlation (NCC) to actual high-field images for T1 (p=0.001) and FLAIR (p<0.001) contrasts. LowGAN generally outperformed the current state-of-the-art for low-field volumetrics. For example, thalamic, lateral ventricle, and total cortical volumes in LowGAN outputs did not differ significantly from 3T measurements. Synthetic outputs preserved MS lesions and captured a known inverse relationship between total lesion volume and thalamic volume. Conclusions LowGAN generates synthetic high-field images with comparable visual and quantitative quality to actual high-field scans. Enhancing portable MRI image quality could add value and boost clinician confidence, enabling wider adoption of this technology.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
| | - T Campbell Arnold
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
| | - Serhat V Okar
- National Institute of Neurological Disorders and Stroke, National Institutes of Health
| | - Chetan Vadali
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Radiology, University of Pennsylvania
| | - Karan D Kawatra
- National Institute of Neurological Disorders and Stroke, National Institutes of Health
| | - Zheng Ren
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Quy Cao
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania
| | - Matthew K Schindler
- Perelman School of Medicine, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Kathryn A Davis
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Brian Litt
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Daniel S Reich
- National Institute of Neurological Disorders and Stroke, National Institutes of Health
| | - Joel M Stein
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Radiology, University of Pennsylvania
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