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Kang B, Lee W, Seo H, Heo HY, Park H. Self-supervised learning for denoising of multidimensional MRI data. Magn Reson Med 2024; 92:1980-1994. [PMID: 38934408 PMCID: PMC11341249 DOI: 10.1002/mrm.30197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/27/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024]
Abstract
PURPOSE To develop a fast denoising framework for high-dimensional MRI data based on a self-supervised learning scheme, which does not require ground truth clean image. THEORY AND METHODS Quantitative MRI faces limitations in SNR, because the variation of signal amplitude in a large set of images is the key mechanism for quantification. In addition, the complex non-linear signal models make the fitting process vulnerable to noise. To address these issues, we propose a fast deep-learning framework for denoising, which efficiently exploits the redundancy in multidimensional MRI data. A self-supervised model was designed to use only noisy images for training, bypassing the challenge of clean data paucity in clinical practice. For validation, we used two different datasets of simulated magnetization transfer contrast MR fingerprinting (MTC-MRF) dataset and in vivo DWI image dataset to show the generalizability. RESULTS The proposed method drastically improved denoising performance in the presence of mild-to-severe noise regardless of noise distributions compared to previous methods of the BM3D, tMPPCA, and Patch2self. The improvements were even pronounced in the following quantification results from the denoised images. CONCLUSION The proposed MD-S2S (Multidimensional-Self2Self) denoising technique could be further applied to various multi-dimensional MRI data and improve the quantification accuracy of tissue parameter maps.
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Affiliation(s)
- Beomgu Kang
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Wonil Lee
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, USA
| | - Hyunseok Seo
- Bionics Research Center, Biomedical Research Division, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
| | - Hye-Young Heo
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - HyunWook Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea
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Hu S, Qiu Z, Adams RJ, Zhao W, Boyacioglu R, Calvetti D, McGivney DF, Ma D. Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index. Magn Reson Med 2024; 92:1600-1616. [PMID: 38725131 PMCID: PMC11262985 DOI: 10.1002/mrm.30155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 03/31/2024] [Accepted: 04/24/2024] [Indexed: 07/07/2024]
Abstract
PURPOSE For effective optimization of MR fingerprinting (MRF) pulse sequences, estimating and minimizing errors from actual scan conditions are crucial. Although virtual-scan simulations offer an approximation to these errors, their computational demands become expensive for high-dimensional MRF frameworks, where interactions between more than two tissue properties are considered. This complexity makes sequence optimization impractical. We introduce a new mathematical model, the systematic error index (SEI), to address the scalability challenges for high-dimensional MRF sequence design. METHODS By eliminating the need to perform dictionary matching, the SEI model approximates quantification errors with low computational costs. The SEI model was validated in comparison with virtual-scan simulations. The SEI model was further applied to optimize three high-dimensional MRF sequences that quantify two to four tissue properties. The optimized scans were examined in simulations and healthy subjects. RESULTS The proposed SEI model closely approximated the virtual-scan simulation outcomes while achieving hundred- to thousand-times acceleration in the computational speed. In both simulation and in vivo experiments, the optimized MRF sequences yield higher measurement accuracy with fewer undersampling artifacts at shorter scan times than the heuristically designed sequences. CONCLUSION We developed an efficient method for estimating real-world errors in MRF scans with high computational efficiency. Our results illustrate that the SEI model could approximate errors both qualitatively and quantitatively. We also proved the practicality of the SEI model of optimizing sequences for high-dimensional MRF frameworks with manageable computational power. The optimized high-dimensional MRF scans exhibited enhanced robustness against undersampling and system imperfections with faster scan times.
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Affiliation(s)
- Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | - Zhilang Qiu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | - Richard J. Adams
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | - Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | - Rasim Boyacioglu
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106
| | - Daniela Calvetti
- Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, OH 44106
| | - Debra F. McGivney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
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Choi Y, Ko JS, Park JE, Jeong G, Seo M, Jun Y, Fujita S, Bilgic B. Beyond the Conventional Structural MRI: Clinical Application of Deep Learning Image Reconstruction and Synthetic MRI of the Brain. Invest Radiol 2024:00004424-990000000-00248. [PMID: 39159333 DOI: 10.1097/rli.0000000000001114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/21/2024]
Abstract
ABSTRACT Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep learning reconstruction (DLR) and quantitative MRI techniques beyond conventional structural imaging. DLR using deep neural networks facilitates accelerated imaging with improved signal-to-noise ratio and spatial resolution, enhancing image quality with short scan times. DLR focuses on supervised learning applied to clinical implementation and applications. Quantitative MRI techniques, exemplified by 2D multidynamic multiecho, 3D quantification using interleaved Look-Locker acquisition sequences with T2 preparation pulses, and magnetic resonance fingerprinting, enable precise calculation of brain-tissue parameters and further advance diagnostic accuracy and efficiency. Potential DLR instabilities and quantification and bias limitations will be discussed. This review underscores the synergistic potential of DLR and quantitative MRI, offering prospects for improved brain imaging beyond conventional methods.
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Affiliation(s)
- Yangsean Choi
- From the Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea (Y.C., J.S.K., J.E.P.); AIRS Medical LLC, Seoul, Republic of Korea (G.J.); Department of Radiology, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea (M.S.); Department of Radiology, Harvard Medical School, Boston, MA (Y.J., S.F., B.B.); Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA (Y.J., S.F., B.B.); and Harvard/MIT Health Sciences and Technology, Cambridge, MA (B.B.)
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Liu C, Li Z, Chen Z, Zhao B, Zheng Z, Song X. Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction. Magn Reson Med 2024; 92:688-701. [PMID: 38623899 DOI: 10.1002/mrm.30089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/31/2024] [Accepted: 03/02/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE To develop a highly accelerated CEST Z-spectral acquisition method using a specifically-designed k-space sampling pattern and corresponding deep-learning-based reconstruction. METHODS For k-space down-sampling, a customized pattern was proposed for CEST, with the randomized probability following a frequency-offset-dependent (FOD) function in the direction of saturation offset. For reconstruction, the convolution network (CNN) was enhanced with a Partially Separable (PS) function to optimize the spatial domain and frequency domain separately. Retrospective experiments on a self-acquired human brain dataset (13 healthy adults and 15 brain tumor patients) were conducted using k-space resampling. The prospective performance was also assessed on six healthy subjects. RESULTS In retrospective experiments, the combination of FOD sampling and PS network (FOD + PSN) showed the best quantitative metrics for reconstruction, outperforming three other combinations of conventional sampling with varying density and a regular CNN (nMSE and SSIM, p < 0.001 for healthy subjects). Across all acceleration factors from 4 to 14, the FOD + PSN approach consistently outperformed the comparative methods in four contrast maps including MTRasym, MTRrex, as well as the Lorentzian Difference maps of amide and nuclear Overhauser effect (NOE). In the subspace replacement experiment, the error distribution demonstrated the denoising benefits achieved in the spatial subspace. Finally, our prospective results obtained from healthy adults and brain tumor patients (14×) exhibited the initial feasibility of our method, albeit with less accurate reconstruction than retrospective ones. CONCLUSION The combination of FOD sampling and PSN reconstruction enabled highly accelerated CEST MRI acquisition, which may facilitate CEST metabolic MRI for brain tumor patients.
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Affiliation(s)
- Chuyu Liu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Zhongsen Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Zhensen Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Benqi Zhao
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Zhuozhao Zheng
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Xiaolei Song
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
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Heo HY, Singh M, Mahmud SZ, Blair L, Kamson DO, Zhou J. Unraveling contributions to the Z-spectrum signal at 3.5 ppm of human brain tumors. Magn Reson Med 2024. [PMID: 39086185 DOI: 10.1002/mrm.30241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/26/2024] [Accepted: 07/17/2024] [Indexed: 08/02/2024]
Abstract
PURPOSE To evaluate the influence of the confounding factors, direct water saturation (DWS), and magnetization transfer contrast (MTC) effects on measured Z-spectra and amide proton transfer (APT) contrast in brain tumors. METHODS High-grade glioma patients were scanned using an RF saturation-encoded 3D MR fingerprinting (MRF) sequence at 3 T. For MRF reconstruction, a recurrent neural network was designed to learn free water and semisolid macromolecule parameter mappings of the underlying multiple tissue properties from saturation-transfer MRF signals. The DWS spectra and MTC spectra were synthesized by solving Bloch-McConnell equations and evaluated in brain tumors. RESULTS The dominant contribution to the saturation effect at 3.5 ppm was from DWS and MTC effects, but 25%-33% of the saturated signal in the gadolinium-enhancing tumor (13%-20% for normal tissue) was due to the APT effect. The APT# signal of the gadolinium-enhancing tumor was significantly higher than that of the normal-appearing white matter (10.1% vs. 8.3% at 1 μT and 11.2% vs. 7.8% at 1.5 μT). CONCLUSION The RF saturation-encoded MRF allowed us to separate contributions to the saturation signal at 3.5 ppm in the Z-spectrum. Although free water and semisolid MTC are the main contributors, significant APT contrast between tumor and normal tissues was observed.
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Affiliation(s)
- Hye-Young Heo
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Munendra Singh
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sultan Z Mahmud
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Lindsay Blair
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA
| | - David Olayinka Kamson
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jinyuan Zhou
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
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Heckel R, Jacob M, Chaudhari A, Perlman O, Shimron E. Deep learning for accelerated and robust MRI reconstruction. MAGMA (NEW YORK, N.Y.) 2024; 37:335-368. [PMID: 39042206 DOI: 10.1007/s10334-024-01173-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/24/2024] [Accepted: 05/28/2024] [Indexed: 07/24/2024]
Abstract
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.
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Affiliation(s)
- Reinhard Heckel
- Department of computer engineering, Technical University of Munich, Munich, Germany
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa, 52242, IA, USA
| | - Akshay Chaudhari
- Department of Radiology, Stanford University, Stanford, 94305, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Efrat Shimron
- Department of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa, 3200004, Israel.
- Department of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, 3200004, Israel.
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Martinez JA, Yu VY, Tringale KR, Otazo R, Cohen O. Phase-sensitive deep reconstruction method for rapid multiparametric MR fingerprinting and quantitative susceptibility mapping in the brain. Magn Reson Imaging 2024; 109:147-157. [PMID: 38513790 PMCID: PMC11042874 DOI: 10.1016/j.mri.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 03/23/2024]
Abstract
INTRODUCTION This study explores the potential of Magnetic Resonance Fingerprinting (MRF) with a novel Phase-Sensitivity Deep Reconstruction Network (PS-DRONE) for simultaneous quantification of T1, T2, Proton Density, B1+, phase and quantitative susceptibility mapping (QSM). METHODS Data were acquired at 3 T in vitro and in vivo using an optimized EPI-based MRF sequence. Phantom experiments were conducted using a standardized phantom for T1 and T2 maps and a custom-made agar-based gadolinium phantom for B1 and QSM maps. In vivo experiments included five healthy volunteers and one patient diagnosed with brain metastasis. PSDRONE maps were compared to reference maps obtained through standard imaging sequences. RESULTS Total scan time was 2 min for 32 slices and a resolution of [1 mm, 1 mm, 4.5 mm]. The reconstruction of T1, T2, Proton Density, B1+ and phase maps were reconstructed within 1 s. In the phantoms, PS-DRONE analysis presented accurate and strongly correlated T1 and T2 maps (r = 0.99) compared to the reference maps. B1 maps from PS-DRONE showed slightly higher values, though still correlated (r = 0.6) with the reference. QSM values showed a small bias but were strongly correlated (r = 0.99) with reference data. In the in vivo analysis, PS-DRONE-derived T1 and T2 values for gray and white matter matched reference values in healthy volunteers. PS-DRONE B1 and QSM maps showed strong correlations with reference values. CONCLUSION The PS-DRONE network enables concurrent acquisition of T1, T2, PD, B1+, phase and QSM maps, within 2 min of acquisition time and 1 s of reconstruction time.
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Affiliation(s)
- Jessica A Martinez
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA.
| | - Victoria Y Yu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Kathryn R Tringale
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
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Cohen O, Kargar S, Woo S, Vargas A, Otazo R. DCE-Qnet: Deep Network Quantification of Dynamic Contrast Enhanced (DCE) MRI. ARXIV 2024:arXiv:2405.12360v1. [PMID: 38827459 PMCID: PMC11142325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Introduction Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. Methods A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in 10 healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor. Results The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5-51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1-47%. Conclusion The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 minutes per scan and more accurate quantification.
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Affiliation(s)
- Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Soudabeh Kargar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sungmin Woo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Zhou IY, Ji Y, Zhao Y, Malvika V, Sun PZ, Zu Z. Specific and rapid guanidinium CEST imaging using double saturation power and QUASS analysis in a rodent model of global ischemia. Magn Reson Med 2024; 91:1512-1527. [PMID: 38098305 PMCID: PMC10872646 DOI: 10.1002/mrm.29960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 10/17/2023] [Accepted: 11/20/2023] [Indexed: 02/03/2024]
Abstract
PURPOSE Guanidinium CEST is sensitive to metabolic changes and pH variation in ischemia, and it can offer advantages over conventional pH-sensitive amide proton transfer (APT) imaging by providing hyperintense contrast in stroke lesions. However, quantifying guanidinium CEST is challenging due to multiple overlapping components and a close frequency offset from water. This study aims to evaluate the applicability of a new rapid and model-free CEST quantification method using double saturation power, termed DSP-CEST, for isolating the guanidinium CEST effect from confounding factors in ischemia. To further reduce acquisition time, the DSP-CEST was combined with a quasi-steady state (QUASS) CEST technique to process non-steady-state CEST signals. METHODS The specificity and accuracy of the DSP-CEST method in quantifying the guanidinium CEST effect were assessed by comparing simulated CEST signals with/without the contribution from confounding factors. The feasibility of this method for quantifying guanidinium CEST was evaluated in a rat model of global ischemia induced by cardiac arrest and compared to a conventional multiple-pool Lorentzian fit method. RESULTS The DSP-CEST method was successful in removing all confounding components and quantifying the guanidinium CEST signal increase in ischemia. This suggests that the DSP-CEST has the potential to provide hyperintense contrast in stroke lesions. Additionally, the DSP-CEST was shown to be a rapid method that does not require the acquisition of the entire or a portion of the CEST Z-spectrum that is required in conventional model-based fitting approaches. CONCLUSION This study highlights the potential of DSP-CEST as a valuable tool for rapid and specific detection of viable tissues.
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Affiliation(s)
- Iris Y. Zhou
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, US
| | - Yang Ji
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Viswanathan Malvika
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
| | - Phillip Zhe Sun
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, US
- Primate Imaging Center, Emory National Primate Research Center, Emory University, Atlanta, GA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
- Department of Biomedical Engineering, Vanderbilt University, Nashville, US
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Monga A, Singh D, de Moura HL, Zhang X, Zibetti MVW, Regatte RR. Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review. Bioengineering (Basel) 2024; 11:236. [PMID: 38534511 DOI: 10.3390/bioengineering11030236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/28/2024] Open
Abstract
Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.
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Affiliation(s)
- Anmol Monga
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Dilbag Singh
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Hector L de Moura
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Xiaoxia Zhang
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Marcelo V W Zibetti
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Ravinder R Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
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Sabeghi P, Zarand P, Zargham S, Golestany B, Shariat A, Chang M, Yang E, Rajagopalan P, Phung DC, Gholamrezanezhad A. Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors. Cancers (Basel) 2024; 16:576. [PMID: 38339327 PMCID: PMC10854543 DOI: 10.3390/cancers16030576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
This study delineates the pivotal role of imaging within the field of neurology, emphasizing its significance in the diagnosis, prognostication, and evaluation of treatment responses for central nervous system (CNS) tumors. A comprehensive understanding of both the capabilities and limitations inherent in emerging imaging technologies is imperative for delivering a heightened level of personalized care to individuals with neuro-oncological conditions. Ongoing research in neuro-oncological imaging endeavors to rectify some limitations of radiological modalities, aiming to augment accuracy and efficacy in the management of brain tumors. This review is dedicated to the comparison and critical examination of the latest advancements in diverse imaging modalities employed in neuro-oncology. The objective is to investigate their respective impacts on diagnosis, cancer staging, prognosis, and post-treatment monitoring. By providing a comprehensive analysis of these modalities, this review aims to contribute to the collective knowledge in the field, fostering an informed approach to neuro-oncological care. In conclusion, the outlook for neuro-oncological imaging appears promising, and sustained exploration in this domain is anticipated to yield further breakthroughs, ultimately enhancing outcomes for individuals grappling with CNS tumors.
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Affiliation(s)
- Paniz Sabeghi
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Paniz Zarand
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran 1985717411, Iran;
| | - Sina Zargham
- Department of Basic Science, California Northstate University College of Medicine, 9700 West Taron Drive, Elk Grove, CA 95757, USA;
| | - Batis Golestany
- Division of Biomedical Sciences, Riverside School of Medicine, University of California, 900 University Ave., Riverside, CA 92521, USA;
| | - Arya Shariat
- Kaiser Permanente Los Angeles Medical Center, 4867 W Sunset Blvd, Los Angeles, CA 90027, USA;
| | - Myles Chang
- Keck School of Medicine, University of Southern California, 1975 Zonal Avenue, Los Angeles, CA 90089, USA;
| | - Evan Yang
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Priya Rajagopalan
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Daniel Chang Phung
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
| | - Ali Gholamrezanezhad
- Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo St., Los Angeles, CA 90033, USA; (P.S.); (E.Y.); (P.R.); (D.C.P.)
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12
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Christodoulou AG, Cruz G, Arami A, Weingärtner S, Artico J, Peters D, Seiberlich N. The future of cardiovascular magnetic resonance: All-in-one vs. real-time (Part 1). J Cardiovasc Magn Reson 2024; 26:100997. [PMID: 38237900 PMCID: PMC11211239 DOI: 10.1016/j.jocmr.2024.100997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 01/10/2024] [Indexed: 02/26/2024] Open
Abstract
Cardiovascular magnetic resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically "all-in-one" CMR, where several contrasts and/or motion states are acquired simultaneously, and "real-time" CMR, in which the examination is accelerated to avoid the need for breathholding and/or cardiac gating. The goal of this two-part manuscript is to describe these two different types of emerging rapid CMR. To this end, the vision of each is described, along with techniques which have been devised and tested along the pathway of clinical implementation. The pros and cons of the different methods are presented, and the remaining open needs of each are detailed. Part 1 will tackle the "all-in-one" approaches, and Part 2 the "real-time" approaches along with an overall summary of these emerging methods.
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Affiliation(s)
- Anthony G Christodoulou
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Gastao Cruz
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Ayda Arami
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | | | - Dana Peters
- Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Nicole Seiberlich
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
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13
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Rivlin M, Perlman O, Navon G. Metabolic brain imaging with glucosamine CEST MRI: in vivo characterization and first insights. Sci Rep 2023; 13:22030. [PMID: 38086821 PMCID: PMC10716494 DOI: 10.1038/s41598-023-48515-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
The utility of chemical exchange saturation transfer (CEST) MRI for monitoring the uptake of glucosamine (GlcN), a safe dietary supplement, has been previously demonstrated in detecting breast cancer in both murine and human subjects. Here, we studied and characterized the detectability of GlcN uptake and metabolism in the brain. Following intravenous GlcN administration in mice, CEST brain signals calculated by magnetization transfer ratio asymmetry (MTRasym) analysis, were significantly elevated, mainly in the cortex, hippocampus, and thalamus. The in vivo contrast remained stable during 40 min of examination, which can be attributed to GlcN uptake and its metabolic products accumulation as confirmed using 13C NMR spectroscopic studies of brain extracts. A Lorentzian multi-pool fitting analysis revealed an increase in the hydroxyl, amide, and relayed nuclear Overhauser effect (rNOE) signal components after GlcN treatment. With its ability to cross the blood-brain barrier (BBB), the GlcN CEST technique has the potential to serve as a metabolic biomarker for the diagnosis and monitoring various brain disorders.
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Affiliation(s)
- Michal Rivlin
- School of Chemistry, Tel-Aviv University, Tel-Aviv, Israel
| | - Or Perlman
- Department of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Gil Navon
- School of Chemistry, Tel-Aviv University, Tel-Aviv, Israel.
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14
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Nagar D, Vladimirov N, Farrar CT, Perlman O. Dynamic and rapid deep synthesis of chemical exchange saturation transfer and semisolid magnetization transfer MRI signals. Sci Rep 2023; 13:18291. [PMID: 37880343 PMCID: PMC10600114 DOI: 10.1038/s41598-023-45548-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/20/2023] [Indexed: 10/27/2023] Open
Abstract
Model-driven analysis of biophysical phenomena is gaining increased attention and utility for medical imaging applications. In magnetic resonance imaging (MRI), the availability of well-established models for describing the relations between the nuclear magnetization, tissue properties, and the externally applied magnetic fields has enabled the prediction of image contrast and served as a powerful tool for designing the imaging protocols that are now routinely used in the clinic. Recently, various advanced imaging techniques have relied on these models for image reconstruction, quantitative tissue parameter extraction, and automatic optimization of acquisition protocols. In molecular MRI, however, the increased complexity of the imaging scenario, where the signals from various chemical compounds and multiple proton pools must be accounted for, results in exceedingly long model simulation times, severely hindering the progress of this approach and its dissemination for various clinical applications. Here, we show that a deep-learning-based system can capture the nonlinear relations embedded in the molecular MRI Bloch-McConnell model, enabling a rapid and accurate generation of biologically realistic synthetic data. The applicability of this simulated data for in-silico, in-vitro, and in-vivo imaging applications is then demonstrated for chemical exchange saturation transfer (CEST) and semisolid macromolecule magnetization transfer (MT) analysis and quantification. The proposed approach yielded 63-99% acceleration in data synthesis time while retaining excellent agreement with the ground truth (Pearson's r > 0.99, p < 0.0001, normalized root mean square error < 3%).
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Affiliation(s)
- Dinor Nagar
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Nikita Vladimirov
- Department of Biomedical Engineering, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Christian T Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, 6997801, Tel Aviv, Israel.
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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15
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Cohen O, Otazo R. Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule. NMR IN BIOMEDICINE 2023; 36:e4954. [PMID: 37070221 PMCID: PMC10896067 DOI: 10.1002/nbm.4954] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/10/2023] [Accepted: 04/11/2023] [Indexed: 05/06/2023]
Abstract
Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging technique but suffers from long scan times and complicated processing. CEST was recently combined with magnetic resonance fingerprinting (MRF) to address these shortcomings. However, the CEST-MRF signal depends on multiple acquisition and tissue parameters so selecting an optimal acquisition schedule is challenging. In this work, we propose a novel dual-network deep learning framework to optimize the CEST-MRF acquisition schedule. The quality of the optimized schedule was assessed in a digital brain phantom and compared with alternate deep learning optimization approaches. The effect of schedule length on the reconstruction error was also investigated. A healthy subject was scanned with optimized and random schedules and with a conventional CEST sequence for comparison. The optimized schedule was also tested in a subject with metastatic renal cell carcinoma. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter (WM) and grey matter (GM). The optimized schedule was 12% shorter but yielded equal or lower normalized root mean square error for all parameters. The proposed optimization also provided a lower error compared with alternate methodologies. Longer schedules generally yielded lower error. In vivo maps obtained with the optimized schedule showed reduced noise and improved delineation of GM and WM. CEST curves synthesized from the optimized parameters were highly correlated (r = 0.99) with measured conventional CEST. The mean concordance correlation coefficient in WM/GM for all tissue parameters was 0.990/0.978 for the optimized schedule but only 0.979/0.975 for the random schedule. The proposed schedule optimization is widely applicable to MRF pulse sequences and provides accurate and reproducible tissue maps with reduced noise at a shorter scan time than a randomly generated schedule.
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Affiliation(s)
- Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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16
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Gaur S, Panda A, Fajardo JE, Hamilton J, Jiang Y, Gulani V. Magnetic Resonance Fingerprinting: A Review of Clinical Applications. Invest Radiol 2023; 58:561-577. [PMID: 37026802 PMCID: PMC10330487 DOI: 10.1097/rli.0000000000000975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
ABSTRACT Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance imaging that allows for efficient simultaneous measurements of multiple tissue properties, which are then used to create accurate and reproducible quantitative maps of these properties. As the technique has gained popularity, the extent of preclinical and clinical applications has vastly increased. The goal of this review is to provide an overview of currently investigated preclinical and clinical applications of MRF, as well as future directions. Topics covered include MRF in neuroimaging, neurovascular, prostate, liver, kidney, breast, abdominal quantitative imaging, cardiac, and musculoskeletal applications.
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Affiliation(s)
- Sonia Gaur
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Ananya Panda
- All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | | | - Jesse Hamilton
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Yun Jiang
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Vikas Gulani
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
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17
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Kang B, Singh M, Park H, Heo HY. Only-train-once MR fingerprinting for B 0 and B 1 inhomogeneity correction in quantitative magnetization-transfer contrast. Magn Reson Med 2023; 90:90-102. [PMID: 36883726 PMCID: PMC10149616 DOI: 10.1002/mrm.29629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 03/09/2023]
Abstract
PURPOSE To develop a fast, deep-learning approach for quantitative magnetization-transfer contrast (MTC)-MR fingerprinting (MRF) that simultaneously estimates multiple tissue parameters and corrects the effects of B0 and B1 variations. METHODS An only-train-once recurrent neural network was designed to perform the fast tissue-parameter quantification for a large range of different MRF acquisition schedules. It enabled a dynamic scan-wise linear calibration of the scan parameters using the measured B0 and B1 maps, which allowed accurate, multiple-tissue parameter mapping. MRF images were acquired from 8 healthy volunteers at 3 T. Estimated parameter maps from the MRF images were used to synthesize the MTC reference signal (Zref ) through Bloch equations at multiple saturation power levels. RESULTS The B0 and B1 errors in MR fingerprints, if not corrected, would impair the tissue quantification and subsequently corrupt the synthesized MTC reference images. Bloch equation-based numerical phantom studies and synthetic MRI analysis demonstrated that the proposed approach could correctly estimate water and semisolid macromolecule parameters, even with severe B0 and B1 inhomogeneities. CONCLUSION The only-train-once deep-learning framework can improve the reconstruction accuracy of brain-tissue parameter maps and be further combined with any conventional MRF or CEST-MRF method.
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Affiliation(s)
- Beomgu Kang
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Munendra Singh
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - HyunWook Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea
| | - Hye-Young Heo
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
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18
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Brock KK, Chen SR, Sheth RA, Siewerdsen JH. Imaging in Interventional Radiology: 2043 and Beyond. Radiology 2023; 308:e230146. [PMID: 37462500 PMCID: PMC10374939 DOI: 10.1148/radiol.230146] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 07/21/2023]
Abstract
Since its inception in the early 20th century, interventional radiology (IR) has evolved tremendously and is now a distinct clinical discipline with its own training pathway. The arsenal of modalities at work in IR includes x-ray radiography and fluoroscopy, CT, MRI, US, and molecular and multimodality imaging within hybrid interventional environments. This article briefly reviews the major developments in imaging technology in IR over the past century, summarizes technologies now representative of the standard of care, and reflects on emerging advances in imaging technology that could shape the field in the century ahead. The role of emergent imaging technologies in enabling high-precision interventions is also briefly reviewed, including image-guided ablative therapies.
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Affiliation(s)
- Kristy K. Brock
- From the Departments of Imaging Physics (K.K.B., J.H.S.),
Interventional Radiology (S.R.C., R.A.S.), Neurosurgery (J.H.S.), and Radiation
Physics (J.H.S.), The University of Texas MD Anderson Cancer Center, 1400
Pressler St, FCT14.6050 Pickens Academic Tower, Houston, TX 77030-4000
| | - Stephen R. Chen
- From the Departments of Imaging Physics (K.K.B., J.H.S.),
Interventional Radiology (S.R.C., R.A.S.), Neurosurgery (J.H.S.), and Radiation
Physics (J.H.S.), The University of Texas MD Anderson Cancer Center, 1400
Pressler St, FCT14.6050 Pickens Academic Tower, Houston, TX 77030-4000
| | - Rahul A. Sheth
- From the Departments of Imaging Physics (K.K.B., J.H.S.),
Interventional Radiology (S.R.C., R.A.S.), Neurosurgery (J.H.S.), and Radiation
Physics (J.H.S.), The University of Texas MD Anderson Cancer Center, 1400
Pressler St, FCT14.6050 Pickens Academic Tower, Houston, TX 77030-4000
| | - Jeffrey H. Siewerdsen
- From the Departments of Imaging Physics (K.K.B., J.H.S.),
Interventional Radiology (S.R.C., R.A.S.), Neurosurgery (J.H.S.), and Radiation
Physics (J.H.S.), The University of Texas MD Anderson Cancer Center, 1400
Pressler St, FCT14.6050 Pickens Academic Tower, Houston, TX 77030-4000
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19
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Wittsack HJ, Radke KL, Stabinska J, Ljimani A, Müller-Lutz A. calf - Software for CEST Analysis with Lorentzian Fitting. J Med Syst 2023; 47:39. [PMID: 36961580 PMCID: PMC10038975 DOI: 10.1007/s10916-023-01931-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 02/27/2023] [Indexed: 03/25/2023]
Abstract
Analysis of chemical exchange saturation transfer (CEST) MRI data requires sophisticated methods to obtain reliable results about metabolites in the tissue under study. CEST generates z-spectra with multiple components, each originating from individual molecular groups. The individual lines with Lorentzian line shape are mostly overlapping and disturbed by various effects. We present an elaborate method based on an adaptive nonlinear least squares algorithm that provides robust quantification of z-spectra and incorporates prior knowledge in the fitting process. To disseminate CEST to the research community, we developed software as part of this study that runs on the Microsoft Windows operating system and will be made freely available to the community. Special attention has been paid to establish a low entrance threshold and high usability, so that even less experienced users can successfully analyze CEST data.
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Affiliation(s)
- Hans-Jörg Wittsack
- Medical Faculty, Department of Diagnostic and Interventional Radiology, University Dusseldorf, D-40225, Dusseldorf, Germany.
| | - Karl Ludger Radke
- Medical Faculty, Department of Diagnostic and Interventional Radiology, University Dusseldorf, D-40225, Dusseldorf, Germany
| | - Julia Stabinska
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, 21205, USA
- Division of MR Research, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Alexandra Ljimani
- Medical Faculty, Department of Diagnostic and Interventional Radiology, University Dusseldorf, D-40225, Dusseldorf, Germany
| | - Anja Müller-Lutz
- Medical Faculty, Department of Diagnostic and Interventional Radiology, University Dusseldorf, D-40225, Dusseldorf, Germany
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20
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Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response. Int J Mol Sci 2023; 24:ijms24043151. [PMID: 36834563 PMCID: PMC9959624 DOI: 10.3390/ijms24043151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several indications has yielded improved prognosis for cases where traditional therapy has shown limited efficiency. However, many patients still fail to benefit from this treatment modality, and the exact mechanisms responsible for tumor response are unknown. Noninvasive treatment monitoring is crucial for longitudinal tumor characterization and the early detection of non-responders. While various medical imaging techniques can provide a morphological picture of the lesion and its surrounding tissue, a molecular-oriented imaging approach holds the key to unraveling biological effects that occur much earlier in the immunotherapy timeline. Magnetic resonance imaging (MRI) is a highly versatile imaging modality, where the image contrast can be tailored to emphasize a particular biophysical property of interest using advanced engineering of the imaging pipeline. In this review, recent advances in molecular-MRI based cancer immunotherapy monitoring are described. Next, the presentation of the underlying physics, computational, and biological features are complemented by a critical analysis of the results obtained in preclinical and clinical studies. Finally, emerging artificial intelligence (AI)-based strategies to further distill, quantify, and interpret the image-based molecular MRI information are discussed in terms of perspectives for the future.
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