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Zhao Y, Li Y, Guo R, Jin W, Sutton B, Ma C, El Fakhri G, Li Y, Luo J, Liang ZP. Accelerated 3D metabolite T 1 mapping of the brain using variable-flip-angle SPICE. Magn Reson Med 2024; 92:1310-1322. [PMID: 38923032 DOI: 10.1002/mrm.30200] [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: 02/12/2024] [Revised: 05/02/2024] [Accepted: 06/04/2024] [Indexed: 06/28/2024]
Abstract
PURPOSE To develop a practical method to enable 3D T1 mapping of brain metabolites. THEORY AND METHODS Due to the high dimensionality of the imaging problem underlying metabolite T1 mapping, measurement of metabolite T1 values has been currently limited to a single voxel or slice. This work achieved 3D metabolite T1 mapping by leveraging a recent ultrafast MRSI technique called SPICE (spectroscopic imaging by exploiting spatiospectral correlation). The Ernst-angle FID MRSI data acquisition used in SPICE was extended to variable flip angles, with variable-density sparse sampling for efficient encoding of metabolite T1 information. In data processing, a novel generalized series model was used to remove water and subcutaneous lipid signals; a low-rank tensor model with prelearned subspaces was used to reconstruct the variable-flip-angle metabolite signals jointly from the noisy data. RESULTS The proposed method was evaluated using both phantom and healthy subject data. Phantom experimental results demonstrated that high-quality 3D metabolite T1 maps could be obtained and used for correction of T1 saturation effects. In vivo experimental results showed metabolite T1 maps with a large spatial coverage of 240 × 240 × 72 mm3 and good reproducibility coefficients (< 11%) in a 14.5-min scan. The metabolite T1 times obtained ranged from 0.99 to 1.44 s in gray matter and from 1.00 to 1.35 s in white matter. CONCLUSION We successfully demonstrated the feasibility of 3D metabolite T1 mapping within a clinically acceptable scan time. The proposed method may prove useful for both T1 mapping of brain metabolites and correcting the T1-weighting effects in quantitative metabolic imaging.
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Affiliation(s)
- Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Siemens Medical Solutions USA, Inc., Urbana, Illinois, USA
| | - Wen Jin
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Brad Sutton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Chao Ma
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Georges El Fakhri
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Luo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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Xu Q, Chen Y, Miller S, Bajaj K, Santana J, Badawy M, Lyu H, Liu Y, He N, Yan F, Haacke EM. In Vivo visualization of white matter fiber tracts in the brainstem using low flip angle double echo 3D gradient echo imaging at 3T. Neuroimage 2024; 300:120857. [PMID: 39299660 DOI: 10.1016/j.neuroimage.2024.120857] [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: 07/15/2024] [Revised: 09/11/2024] [Accepted: 09/17/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND White matter (WM) fiber tracts in the brainstem communicate with various regions in the cerebrum, cerebellum, and spinal cord. Clinically, small lesions, malformations, or histopathological changes in the brainstem can cause severe neurological disorders. A direct and non-invasive assessment approach could bring valuable information about the intricate anatomical variations of the white matter fiber tracts and nuclei. Although tractography from diffusion tensor imaging has been commonly used to map the WM fiber tracts connectivity, it is difficult to differentiate the complex WM tracts anatomically. Both high field MRI methods and ultrahigh-field MRI methods at 7T and 11.7 T have been used to enhance the contrast of WM fiber tracts. Despite their promising results, it is still challenging to achieve wide clinical adoption at 3T. In this study, we explored a clinically feasible method using a proton density weighted (PDW) 3D gradient echo (GRE) sequence to directly image the WM fiber tracts in the brainstem at 3T in vivo. METHODS We optimized a 3D high resolution, double echo, short TR, PDW GRE sequence on 5 healthy volunteers using a clinical 3T scanner to visualize the complicated anatomy of WM fiber tracts in the brain stem. Tissue properties including T1, proton density and T2* from in vivo quantitative MRI data were used for simulations to determine the optimal flip angle for the sequence. The visualization of multiple WM fiber tracts in the brainstem was assessed qualitatively and quantitatively using relative contrast and contrast-to-noise ratio (CNR). To improve the CNR, the final images were created by averaging over all echoes from two consecutive scans at the optimal flip angle. The results were compared to anatomical atlases and histology sections to identify the major fiber tracts. All the identified major fiber tracts were labeled on axial, sagittal and coronal slices. RESULTS The WM fiber tracts were found to have distinct hypointense signal throughout the brainstem and most of the major WM fiber tracts, such as the corticospinal tract, medial lemniscus, medial longitudinal fasciculus, and central tegmental tract, in the brainstem up to and including the thalamus were identified in all subjects. Both qualitative and quantitative evaluations showed that the 3° scan offered the best contrast for WM fiber tracts for a TR of 20 ms. The average over the first two echo times and two consecutive 3° scans gave a CNR of 47.8 ± 6.2 for the pyramidal tracts in particular and CNRs values greater than 6.5 ± 2.4 for the rest of the fiber tracts. CONCLUSIONS All the major fiber tracts in the brainstem could be visualized. Given the reasonably short scan time of 10 min at 3T, double echo PDW GRE sequence is a very practical approach for clinical adoption.
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Affiliation(s)
- Qiuyun Xu
- Department of Radiology, Wayne State University, 3990 John R, 4201 St Antoine, Detroit Receiving Hospital 3L-8, Detroit, MI, 48201, USA
| | - Yongsheng Chen
- Department of Neurology, Detroit Medical Center, Wayne State University, University Health Center-8th floor, 4201 St Antoine, Detroit, MI, 48201, USA
| | - Stephan Miller
- Department of Radiology, Detroit Medical Center, Wayne State University School of Medicine, 3901 Beaubien Boulevard, Detroit, MI, 48201, USA
| | - Kunal Bajaj
- Department of Radiology, Detroit Medical Center, Wayne State University School of Medicine, 3901 Beaubien Boulevard, Detroit, MI, 48201, USA
| | - Jairo Santana
- Department of Radiology, Detroit Medical Center, Wayne State University School of Medicine, 3901 Beaubien Boulevard, Detroit, MI, 48201, USA
| | - Mohamed Badawy
- Department of Radiology, Detroit Medical Center, Wayne State University School of Medicine, 3901 Beaubien Boulevard, Detroit, MI, 48201, USA
| | - Haiying Lyu
- Department of Radiology, Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yu Liu
- Department of Radiology, Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - E Mark Haacke
- Department of Radiology, Wayne State University, 3990 John R, 4201 St Antoine, Detroit Receiving Hospital 3L-8, Detroit, MI, 48201, USA; Department of Neurology, Detroit Medical Center, Wayne State University, University Health Center-8th floor, 4201 St Antoine, Detroit, MI, 48201, USA; Department of Radiology, Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Department of Biomedical Engineering, Wayne State University, 3990 John R, 4201 St Antoine, Detroit Receiving Hospital 3L-8, Detroit, MI, 48201, USA.
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Stige KE, Kverneng SU, Sharma S, Skeie GO, Sheard E, Søgnen M, Geijerstam SA, Vetås T, Wahlvåg AG, Berven H, Buch S, Reese D, Babiker D, Mahdi Y, Wade T, Miranda GP, Ganguly J, Tamilselvam YK, Chai JR, Bansal S, Aur D, Soltani S, Adams S, Dölle C, Dick F, Berntsen EM, Grüner R, Brekke N, Riemer F, Goa PE, Haugarvoll K, Haacke EM, Jog M, Tzoulis C. The STRAT-PARK cohort: A personalized initiative to stratify Parkinson's disease. Prog Neurobiol 2024; 236:102603. [PMID: 38604582 DOI: 10.1016/j.pneurobio.2024.102603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/15/2024] [Accepted: 04/07/2024] [Indexed: 04/13/2024]
Abstract
The STRAT-PARK initiative aims to provide a platform for stratifying Parkinson's disease (PD) into biological subtypes, using a bottom-up, multidisciplinary biomarker-based and data-driven approach. PD is a heterogeneous entity, exhibiting high interindividual clinicopathological variability. This diversity suggests that PD may encompass multiple distinct biological entities, each driven by different molecular mechanisms. Molecular stratification and identification of disease subtypes is therefore a key priority for understanding and treating PD. STRAT-PARK is a multi-center longitudinal cohort aiming to recruit a total of 2000 individuals with PD and neurologically healthy controls from Norway and Canada, for the purpose of identifying molecular disease subtypes. Clinical assessment is performed annually, whereas biosampling, imaging, and digital and neurophysiological phenotyping occur every second year. The unique feature of STRAT-PARK is the diversity of collected biological material, including muscle biopsies and platelets, tissues particularly useful for mitochondrial biomarker research. Recruitment rate is ∼150 participants per year. By March 2023, 252 participants were included, comprising 204 cases and 48 controls. STRAT-PARK is a powerful stratification initiative anticipated to become a global research resource, contributing to personalized care in PD.
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Affiliation(s)
- Kjersti Eline Stige
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway; The Department of Neuromedicine and Movement Sciences, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Neurology and Clinical Neurophysiology, St Olav's University Hospital, Trondheim 7006, Norway
| | - Simon Ulvenes Kverneng
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Soumya Sharma
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Geir-Olve Skeie
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Erika Sheard
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Mona Søgnen
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Solveig Af Geijerstam
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Therese Vetås
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Anne Grete Wahlvåg
- Department of Neurology and Clinical Neurophysiology, St Olav's University Hospital, Trondheim 7006, Norway
| | - Haakon Berven
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Sagar Buch
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - David Reese
- Imaging Research Laboratories, Robarts Research Institute, Ontario, London N6A 5B7, Canada
| | - Dina Babiker
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Yekta Mahdi
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Trevor Wade
- Department of Medical Biophysics, Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, Ontario, London N6A 6B7, Canada
| | - Gala Prado Miranda
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Jacky Ganguly
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Yokhesh Krishnasamy Tamilselvam
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada; Department of Electrical and Computer Engineering, Canadian Surgical Technologies and Advanced Robotics (CSTAR), University of Western Ontario (UWO), Ontario, London, Canada
| | - Jia Ren Chai
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Saurabh Bansal
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Dorian Aur
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Sima Soltani
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Scott Adams
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada; School of Communication Sciences & Disorders, Faculty of Health Sciences, Western University, Canada
| | - Christian Dölle
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Fiona Dick
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Erik Magnus Berntsen
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim 7006, Norway; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Renate Grüner
- Department of Physics and Technology, University of Bergen, Bergen 5007, Norway; Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Post Office Box 1400, Bergen 5021, Norway
| | - Njål Brekke
- Department of Physics and Technology, University of Bergen, Bergen 5007, Norway; Radiology Department, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Frank Riemer
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Post Office Box 1400, Bergen 5021, Norway
| | - Pål Erik Goa
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim 7006, Norway; Department of Physics, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Kristoffer Haugarvoll
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - E Mark Haacke
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA; Department of Radiology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Mandar Jog
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Charalampos Tzoulis
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway.
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Liu P, Wang X, Zhang Y, Huang P, Jin Z, Cheng Z, Chen Y, Xu Q, Ghassaban K, Liu Y, Chen S, He N, Yan F, Haacke EM. PENCIL imaging: A novel approach for neuromelanin sensitive MRI in Parkinson's disease. Neuroimage 2024; 291:120588. [PMID: 38537765 DOI: 10.1016/j.neuroimage.2024.120588] [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: 08/31/2023] [Revised: 03/11/2024] [Accepted: 03/25/2024] [Indexed: 04/01/2024] Open
Abstract
BACKGROUND Parkinson's disease (PD) is associated with the loss of neuromelanin (NM) and increased iron in the substantia nigra (SN). Magnetization transfer contrast (MTC) is widely used for NM visualization but has limitations in brain coverage and scan time. This study aimed to develop a new approach called Proton-density Enhanced Neuromelanin Contrast in Low flip angle gradient echo (PENCIL) imaging to visualize NM in the SN. METHODS This study included 30 PD subjects and 50 healthy controls (HCs) scanned at 3T. PENCIL and MTC images were acquired. NM volume in the SN pars compacta (SNpc), normalized image contrast (Cnorm), and contrast-to-noise ratio (CNR) were calculated. The change of NM volume in the SNpc with age was analyzed using the HC data. A group analysis compared differences between PD subjects and HCs. Receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculations were used to evaluate the diagnostic performance of NM volume and CNR in the SNpc. RESULTS PENCIL provided similar visualization and structural information of NM compared to MTC. In HCs, PENCIL showed higher NM volume in the SNpc than MTC, but this difference was not observed in PD subjects. PENCIL had higher CNR, while MTC had higher Cnorm. Both methods revealed a similar pattern of NM volume in SNpc changes with age. There were no significant differences in AUCs between NM volume in SNpc measured by PENCIL and MTC. Both methods exhibited comparable diagnostic performance in this regard. CONCLUSIONS PENCIL imaging provided improved CNR compared to MTC and showed similar diagnostic performance for differentiating PD subjects from HCs. The major advantage is PENCIL has rapid whole-brain coverage and, when using STAGE imaging, offers a one-stop quantitative assessment of tissue properties.
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Affiliation(s)
- Peng Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Xinhui Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Youmin Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Pei Huang
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Zhijia Jin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, 4201St. Antoine, Detroit, MI 48201, USA
| | - Qiuyun Xu
- SpinTech MRI, Bingham Farms, MI 48025, USA
| | | | - Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Shengdi Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China.
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China; Faculty of Medical Imaging Technology, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - E Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China; Department of Neurology, Wayne State University School of Medicine, 4201St. Antoine, Detroit, MI 48201, USA; Department of Radiology, Wayne State University School of Medicine, 3990 John R Street, MRI Concourse, Detroit, MI 48201, USA.
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5
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Haacke EM, Xu Q, Kokeny P, Gharabaghi S, Chen Y, Wu B, Liu Y, He N, Yan F. Strategically Acquired Gradient Echo (STAGE) Imaging, part IV: Constrained Reconstruction of White Noise (CROWN) Processing as a Means to Improve Signal-to-Noise in STAGE Imaging at 3 Tesla. Magn Reson Imaging 2024; 107:55-68. [PMID: 38181834 DOI: 10.1016/j.mri.2024.01.001] [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: 08/03/2023] [Revised: 10/30/2023] [Accepted: 01/01/2024] [Indexed: 01/07/2024]
Abstract
Increasing the signal-to-noise ratio (SNR) has always been of critical importance for magnetic resonance imaging. Although increasing field strength provides a linear increase in SNR, it is more and more costly as field strength increases. Therefore, there is a major effort today to use signal processing methods to improve SNR since it is more efficient and economical. There are a variety of methods to improve SNR such as averaging the data at the expense of imaging time, or collecting the data with a lower resolution, all of these methods, including imaging processing methods, usually come at the expense of loss of image detail or image blurring. Therefore, we developed a new mathematical approach called CROWN (Constrained Reconstruction of White Noise) to enhance SNR without loss of structural detail and without affecting scanning time. In this study, we introduced and tested the concept behind CROWN specifically for STAGE (strategically acquired gradient echo) imaging. The concept itself is presented first, followed by simulations to demonstrate its theoretical effectiveness. Then the SNR improvement on proton spin density (PSD) and R2⁎ maps was investigated using brain STAGE data acquired from 10 healthy controls (HCs) and 10 patients with Parkinson's disease (PD). For the PSD and R2* maps, the SNR and CNR between white matter and gray matter were improved by a factor of 1.87 ± 0.50 and 1.72 ± 0.88, respectively. The white matter hyperintensity lesions in PD patients were more clearly defined after CROWN processing. Using these improved maps, simulated images for any repeat time, echo time or flip angle can be created with improved SNR. The potential applications of this technology are to trade off the increased SNR for higher resolution images and/or faster imaging.
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Affiliation(s)
- E Mark Haacke
- SpinTech MRI, Bingham Farms, MI 48025, United States of America; Wayne State University, Department of Neurology, Detroit, MI 48201, United States of America; Wayne State University, Department of Radiology, Detroit, MI 48201, United States of America; Zhuyan Limited, Shanghai, China.
| | - Qiuyun Xu
- SpinTech MRI, Bingham Farms, MI 48025, United States of America
| | - Paul Kokeny
- SpinTech MRI, Bingham Farms, MI 48025, United States of America
| | - Sara Gharabaghi
- SpinTech MRI, Bingham Farms, MI 48025, United States of America
| | - Yongsheng Chen
- Wayne State University, Department of Neurology, Detroit, MI 48201, United States of America
| | - Bo Wu
- Zhuyan Limited, Shanghai, China
| | - Yu Liu
- Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Department of Radiology, Shanghai, China
| | - Naying He
- Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Department of Radiology, Shanghai, China
| | - Fuhua Yan
- Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Department of Radiology, Shanghai, China
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Eajazi A, Weinschenk C, Chhabra A. Imaging Biomarkers of Peripheral Nerves: Focus on Magnetic Resonance Neurography and Ultrasonography. Semin Musculoskelet Radiol 2024; 28:92-102. [PMID: 38330973 DOI: 10.1055/s-0043-1776427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2024]
Abstract
Peripheral neuropathy is a prevalent and debilitating condition affecting millions of individuals globally. Magnetic resonance neurography (MRN) and ultrasonography (US) are noninvasive methods offering comprehensive visualization of peripheral nerves, using anatomical and functional imaging biomarkers to ensure accurate evaluation. For optimized MRN, superior and high-resolution two-dimensional and three-dimensional imaging protocols are essential. The anatomical MRN and US imaging markers include quantitative measures of nerve and fascicular size and signal, and qualitative markers of course and morphology. Among them, quantitative markers of T2-signal intensity ratio are sensitive to nerve edema-like signal changes, and the T1-mapping technique reveals nerve and muscle tissue fatty and fibrous compositional alterations.The functional markers are derived from physiologic properties of nerves, such as diffusion characteristics or blood flow. They include apparent diffusion coefficient from diffusion-weighted imaging and fractional anisotropy and tractography from diffusion tensor imaging to delve into peripheral nerve microstructure and integrity. Peripheral nerve perfusion using dynamic contrast-enhanced magnetic resonance imaging estimates perfusion parameters, offering insights into nerve health and neuropathies involving edema, inflammation, demyelination, and microvascular alterations in conditions like type 2 diabetes, linking nerve conduction pathophysiology to vascular permeability alterations.Imaging biomarkers thus play a pivotal role in the diagnosis, prognosis, and monitoring of nerve pathologies, thereby ensuring comprehensive assessment and elevating patient care. These biomarkers provide valuable insights into nerve structure, function, and pathophysiology, contributing to the accurate diagnosis and management planning for peripheral neuropathy.
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Affiliation(s)
- Alireza Eajazi
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas
| | - Cindy Weinschenk
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas
| | - Avneesh Chhabra
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas
- Department of Radiology & Orthopedic Surgery, UT Southwestern Medical Center, Dallas, Texas
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Buch S, Subramanian K, Chen T, Chen Y, Larvie M, Bernitsas E, Haacke EM. Characterization of white matter lesions in multiple sclerosis using proton density and T1-relaxation measures. Magn Reson Imaging 2024; 106:110-118. [PMID: 38145698 DOI: 10.1016/j.mri.2023.12.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 12/18/2023] [Indexed: 12/27/2023]
Abstract
PURPOSE Although lesion dissemination in time is a defining characteristic of multiple sclerosis (MS), there is a limited understanding of lesion heterogeneity. Currently, conventional sequences such as fluid attenuated inversion recovery (FLAIR) and T1-weighted (T1W) data are used to assess MS lesions qualitatively. Estimating water content could provide a measure of local tissue rarefaction, or reduced tissue density, resulting from chronic inflammation. Our goal was to utilize the proton spin density (PD), derived from a rapid, multi-contrast STAGE (strategically acquired gradient echo) protocol to characterize white matter (WM) lesions seen on T2W, FLAIR and T1W data. MATERIALS AND METHODS Twenty (20) subjects with relapsing-remitting MS were scanned at 3 T using T1W, T2-weighted, FLAIR and strategically acquired gradient echo (STAGE) sequences. PD and T1 maps were derived from the STAGE data. Disease severity scores, including Extended Disability Status Scale (EDSS) and Multiple Sclerosis Functional Composite (MSFC), were correlated with total, high PD and high T1 lesion volumes. A probability map of high PD regions and all lesions across all subjects was generated. Five perilesional normal appearing WM (NAWM) bands surrounding the lesions were generated to compare the median PD and T1 values in each band with the lesional values and the global WM. RESULTS T1W intensity was negatively correlated with PD as expected (R = -0.87, p < 0.01, R2 = 0.756) and the FLAIR signal was suppressed for high PD volumes within the lesions, roughly for PD ≥ 0.85. The threshold for high PD and T1 regions was set to 0.909 and 1953.6 ms, respectively. High PD regions showed a high probability of occurrence near the boundary of the lateral ventricles. EDSS score and nine-hole peg test (dominant and non-dominant hand) were significantly correlated with the total lesion volume and the volumes of high PD and T1 regions (p < 0.05). There was a significant difference in PD/T1 values between the high PD/T1 regions within the lesions and the remaining lesional tissue (p < 0.001). In addition, the PD values of the first NAWM perilesional band directly adjacent to the lesional boundary displayed a significant difference (p < 0.05) compared to the global WM. CONCLUSION Lesions with high PD and T1s had the highest probability of occurrence at the boundary of the lateral ventricles and likely represent chronic lesions with significant local tissue rarefaction. Moreover, the perilesional NAWM exhibited subtly increasing PD and T1 values from the NAWM up to the lesion boundary. Unlike on the T1 maps, the perilesional band adjacent to the lesion boundary possessed a significantly higher PD value than the global WM PD values. This shows that PD maps were sensitive to the subtle changes in NAWM surrounding the lesions.
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Affiliation(s)
- Sagar Buch
- Department of Neurology, Wayne State University, Detroit, MI, USA
| | | | - Teresa Chen
- College of Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Yongsheng Chen
- Department of Neurology, Wayne State University, Detroit, MI, USA
| | - Mykol Larvie
- Department of Radiology, Wayne State University, Detroit, MI, USA
| | | | - E Mark Haacke
- Department of Neurology, Wayne State University, Detroit, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA.
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Wang C, He N, Zhang Y, Li Y, Huang P, Liu Y, Jin Z, Cheng Z, Liu Y, Wang Y, Zhang C, Haacke EM, Chen S, Yan F, Yang G. Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging. J Magn Reson Imaging 2024. [PMID: 38236577 DOI: 10.1002/jmri.29245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/05/2024] [Accepted: 01/08/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD) diagnostic marker. However, it is challenging to visualize and assess the N1 sign in clinical practice. PURPOSE To automatically detect the presence or absence of the N1 sign from true susceptibility weighted imaging by using deep-learning method. STUDY TYPE Prospective. POPULATION/SUBJECTS 453 subjects, including 225 PD patients, 120 healthy controls (HCs), and 108 patients with other movement disorders, were prospectively recruited including 227 males and 226 females. They were divided into training, validation, and test cohorts of 289, 73, and 91 cases, respectively. FIELD STRENGTH/SEQUENCE 3D gradient echo SWI sequence at 3T; 3D multiecho strategically acquired gradient echo imaging at 3T; NM-sensitive 3D gradient echo sequence with MTC pulse at 3T. ASSESSMENT A neuroradiologist with 5 years of experience manually delineated substantia nigra regions. Two raters with 2 and 36 years of experience assessed the N1 sign on true susceptibility weighted imaging (tSWI), QSM with high-pass filter, and magnitude data combined with MTC data. We proposed NINet, a neural model, for automatic N1 sign identification in tSWI images. STATISTICAL TESTS We compared the performance of NINet to the subjective reference standard using Receiver Operating Characteristic analyses, and a decision curve analysis assessed identification accuracy. RESULTS NINet achieved an area under the curve (AUC) of 0.87 (CI: 0.76-0.89) in N1 sign identification, surpassing other models and neuroradiologists. NINet localized the putative N1 sign within tSWI images with 67.3% accuracy. DATA CONCLUSION Our proposed NINet model's capability to determine the presence or absence of the N1 sign, along with its localization, holds promise for enhancing diagnostic accuracy when evaluating PD using MR images. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Chenglong Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Youmin Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Pei Huang
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhijia Jin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yun Liu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yida Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - E Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA
| | - Shengdi Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
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Shen L, Lu X, Wang H, Wu G, Guo Y, Zheng S, Ren L, Zhang H, Huang L, Ren B, Zhu J, Xia S. Impaired T1 mapping and Tmax during the first 7 days after ischemic stroke. A retrospective observational study. J Stroke Cerebrovasc Dis 2023; 32:107383. [PMID: 37844455 DOI: 10.1016/j.jstrokecerebrovasdis.2023.107383] [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: 06/29/2023] [Revised: 09/14/2023] [Accepted: 09/19/2023] [Indexed: 10/18/2023] Open
Abstract
OBJECTIVE To measure the relative T1 (rT1) value in different hypo-perfused regions after ischemic stroke using T1 mapping derived by Strategically Acquired Gradient Echo (STAGE) and assess its relationship with onset time and severity of ischemia. MATERIALS AND METHODS Sixty-three patients with acute anterior circulation ischemic stroke from 2017 to 2022 who underwent STAGE, diffusion weighted imaging (DWI) and dynamic susceptibility contrast perfusion weighted imaging (DSC-PWI) within 7 days were retrospectively enrolled. The areas with reduced diffusion and hypo-perfusion were segmented based on apparent diffusion coefficient (ADC) value < 0.62 × 10-3mm2/s and time-to-maximum (Tmax) thresholds (4, 6, 8, and 10 seconds). We measured the T1 value in the diffusion reduced and every 2 s Tmax strata regions and calculated rT1 (T1ipsi/T1contra) to explore the relationship between rT1 value, Tmax, and onset time. RESULTS rT1 value was increased in diffusion reduced (1.42) and hypo-perfused regions (1.02, 1.06, 1.12, 1.27, Tmax 4-6 s, 6-8 s, 8-10 s, > 10 s, respectively; all different from 1, P < 0.001). rT1 value was positively correlated with Tmax (rs = 0.61, P < 0.001) and onset time in area with reduced diffusion (rs = 0.39, P = 0.014). CONCLUSIONS Increased rT1 value in different hypo-perfused brain regions using T1 mapping derived by STAGE may reflect the edema; it was associated with the severity of Tmax and showed a weak correlation with the onset time in diffusion reduced areas.
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Affiliation(s)
- Lianfang Shen
- Department of Radiology, The First Central Clinical School, Tianjin Medical University, Tianjin, China
| | - Xiudi Lu
- Department of Radiology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Huiying Wang
- The School of Medicine, Nankai University, Tianjin, China
| | - Gemuer Wu
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Yu Guo
- Department of Radiology, Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Shaowei Zheng
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Lei Ren
- Department of Radiology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Huanlei Zhang
- Department of Radiology, Yidu Central Hospital of Weifang, Qingzhou City, Shandong, China
| | - Lixiang Huang
- Department of Radiology, Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China
| | - Bo Ren
- College of Computer Science, Nankai University, Tianjin, China
| | - Jinxia Zhu
- MR Collaboration, Siemens Healthcare Ltd, Beijing, China
| | - Shuang Xia
- Department of Radiology, Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
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10
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Zhang T, Zhao Y, Jin W, Li Y, Guo R, Ke Z, Luo J, Li Y, Liang ZP. B 1 mapping using pre-learned subspaces for quantitative brain imaging. Magn Reson Med 2023; 90:2089-2101. [PMID: 37345702 DOI: 10.1002/mrm.29764] [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: 04/06/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE To develop a machine learning-based method for estimation of both transmitter and receiver B1 fields desired for correction of the B1 inhomogeneity effects in quantitative brain imaging. THEORY AND METHODS A subspace model-based machine learning method was proposed for estimation of B1t and B1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B1 fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B1 fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B1 inhomogeneity correction in quantitative brain imaging scenarios, including T1 and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data. RESULTS In both phantom and healthy subject data, the proposed method produced high-quality B1 maps. B1 correction on SPGR data using the estimated B1 maps produced significantly improved T1 and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B1 estimation and correction results than conventional methods. The B1 maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues. CONCLUSION This work presents a novel method to estimate B1 variations using probabilistic subspace models and machine learning. The proposed method may make correction of B1 inhomogeneity effects more robust in practical applications.
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Affiliation(s)
- Tianxiao Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Wen Jin
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Siemens Medical Solutions USA, Inc., Urbana, Illinois, USA
| | - Ziwen Ke
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Luo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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11
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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12
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Chen Z, Zhai X, Chen Z. Computed cancer magnetic susceptibility imaging (canχ): Computational inverse mappings of cancer MRI. Magn Reson Imaging 2023; 102:86-95. [PMID: 37075866 DOI: 10.1016/j.mri.2023.04.003] [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: 01/05/2023] [Revised: 03/31/2023] [Accepted: 04/16/2023] [Indexed: 04/21/2023]
Abstract
PURPOSE We report a new cancer imaging modality in the contrast of tissue intrinsic susceptibility property by computed inverse magnetic resonance imaging (CIMRI). METHODS In MRI physics, an MRI signal is formed from tissue magnetism source (primarily magnetic susceptibility χ) through a cascade of MRI-introduced transformations (e.g. dipole-convolved magnetization) involving MRI setting parameters (e.g. echo time). In two-step computational inverse mappings (from phase image to internal fieldmap to susceptibility source), we could remove the MRI transformations and imaging parameters, thereby obtaining χ-depicted cancer images (canχ) from MRI phase images. Canχ is computationally implemented from clinical cancer MRI phase image by CIMRI. RESULTS As a result of MRI effect removal through computational inverse mappings, the reconstructed χ map (canχ) could provide a new cancerous tissue depiction in contrast of tissue intrinsic magnetism property (i.e. diamagnetism vs paramagnetism) as in an off-scanner state (e.g. in absence of main field B0). CONCLUSION Through retrospective clinical cancer MRI data analysis, we reported on the canχ method in technical details and demonstrated its feasibility of innovating cancer imaging in the contrast of tissue intrinsic paramagnetism/diamagnetism property (in a cancer tissue state free from MRI effect).
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Affiliation(s)
- Zikuan Chen
- Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA 91010, United States of America; Zinv LLC, Albuquerque, NM 87108, United States of America.
| | - Xiulan Zhai
- Zinv LLC, Albuquerque, NM 87108, United States of America
| | - Zeyuan Chen
- Department of Computer Sciences, University of California-Davis, Davis, CA 95616, United States of America; Microsoft Corporation, Seattle, WA 98052, United States of America.
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13
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Deveshwar N, Rajagopal A, Sahin S, Shimron E, Larson PEZ. Synthesizing Complex-Valued Multicoil MRI Data from Magnitude-Only Images. Bioengineering (Basel) 2023; 10:358. [PMID: 36978749 PMCID: PMC10045391 DOI: 10.3390/bioengineering10030358] [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: 02/03/2023] [Revised: 03/08/2023] [Accepted: 03/12/2023] [Indexed: 03/18/2023] Open
Abstract
Despite the proliferation of deep learning techniques for accelerated MRI acquisition and enhanced image reconstruction, the construction of large and diverse MRI datasets continues to pose a barrier to effective clinical translation of these technologies. One major challenge is in collecting the MRI raw data (required for image reconstruction) from clinical scanning, as only magnitude images are typically saved and used for clinical assessment and diagnosis. The image phase and multi-channel RF coil information are not retained when magnitude-only images are saved in clinical imaging archives. Additionally, preprocessing used for data in clinical imaging can lead to biased results. While several groups have begun concerted efforts to collect large amounts of MRI raw data, current databases are limited in the diversity of anatomy, pathology, annotations, and acquisition types they contain. To address this, we present a method for synthesizing realistic MR data from magnitude-only data, allowing for the use of diverse data from clinical imaging archives in advanced MRI reconstruction development. Our method uses a conditional GAN-based framework to generate synthetic phase images from input magnitude images. We then applied ESPIRiT to derive RF coil sensitivity maps from fully sampled real data to generate multi-coil data. The synthetic data generation method was evaluated by comparing image reconstruction results from training Variational Networks either with real data or synthetic data. We demonstrate that the Variational Network trained on synthetic MRI data from our method, consisting of GAN-derived synthetic phase and multi-coil information, outperformed Variational Networks trained on data with synthetic phase generated using current state-of-the-art methods. Additionally, we demonstrate that the Variational Networks trained with synthetic k-space data from our method perform comparably to image reconstruction networks trained on undersampled real k-space data.
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Affiliation(s)
- Nikhil Deveshwar
- UC Berkeley-UCSF Graduate Program in Bioengineering, Berkeley, CA 94701, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94016, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94701, USA
| | - Abhejit Rajagopal
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94016, USA
| | - Sule Sahin
- UC Berkeley-UCSF Graduate Program in Bioengineering, Berkeley, CA 94701, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94016, USA
| | - Efrat Shimron
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94701, USA
| | - Peder E. Z. Larson
- UC Berkeley-UCSF Graduate Program in Bioengineering, Berkeley, CA 94701, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94016, USA
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14
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Zhang Y, Huang P, Wang X, Xu Q, Liu Y, Jin Z, Li Y, Cheng Z, Tang R, Chen S, He N, Yan F, Haacke EM. Visualizing the deep cerebellar nuclei using quantitative susceptibility mapping: An application in healthy controls, Parkinson's disease patients and essential tremor patients. Hum Brain Mapp 2023; 44:1810-1824. [PMID: 36502376 PMCID: PMC9921226 DOI: 10.1002/hbm.26178] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 10/20/2022] [Accepted: 11/27/2022] [Indexed: 12/14/2022] Open
Abstract
The visualization and identification of the deep cerebellar nuclei (DCN) (dentate [DN], interposed [IN] and fastigial nuclei [FN]) are particularly challenging. We aimed to visualize the DCN using quantitative susceptibility mapping (QSM), predict the contrast differences between QSM and T2* weighted imaging, and compare the DCN volume and susceptibility in movement disorder populations and healthy controls (HCs). Seventy-one Parkinson's disease (PD) patients, 39 essential tremor patients, and 80 HCs were enrolled. The PD patients were subdivided into tremor dominant (TD) and postural instability/gait difficulty (PIGD) groups. A 3D strategically acquired gradient echo MR imaging protocol was used for each subject to obtain the QSM data. Regions of interest were drawn manually on the QSM data to calculate the volume and susceptibility. Correlation analysis between the susceptibility and either age or volume was performed and the intergroup differences of the volume and magnetic susceptibility in all the DCN structures were evaluated. For the most part, all the DCN structures were clearly visualized on the QSM data. The susceptibility increased as a function of volume for both the HC group and disease groups in the DN and IN (p < .001) but not the FN (p = .74). Only the volume of the FN in the TD-PD group was higher than that in the HCs (p = .012), otherwise, the volume and susceptibility among these four groups did not differ significantly. In conclusion, QSM provides clear visualization of the DCN structures. The results for the volume and susceptibility of the DCN can be used as baseline references in future studies of movement disorders.
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Affiliation(s)
- Youmin Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Pei Huang
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinhui Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiuyun Xu
- Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA
| | - Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhijia Jin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rongbiao Tang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shengdi Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - E Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA.,Department of Radiology, Wayne State University, Detroit, Michigan, USA
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15
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Guo H, Zhou J, Liu P, Chen H. Phase-constrained reconstruction method with compressed sensing for multi-parametric quantitative magnetic resonance imaging. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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16
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Sun C, Ghassaban K, Song J, Chen Y, Zhang C, Qu F, Zhu J, Wang G, Haacke EM. Quantifying calcium changes in the fetal spine using quantitative susceptibility mapping as extracted from STAGE imaging. Eur Radiol 2023; 33:606-614. [PMID: 36044065 PMCID: PMC10662431 DOI: 10.1007/s00330-022-09042-5] [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: 03/18/2022] [Revised: 06/11/2022] [Accepted: 07/19/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES To evaluate calcium deposition in the fetal spine in vivo during the second and third trimesters using quantitative susceptibility mapping (QSM). METHODS Fifty-four pregnant women in their second and third trimesters underwent a 2D multi-echo STrategically Acquired Gradient Echo (STAGE) MR imaging protocol at 3T covering the fetal spine. The first echo data was used for QSM processing. A linear regression model was used to assess the correlation between magnetic susceptibility and gestational age (GA). A paired sample t-test was used to compare the consistency of QSM measurements from each sequence. RESULTS The magnetic susceptibility of the fetal spine decreased linearly with advancing GA, with a slope of -52.3 parts per billion (ppb)/week and a Pearson correlation coefficient (r) of 0.83 (p < 0.001). In 37 subjects for whom the STAGE local QSM data were available from both flip angles, the average magnetic susceptibility values were -1111 ± 278 ppb and -1081 ± 262 ppb for FA = 8° and FA = 40°, respectively. These means were not statistically different according to a paired sample t-test (p = 0.156). CONCLUSIONS QSM is a reliable technique for evaluating calcium deposition and bone mineral density of fetal vertebrae. Our results demonstrate an increase in fetal calcium levels as a function of GA. These measures might be able to provide reference values for calcium content in the fetal spine during the second and third trimesters. KEY POINTS • Calcium deposition and mineralization in the fetal spine, evaluated by vertebral magnetic susceptibility, increased with advancing gestational age. • Our results provide reference values for calcium content in the fetal spine during the second and third trimesters.
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Affiliation(s)
- Cong Sun
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Kiarash Ghassaban
- Department of Radiology, Wayne State University, Detroit, MI, USA
- SpinTech MRI Inc., Bingham Farms, MI, USA
| | - Jiaguang Song
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Yufan Chen
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Chao Zhang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan, China
| | - Feifei Qu
- MR Collaboration, Siemens Healthineers Ltd., Shanghai, China
| | - Jinxia Zhu
- MR Collaboration, Siemens Healthineers Ltd., Beijing, China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324, Jingwu Road, Jinan, 250021, Shandong, China.
| | - E Mark Haacke
- Department of Radiology, Wayne State University, Detroit, MI, USA.
- SpinTech MRI Inc., Bingham Farms, MI, USA.
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17
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Ye Y, Lyu J, Hu Y, Zhang Z, Xu J, Zhang W, Yuan J, Zhou C, Fan W, Zhang X. Augmented T 1 -weighted steady state magnetic resonance imaging. NMR IN BIOMEDICINE 2022; 35:e4729. [PMID: 35297115 DOI: 10.1002/nbm.4729] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 06/14/2023]
Abstract
T1 contrasts obtained using short-TR incoherent steady state gradient echo (GRE) methods are generally suboptimal, to which non-T1 factors in the signals play a major part. In this work, we proposed an augmented T1 -weighted (aT1 W) method to extract the signal ratio between routine GRE T1 W and proton density-weighted signals that effectively removes the non-T1 effects from the original T1 W signals, including proton density, T2 * decay, and coil sensitivity. A recently proposed multidimensional integration (MDI) technique was incorporated in the aT1 W calculation for better signal-to-noise ratio (SNR) performance. For comparison between aT1 W and T1 W results, Monte Carlo noise analysis was performed via simulation and on scanned data, and region-of-interest (ROI) analysis and comparison was performed on the system phantom. For brain scans, the image contrast, noise behavior, and SNR of aT1 W images were compared with routine GRE and inversion-recovery-based T1 W images. The proposed aT1 W method yielded saliently improved T1 contrasts (potentially > 30% higher contrast-to-noise ratio [CNR]) than routine GRE T1 W images. Good spatial homogeneity and signal consistency as well as high SNR/CNR were achieved in aT1 W images using the MDI technique. For contrast-enhanced (CE) imaging, aT1 W offered stronger post-CE contrast and better boundary delineation than T1 MPRAGE images while using a shorter scan time.
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Affiliation(s)
| | | | - Yichen Hu
- UIH America, Inc., Houston, Texas, USA
| | | | - Jian Xu
- UIH America, Inc., Houston, Texas, USA
| | | | - Jianmin Yuan
- Central Research Institute, UIH Group, Shanghai, China
| | - Chao Zhou
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, China
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Wei Fan
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, China
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Xu Zhang
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, Guangdong, China
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
- Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
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18
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Bartnik-Olson BL, Blood AB, Terry MH, Hanson SFL, Day C, Kido D, Kim P. Quantitative susceptibility mapping as a measure of cerebral oxygenation in neonatal piglets. J Cereb Blood Flow Metab 2022; 42:891-900. [PMID: 34878947 PMCID: PMC9254037 DOI: 10.1177/0271678x211065199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 11/01/2021] [Accepted: 11/05/2021] [Indexed: 11/15/2022]
Abstract
Prominence of cerebral veins using susceptibility weighted magnetic resonance imaging (SWI) has been used as a qualitative indicator of cerebral venous oxygenation (CvO2). Quantitative susceptibility mapping (QSM) adds more precision to the assessment of CvO2, but has not been applied to neonatal hypoxic ischemic injury (HII). We proposed to study QSM measures of venous susceptibility and their correlation with direct measures of brain oxygenation and cerebral blood flow (CBF) in the neonatal piglet. The association of QSM intravascular cerebral venous susceptibility, with brain tissue O2 tension, CBF, cortical tissue oxyhemoglobin saturation, and the partial pressure of oxygen in arterial blood measurement during various oxygenation states was determined by linear regression. Compared to normoxia, venous susceptibility in the straight sinus increased 56.8 ± 25.4% during hypoxia, while decreasing during hyperoxia (23.5 ± 32.9%) and hypercapnia (23.3 ± 73.1%), which was highly correlated to all other measures of oxygenation (p < 0.0001) but did not correlate to CBF (p = 0.82). These findings demonstrate a strong relationship between venous susceptibility and brain tissue O2 tension. Our results suggest that QSM-derived venous susceptibility is sensitive to cerebral oxygenation status across various oxygenation states.
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Affiliation(s)
| | - Arlin B Blood
- Department of Pediatrics, Loma Linda University School of
Medicine, Center for Perinatal Biology, Loma Linda, CA, USA
| | - Michael H Terry
- Department of Pulmonary & Critical Care, Loma Linda
University Medical Center, Loma Linda, CA, USA
| | - Shawn FL Hanson
- Center for Perinatal Biology, Loma Linda University School of
Medicine, Loma Linda, CA, USA
| | - Christopher Day
- Department of Pediatrics, Office of Graduate Medical Education,
Loma Linda, CA, USA
| | - Daniel Kido
- Department of Radiology, Loma Linda University Medical Center,
Loma Linda, CA, USA
| | - Paggie Kim
- Department of Radiology, Loma Linda University Medical Center,
Loma Linda, CA, USA
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19
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Cao T, Ma S, Wang N, Gharabaghi S, Xie Y, Fan Z, Hogg E, Wu C, Han F, Tagliati M, Haacke EM, Christodoulou AG, Li D. Three-dimensional simultaneous brain mapping of T1, T2, T2∗ and magnetic susceptibility with MR Multitasking. Magn Reson Med 2022; 87:1375-1389. [PMID: 34708438 PMCID: PMC8776611 DOI: 10.1002/mrm.29059] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 09/08/2021] [Accepted: 10/07/2021] [Indexed: 01/24/2023]
Abstract
PURPOSE To develop a new technique that enables simultaneous quantification of whole-brain T1 , T2 , T 2 ∗ , as well as susceptibility and synthesis of six contrast-weighted images in a single 9.1-minute scan. METHODS The technique uses hybrid T2 -prepared inversion-recovery pulse modules and multi-echo gradient-echo readouts to collect k-space data with various T1, T2, and T 2 ∗ weightings. The underlying image is represented as a six-dimensional low-rank tensor consisting of three spatial dimensions and three temporal dimensions corresponding to T1 recovery, T2 decay, and multi-echo behaviors, respectively. Multiparametric maps were fitted from reconstructed image series. The proposed method was validated on phantoms and healthy volunteers, by comparing quantitative measurements against corresponding reference methods. The feasibility of generating six contrast-weighted images was also examined. RESULTS High quality, co-registered T1 , T2 , and T 2 ∗ susceptibility maps were generated that closely resembled the reference maps. Phantom measurements showed substantial consistency (R2 > 0.98) with the reference measurements. Despite the significant differences of T1 (p < .001), T2 (p = .002), and T 2 ∗ (p = 0.008) between our method and the references for in vivo studies, excellent agreement was achieved with all intraclass correlation coefficients greater than 0.75. No significant difference was found for susceptibility (p = .900). The framework is also capable of synthesizing six contrast-weighted images. CONCLUSION The MR Multitasking-based 3D brain mapping of T1 , T2 , T 2 ∗ , and susceptibility agrees well with the reference and is a promising technique for multicontrast and quantitative imaging.
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Affiliation(s)
- Tianle Cao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Sen Ma
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Sara Gharabaghi
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Elliot Hogg
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Chaowei Wu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Fei Han
- Siemens Medical Solutions USA, Inc., Los Angeles, California, USA
| | - Michele Tagliati
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - E. Mark Haacke
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA
- The MRI Institute for Biomedical Research, Bingham Farms, MI, USA
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
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20
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Ye Y, Lyu J, Hu Y, Zhang Z, Xu J, Zhang W. MULTI-parametric MR imaging with fLEXible design (MULTIPLEX). Magn Reson Med 2022; 87:658-673. [PMID: 34464011 DOI: 10.1002/mrm.28999] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/17/2021] [Accepted: 08/18/2021] [Indexed: 11/10/2022]
Abstract
PURPOSE To introduce a gradient echo (GRE) -based method, namely MULTIPLEX, for single-scan 3D multi-parametric MRI with high resolution, signal-to-noise ratio (SNR), accuracy, efficiency, and acquisition flexibility. THEORY With a comprehensive design with dual-repetition time (TR), dual flip angle (FA), multi-echo, and optional flow modulation features, the MULTIPLEX signals contain information on radiofrequency (RF) B1t fields, proton density, T1 , susceptibility and blood flows, facilitating multiple qualitative images and parametric maps. METHODS MULTIPLEX was evaluated on system phantom and human brains, via visual inspection for image contrasts and quality or quantitative evaluation via simulation, phantom scans and literature comparison. Region-of-interest (ROI) analysis was performed on parametric maps of the system phantom and brain scans, extracting the mean and SD of the T1 , T2∗ , proton density (PD), and/or quantitative susceptibility mapping (QSM) values for comparison with reference values or literature. RESULTS One MULTIPLEX scan offers multiple sets of images, including but not limited to: composited PDW/T1 W/ T2∗ W, aT1 W, SWI, MRA (optional), B1t map, T1 map, T2∗ / R2∗ maps, PD map, and QSM. The quantitative error of phantom T1 , T2∗ and PD mapping were <5%, and those in brain scans were in good agreement with literature. MULTIPLEX scan times for high resolution (0.68 × 0.68 × 2 mm3 ) whole brain coverage were about 7.5 min, while processing times were <1 min. With flow modulation, additional MRA images can be obtained without affecting the quality or accuracy of other images. CONCLUSION The proposed MUTLIPLEX method possesses great potential for multi-parametric MR imaging.
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Affiliation(s)
| | | | - Yichen Hu
- UIH America, Inc., Houston, Texas, USA
| | | | - Jian Xu
- UIH America, Inc., Houston, Texas, USA
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21
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Haacke EM, Bernitsas E, Subramanian K, Utriainen D, Palutla VK, Yerramsetty K, Kumar P, Sethi SK, Chen Y, Latif Z, Jella P, Gharabaghi S, Wang Y, Zhang X, Comley RA, Beaver J, Luo Y. A Comparison of Magnetic Resonance Imaging Methods to Assess Multiple Sclerosis Lesions: Implications for Patient Characterization and Clinical Trial Design. Diagnostics (Basel) 2021; 12:diagnostics12010077. [PMID: 35054244 PMCID: PMC8775217 DOI: 10.3390/diagnostics12010077] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/20/2021] [Accepted: 12/22/2021] [Indexed: 11/16/2022] Open
Abstract
Magnetic resonance imaging (MRI) is a sensitive imaging modality for identifying inflammatory and/or demyelinating lesions, which is critical for a clinical diagnosis of MS and evaluating drug responses. There are many unique means of probing brain tissue status, including conventional T1 and T2 weighted imaging (T1WI, T2WI), T2 fluid attenuated inversion recovery (FLAIR), magnetization transfer, myelin water fraction, diffusion tensor imaging (DTI), phase-sensitive inversion recovery and susceptibility weighted imaging (SWI), but no study has combined all of these modalities into a single well-controlled investigation. The goals of this study were to: compare different MRI measures for lesion visualization and quantification; evaluate the repeatability of various imaging methods in healthy controls; compare quantitative susceptibility mapping (QSM) with myelin water fraction; measure short-term longitudinal changes in the white matter of MS patients and map out the tissue properties of the white matter hyperintensities using STAGE (strategically acquired gradient echo imaging). Additionally, the outcomes of this study were anticipated to aid in the choice of an efficient imaging protocol reducing redundancy of information and alleviating patient burden. Of all the sequences used, T2 FLAIR and T2WI showed the most lesions. To differentiate the putative demyelinating lesions from inflammatory lesions, the fusion of SWI and T2 FLAIR was used. Our study suggests that a practical and efficient imaging protocol combining T2 FLAIR, T1WI and STAGE (with SWI and QSM) can be used to rapidly image MS patients to both find lesions and study the demyelinating and inflammatory characteristics of the lesions.
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Affiliation(s)
- Ewart Mark Haacke
- The MRI Institute for Biomedical Research, Bingham Farms, MI 48025, USA; (D.U.); (S.K.S.)
- Department of Radiology, Wayne State University, Detroit, MI 48201, USA; (K.S.); (Z.L.); (P.J.); (Y.W.)
- Department of Neurology, Wayne State University, Detroit, MI 48201, USA; (E.B.); (Y.C.)
- SpinTech Inc., Bingham Farms, MI 48025, USA
- MR Innovations Inc., Bingham Farms, MI 48025, USA;
- Correspondence:
| | - Evanthia Bernitsas
- Department of Neurology, Wayne State University, Detroit, MI 48201, USA; (E.B.); (Y.C.)
| | - Karthik Subramanian
- Department of Radiology, Wayne State University, Detroit, MI 48201, USA; (K.S.); (Z.L.); (P.J.); (Y.W.)
| | - David Utriainen
- The MRI Institute for Biomedical Research, Bingham Farms, MI 48025, USA; (D.U.); (S.K.S.)
- SpinTech Inc., Bingham Farms, MI 48025, USA
| | - Vinay Kumar Palutla
- MR Medical Imaging Innovations India Pvt. Ltd., Hyderabad 500081, India; (V.K.P.); (K.Y.); (P.K.)
| | - Kiran Yerramsetty
- MR Medical Imaging Innovations India Pvt. Ltd., Hyderabad 500081, India; (V.K.P.); (K.Y.); (P.K.)
| | - Prashanth Kumar
- MR Medical Imaging Innovations India Pvt. Ltd., Hyderabad 500081, India; (V.K.P.); (K.Y.); (P.K.)
| | - Sean K. Sethi
- The MRI Institute for Biomedical Research, Bingham Farms, MI 48025, USA; (D.U.); (S.K.S.)
- Department of Radiology, Wayne State University, Detroit, MI 48201, USA; (K.S.); (Z.L.); (P.J.); (Y.W.)
- SpinTech Inc., Bingham Farms, MI 48025, USA
| | - Yongsheng Chen
- Department of Neurology, Wayne State University, Detroit, MI 48201, USA; (E.B.); (Y.C.)
| | - Zahid Latif
- Department of Radiology, Wayne State University, Detroit, MI 48201, USA; (K.S.); (Z.L.); (P.J.); (Y.W.)
| | - Pavan Jella
- Department of Radiology, Wayne State University, Detroit, MI 48201, USA; (K.S.); (Z.L.); (P.J.); (Y.W.)
| | | | - Ying Wang
- Department of Radiology, Wayne State University, Detroit, MI 48201, USA; (K.S.); (Z.L.); (P.J.); (Y.W.)
- MR Innovations Inc., Bingham Farms, MI 48025, USA;
| | - Xiaomeng Zhang
- AbbVie Inc., North Chicago, IL 60064, USA; (X.Z.); (R.A.C.); (J.B.); (Y.L.)
| | - Robert A. Comley
- AbbVie Inc., North Chicago, IL 60064, USA; (X.Z.); (R.A.C.); (J.B.); (Y.L.)
| | - John Beaver
- AbbVie Inc., North Chicago, IL 60064, USA; (X.Z.); (R.A.C.); (J.B.); (Y.L.)
| | - Yanping Luo
- AbbVie Inc., North Chicago, IL 60064, USA; (X.Z.); (R.A.C.); (J.B.); (Y.L.)
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22
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Fahmy LM, Chen Y, Xuan S, Haacke EM, Hu J, Jiang Q. All Central Nervous System Neuro- and Vascular-Communication Channels Are Surrounded With Cerebrospinal Fluid. Front Neurol 2021; 12:614636. [PMID: 34220663 PMCID: PMC8247447 DOI: 10.3389/fneur.2021.614636] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 04/29/2021] [Indexed: 12/15/2022] Open
Abstract
Background: Recent emerging evidence has highlighted the potential critical role of cerebrospinal fluid (CSF) in cerebral waste clearance and immunomodulation. It is already very well-established that the central nervous system (CNS) is completely submerged in CSF on a macro-level; but to what extent is this true on a micro-level? Specifically, within the peri-neural and peri-vascular spaces within the CNS parenchyma. Therefore, the objective of this study was to use magnetic resonance imaging (MRI) to simultaneously map the presence of CSF within all peri-neural (cranial and spinal nerves) and peri-vascular spaces in vivo in humans. Four MRI protocols each with five participants were used to image the CSF in the brain and spinal cord. Our findings indicated that all CNS neuro- and vascular-communication channels are surrounded with CSF. In other words, all peri-neural spaces surrounding the cranial and spinal nerves as well as all peri-vascular spaces surrounding MRI-visible vasculature were filled with CSF. These findings suggest that anatomically, substance exchange between the brain parenchyma and outside tissues including lymphatic ones can only occur through CSF pathways and/or vascular pathways, warranting further investigation into its implications in cerebral waste clearance and immunity.
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Affiliation(s)
- Lara M Fahmy
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, United States
| | - Yongsheng Chen
- Department of Neurology, Wayne State University, Detroit, MI, United States
| | - Stephanie Xuan
- Department of Radiology, Wayne State University, Detroit, MI, United States
| | - E Mark Haacke
- Department of Radiology, Wayne State University, Detroit, MI, United States
| | - Jiani Hu
- Department of Radiology, Wayne State University, Detroit, MI, United States
| | - Quan Jiang
- Department of Neurology, Henry Ford Health System, Detroit, MI, United States
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23
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Chen Y, Moiseev D, Kong WY, Bezanovski A, Li J. Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction. J Magn Reson Imaging 2021; 53:1539-1549. [PMID: 33448058 DOI: 10.1002/jmri.27508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 12/28/2020] [Accepted: 12/29/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle using MRI has emerged as a monitoring biomarker for axonal loss in patients with peripheral neuropathies. In this study, we are testing whether deep learning-based model can automate quantification of the FF in individual muscles. While individual muscle is smaller with irregular shape, manually segmented muscle MRI images have been accumulated in this lab; and make the deep learning feasible. PURPOSE To automate segmentation on muscle MRI images through deep learning for quantifying individual muscle FF in patients with peripheral neuropathies. STUDY TYPE Retrospective. SUBJECTS 24 patients and 19 healthy controls. FIELD STRENGTH/SEQUENCES 3T; Interleaved 3D GRE. ASSESSMENT A 3D U-Net model was implemented in segmenting muscle MRI images. This was enabled by leveraging a large set of manually segmented muscle MRI images. B1+ and B1- maps were used to correct image inhomogeneity. Accuracy of the automation was evaluated using Pixel Accuracy (PA), Dice Coefficient (DC) in binary masks; and Bland-Altman and Pearson correlation by comparing FF values between manual and automated methods. STATISTICAL TESTS PA and DC were reported with their median value and standard deviation. Two methods were compared using the ± 95% confidence intervals (CI) of Bland-Altman analysis and the Pearson's coefficient (r2 ). RESULTS DC values were from 0.83 ± 0.17 to 0.98 ± 0.02 in thigh and from 0.63 ± 0.18 to 0.96 ± 0.02 in calf muscles. For FF values, the overall ± 95% CI and r2 were [0.49, -0.56] and 0.989 in thigh and [0.84, -0.71] and 0.971 in the calf. DATA CONCLUSION Automated results well agreed with the manual results in quantifying FF for individual muscles. This method mitigates the formidable time consumption and intense labor in manual segmentations; and enables the use of individual muscle FF as outcome measures in upcoming longitudinal studies. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Daniel Moiseev
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Wan Yee Kong
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Alexandar Bezanovski
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Jun Li
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, Michigan, USA
- Department of Biochemistry, Microbiology and Immunology, Wayne State University School of Medicine, Detroit, Michigan, USA
- John D. Dingell VA Medical Center, Detroit, Michigan, USA
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24
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Qu F, Sun T, Chen Y, Yadav BK, Jiang L, Qian Z, Haacke EM. Fetal brain tissue characterization at 1.5 T using STrategically Acquired Gradient Echo (STAGE) imaging. Eur Radiol 2021; 31:5586-5594. [PMID: 33523305 DOI: 10.1007/s00330-020-07618-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 11/13/2020] [Accepted: 12/07/2020] [Indexed: 11/25/2022]
Abstract
OBJECTIVES To estimate human fetal brain MRI tissue properties including apparent T1 (T1app) and apparent proton density (PDapp) by using a rapid multi-contrast acquisition protocol called STrategically Acquired Gradient Echo (STAGE) imaging. METHODS STAGE data were collected using two flip angles (15° and 60°, with a TR = 600 ms) for 30 pregnant women at 1.5 T (15 healthy controls: gestational age (GA) range 19 + 1/7 weeks to 34 + 5/7 weeks; 11 abnormal subjects with ventriculomegaly: GA range 21 + 5/7 weeks to 31 + 5/7 weeks; 4 subjects with other abnormalities). Both T1app and PDapp maps of the fetal brain were calculated from the STAGE data. A region-of-interest-based approach was used to measure T1app and PDapp in the subplate/intermediate zone (SP/IZ), cortical plate (CP), and cerebrospinal fluid (CSF) in the fetal brain. RESULTS The ratios of T1appSP/IZ/T1appCP and PDappSP/IZ/PDappCP were larger than unity while T1appSP/IZ/T1appCSF and PDappSP/IZ/PDappCSF were both less than unity. CONCLUSIONS STAGE imaging provides a potential practical approach to estimate multi-parametric properties of the human fetal brain. KEY POINTS • STAGE is feasible in measuring fetal brain tissue properties. • Water content in cortical plate and subplate/intermediate zone approaches that of cerebrospinal fluid in early gestational ages.
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Affiliation(s)
- Feifei Qu
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Taotao Sun
- Department of Radiology, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
- Shandong Medical Imaging Research Institute, Shandong University, Jinan, Shandong, China
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Brijesh Kumar Yadav
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Ling Jiang
- Department of Radiology, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Zhaoxia Qian
- Department of Radiology, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China.
- Shandong Medical Imaging Research Institute, Shandong University, Jinan, Shandong, China.
| | - E Mark Haacke
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA.
- Department of Biomedical Engineering, Wayne State University College of Engineering, Detroit, MI, USA.
- SpinTech, Inc., Bingham Farms, MI, USA.
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25
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He N, Ghassaban K, Huang P, Jokar M, Wang Y, Cheng Z, Jin Z, Li Y, Sethi SK, He Y, Chen Y, Gharabaghi S, Chen S, Yan F, Haacke EM. Imaging iron and neuromelanin simultaneously using a single 3D gradient echo magnetization transfer sequence: Combining neuromelanin, iron and the nigrosome-1 sign as complementary imaging biomarkers in early stage Parkinson's disease. Neuroimage 2021; 230:117810. [PMID: 33524572 DOI: 10.1016/j.neuroimage.2021.117810] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 01/15/2021] [Accepted: 01/23/2021] [Indexed: 10/22/2022] Open
Abstract
Diagnosing early stage Parkinson's disease (PD) is still a clinical challenge. Previous studies using iron, neuromelanin (NM) or the Nigrosome-1 (N1) sign in the substantia nigra (SN) by themselves have been unable to provide sufficiently high diagnostic performance for these methods to be adopted clinically. Our goal in this study was to extract the NM complex volume, iron content and volume representing the entire SN, and the N1 sign as potential complementary imaging biomarkers using a single 3D magnetization transfer contrast (MTC) gradient echo sequence and to evaluate their diagnostic performance and clinical correlations in early stage PD. A total of 40 early stage idiopathic PD subjects and 40 age- and sex-matched healthy controls (HCs) were imaged at 3T. NM boundaries (representing the SN pars compacta (SNpc) and parabrachial pigmented nucleus) and iron boundaries representing the total SN (SNpc and SN pars reticulata) were determined semi-automatically using a dynamic programming (DP) boundary detection algorithm. Receiver operating characteristic analyses were performed to evaluate the utility of these imaging biomarkers in diagnosing early stage PD. A correlation analysis was used to study the relationship between these imaging measures and the clinical scales. We also introduced the concept of NM and total iron overlap volumes to demonstrate the loss of NM relative to the iron containing SN. Furthermore, all 80 cases were evaluated for the N1 sign independently. The NM and SN volumes were lower while the iron content was higher in the SN for PD subjects compared to HCs. Interestingly, the PD subjects with bilateral loss of the N1 sign had the highest iron content. The area under the curve (AUC) values for the average of both hemispheres for single measures were: .960 for NM complex volume; .788 for total SN volume; .740 for SN iron content and .891 for the N1 sign. Combining NM complex volume with each of the following measures through binary logistic regression led to AUC values for the averaged right and left sides of: .976 for total iron content; .969 for total SN volume, .965 for overlap volume and .983 for the N1 sign. We found a negative correlation between SN volume and UPDRS-III (R2 = .22, p = .002). While the N1 sign performed well, it does not contain any information about iron content or NM quantitatively, therefore, marrying this sign with the NM and iron measures provides a better physiological explanation of what is happening when the N1 sign disappears in PD subjects. In summary, the combination of NM complex volume, SN volume, iron content and the N1 sign as derived from a single MTC sequence provides complementary information for understanding and diagnosing early stage PD.
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Affiliation(s)
- Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China.
| | - Kiarash Ghassaban
- Department of Radiology, Wayne State University, 3990 John R, Detroit, Michigan 48201, USA; Department of Biomedical Engineering, Wayne State University, 3990 John R, Detroit, Michigan 48201, USA
| | - Pei Huang
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | - Ying Wang
- Department of Radiology, Wayne State University, 3990 John R, Detroit, Michigan 48201, USA; SpinTech, Inc., Bingham Farms, Michigan 48025, USA
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China
| | - Zhijia Jin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China
| | - Sean K Sethi
- Department of Radiology, Wayne State University, 3990 John R, Detroit, Michigan 48201, USA; SpinTech, Inc., Bingham Farms, Michigan 48025, USA
| | - Yixi He
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yongsheng Chen
- Department of Neurology, Wayne State University, 4201 St. Antoine, Detroit, Michigan 48201, USA
| | | | - Shengdi Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China.
| | - E Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China; Department of Radiology, Wayne State University, 3990 John R, Detroit, Michigan 48201, USA; Department of Biomedical Engineering, Wayne State University, 3990 John R, Detroit, Michigan 48201, USA; SpinTech, Inc., Bingham Farms, Michigan 48025, USA; Department of Neurology, Wayne State University, 4201 St. Antoine, Detroit, Michigan 48201, USA
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Mohammadi S, Callaghan MF. Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging. J Neurosci Methods 2021; 348:108990. [PMID: 33129894 PMCID: PMC7840525 DOI: 10.1016/j.jneumeth.2020.108990] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/21/2020] [Accepted: 10/20/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. NEW METHOD This is the second review on the topic of g-ratio mapping using MRI. RESULTS This review summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. COMPARISON WITH EXISTING METHODS Using simulations based on recently published data, this review reveals caveats to the state-of-the-art calibration methods that have been used for in vivo g-ratio mapping. It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. CONCLUSIONS We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the full potential of many novel techniques yet to be investigated.
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Affiliation(s)
- Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
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Pirastru A, Chen Y, Pelizzari L, Baglio F, Clerici M, Haacke EM, Laganà MM. Quantitative MRI using STrategically Acquired Gradient Echo (STAGE): optimization for 1.5 T scanners and T1 relaxation map validation. Eur Radiol 2021; 31:4504-4513. [PMID: 33409790 DOI: 10.1007/s00330-020-07515-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 09/24/2020] [Accepted: 11/12/2020] [Indexed: 11/24/2022]
Abstract
OBJECTIVES The strategically acquired gradient echo (STAGE) protocol, developed for 3T scanners, allows one to derive quantitative maps such as T1, T2*, proton density, and quantitative susceptibility mapping in about 5 min. Our aim was to adapt the STAGE sequences for 1.5T scanners which are still commonly used in clinical practice. Furthermore, the accuracy and repeatability of the STAGE-derived T1 estimate were tested. METHODS Flip angle (FA) optimization was performed using a theoretical simulation by maximizing signal-to-noise ratio, contrast-to-noise ratio, and T1 precision. The FA choice was further refined with the ISMRM/NIST phantom and in vivo acquisitions. The accuracy of the T1 estimate was assessed by comparing STAGE-derived T1 values with T1 maps obtained with an inversion recovery sequence. T1 accuracy was investigated for both the phantom and in vivo data. Finally, one subject was acquired 10 times once a week and a group of 27 subjects was scanned once. The T1 coefficient of variation (COV) was computed to assess scan-rescan and physiological variability, respectively. RESULTS The FA1,2 = 7°,38° were identified as the optimal FA pair at 1.5T. The T1 estimate errors were below 3% and 5% for phantom and in vivo measurements, respectively. COV for different tissues ranged from 1.8 to 4.8% for physiological variability, and between 0.8 and 2% for scan-rescan repeatability. CONCLUSION The optimized STAGE protocol can provide accurate and repeatable T1 mapping along with other qualitative images and quantitative maps in about 7 min on 1.5T scanners. This study provides the groundwork to assess the role of STAGE in clinical settings. KEY POINTS • The STAGE imaging protocol was optimized for use on 1.5T field strength scanners. • A practical STAGE protocol makes it possible to derive quantitative maps (i.e., T1, T2*, PD, and QSM) in about 7 min at 1.5T. • The T1 estimate derived from the STAGE protocol showed good accuracy and repeatability.
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Affiliation(s)
- Alice Pirastru
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Via Alfonso Capecelatro, 66, 20148, Milan, Italy
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, 4201 St Antoine St, Detroit, MI 48201, USA
| | - Laura Pelizzari
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Via Alfonso Capecelatro, 66, 20148, Milan, Italy
| | - Francesca Baglio
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Via Alfonso Capecelatro, 66, 20148, Milan, Italy
| | - Mario Clerici
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Via Alfonso Capecelatro, 66, 20148, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Via Francesco Sforza, 35, Milan, 20122, Italy
| | - E Mark Haacke
- Department of Neurology, Wayne State University School of Medicine, 4201 St Antoine St, Detroit, MI 48201, USA.,The MRI Institute for Biomedical Research, 30200 Telegraph Rd, Bingham Farms, MI 48025, USA.,Magnetic Resonance Innovations Inc, 30200 Telegraph Rd, Bingham Farms, MI 48025, USA.,Department of Radiology, Wayne State University School of Medicine, 3990 John R St, Detroit, MI 48201, USA
| | - Maria Marcella Laganà
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Via Alfonso Capecelatro, 66, 20148, Milan, Italy.
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Gharabaghi S, Liu S, Wang Y, Chen Y, Buch S, Jokar M, Wischgoll T, Kashou NH, Zhang C, Wu B, Cheng J, Haacke EM. Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging. Front Neurosci 2020; 14:581474. [PMID: 33192267 PMCID: PMC7645168 DOI: 10.3389/fnins.2020.581474] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/16/2020] [Indexed: 11/13/2022] Open
Abstract
Purpose To develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging. Methods The proposed QSM reconstruction algorithm, referred to as “structurally constrained Susceptibility Weighted Imaging and Mapping” scSWIM, performs an ℓ1 and ℓ2 regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The measured susceptibility values from scSWIM for both simulated and in vivo data were compared to the: original susceptibility model (for simulated data only), the multi orientation COSMOS (for in vivo data only), truncated k-space division (TKD), iterative susceptibility weighted imaging and mapping (iSWIM), and morphology enabled dipole inversion (MEDI) algorithms. Goodness of fit was quantified by measuring the root mean squared error (RMSE) and structural similarity index (SSIM). Additionally, scSWIM was assessed in ten healthy subjects. Results The unique contrast and tissue boundaries from T1WE and iSWIM enable the accurate definition of edges of high susceptibility regions. For the simulated brain model without the addition of microbleeds and calcium, the RMSE was best at 5.21ppb for scSWIM and 8.74ppb for MEDI thanks to the reduced streaking artifacts. However, by adding the microbleeds and calcium, MEDI’s performance dropped to 47.53ppb while scSWIM performance remained the same. The SSIM was highest for scSWIM (0.90) and then MEDI (0.80). The deviation from the expected susceptibility in deep gray matter structures for simulated data relative to the model (and for the in vivo data relative to COSMOS) as measured by the slope was lowest for scSWIM + 1%(−1%); MEDI + 2%(−11%) and then iSWIM −5%(−10%). Finally, scSWIM measurements in the basal ganglia of healthy subjects were in agreement with literature. Conclusion This study shows that using a data fidelity term and structural constraints results in reduced noise and streaking artifacts while preserving structural details. Furthermore, the use of STAGE imaging with multi-echo and multi-flip data helps to improve the signal-to-noise ratio in QSM data and yields less artifacts.
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Affiliation(s)
- Sara Gharabaghi
- Department of Computer Science and Engineering, Wright State University, Dayton, OH, United States.,Magnetic Resonance Innovations, Inc., Bingham Farms, MI, United States
| | - Saifeng Liu
- The MRI Institute for Biomedical Research, Bingham Farms, MI, United States
| | - Ying Wang
- The MRI Institute for Biomedical Research, Bingham Farms, MI, United States.,Department of Radiology, Wayne State University, Detroit, MI, United States
| | - Yongsheng Chen
- Department of Neurology, Wayne State University, Detroit, MI, United States
| | - Sagar Buch
- Department of Radiology, Wayne State University, Detroit, MI, United States
| | - Mojtaba Jokar
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, United States
| | - Thomas Wischgoll
- Department of Computer Science and Engineering, Wright State University, Dayton, OH, United States
| | - Nasser H Kashou
- Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, Dayton, OH, United States
| | - Chunyan Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Bo Wu
- Shanghai Zhu Yan Medical Technology Ltd., Shanghai, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - E Mark Haacke
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, United States.,The MRI Institute for Biomedical Research, Bingham Farms, MI, United States.,Department of Radiology, Wayne State University, Detroit, MI, United States.,Department of Neurology, Wayne State University, Detroit, MI, United States.,Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States
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Ji S, Yang D, Lee J, Choi SH, Kim H, Kang KM. Synthetic MRI: Technologies and Applications in Neuroradiology. J Magn Reson Imaging 2020; 55:1013-1025. [PMID: 33188560 DOI: 10.1002/jmri.27440] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 10/29/2020] [Accepted: 10/29/2020] [Indexed: 12/14/2022] Open
Abstract
Synthetic MRI is a technique that synthesizes contrast-weighted images from multicontrast MRI data. There have been advances in synthetic MRI since the technique was introduced. Although a number of synthetic MRI methods have been developed for quantifying one or more relaxometric parameters and for generating multiple contrast-weighted images, this review focuses on several methods that quantify all three relaxometric parameters (T1 , T2 , and proton density) and produce multiple contrast-weighted images. Acquisition, quantification, and image synthesis techniques are discussed for each method. We discuss the image quality and diagnostic accuracy of synthetic MRI methods and their clinical applications in neuroradiology. Based on this analysis, we highlight areas that need to be addressed for synthetic MRI to be widely implemented in the clinic. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Sooyeon Ji
- Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Dongjin Yang
- Department of Radiology, Daegu Fatima Hospital, Daegu, Republic of Korea
| | - Jongho Lee
- Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Seung Hong Choi
- Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyeonjin Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
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Optimizing neuromelanin contrast in the substantia nigra and locus coeruleus using a magnetization transfer contrast prepared 3D gradient recalled echo sequence. Neuroimage 2020; 218:116935. [DOI: 10.1016/j.neuroimage.2020.116935] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 05/06/2020] [Accepted: 05/07/2020] [Indexed: 12/12/2022] Open
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Plaque characteristics of middle cerebral artery assessed using strategically acquired gradient echo (STAGE) and vessel wall MR contribute to misery downstream perfusion in patients with intracranial atherosclerosis. Eur Radiol 2020; 31:65-75. [PMID: 32740814 DOI: 10.1007/s00330-020-07055-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 04/21/2020] [Accepted: 06/30/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To assess plaque vulnerability of the middle cerebral artery (MCA) using strategically acquired gradient echo (STAGE) versus high-resolution vessel wall MRI (hr-vwMRI), and explore the relationship between plaque characteristics and misery downstream perfusion. METHODS Ninety-one patients with single MCA atherosclerotic plaques underwent STAGE and hr-vwMRI were categorized into a group with misery perfusion and a group without based on the Alberta Stroke Program Early CT score (MTT-ASPECTS) with a threshold of 6. Plaque characteristics including inner lumen area (IWA), susceptibility, presence of hyperintensity within plaque (HIP), surface irregularity, stenosis degree, remodeling index, lipid ratio, and enhancement grade were compared between the two groups. The vulnerability of each plaque was retrospectively assessed on both STAGE and hr-vwMRI according to the combination of plaque features. Logistic regression analysis and ROC curve were performed to evaluate the effect of plaque characteristics on the presence of misery perfusion. RESULTS Taking hr-vwMRI as the reference, STAGE showed good efficiency in detecting vulnerable plaques. Patients with misery perfusion had less IWA, higher stenosis degree, more irregular surface and HIP, higher enhancement grade, and susceptibility (p < 0.01 for all). Higher susceptibility and stenosis degree were independent predictors for the occurrence of misery perfusion (p = 0.025, p = 0.048). The AUC was 0.900 for the combination of the two variables. CONCLUSION STAGE shows good efficiency to assess MCA plaque vulnerability versus hr-vwMRI. Plaque susceptibility evaluated using STAGE provides incremental value to predict misery perfusion combined with hr-vwMRI plaque features. KEY POINTS • STAGE has good efficiency in evaluating MCA plaque vulnerability versus hr-vwMRI. • Higher plaque susceptibility assessed using STAGE and higher grade luminal stenosis based on hr-vwMRI attribute to misery downstream perfusion. • STAGE provides incremental value on the understanding of plaque vulnerability in addition to conventional hr-vwMRI.
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32
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Nejad-Davarani SP, Zakariaei N, Chen Y, Haacke EM, Hurst NJ, Salim Siddiqui M, Schultz LR, Snyder JM, Walbert T, Glide-Hurst CK. Rapid multicontrast brain imaging on a 0.35T MR-linac. Med Phys 2020; 47:4064-4076. [PMID: 32434276 DOI: 10.1002/mp.14251] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 04/03/2020] [Accepted: 05/13/2020] [Indexed: 12/27/2022] Open
Abstract
PURPOSE Magnetic resonance-guided radiation therapy (MRgRT) has shown great promise for localization and real-time tumor monitoring. However, to date, quantitative imaging has been limited for low field MRgRT. This work benchmarks quantitative T1, R2*, and Proton Density (PD)mapping in a phantom on a 0.35 T MR-linac and implements a novel acquisition method, STrategically Acquired Gradient Echo (STAGE). To further validate STAGE in a clinical setting, a pilot study was undertaken in a cohort of brain tumor patients to elucidate opportunities for longitudinal functional imaging with an MR-linac in the brain. METHODS STAGE (two triple-echo gradient echo (GRE) acquisitions) was optimized for a 0.35T low-field MR-linac. Simulations were performed to choose two flip angles to optimize signal-to-noise ratio (SNR) and T1-mapping precision. Tradeoffs between SNR, scan time, and spatial resolution for whole-brain coverage were evaluated in healthy volunteers. Data were inputted into a STAGE processing pipeline to yield four qualitative images (T1-weighted, enhanced T1-weighted, proton-density (PD) weighted, and simulated FLuid-Attenuated Inversion Recovery (sFLAIR)), and three quantitative datasets (T1, PD, and R2*). A benchmarking ISMRM/NIST phantom consisting of vials with variable NiCl2 and MnCl2 concentrations was scanned using variable flip angles (VFA) (2-60 degrees) and inversion recovery (IR) methods at 0.35 T. STAGE and VFA T1 values of vials were compared to IR T1 values. As measures of agreement with reference values and repeatability, relative error (RE) and coefficient of variability (CV) were calculated, respectively, for quantitative MR values within the phantom vials (spheres). To demonstrate feasibility, longitudinal STAGE data (pretreatment, weekly, and ~ 2 months post-treatment) were acquired in an IRB-approved pilot study of brain tumor cases via the generation of temporal and differential quantitative MRI maps. RESULTS In the phantom, RE of measured VFA T1 and STAGE relative to IR reference values were 7.0 ± 2.5% and 9.5 ± 2.2% respectively. RE for the PD vials was 8.1 ± 6.8% and CV for phantom R2* measurements was 10.1 ± 9.9%. Simulations and volunteer experiments yielded final STAGE parameters of FA = 50°/10°, 1 × 1 × 3 mm3 resolution, TR = 40 ms, TE = 5/20/34 ms in 10 min (64 slices). In the pilot study of brain tumor patients, differential maps for R2* and T1 maps were sensitive to local tumor changes and appeared similar to 3 T follow-up MRI datasets. CONCLUSION Quantitative T1, R2*, and PD mapping are promising at 0.35 T agreeing well with reference data. STAGE phantom data offer quantitative representations comparable to traditional methods in a fraction of the acquisition time. Initial feasibility of implementing STAGE at 0.35 T in a patient brain tumor cohort suggests that detectable changes can be observed over time. With confirmation in a larger cohort, results may be implemented to identify areas of recurrence and facilitate adaptive radiation therapy.
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Affiliation(s)
- Siamak P Nejad-Davarani
- Department of Radiation Oncology, Henry Ford Cancer Institute, 2799 West Grand Blvd., Detroit, MI, 48202, USA
| | - Niloufar Zakariaei
- Department of Radiation Oncology, Henry Ford Cancer Institute, 2799 West Grand Blvd., Detroit, MI, 48202, USA
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, 4201 St Antoine Blvd, 8C UHC, Detroit, MI, 48201, USA
| | - E Mark Haacke
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, 48201, USA.,The MRI Institute for Biomedical Research, 30200 Telegraph Rd, STE 104, Bingham Farms, Detroit, MI, 48025, USA
| | - Newton J Hurst
- Department of Radiation Oncology, Henry Ford Cancer Institute, 2799 West Grand Blvd., Detroit, MI, 48202, USA
| | - M Salim Siddiqui
- Department of Radiation Oncology, Henry Ford Cancer Institute, 2799 West Grand Blvd., Detroit, MI, 48202, USA
| | - Lonni R Schultz
- Department of Public Health Sciences, Henry Ford Health System, One Ford Place, Ste 3E, Detroit, MI, 48202, USA
| | - James M Snyder
- Departments of Neurosurgery and Neurology, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Tobias Walbert
- Departments of Neurosurgery and Neurology, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, Henry Ford Cancer Institute, 2799 West Grand Blvd., Detroit, MI, 48202, USA
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Abstract
Magnetic resonance imaging (MRI) has been used extensively in revealing pathological changes in the central nervous system. However, to date, MRI is very much underutilized in evaluating the peripheral nervous system (PNS). This underutilization is generally due to two perceived weaknesses in MRI: first, the need for very high resolution to image the small structures within the peripheral nerves to visualize morphological changes; second, the lack of normative data in MRI of the PNS and this makes reliable interpretation of the data difficult. This article reviews current state-of-the-art capabilities in
in vivo MRI of human peripheral nerves. It aims to identify areas where progress has been made and those that still require further improvement. In particular, with many new therapies on the horizon, this review addresses how MRI can be used to provide non-invasive and objective biomarkers in the evaluation of peripheral neuropathies. Although a number of techniques are available in diagnosing and tracking pathologies in the PNS, those techniques typically target the distal peripheral nerves, and distal nerves may be completely degenerated during the patient’s first clinic visit. These techniques may also not be able to access the proximal nerves deeply embedded in the tissue. Peripheral nerve MRI would be an alternative to circumvent these problems. In order to address the pressing clinical needs, this review closes with a clinical protocol at 3T that will allow high-resolution, high-contrast, quantitative MRI of the proximal peripheral nerves.
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Affiliation(s)
- Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - E Mark Haacke
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Jun Li
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, 48201, USA.,Center for Molecular Medicine & Genetics, Wayne State University School of Medicine, Detroit, MI, 48201, USA.,Department of Biochemistry, Microbiology and Immunology, Wayne State University School of Medicine, Detroit, MI, 48201, USA.,John D. Dingell VA Medical Center, Detroit, MI, 48201, USA
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34
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He N, Sethi SK, Zhang C, Li Y, Chen Y, Sun B, Yan F, Haacke EM. Visualizing the lateral habenula using susceptibility weighted imaging and quantitative susceptibility mapping. Magn Reson Imaging 2019; 65:55-61. [PMID: 31655137 DOI: 10.1016/j.mri.2019.09.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Revised: 09/03/2019] [Accepted: 09/15/2019] [Indexed: 12/22/2022]
Abstract
The habenulae consist of a pair of small nuclei which bridge the limbic forebrain and midbrain monoaminergic centers. They are implicated in major depressive disorders due to abnormal phasic response when provoked by a conditioned stimulus. The lateral habenula (Lhb) is believed to be involved in dopamine metabolism and is now a target for deep brain stimulation, a treatment which has shown promising anti-depression effects. We imaged the habenulae with susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) in order to localize the lateral habenula. Fifty-six healthy controls were recruited for this study. For the quantitative assessment, we traced the structure to compute volume from magnitude images and mean susceptibility bilaterally for the habenula on QSM. Thresholding methods were used to delineate the Lhb habenula on QSM. SWI, true SWI (tSWI), and QSM data were subjectively reviewed for increased Lhb contrast. SWI, QSM, and tSWI showed bilateral signal changes in the posterior location of the habenulae relative to the anterior location, which may indicate increased putative iron content within the Lhb. This signal behavior was shown in 41/44 (93%) subjects. In summary, it is possible to localize the lateral component of the habenula using SWI and QSM at 3 T.
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Affiliation(s)
- Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sean K Sethi
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA; The MRI Institute for Biomedical Research, Bingham Farms, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA
| | - Chencheng Zhang
- Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Bomin Sun
- Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - E Mark Haacke
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA; The MRI Institute for Biomedical Research, Bingham Farms, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA
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35
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Haacke EM, Chen Y, Utriainen D, Wu B, Wang Y, Xia S, He N, Zhang C, Wang X, Lagana MM, Luo Y, Fatemi A, Liu S, Gharabaghi S, Wu D, Sethi SK, Huang F, Sun T, Qu F, Yadav BK, Ma X, Bai Y, Wang M, Cheng J, Yan F. STrategically Acquired Gradient Echo (STAGE) imaging, part III: Technical advances and clinical applications of a rapid multi-contrast multi-parametric brain imaging method. Magn Reson Imaging 2019; 65:15-26. [PMID: 31629075 DOI: 10.1016/j.mri.2019.09.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Revised: 09/11/2019] [Accepted: 09/15/2019] [Indexed: 12/15/2022]
Abstract
One major thrust in radiology today is image standardization with a focus on rapidly acquired quantitative multi-contrast information. This is critical for multi-center trials, for the collection of big data and for the use of artificial intelligence in evaluating the data. Strategically acquired gradient echo (STAGE) imaging is one such method that can provide 8 qualitative and 7 quantitative pieces of information in 5 min or less at 3 T. STAGE provides qualitative images in the form of proton density weighted images, T1 weighted images, T2* weighted images and simulated double inversion recovery (DIR) images. STAGE also provides quantitative data in the form of proton spin density, T1, T2* and susceptibility maps as well as segmentation of white matter, gray matter and cerebrospinal fluid. STAGE uses vendors' product gradient echo sequences. It can be applied from 0.35 T to 7 T across all manufacturers producing similar results in contrast and quantification of the data. In this paper, we discuss the strengths and weaknesses of STAGE, demonstrate its contrast-to-noise (CNR) behavior relative to a large clinical data set and introduce a few new image contrasts derived from STAGE, including DIR images and a new concept referred to as true susceptibility weighted imaging (tSWI) linked to fluid attenuated inversion recovery (FLAIR) or tSWI-FLAIR for the evaluation of multiple sclerosis lesions. The robustness of STAGE T1 mapping was tested using the NIST/NIH phantom, while the reproducibility was tested by scanning a given individual ten times in one session and the same subject scanned once a week over a 12-week period. Assessment of the CNR for the enhanced T1W image (T1WE) showed a significantly better contrast between gray matter and white matter than conventional T1W images in both patients with Parkinson's disease and healthy controls. We also present some clinical cases using STAGE imaging in patients with stroke, metastasis, multiple sclerosis and a fetus with ventriculomegaly. Overall, STAGE is a comprehensive protocol that provides the clinician with numerous qualitative and quantitative images.
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Affiliation(s)
- E Mark Haacke
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA; The MRI Institute for Biomedical Research, Bingham Farms, MI, USA; Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA.
| | - Yongsheng Chen
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA; Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
| | - David Utriainen
- The MRI Institute for Biomedical Research, Bingham Farms, MI, USA; Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA
| | - Bo Wu
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA
| | - Yu Wang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China; Neusoft Medical Systems Co., Ltd., Shanghai, China
| | - Shuang Xia
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunyan Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiao Wang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | | | - Yu Luo
- Department of Radiology, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, China
| | - Ali Fatemi
- Departments of Radiology and Radiation Oncology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Saifeng Liu
- The MRI Institute for Biomedical Research, Bingham Farms, MI, USA
| | - Sara Gharabaghi
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Sean K Sethi
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA; The MRI Institute for Biomedical Research, Bingham Farms, MI, USA; Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA
| | - Feng Huang
- Neusoft Medical Systems Co., Ltd., Shanghai, China
| | - Taotao Sun
- Department of Radiology, International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Feifei Qu
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Brijesh K Yadav
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Xiaoyue Ma
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China; Department of Radiology, Zhengzhou University People's Hospital, Zhengzhou, China
| | - Yan Bai
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China; Department of Radiology, Zhengzhou University People's Hospital, Zhengzhou, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China; Department of Radiology, Zhengzhou University People's Hospital, Zhengzhou, China.
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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36
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Melazzini L, Codari M, Vitali P, Sardanelli F. Brain vascular changes in adults with congenital heart disease: A systematic review. NEUROIMAGE-CLINICAL 2019; 23:101873. [PMID: 31158693 PMCID: PMC6545412 DOI: 10.1016/j.nicl.2019.101873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 05/23/2019] [Accepted: 05/24/2019] [Indexed: 12/03/2022]
Abstract
Less information is available on brain integrity in adults with congenital heart disease than on brain changes in newborns and children with heart defects. Nevertheless, the number of adults with congenital heart disease is increasing rapidly and it has been shown that adults with congenital heart disease develop dementia almost twice as frequently as adults in the general population. In the context of a rapidly growing congenital heart disease population, neuroradiological-oriented investigations of biomarkers distinctive for vascular damage, brain aging, and possible cognitive impairment is a crucial challenge. We provide an overview of the existing literature on neuroimaging studies in adults with congenital heart disease and discuss methodology issues to further investigate this subject. Overall, we aim to raise awareness of the importance of brain health studies in adults with congenital heart disease given the likely increasing impact on social and healthcare systems. The number of adults with congenital heart disease is increasing rapidly. There is considerable concern regarding global health outcomes in this population. Evidence shows that patients with congenital heart disease are more prone to cerebrovascular accidents. Neuroimaging tools may aid the detection of subtle vascular brain changes before cognitive impairment occurs.
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Affiliation(s)
- Luca Melazzini
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Mangiagalli 31, 20133, Milan, Italy.
| | - Marina Codari
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
| | - Paolo Vitali
- Department of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy.
| | - Francesco Sardanelli
- Department of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy; Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Morandi 30, 20097, San Donato Milanese, Milan, Italy.
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37
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Lorio S, Tierney TM, McDowell A, Arthurs OJ, Lutti A, Weiskopf N, Carmichael DW. Flexible proton density (PD) mapping using multi-contrast variable flip angle (VFA) data. Neuroimage 2018; 186:464-475. [PMID: 30465865 DOI: 10.1016/j.neuroimage.2018.11.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 11/13/2018] [Accepted: 11/16/2018] [Indexed: 12/13/2022] Open
Abstract
Quantitative proton density (PD) maps measure the amount of free water, which is important for non-invasive tissue characterization in pathology and across lifespan. PD mapping requires the estimation and subsequent removal of factors influencing the signal intensity other than PD. These factors include the T1, T2* relaxation effects, transmit field inhomogeneities, receiver coil sensitivity profile (RP) and the spatially invariant factor that is required to scale the data. While the transmit field can be reliably measured, the RP estimation is usually based on image post-processing techniques due to limitations of its measurement at magnetic fields higher than 1.5 T. The post-processing methods are based on unified bias-field/tissue segmentation, fitting the sensitivity profile from images obtained with different coils, or on the linear relationship between T1 and PD. The scaling factor is derived from the signal within a specific tissue compartment or reference object. However, these approaches for calculating the RP and scaling factor have limitations particularly in severe pathology or over a wide age range, restricting their application. We propose a new approach for PD mapping based on a multi-contrast variable flip angle acquisition protocol and a data-driven estimation method for the RP correction and map scaling. By combining all the multi-contrast data acquired at different echo times, we are able to fully correct the MRI signal for T2* relaxation effects and to decrease the variance and the entropy of PD values within tissue class of the final map. The RP is determined from the corrected data applying a non-parametric bias estimation, and the scaling factor is based on the median intensity of an external calibration object. Finally, we compare the signal intensity and homogeneity of the multi-contrast PD map with the well-established effective PD (PD*) mapping, for which the RP is based on concurrent bias field estimation and tissue classification, and the scaling factor is estimated from the mean white matter signal. The multi-contrast PD values homogeneity and accuracy within the cerebrospinal fluid (CSF) and deep brain structures are increased beyond that obtained using PD* maps. We demonstrate that the multi-contrast RP approach is insensitive to anatomical or a priori tissue information by applying it in a patient with extensive brain abnormalities and for whole body PD mapping in post-mortem foetal imaging.
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Affiliation(s)
- Sara Lorio
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
| | - Tim M Tierney
- Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, London, UK
| | - Amy McDowell
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Owen J Arthurs
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK; Department of Radiology, Great Ormond Street Hospital for Children, London, UK
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - David W Carmichael
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK; EPSRC / Wellcome Centre for Medical Engineering, Biomedical Engineering, King's College, London, UK
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38
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Quantifying iron content in magnetic resonance imaging. Neuroimage 2018; 187:77-92. [PMID: 29702183 DOI: 10.1016/j.neuroimage.2018.04.047] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 04/13/2018] [Accepted: 04/20/2018] [Indexed: 01/19/2023] Open
Abstract
Measuring iron content has practical clinical indications in the study of diseases such as Parkinson's disease, Huntington's disease, ferritinopathies and multiple sclerosis as well as in the quantification of iron content in microbleeds and oxygen saturation in veins. In this work, we review the basic concepts behind imaging iron using T2, T2*, T2', phase and quantitative susceptibility mapping in the human brain, liver and heart, followed by the applications of in vivo iron quantification in neurodegenerative diseases, iron tagged cells and ultra-small superparamagnetic iron oxide (USPIO) nanoparticles.
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