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Maier O, Spann SM, Pinter D, Gattringer T, Hinteregger N, Thallinger GG, Enzinger C, Pfeuffer J, Bredies K, Stollberger R. Non-linear fitting with joint spatial regularization in arterial spin labeling. Med Image Anal 2021; 71:102067. [PMID: 33930830 DOI: 10.1016/j.media.2021.102067] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 03/26/2021] [Accepted: 04/01/2021] [Indexed: 10/21/2022]
Abstract
Multi-Delay single-shot arterial spin labeling (ASL) imaging provides accurate cerebral blood flow (CBF) and, in addition, arterial transit time (ATT) maps but the inherent low SNR can be challenging. Especially standard fitting using non-linear least squares often fails in regions with poor SNR, resulting in noisy estimates of the quantitative maps. State-of-the-art fitting techniques improve the SNR by incorporating prior knowledge in the estimation process which typically leads to spatial blurring. To this end, we propose a new estimation method with a joint spatial total generalized variation regularization on CBF and ATT. This joint regularization approach utilizes shared spatial features across maps to enhance sharpness and simultaneously improves noise suppression in the final estimates. The proposed method is evaluated at three levels, first on synthetic phantom data including pathologies, followed by in vivo acquisitions of healthy volunteers, and finally on patient data following an ischemic stroke. The quantitative estimates are compared to two reference methods, non-linear least squares fitting and a state-of-the-art ASL quantification algorithm based on Bayesian inference. The proposed joint regularization approach outperforms the reference implementations, substantially increasing the SNR in CBF and ATT while maintaining sharpness and quantitative accuracy in the estimates.
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Affiliation(s)
- Oliver Maier
- Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16/III, Graz 8010, Austria.
| | - Stefan M Spann
- Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16/III, Graz 8010, Austria.
| | - Daniela Pinter
- Department of Neurology, Division of General Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria.
| | - Thomas Gattringer
- Department of Neurology, Division of General Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria; Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria.
| | - Nicole Hinteregger
- Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria.
| | - Gerhard G Thallinger
- Institute of Biomedical Informatics, Graz University of Technology, Stremayrgasse 16/I, Graz 8010, Austria; BioTechMed-Graz, Mozartgasse 12/II, Graz 8010, Austria.
| | - Christian Enzinger
- Department of Neurology, Division of General Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria; Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 22, Graz 8036, Austria.
| | - Josef Pfeuffer
- Application Development, Siemens Healthcare, Henkestraße 127, Erlangen 91052, Germany.
| | - Kristian Bredies
- Institute of Mathematics and Scientific Computing, University of Graz, Heinrichstraße 36, Graz 8010, Austria; BioTechMed-Graz, Mozartgasse 12/II, Graz 8010, Austria.
| | - Rudolf Stollberger
- Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16/III, Graz 8010, Austria; BioTechMed-Graz, Mozartgasse 12/II, Graz 8010, Austria.
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Maziero D, Straza MW, Ford JC, Bovi JA, Diwanji T, Stoyanova R, Paulson ES, Mellon EA. MR-Guided Radiotherapy for Brain and Spine Tumors. Front Oncol 2021; 11:626100. [PMID: 33763361 PMCID: PMC7982530 DOI: 10.3389/fonc.2021.626100] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 02/12/2021] [Indexed: 12/19/2022] Open
Abstract
MRI is the standard modality to assess anatomy and response to treatment in brain and spine tumors given its superb anatomic soft tissue contrast (e.g., T1 and T2) and numerous additional intrinsic contrast mechanisms that can be used to investigate physiology (e.g., diffusion, perfusion, spectroscopy). As such, hybrid MRI and radiotherapy (RT) devices hold unique promise for Magnetic Resonance guided Radiation Therapy (MRgRT). In the brain, MRgRT provides daily visualizations of evolving tumors that are not seen with cone beam CT guidance and cannot be fully characterized with occasional standalone MRI scans. Significant evolving anatomic changes during radiotherapy can be observed in patients with glioblastoma during the 6-week fractionated MRIgRT course. In this review, a case of rapidly changing symptomatic tumor is demonstrated for possible therapy adaptation. For stereotactic body RT of the spine, MRgRT acquires clear isotropic images of tumor in relation to spinal cord, cerebral spinal fluid, and nearby moving organs at risk such as bowel. This visualization allows for setup reassurance and the possibility of adaptive radiotherapy based on anatomy in difficult cases. A review of the literature for MR relaxometry, diffusion, perfusion, and spectroscopy during RT is also presented. These techniques are known to correlate with physiologic changes in the tumor such as cellularity, necrosis, and metabolism, and serve as early biomarkers of chemotherapy and RT response correlating with patient survival. While physiologic tumor investigations during RT have been limited by the feasibility and cost of obtaining frequent standalone MRIs, MRIgRT systems have enabled daily and widespread physiologic measurements. We demonstrate an example case of a poorly responding tumor on the 0.35 T MRIgRT system with relaxometry and diffusion measured several times per week. Future studies must elucidate which changes in MR-based physiologic metrics and at which timepoints best predict patient outcomes. This will lead to early treatment intensification for tumors identified to have the worst physiologic responses during RT in efforts to improve glioblastoma survival.
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Affiliation(s)
- Danilo Maziero
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Michael W Straza
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - John C Ford
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Joseph A Bovi
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Tejan Diwanji
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Radka Stoyanova
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Eric S Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Eric A Mellon
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
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