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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Afzali-Hashemi L, Baas KPA, Schrantee A, Coolen BF, van Osch MJP, Spann SM, Nur E, Wood JC, Biemond BJ, Nederveen AJ. Impairment of Cerebrovascular Hemodynamics in Patients With Severe and Milder Forms of Sickle Cell Disease. Front Physiol 2021; 12:645205. [PMID: 33959037 PMCID: PMC8093944 DOI: 10.3389/fphys.2021.645205] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/11/2021] [Indexed: 11/18/2022] Open
Abstract
In patients with sickle cell disease (SCD), cerebral blood flow (CBF) is elevated to counteract anemia and maintain oxygen supply to the brain. This may exhaust the vasodilating capacity of the vessels, possibly increasing the risk of silent cerebral infarctions (SCI). To further investigate cerebrovascular hemodynamics in SCD patients, we assessed CBF, arterial transit time (ATT), cerebrovascular reactivity of CBF and ATT (CVRCBF and CVRATT) and oxygen delivery in patients with different forms of SCD and matched healthy controls. We analyzed data of 52 patients with severe SCD (HbSS and HbSβ0-thal), 20 patients with mild SCD (HbSC and HbSβ+-thal) and 10 healthy matched controls (HbAA and HbAS). Time-encoded arterial spin labeling (ASL) scans were performed before and after a vasodilatory challenge using acetazolamide (ACZ). To identify predictors of CBF and ATT after vasodilation, regression analyses were performed. Oxygen delivery was calculated and associated with hemoglobin and fetal hemoglobin (HbF) levels. At baseline, severe SCD patients showed significantly higher CBF and lower ATT compared to both the mild SCD patients and healthy controls. As CBFpostACZ was linearly related to CBFpreACZ, CVRCBF decreased with disease severity. CVRATT was also significantly affected in severe SCD patients compared to mild SCD patients and healthy controls. Considering all groups, women showed higher CBFpostACZ than men (p < 0.01) independent of baseline CBF. Subsequently, post ACZ oxygen delivery was also higher in women (p < 0.05). Baseline, but not post ACZ, GM oxygen delivery increased with HbF levels. Our data showed that baseline CBF and ATT and CVRCBF and CVRATT are most affected in severe SCD patients and to a lesser extent in patients with milder forms of SCD compared to healthy controls. Cerebrovascular vasoreactivity was mainly determined by baseline CBF, sex and HbF levels. The higher vascular reactivity observed in women could be related to their lower SCI prevalence, which remains an area of future work. Beneficial effects of HbF on oxygen delivery reflect changes in oxygen dissociation affinity from hemoglobin and were limited to baseline conditions suggesting that high HbF levels do not protect the brain upon a hemodynamic challenge, despite its positive effect on hemolysis.
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Affiliation(s)
- Liza Afzali-Hashemi
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location AMC, Amsterdam, Netherlands
| | - Koen P A Baas
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location AMC, Amsterdam, Netherlands
| | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location AMC, Amsterdam, Netherlands
| | - Bram F Coolen
- Department of Biomedical Engineering and Physics, Amsterdam UMC, location AMC, Amsterdam Cardiovascular Sciences, Amsterdam, Netherlands
| | - Matthias J P van Osch
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands
| | - Stefan M Spann
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria
| | - Erfan Nur
- Department of Hematology, Amsterdam UMC, Location AMC, Amsterdam, Netherlands
| | - John C Wood
- Division of Cardiology, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Bart J Biemond
- Department of Hematology, Amsterdam UMC, Location AMC, Amsterdam, Netherlands
| | - Aart J Nederveen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location AMC, Amsterdam, Netherlands
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Spann SM, Kazimierski KS, Aigner CS, Kraiger M, Bredies K, Stollberger R. Spatio-temporal TGV denoising for ASL perfusion imaging. Neuroimage 2017; 157:81-96. [PMID: 28559192 DOI: 10.1016/j.neuroimage.2017.05.054] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 05/24/2017] [Accepted: 05/25/2017] [Indexed: 12/21/2022] Open
Abstract
In arterial spin labeling (ASL) a perfusion weighted image is achieved by subtracting a label image from a control image. This perfusion weighted image has an intrinsically low signal to noise ratio and numerous measurements are required to achieve reliable image quality, especially at higher spatial resolutions. To overcome this limitation various denoising approaches have been published using the perfusion weighted image as input for denoising. In this study we propose a new spatio-temporal filtering approach based on total generalized variation (TGV) regularization which exploits the inherent information of control and label pairs simultaneously. In this way, the temporal and spatial similarities of all images are used to jointly denoise the control and label images. To assess the effect of denoising, virtual ground truth data were produced at different SNR levels. Furthermore, high-resolution in-vivo pulsed ASL data sets were acquired and processed. The results show improved image quality, quantitative accuracy and robustness against outliers compared to seven state of the art denoising approaches.
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Affiliation(s)
- Stefan M Spann
- Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16, 8010 Graz, Austria
| | - Kamil S Kazimierski
- Institute for Mathematics and Scientific Computing, University of Graz, NAWI Graz, Heinrichstrasse 36, 8010 Graz, Austria
| | - Christoph S Aigner
- Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16, 8010 Graz, Austria; BioTechMed-Graz, Graz, Austria
| | - Markus Kraiger
- Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16, 8010 Graz, Austria
| | - Kristian Bredies
- Institute for Mathematics and Scientific Computing, University of Graz, NAWI Graz, Heinrichstrasse 36, 8010 Graz, Austria
| | - Rudolf Stollberger
- Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16, 8010 Graz, Austria; BioTechMed-Graz, Graz, Austria.
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