Jia Guo^1,2
1Bioengineering, University of California Riverside, Riverside, CA, United States, ²Center for Advanced Neuroimaging, UCR, Riverside, CA, United States

High temporal signal-to-noise ratio (tSNR) is desired for arterial spin labeling (ASL) methods. The tSNR of velocity-selective arterial spin labeling (VSASL) was limited in practice due to its sensitivity to error sources such as motion, eddy currents and diffusion effects. A novel VSASL strategy was invented to enable dual-module (dm-) VS saturation (VSS) and VS inversion (VSI) labeling for further improved SNR efficiency with VSASL. This study focused on the tSNR performance analysis and found the tSNR was doubled in gray matter using the new dm-VSASL. The explanation of such significant improvement was explored and validated by in vivo experiments.

Qihao Zhang¹, Dominick Romano², Thanh Nguyen², Pascal Spincemaille³, and Yi Wang^3
1Cornell University, Ithaca, NY, NY, United States, ²Cornell University, New York, NY, United States, ³Weill Cornell Medical College, New York, NY, United States

We propose to estimate perfusion parameters (perfusion $$$F$$$, permeability $$$K^{trans}$$$, vascular space volume $$$V_p$$$     and extravascular extracellular volume $$$V_e$$$) from contrast enhanced MRI using Quantitative Transport and Exchange network (QTEnet), a deep learning method that does not require an arterial input function. Training data were generated by solving the transport equation in simulated high-resolution vasculature and computing the corresponding 4D tracer propagation. A 3D U-net was trained to reconstruct perfusion parameters from the tracer propagation images. Tracer propagation simulated in experimentally obtained tumor vasculature was used for valiation, and the method was then applied to glioma DCE MRI data.

Ben R Dickie¹, Steven Sourbron², Petra J van Houdt³, Laura Bell⁴, Rianne A van der Heijden⁵, Andrey Fedorov⁶, Jonathan Arvidsson⁷, Charlotte Debus⁸, Ingomar Gutmann⁹, Chad Quarles¹⁰, Ralf Floca¹¹, Zaki ahmed¹², David Buckley¹³, and Ina Kompan^11
1Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, United Kingdom, ²University of Sheffield, Sheffield, United Kingdom, ³the Netherlands Cancer Institute, Amsterdam, Netherlands, ⁴Genentech, Inc, San Francisco, CA, United States, ⁵Erasmus MC University Medical Center, Rotterdam, Netherlands, ⁶Harvard Medical School, Boston, MA, United States, ⁷University of Gothenburg, Gothenburg, Sweden, ⁸Karlsruhe Institute for Technology, Karlsruhe, Germany, ⁹University of Vienna, Vienna, Austria, ¹⁰Barrow Neurological Institute, Phoenix, AZ, United States, ¹¹German Cancer Research Center DKFZ, Heidelberg, Germany, ¹²Mayo Clinic, Rochester, MN, United States, ¹³The University of Leeds, Leeds, United Kingdom

The Open Science Initiative for Perfusion Imaging (OSIPI) aims to improve the reproducibility of perfusion MRI research through creation of acquisition and analysis standards. Specifically, Task Force 4.2 was established to develop a lexicon of variables and processes to facilitate standardised reporting of dynamic contrast-enhanced (DCE-) and dynamic susceptibility contrast (DSC-) MRI analysis pipelines.  Here we report progress towards these objectives by presenting the current DCE/DSC lexicon structure, and provide a use-case to encode a simple analysis pipeline.

Junyu Wang¹, Patricia Rodriguez Lozano², and Michael Salerno^1,2,3,4
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, ²Medicine, University of Virginia, Charlottesville, VA, United States, ³Radiology, University of Virginia, Charlottesville, VA, United States, ⁴Medicine, Stanford University, Stanford, CA, United States

First-pass contrast-enhanced myocardial perfusion imaging is useful for evaluating coronary artery disease (CAD). Spiral perfusion imaging techniques, using a motion-compensated (SMS-Slice-) L1-SPIRiT reconstruction, are capable of whole-heart high-resolution perfusion imaging. However, this reconstruction is performed off-line and takes ~30 minutes per slice. To address this, we developed a DEep learning-based rapid Spiral Image REconstruction technique (DESIRE) for spiral perfusion imaging at 1.5 T, for both single-slice (SS) and simultaneous multi-slice (SMS) MB=3 and MB=4 acquisitions, to provide rapid and high-quality reconstruction and make rapid online reconstruction feasible. High image quality was demonstrated using the proposed technique.

Lexiaozi Fan^1,2, Huili Yang^1,2, Li-Yueh Hsu³, Aggelos K Katsaggelos^1,4,5, Bradley D Allen¹, Daniel C Lee⁶, and Daniel Kim^1,2
1Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States, ²Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States, ³Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD, United States, ⁴Department of Electrical Engineering, Northwestern University, Evanston, IL, United States, ⁵Department of Computer Science, Northwestern University, Evanston, IL, United States, ⁶Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States

Motion correction (MoCo) is an important pre-processing step for pixel-by-pixel myocardial blood flow (MBF) quantification from cardiac perfusion MRI. It may also improve throughput of visual evaluation of perfusion images. One commonly used method for MoCo is optical flow (OF), which requires a moderate level of computational demand. In this study, we sought to perform rapid MoCo of respiratory motion on cardiac perfusion images using deep learning (DL). Our results show that the proposed DL MoCo performs 418-times faster than the reference OF approach without loss in accuracy.

Julian Glandorf¹, Filip Klimes¹, Andreas Voskrebenzev¹, Marcel Gutberlet¹, Agilo Luitger Kern¹, Norman Kornemann¹, Nima Mahmoudi², Mike Peter Wattjes², Frank Wacker¹, and Jens Vogel-Claussen^1
1Institut für Diagnostische und Interventionelle Radiologie, Hannover Medical School, Hannover, Germany, ²Institut für Diagnostische und Interventionelle Neuroradiologie, Hannover Medical School, Hannover, Germany

To test the feasibility of brain perfusion imaging exploiting flow-related enhancement during fast gradient echo sequences. Imaging was performed using Fast Low-Angle Shot (FLASH) and balanced steady-state free precession (bSSFP) sequences of a single axial slice with various imaging parameters at 1.5T and 3T. Perfusion-weighted maps were generated based on the Fourier decomposition method “Phase-REsolved FUnctional Lung” (PREFUL) MRI. The derived imaging protocol was evaluated in patients with metastases and a stroke showing high spatial overlap to post-contrast MRI and CT-perfusion. A cohort of 19 healthy individuals presented moderate, but significant regional correlation and high spatial Dice overlap to pCASL-MRI.  

Jasmine Y Hu^1,2, Sana Vaziri¹, Nikolaj Bogh³, Yaewon Kim¹, Adam W Autry¹, Robert A Bok¹, Yan Li^1,2, Christoffer Laustsen³, Peder EZ Larson^1,2, Duan Xu^1,2, Daniel B Vigneron^1,2, and Jeremy W Gordon^1
1Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, ²Bioengineering, University of California Berkeley, Berkeley, CA, United States, ³MR Research Center, Clinical Medicine, Aarhus University, Aarhus, Denmark

Acquiring high resolution 7.5 mm² hyperpolarized [1-¹³C]pyruvate brain MRI allows for finer spatial delineation of brain structures and can be used to obtain cerebral perfusion parameters. In the healthy volunteers studied, pyruvate rCBV and rCBF were positively correlated to ASL perfusion values. Hyperpolarized pyruvate MRI can be used to assess cerebral metabolism and perfusion within the same study.

Karl Philipp Kunze¹, Nina Mellor², Tracy Moon², Kuberan Pushparajah³, Radhouene Neji¹, and Amedeo Chiribiri^3
1MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom, ²Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom, ³Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom

This abstract describes a feasibility study for a high-resolution perfusion imaging framework, including a dedicated sampling pattern and a motion compensation framework integrated into temporal regularization and implemented in-line on the scanner with GPU support. The approach was employed successfully in patients with suspected myocarditis at rest, showing superior ability to handle both respiratory and cardiac motion at an image resolution of 1.3 mm², as compared to a clinical reference perfusion sequence and supported by Late Gadolinium Enhancement imaging findings.

Rashed Sobhan¹, Glyn Johnson¹, and Donnie Cameron^1,2
1Norwich Medical School, University of East Anglia, Norwich, United Kingdom, ²C.J. Gorter Centre for High Field MRI, Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands

To identify the optimal model-based deconvolution process for DSC-MRI, four models of transit time distribution (TTD) were compared in terms of goodness and stability of fit, consistency of perfusion estimates, computational complexity, and robustness against  noise. Although all models gave similar fits, the gamma function converged faster and more consistently to the global minimum, regardless of the initial guess. Moreover, it gave more accurate and precise perfusion estimates in the presence of noise. We conclude that the gamma function is the most suitable TTD model for perfusion analysis, and may prove useful in urgent clinical situations and multi-centre studies.

Tonner DeBeer¹, Lori C Jordan², Spencer Waddle¹, Chelsea A Lee², Niral J Patel², Maria Garza³, Larry Taylor Davis⁴, Sumit Pruthi⁴, Randall Sky Jones², and Manus Joseph Donahue^3
1Vanderbilt University, Nashville, TN, United States, ²Pediatrics, Vanderbilt University Medical Center, Nashville, TN, United States, ³Neurology, Vanderbilt University Medical Center, Nashville, TN, United States, ⁴Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States

Most persons with sickle cell disease (SCD) lack conventional stroke risk factors, yet nearly 50% have evidence of brain infarcts by age 30 years, indicating alternative etiologies for ischemia. We investigated whether accelerated red cell transit affects oxygen extraction and improves following transfusion-induced increases in hemoglobin. Findings suggest that evidence of accelerated capillary transit is present on arterial spin labeling (ASL) MRI, and reduces following transfusion-induced increases in hemoglobin. Furthermore, the relationship between dural ASL signal and brain oxygen extraction evolves following transfusion, suggesting that oxygen delivery is complexly dependent on blood oxygen content and capillary dynamics.