Diego Felipe Martinez¹, Arjama Halder^1,2, Will Bradfield Handler¹, and Blaine Alexander Chronik^1,2
1The xMR Labs, Physics and Astronomy, Western University, London, ON, Canada, ²Medical Biophysics, Western University, London, ON, Canada

Cryogen-free low-field MR systems can address accessibility issues associated with MRI as they can relax siting requirements and lower operating costs. An important concern in these systems is heating of the magnet during imaging sequences, which could cause field stability issues or quench the magnet. The interaction between the gradient system and the main magnet was investigated using temperature probes. Trapezoidal based pulses were run on components of the gradient at various strengths, showing the x-axis coil as the most impactful on the main magnet. In all experiments, the heating was <0.015 K, with “worst case” heating guiding further study. 

Dattesh Dayanand Shanbhag¹, Arathi Sreekumari¹, Soumya Ghose², Chitresh Bhushan², and Uday Patil^3
1GE Healthcare, Bangalore, India, ²GE Global Research, Niskayuna, NY, United States, ³General Electric Company, Bangalore, India

In this work , we describe a deep learning-based methodology to generate vertebrae labels directly from the standard 2D tri-planar localizer images without the need any additional scanning or explicitly segmenting the vertebrae. This is accomplished by using deep-learning setup a to identify vertebrae labels directly on the localizer images. The method is demonstrated on lumbar spine localizer data to identify Thoracic-12 (T12), Lumbar-4 (L4) , and Sacral-1 (S1) vertebrae locations. In a test cohort of 50 lumbar MR spine exams, we report labeling accuracy of 92%, 98% and 96% for T12, L4 and S1 vertebrae respectively on localizer images.

Sanjay Dhawan^1,2, Ishrat Afshan^1,2, Rupsa Bhattacharjee³, and Indrajit Saha^3
1Clearmedi Healthcare Pvt. Ltd., Gurugram, India, ²Department of Radiology, Paras Hospitals, Gurugram, India, ³Philips Health Systems, Philips India Limited, Gurugram, India

The focus of MRI stroke-research is concentrated in identifying relevant sequence for stroke-screening, accelerating the acquisitions by latest innovations like Compressed-SENSE and developing methodologies to extract meaningful clinical-relevance (i.e., penumbra or mismatch-ratio) using minimum number sequences. Our objective is to evaluate the impact of including T2-weighted-images in fast acute-ischemic-stroke MRI protocols, to comment on role of T2w vis-à-vis Flair in terms of clinical findings for making an informed decision on the inclusion-criteria of ideal MR sequences in stroke MR protocol. Flair proves to be better than T2, in depicting small/subtle signal changes in critical time-window, beneficial for acute-stroke patient-treatment-planning.

Keerthi Sravan Ravi^1,2, Thomas O'Reilly³, John Thomas Vaughan Jr.², Andrew Webb³, and Sairam Geethanath^2
1Biomedical Engineering, Columbia University, New York, NY, United States, ²Columbia Magnetic Resonance Research Center, New York, NY, United States, ³Leiden University Medical Center, Leiden, Netherlands

Low-field MRI scanners are more viable in resource-strained regions such as Africa, sacrificing SNR for gains on the economics of purchase, siting and service. Pulse sequences need to be optimized for maximum efficiency on these scanners. This work introduces seq2prospa, a Python-based tool facilitating the conversion of pulse sequences designed using PyPulseq into a low-field spectrometer-friendly format. We perform an in-vitro and in-vivo acquisition experiment to image a structural phantom and a human hand respectively. Data acquired from a 2D GRE sequence designed natively in Prospa is compared with data acquired from a PyPulseq-converted 2D GRE sequence.

L Cheng¹, Tjada Schult¹, Mara Lauer¹, Yannick Berker², Marcella Cardoso¹, Lindsey Vandergrift¹, Piet Habbel³, Johannes Nowak⁴, Martin Aryee¹, Mari Mino-Kenudson¹, and David Christiani^5
1Mass General Hospital, Boston, MA, United States, ²German Cancer Research Center, Heidelberg, Germany, ³Charite Medical University, Berlin, Germany, ⁴Julius-Maximilians University, Wuerzburg, Germany, ⁵Harvard T.H. Chan School of Public Health, Boston, MA, United States

Lung cancer (LuCa), the leading cause of cancer deaths, are often diagnosed late due to the lack of screening. Low-dose spiral CT can detect small and early stage LuCa lesions, but cannot practically be used as an annual LuCa screening tool. Metabolomics detects global metabolite variations under physiology and pathology. Metabolomic profiles measured from blood may reveal LuCa at early stages as a screening tool to triage suspicious patients to CT tests. Blood sera obtained from LuCa patients prior to their diagnosis were studied with MRS to establish LuCa screening metabolomic profiles to discover LuCa earlier and reduce death rates.

Alexandra W. Acher¹, Emily Winslow², and Scott B. Reeder^1
1University of Wisconsin School of Medicine and Public Health, Madison, WI, United States, ²Georgetown University, Washington DC, MD, United States

Colon cancer is the 3rd most common cancer in the United States. Unfortunately, most patients present with Stage III or IV disease and disease recurrence is high despite advanced in systemic therapies. Despite evidence that gadoxetic acid-enhanced MRI is superior for the detection and diagnosis of colon cancer liver metastases, computed tomography remains the recommended staging and surveillance modality. The purpose of this abstract is to synthesize and analyze the evidence regarding the accuracy of liver MRI to stage and surveil CCLM, and to identify knowledge gaps to inform future research on staging and surveillance imaging for CCLM.

Dahan Kim¹, Dinghui Wang¹, and James G Pipe^1
1Department of Radiology, Mayo Clinic, Rochester, MN, United States

We present a framework for maintaining constant SNR and T1 contrast in SPGR and MP-RAGE scans that employ non-identical scan parameters. Central to this work are the concept of time constant TA and simplified SNR expression. By adjusting the flip angle to keep TA constant and adjusting the number of spiral arms to match SNR, we demonstrated constant SNR and T1 contrast under varied ADC time, TR, and FOV. This work ultimately aims for reducing image variability, automating scanning, reducing technologist intervention, and increasing patient safety.

Gehua Tong¹, Sairam Geethanath², Enlin Qian¹, and John Thomas Vaughan, Jr. ^2
1Biomedical Engineering, Columbia University, New York, NY, United States, ²Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States

A framework for testing, validating, and sharing open-source MR sequences was developed to improve their accessibility, repeatability, and safety. Accessibility is improved by requiring documentation of sequence usage and data processing steps, repeatability by requiring test experiments and examples to replicate, and safety by requiring simulation or records of SAR and PNS levels. Forms and guidelines are provided to help developers and users package, share, and apply novel sequences efficiently. The framework was demonstrated for two common sequences, Inversion Recovery Spin Echo (IRSE) and Turbo Spin Echo (SE), and they were packaged and shared in an open-source repository.

Penny L Hubbard Cristinacce^1,2, Damien J McHugh^1,2, Matthew R Orton^2,3, Arnold JV Benjamin^2,4, Ross A Little^1,2, James P. B. O'Connor^1,2,3, and Maite Jauregui-Osoro^2,5
1The University of Manchester, Manchester, United Kingdom, ²National Cancer Imaging Translational Accelerator (NCITA), CRUK, United Kingdom, ³Institute of Cancer Research, Sutton, United Kingdom, ⁴University of Cambridge, Cambridge, United Kingdom, ⁵Imperial College London, London, United Kingdom

National Cancer Imaging Translational Accelerator (NCITA) ran a survey on quality assurance (QA) and quality control (QC) procedures for human MRI scanners and associated equipment at research institutions across the UK. The survey was conducted by the NCITA QA/QC Unit, which is developing an MRI core lab to facilitate the validation and standardisation of quality-assured imaging biomarkers from first-in-human studies to the assessment of multicentre reproducibility. The results showed institutions emphasise the earlier part of the QA/QC pipeline (i.e. scanner maintenance and data acquisition QA), with fewer institutions performing comprehensive QC on acquired/analysed data or any software quality management.

Laura C. Bell¹, Henk Mutsaerts², Andrey Fedorov³, Zaki Ahmed⁴, Patricia Clement⁵, Simon Levy⁶, Frank G Zollner⁷, Jan Petr⁸, Sudipto Dolui⁹, Kathleen Schmainda¹⁰, Melissa Prah¹⁰, Matthias Schabel¹¹, Ananth Madhuranthakam¹², Li Zhao¹³, Michael Thrippleton¹⁴, Petra van Houdt¹⁵, James Holmes¹⁶, C. Chad Quarles¹, Greg Cron¹⁷, David L Thomas¹⁸, Yuriko Suzuki¹⁹, Ina Kompan²⁰, David L Buckley²¹, Paula Croal²², Udunna Anazodo²³, Anahita Fathi Kazerooni⁹, Hamidreza Saligheh Rad²⁴, Charlotte Debus²⁵, and Steven P Sourbron^26
1Barrow Neurological Institute, Phoenix, AZ, United States, ²Amsterdam University Medical Cente, Amsterdam, Netherlands, ³Brigham and Women’s Hospita, Boston, MA, United States, ⁴Mayo Clinic, Rochester, MN, United States, ⁵Ghent University, Ghent, Belgium, ⁶Aix-Marseille Univ, Marseille, France, ⁷Heidelberg University, Mannheim, Germany, ⁸Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany, ⁹University of Pennsylvania, Philadelphia, PA, United States, ¹⁰Medical College of Wisconsin, Milwaukee, WI, United States, ¹¹Oregon Health and Science University, Portland, OR, United States, ¹²UT Southwestern Medical Center, Dallas, TX, United States, ¹³Children’s National Medical Center, Washington DC, DC, United States, ¹⁴University of Edinburgh, Edinburgh, United Kingdom, ¹⁵the Netherlands Cancer Institute, Amsterdam, Netherlands, ¹⁶University of Wisconsin - Madison, Madison, WI, United States, ¹⁷Ottawa Hospital Research Institute, Ottawa, ON, Canada, ¹⁸University College London, London, United Kingdom, ¹⁹University of Oxford, Oxford, United Kingdom, ²⁰German Cancer Research Center, Heidelberg, Germany, ²¹University of Leeds, Leeds, United Kingdom, ²²University of Nottingham, Nottingham, United Kingdom, ²³Western University, London, ON, Canada, ²⁴Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), ²⁵Karlsruhe Institute of Technology, Karlsruhe, Germany, ²⁶University of Sheffield, Sheffield, United Kingdom

Open Source Initiative for Perfusion Imaging (OSIPI) was founded by the ISMRM Perfusion Study Group as a community-driven initiative. Supported by six distinct aims, its mission is “to promote the sharing of perfusion imaging open-source software in order to eliminate the practice of duplicate development, improve the reproducibility of perfusion imaging research, and speed up the translation into tools for discovery science, drug development, and clinical practice”. OSIPI seeks to provide centralized resources to deliver reproducible perfusion research. In addition, it provides a platform for exchange where new and more advanced methods may be validated for perfusion accuracy. 

Mohammad Samim¹, Mahesh Bharath Keerthivasan², Iman Khodarahmi¹, Marisa Ilag¹, Mary Bruno¹, Hersh Chandrana¹, Inge Brinkmann², and Jan Fritz^1
1Department of Radiology, NYU Langone Medical Center, New York, NY, United States, ²Siemens Medical Solutions USA Inc, Malvern, PA, United States

Next-generation low-field magnetic resonance imaging (MRI) systems hold great potential to reduce the cost of ownership and improve access to MRI worldwide. We compared MRI of the lumbar spine at 0.55T MRI with two different gradient performance modes to 1.5T MRI in 10 volunteer participants. Regardless of field strength and gradient performances, all MRI studies had sufficient signal-to-noise-ratios, contrast-to-noise-ratios, image quality, and visibility of anatomical structures with only small differences. Our initial results support the great potential of next-generation low-field MR systems to achieve similar image quality than state-of-the-art clinical 1.5-T MRI systems to detect the lumbar spine abnormalities.

Samantha By¹, E. Brian Welch¹, Houchun Harry Hu¹, Rafael O'Halloran¹, Laura Sacolick¹, Carole Lazarus¹, Hadrien Dyvorne¹, Jonathan Rothberg¹, Khan Siddiqui¹, and Lorraine Cullen^2
1Hyperfine, Guilford, CT, United States, ²Gaylord Specialty Healthcare, Wallingford, CT, United States

 Portable, point-of-care MRI offers many new opportunities, such as scanning at the bedside or serial monitoring of a patient (i.e. daily or weekly). We demonstrate feasibility and initial results of a portable 64 mT MRI in a stroke rehabilitation setting. Six recovering stroke patients received such exams; four of them were serially monitored, each receiving four exams. Each exam consisted of T2-W, T1-W, FLAIR and DWI imaging. Different cases are highlighted to demonstrate the utility of point-of-care MRI in a rehabilitation setting.

Nandor K Pinter^1,2, Sandeep Ganji³, Jan Petr⁴, Bela Ajtai ⁵, Harry Friel³, Joseph Fritz⁵, Laszlo Mechtler⁵, Alexander Fischer⁶, Frederik Barkhof^7,8, and Henri Mutsaerts^7,9
1Dent Neurologic Institute, Buffalo, NY, United States, ²Neurosurgery, University at Buffalo, Buffalo, NY, United States, ³Philips Healthcare, Gainesville, FL, United States, ⁴Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany, ⁵Dent Neurologic Institute, Amherst, NY, United States, ⁶Philips Research Europe, Aachen, Germany, ⁷Dept of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands, ⁸Institute of Neurology, University College London, London, United Kingdom, ⁹Ghent Institute for Functional and Metabolic Imaging, Ghent, Belgium

While neurological imaging is mostly done in non-academic centers, the lack of academic resources, special image processing skillset and the fast paced workflow prevent radiologists in these data-rich environments from engaging in high quality clinical research that utilizes quantitative imaging. Clinical adoption of Arterial Spin Labeling could benefit from providing easy-to-use, PACS connected image processing solutions that do not require neuroscience background and provide truly quantified Cerebral Blood Flow values in any outpatient center or public hospital. Our goal is to create such a solution and bridge the gap between academic research and real world practice. 

Thomas O'Reilly¹, Danny de Gans², Lars Leenheer², and Andrew Webb^1
1C.J. Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, Netherlands, ²DEMO, Delft University of Technology, Delft, Netherlands

Low field MRI has gained interest in recent years as a way of providing MRI to under-served and remote settings. MR hardware designed for such applications should be robust, portable and easy to maintain. In this work we present the design of a three-axis gradient amplifier capable of an output current of up to 15 amperes. The gradient amplifier is designed to run off two car batteries and consumes less than 100 watts during imaging. The components in the gradient amplifier are designed to be easily repairable and commonly available.

Jack C. Williams¹, Matthew Gibbons¹, Janet E. Cowan², Peter R. Carroll², and Susan M. Noworolski^1
1Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, ²Urology, University of California San Francisco, San Francisco, CA, United States

This study attempted to fill the dearth of literature establishing the repeatability of DWI and DCE-MRI using a population of non-progressing men on active surveillance for prostate cancer. MRIs from men (n=26) with two DCE-MRI scans in one year were examined and mean ROI values on different imaging maps were analyzed for percent repeatability coefficient (%RC). %RC was lowest for ADC, DCE MRI peak enhancement, T2-weighted images and pharmacokinetically modeled v_e and K^trans.

Hongxiang Lin¹, Matteo Figini¹, Felice D'Arco², Godwin Ogbole³, David W Carmichael^4,5, Ikeoluwa Lagunju⁶, Helen Cross^4,7, Delmiro Fernandez-Reyes^6,8, and Daniel C Alexander^1
1Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, ²Department of Radiology, Great Ormond Street Hospital, London, United Kingdom, ³Department of Radiology, College of Medicine, University of Ibadan, Ibadan, Nigeria, ⁴Great Ormond Street Institute of Child Health, University College London, London, United Kingdom, ⁵Department of Biomedical Engineering, King’s College London, London, United Kingdom, ⁶Department of Paediatrics, College of Medicine, University of Ibadan, Ibadan, Nigeria, ⁷Great Ormond Street Hospital for Children, London, United Kingdom, ⁸Department of Computer Science, University College London, London, United Kingdom

We devise and demonstrate an image quality transfer (IQT) system to estimate, from low-field (<0.5T) magnetic resonance (MR) structural images, the corresponding high-field (1.5T or 3T) images. The intended application scenario is to improve clinical lesion-detection, classification, and decision-making in childhood epilepsy in lower and middle income countries where T1w, T2w and FLAIR sequences on 0.36T scanners remain the clinical standard. Results on synthetic and real data verify substantial resolution and contrast enhancements, aiding conspicuity of pathological lesions.

Mo Kadbi¹, Dawn Berkeley¹, Brian Tymkiw¹, Hung Do¹, and Erin Kelly^1
1Canon Medical System USA, Tustin, CA, United States

In MR physics, there is a fundamental tradeoff between image spatial resolution, signal to noise ratio, and scan time. To acquire images with high resolution and SNR, signal averaging is the most common solution, but results in longer scan time. In this study, a Deep Learning Reconstruction method was employed to remove the noise from clinical images and improve SNR. This SNR improvement was devoted to increase the spatial resolution without the need of signal averaging and increased scan time. Hence, higher resolution images with high image quality can be obtained in shorter time in clinical practice.      

Gregory Avey¹, Laura Eisenmenger¹, Nathan Kim¹, Alexey Samsonov¹, James Holmes¹, Lloyd Estkowski², and Tabassum Kennedy^1
1University of Wisconsin School of Medicine and Public Health, Madison, WI, United States, ²GE Healthcare, Madison, WI, United States

Deep Learning (DL) MRI image reconstruction holds great promise in improving overall MRI image quality and decreasing examination time. Given the nonlinear properties of DL reconstruction it is unknown which methods of decreasing MRI examination time are most suitable when adapting current protocols for DL based examination acceleration. 10 volunteers were scanned with clinical baseline T1 and T2 weighted FSE exams, along with 5 accelerated exam types. The resulting exams were scored to determine differences in image quality. DL image reconstruction allowed for an up to 78% reduction in scan time while matching baseline subjective image quality.

Min Lang¹, Samuel CD Cartmell¹, Azadeh Tabari¹, Daniel Briggs¹, Oleg Pianykh¹, John E Kirsch¹, Stephen F Cauley¹, Wei-Ching Lo², Seretha J Risacher¹, Augusto Lio Goncalves Filho¹, Marc D Succi^1,3, Otto Rapalino¹, Pamela Schaefer¹, John Conklin¹, and Susie Y Huang^1
1Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, ²Siemens Medical Solutions, Boston, MA, United States, ³Medically Engineered Solutions in Healthcare Incubator, Massachusetts General Hospital, Boston, MA, United States

We report our clinical experience of implementing fast MRI sequences into the most performed brain MRI protocols at a large academic center, which resulted in significant decrease of gradient times on both 1.5T and 3T scanners. The overall scan times were reduced by up to 40% for the longest examinations, with all included brain MRI protocols now being under 20 minutes. The potential benefits of reduced scan time include increased imaging volume throughput and improved patient access. During the ongoing COVID-19 pandemic, the decreased scan times also accommodated heightened infection control protocols to help protect both patients and healthcare workers.

Hersh Chandarana¹, Barun Bagga¹, Chenchan Huang¹, Bari Dane¹, Robert Petrocelli¹, Mary Bruno¹, Mahesh Keerthivasan², Himanshu Bhat², Kai Tobias Block^1,3, David Stoffel¹, and Daniel K Sodickson^1
1Radiology, Center for Advanced Imaging Innovation and Research, NYU Grossman School of Medicine, New York, NY, United States, ²Siemens Medical Solutions USA Inc, Malvern, PA, United States, ³Siemens Healthcare GmbH, Erlangen, Germany

MRI is a powerful imaging modality for abdominal examination. However, high costs and accessibility limit its utilization. Recent advances in acquisition and reconstruction techniques coupled with considerations of value have reignited interest in low-field (≤ 1T) MRI systems. In this study, we developed an abdominal imaging protocol on a prototype 0.55T scanner operating with higher or regular (45 mT/m; 200 T/m/sec) and lower (25 mT/m; 40 T/m/sec) gradients in order to investigate the level of tolerable cost reduction. Our study shows that diagnostic non-contrast abdominal imaging with T2W, DW, and T1W contrast can be performed within 10 minutes or less.