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Bio
The main focus of my research is to develop novel acquisition and reconstruction techniques for magnetic resonance imaging (MRI), which have a direct impact in the clinical environment. I believe this translational MR physics is of great clinical importance; helping to enhance the accuracy, efficiency and effectiveness of healthcare through technological advances.
One of my primary aims is to reduce the need for patient cooperation and to lower scan times, particularly in children. This makes CMR more accessible, allowing improvements in scan efficiency and ultimately reduces waiting times for patients. This challenging area of MR physics requires specialist acquisition and reconstruction techniques. My research to-date has transformed the clinical service at Great Ormond Street Hospital, by enabling quick scans; significantly improving patient throughput, with no additional cost. These techniques are also being tested in other UCLP clinical departments, including the Heart Hospital and UCLH.
I have vast experience in non-Cartesian MRI acquisition techniques, and a variety of reconstruction technologies, including parallel imaging, compressive sensing and machine learning. These enable highly accelerated imaging, with accurate reconstructions.
One of my primary aims is to reduce the need for patient cooperation and to lower scan times, particularly in children. This makes CMR more accessible, allowing improvements in scan efficiency and ultimately reduces waiting times for patients. This challenging area of MR physics requires specialist acquisition and reconstruction techniques. My research to-date has transformed the clinical service at Great Ormond Street Hospital, by enabling quick scans; significantly improving patient throughput, with no additional cost. These techniques are also being tested in other UCLP clinical departments, including the Heart Hospital and UCLH.
I have vast experience in non-Cartesian MRI acquisition techniques, and a variety of reconstruction technologies, including parallel imaging, compressive sensing and machine learning. These enable highly accelerated imaging, with accurate reconstructions.
Research Interests
Papers共 112 篇Author StatisticsCo-AuthorSimilar Experts
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Scientific reportsno. 1 (2024): 11774-11774
CoRR (2024)
Cited0Views0EIBibtex
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RADIOLOGY-ARTIFICIAL INTELLIGENCEno. 1 (2024): e230132-e230132
PLoS computational biologyno. 6 (2024): e1012231-e1012231
Magnetic resonance imaging (2024): 184-194
Ruta Virsinskaite,James T. Brown,Tushar Kotecha, Darren Bower,Jennifer A. Steeden,Javier Montalt-Tordera,Olivier Jaubert,Marianna Fontana,J. Gerry Coghlan,Daniel S. Knight, Vivek Muthurangu
medrxiv(2024)
Rebecca R Baker,Vivek Muthurangu,Marilena Rega,Javier Montalt-Tordera, Samuel Rot,Bhavana S Solanky, Claudia A M Gandini Wheeler-Kingshott,Stephen B Walsh,Jennifer A Steeden
Magnetic resonance in medicineno. 1 (2023): 325-336
arXiv (Cornell University)no. 1 (2023): 266-279
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Author Statistics
#Papers: 115
#Citation: 1638
H-Index: 23
G-Index: 37
Sociability: 6
Diversity: 1
Activity: 1
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