Personal profile
Overview
Dr. Ka-Loh Li is a research only research associate in the university of Manchester. Her major research interests are to use MRI techniques to better characterize and monitor therapy response of brain tumours and specifically in developing imaging processing technique using contrast-enhanced MRI. The goal is to transform clinically collected image data into useful medical information to benefit patient management.
Research interests
Gadolinium deposition in normal tissues has been found associated with potential long-term health risks. There is an international drive to try and reduce the use of gadolinium. Her research over the past 5-10 has formed a strong position in the development of approaches which would allow the application of low-dose gadolinium contrast studies, which is a key advantage benefiting CNS patients undergoing repeated exposures.
Her current work is focused on:
1) Improving accuracy of kinetic parameter estimates in whole brain high-spatial resolution DCE-MRI
Whilst highly desirable in neuro-oncology, derivation of accurate, high-spatial resolution, whole brain coverage microvascular parameters with DCE-MRI remains challenging. This study sought to address this through developing and evaluating a novel dual-temporal resolution (DTR) DCE-based technique.
2) low Gd dose high temporal high spatial DCE-MRI
The requirement for full-dose administration of the gadolinium-based contrast agent (GBCA) has been recognized as a key concern in patient management, especially in patients with more benign CNS tumors, such as the vestibular schwannoma (VS) patients, who may receive serial contrast-enhanced scans. We aim to develop technique for:
- The segmentation of tumours from the low dose T1W structural.
- Accurate kinetic parameter estimates in whole brain high-spatial resolution low dose DCE-MRI
My collaborations
Professor Timothy Cootes, Division of Informatics, Imaging & Data Sciences, The University of Manchester
Dr. Daniel Lewis, MRCS, MRCP, MA, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, United Kingdom
Dr. Sha Zhao, MRI spin physicist, Division of Informatics, Imaging & Data Sciences, The University of Manchester
Dr. Xiaoping Zhu, Honorary Senior Lecturer, Division of Informatics, Imaging & Data Sciences, The University of Manchester
Professor Alan Jackson, Division of Informatics, Imaging & Data Sciences, The University of Manchester
Biography
Ka-loh received PhD degree in chemistry/NMR from Clark University, Worcester, MA, USA, and one year postdoctoral training at City University of New York, Staten Island, NY, USA. She worked as a scientific associate at Department of Radiology, New England Deaconess Hospital and research fellow in Radiology of Harvard Medical School 1991-1996. She worked as a research fellow at Department of Clinical Radiology, University of Manchester 1997 – 2000. She worked in the department of Radiology, UCSF, San Francisco, USA 2000 – 2009, as assistant research scientist with PI status. From 2010 to present, she has worked at the University of Manchester. In past 20 years Kaloh has a specific interest in quantitative magnetic resonance imaging (MRI) with a focus on tracer kinetic analysis of data acquired using dynamic contrast-enhanced MRI.
Research Beacons, Institutes and Platforms
- Digital Futures
- Christabel Pankhurst Institute
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
-
SDG 3 Good Health and Well-being
-
SDG 6 Clean Water and Sanitation
Fingerprint
- 1 Similar Profiles
Collaborations and top research areas from the last five years
-
Dynamic contrast-enhanced and diffusion-weighted MR imaging for predicting tumor growth of sporadic vestibular schwannomas: A prospective study
Schouten, S. M., Lewis, D., Cornelissen, S., Li, K.-L., Zhu, X., Maas, M. C., Pegge, S., Jansen, T. T. G., Mulder, J. J. S., Waterval, J. J., Postma, A. A., Pathmanaban, O., Coope, D. J., Derks, J. M. M., Langenhuizen, P. P. J. H., King, A. T., Verheul, J. B. & Kunst, H. P. M., 15 May 2025, In: Neuro-Oncology. 27, 4, p. 1116–1127Research output: Contribution to journal › Article › peer-review
Open Access -
Emerging strategies for the prediction of behaviour, growth, and treatment response in vestibular schwannoma
Lewis, D., Li, K.-L., Djoukhadar, I., Hannan, C. J., Pathmanaban, O. N., Coope, D. J. & King, A. T., 22 Apr 2025, In: Acta Neurochirurgica. 167, 1, p. 116Research output: Contribution to journal › Review article › peer-review
-
Low-dose GBCA administration for brain tumour dynamic contrast enhanced MRI: a feasibility study.
Lewis, D., Li, K.-L., Waqar, M., Coope, D., Pathmanaban, O., King, A., Djoukhadar, I., Zhao, S., Cootes, T., Jackson, A. & Zhu, X., 28 Feb 2024, In: Scientific Reports. 14, 4905 .Research output: Contribution to journal › Article › peer-review
Open Access -
A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis
Li, K.-L., Lewis, D., Zhu, X., Coope, D. J., Djoukhadar, I., King, A. T., Cootes, T. & Jackson, A., 11 Sept 2023, In: Pharmaceuticals. 16, 9, 23 p., 1282.Research output: Contribution to journal › Article › peer-review
Open Access -
IMAGING HABITAT CHANGES AFTER PREOPERATIVE RADIOTHERAPY FOR GLIOBLASTOMA: UTILISATION OF A BIOINFORMATICS AND MACHINE LEARNING PIPELINE TO CHARACTERISE REGIONAL TREATMENT RESPONSE
Waqar, M., Van-Houdt, P., Hessen, E., Lewis, D., Li, K.-L., Zhu, X., Jackson, A., Iqbal, M., Roncaroli, F., Djoukhadar, I., Coope, D. & Borst, G., 16 Sept 2023, In: Neuro-Oncology.Research output: Contribution to journal › Article › peer-review
Datasets
-
Data for: Low Dose T1W DCE-MRI for Early Time Points (ET) Perfusion Measurement in Patients with Intra-Cranial Tumors: A Pilot Study Applying the Microsphere Model to Measure Absolute Cerebral Blood Flow
Li, K.-L. (Contributor), Lewis, D. (Contributor), jackson, A. (Contributor), Zhao, S. (Contributor) & zhu, X. (Contributor), Mendeley Data, 17 Jan 2018
DOI: 10.17632/hg7vppf6g5.1, https://data.mendeley.com/datasets/hg7vppf6g5
Dataset