Dr Thomas Booth
Reader in Neuroimaging
Research interests
- Cancer
- Neuroscience
Biography
Thomas C Booth is a Reader in Neuroimaging in the School of Biomedical Engineering & Imaging Sciences at King’s College London. He is also an Honorary Consultant Diagnostic and Interventional Neuroradiologist at King’s College Hospital, London. His research interests are in (1) neuro-oncology (especially relating to diagnostic AI), (2) neurovascular (robotics) and (3) abnormality detection (especially relating to diagnostic AI). His PhD focus was on brain tumour treatment response assessment using pre-clinical metabolic imaging as well as adult brain tumour MRI structural images using machine learning at the University of Cambridge a decade ago – something he continues to research now as he is reminded continuously how important neuro-oncology diagnostics are in a busy London teaching hospital. On the neurovascular side, stroke imaging and aneurysm procedural work have also become areas of much research and he is developing robotics with his multidisciplinary colleagues.
He is the Chief Investigator on 5 UK multicentre and 4 NIHR portfolio-adopted prospective studies: more than 6000 patients across the UK have been recruited to these studies. His largest study relates to abnormality detection in brain MRI scans using AI.
He chairs or sits on various National and International committees - some relating to funding (e.g. NIHR) and some special interest groups (e.g. relating to brain tumours). He was an awardee of the inaugural Royal College of Radiologists Outstanding Researcher Award.
News
New study finds device-mimicking controller best approach for neuroradiology procedures
Researchers from the School of Biomedical Engineering & Imaging Sciences, King’s College London, have found that a device-mimicking robotic controller is...
Study finds insufficient evidence to recommend AI for abnormality detection
A King’s College study has found the use of Artificial Intelligence (AI) detection models is only adequate as a tool to improve radiologist efficiency rather...
Poor evidence currently for studies using machine learning algorithms to determine effectiveness of glioblastoma treatment, study suggests
Glioblastoma is the most common primary malignant brain tumour with a median overall survival within 1.5 years
Machine learning currently ineffective for detecting brain aneurysms, suggests systematic review and meta-analysis
First systematic review and meta-analysis looking at feasability of machine learning algorithms to detect brain aneurysms
Researchers put forward evidence for use of advanced MRI techniques as brain cancer monitoring biomarkers
The statement is a result of a joint effort of researchers from eight European countries and the US, highlighting evidence gaps and provides a focus for...
New machine learning model flags abnormal brain scans in real-time
The model can reduce reporting times for abnormal examinations by accurately flagging abnormalities at the time of imaging
Researchers receive £1M Medical Research Council grant for automatic brain abnormality detection tool
The work follows earlier findings that it is possible to automate brain MRI image labelling where the researchers found that more than 100,000 exams can be...
New screening tool developed to automatically identify older appearing brains typical of dementia
If a patient has a brain which is diseased and has lost a disproportionate amount of volume, such as in dementia, the tool will show the mismatch between the...
New research into remote robotic surgery
The review highlights some of the key criteria needed for interventional systems needed to be successful
Researchers automate brain MRI image labelling, more than 100,000 exams can be labelled in less than 30 minutes
Researchers automate brain MRI image labelling, more than 100,000 exams can be labelled in less than 30 minutes
News
New study finds device-mimicking controller best approach for neuroradiology procedures
Researchers from the School of Biomedical Engineering & Imaging Sciences, King’s College London, have found that a device-mimicking robotic controller is...
Study finds insufficient evidence to recommend AI for abnormality detection
A King’s College study has found the use of Artificial Intelligence (AI) detection models is only adequate as a tool to improve radiologist efficiency rather...
Poor evidence currently for studies using machine learning algorithms to determine effectiveness of glioblastoma treatment, study suggests
Glioblastoma is the most common primary malignant brain tumour with a median overall survival within 1.5 years
Machine learning currently ineffective for detecting brain aneurysms, suggests systematic review and meta-analysis
First systematic review and meta-analysis looking at feasability of machine learning algorithms to detect brain aneurysms
Researchers put forward evidence for use of advanced MRI techniques as brain cancer monitoring biomarkers
The statement is a result of a joint effort of researchers from eight European countries and the US, highlighting evidence gaps and provides a focus for...
New machine learning model flags abnormal brain scans in real-time
The model can reduce reporting times for abnormal examinations by accurately flagging abnormalities at the time of imaging
Researchers receive £1M Medical Research Council grant for automatic brain abnormality detection tool
The work follows earlier findings that it is possible to automate brain MRI image labelling where the researchers found that more than 100,000 exams can be...
New screening tool developed to automatically identify older appearing brains typical of dementia
If a patient has a brain which is diseased and has lost a disproportionate amount of volume, such as in dementia, the tool will show the mismatch between the...
New research into remote robotic surgery
The review highlights some of the key criteria needed for interventional systems needed to be successful
Researchers automate brain MRI image labelling, more than 100,000 exams can be labelled in less than 30 minutes
Researchers automate brain MRI image labelling, more than 100,000 exams can be labelled in less than 30 minutes