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Thomas Booth
Thomas Booth

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...

    woman in lab coat and office setting wearing VR headset and holding controllers

    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...

    AI abnormality image

    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

    brain imaging

    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

    tom-booth-story-1

    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...

    MRI-2-parter-tom-booth-

    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

    head-mri-exams

    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...

    tom-booth-mrc-grant

    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...

    tom-booth-scans-story

    New research into remote robotic surgery

    The review highlights some of the key criteria needed for interventional systems needed to be successful

    thomas-booth-robotic-surgery

    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

    mri tom booth.jpg

      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...

      woman in lab coat and office setting wearing VR headset and holding controllers

      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...

      AI abnormality image

      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

      brain imaging

      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

      tom-booth-story-1

      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...

      MRI-2-parter-tom-booth-

      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

      head-mri-exams

      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...

      tom-booth-mrc-grant

      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...

      tom-booth-scans-story

      New research into remote robotic surgery

      The review highlights some of the key criteria needed for interventional systems needed to be successful

      thomas-booth-robotic-surgery

      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

      mri tom booth.jpg