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We are working to develop new technologies that combine a new type of camera system, referred to as hyperspectral, with Artificial Intelligence (AI) systems to reveal to neurosurgeons information that is otherwise not visible to the naked eye during surgery. Two studies are currently bringing this “hyperspectral” technology to operating theatres. The NeuroHSI study uses a hyperspectral camera attached to an external scope to show surgeons critical information on tissue blood flow and distinguishes vulnerable structures which need to be protected. The NeuroPPEye study is developing this technology adapted for surgical microscopes, to guide tumour surgery.

This video presents work lead by Christopher E. Mower. The ROS-PyBullet Interface is a framework between the reliable contact simulator PyBullet and the Robot Operating System (ROS) with additional utilities for Human-Robot Interaction in the simulated environment. This work was presented at the Conference on Robot Learning (CoRL), 2022. The corresponding paper can be found at PMLR.

We are seeking a motivated research nurse/coordinator to support our NeuroHSI and NeuroPPEye project.

CRN King’s Neurosurgery research coordinator

The post will be a Band 6 level Neurosurgery affiliated research nurse/coordinator to work within the neuroscience division at KCH. This is a full-time post, initially until end of August 2023 with a view to be extended by 6 - 12 months. The successful applicant will work across several neurosurgery sub-specialities with a particular focus on neuro-oncology and translational healthcare technology in neurosurgery. The applicate will work on research and clinical trials listed in the Department of Heath national portfolio, principally involving the development and evaluation of advanced smart camera technology for use during surgery. The post holder will work under the supervision of Mr Jonathan Shapey (Senior Clinical Lecturer and Consultant Neurosurgeon), Professor Keyoumars Ashkan (Professor of Neurosurgery) and the management of Alexandra Rizos, Neuroscience Research Manager. Some experience in clinical research and knowledge of good clinical practice would be beneficial.

Muhammad led the development of FastGeodis, an open-source package that provides efficient implementations for computing Geodesic and Euclidean distance transforms (or a mixture of both), targetting efficient utilisation of CPU and GPU hardware. This package is able to handle 2D as well as 3D data, where it achieves up to a 20x speedup on a CPU and up to a 74x speedup on a GPU as compared to an existing open-source library that uses a non-parallelisable single-thread CPU implementation. Further in-depth comparison of performance improvements is discussed in the FastGeodis documentation.

• Title: Incorporating Expert-consistent Spatial Structure Relationships in Learning-based Brain Parcellation
• First supervisor: Tom Vercauteren
• Second supervisor: Rachel Sparks
• Clinical Supervisor: Jonathan Shapey
• Start date: February 2024

Aim of the PhD Project

• Develop trustworthy deep learning-based brain segmentation/parcellation.
• Formalise trustworthiness as contracts on spatial relationships between labels that the algorithm must fulfil.
• Establish mathematical/algorithmic frameworks to guarantee that the proposed segmentation/parcellation respect the contracts of trust.
• Implement, validate, and disseminate the proposed algorithms using open-access datasets.

Project summary

Automated segmentation and labelling of brain structures from medical images, in particular Magnetic Resonance Imaging (MRI), plays an important role in many applications ranging from surgical planning to neuroscience studies. For example, in Deep Brain Stimulation (DBS) procedures used to treat some movement disorders, segmentation of the basal ganglia and structures such as the subthalamic nucleus (STN) can help with precise targeting of the neurostimulation electrodes being implanted in the patient’s brain. Going beyond segmentation of a few discrete structures, some applications required a full brain parcellation, i.e., a partition of the entire brain into a set of non-overlapping spatial regions of anatomical or functional significance. Brain parcellation have notably been used to automate the trajectory planning of multiple intracranial electrodes for epilepsy surgery or to support the assessment of brain atrophy patterns for dementia monitoring.

• Title: Artificial intelligence-driven radiosurgery planning for brain metastases
• First supervisor: Jonathan Shapey
• Second supervisor: Tom Vercauteren
• Clinical Supervisor: Ian Paddick
• Start date: October 2023

Aim of the PhD Project

• Implement learning-based registration to curate a spatially-normalised dataset of MR images previously used to deliver stereotactic radiosurgery to brain metastases
• Develop data-driven deep learning frameworks to automatically detect and segment brain metastases while allowing for interactive corrections
• Develop imaging biomarkers to predict tumour response and behaviour following treatment

Project summary

Approximately 25,000 patients are diagnosed with a brain tumour every year in the UK. Brain metastases affect up to 40% of patients with extracranial primary cancer. Furthermore, although there are presently no reliable data, metastatic brain tumours are thought to outnumber primary malignant brain tumours by at least 3:1. Patients with brain metastases require individualized patient management and may include surgery, stereotactic radiosurgery, fractionated radiotherapy and chemotherapy, either alone or in combination.

• Title: Accurate automated quantification of spine evolution — it’s about time!
• First supervisor: Marc Modat
• Second supervisor: Tom Vercauteren
• Clinical Supervisor: Amanda Isaac
• Start date: February 2024

Aim of the PhD Project

• Complement radiological expertise with automated analysis of longitudinal spine changes
• Development of longitudinal registration algorithms to align spine images from different imaging modalities
• Development of processing tools robust to the presence of metal artefact and various fields of view
• Extraction of imaging biomarkers of spine degeneration.

Project summary

Back pain presents because of a wide range of conditions in the spine and is often multifactorial. Spine appearances also change after surgery, and postoperative changes depend on the specific interventions offered to patients and human factors such as healing and mechanical adaptations, which are also unique to each patient. When reviewing medical images for diagnostic, monitoring or prognosis purpose, radiologists are required to evaluate multiple structures, including bone, muscles, and nerves, as well as the surrounding soft tissues and any instrumentation used in surgery. They must use their expertise to assess how each structure has evolved over time visually. Such readings are, therefore, both time-consuming and require dedicated expertise, which is limited to large regional spine centres throughout the UK.

• Title: Physically-informed learning-based beamforming for multi-transducer ultrasound imaging
• First supervisor: Laura Maria Peralta Pereira
• Second supervisor: Tom Vercauteren
• Clinical Supervisor: Dean Huang
• Start date: February 2024

Aim of the PhD Project

• Pursue sparse solutions to handle the channel count required to coherently operate multiple ultrasound transducer and design and implement machine learning strategies to avoid the sparsity-related artefacts in the images.
• Develop advanced beamforming techniques using machine learning approaches informed by ultrasound physics to address the concern of the flexible geometry in a multi-transducer imaging system and achieve unprecedented image quality.
• Explore the application of the techniques on healthy volunteers.

Project description

Medical Ultrasound (US) is a low-cost imaging method that is long-established and widely used for screening, diagnosis, therapy monitoring, and guidance of interventional procedures. However, the usefulness of conventional US systems is limited by physical constraints mainly imposed by the small size of the handheld probe that lead to low-resolution images with a restricted field of view and view-dependent artefacts.