John Duncan

Jingjing Peng, Giorgio Fiore, Yang Liu et al.

Introduction: In neurosurgery, image-guided Neurosurgery Systems (IGNS) highly rely on preoperative brain magnetic resonance images (MRI) to assist surgeons in locating surgical targets and determining surgical paths. However, brain shift invalidates the preoperative MRI after dural opening. Updated intraoperative brain MRI with brain shift compensation is crucial for enhancing the precision of neuronavigation systems and ensuring the optimal outcome of surgical interventions. Methodology: We propose NeuralShift, a U-Net-based model that predicts brain shift entirely from pre-operative MRI for patients undergoing temporal lobe resection. We evaluated our results using Target Registration Errors (TREs) computed on anatomical landmarks located on the resection side and along the midline, and DICE scores comparing predicted intraoperative masks with masks derived from intraoperative MRI. Results: Our experimental results show that our model can predict the global deformation of the brain (DICE of 0.97) with accurate local displacements (achieve landmark TRE as low as 1.12 mm), compensating for large brain shifts during temporal lobe removal neurosurgery. Conclusion: Our proposed model is capable of predicting the global deformation of the brain during temporal lobe resection using only preoperative images, providing potential opportunities to the surgical team to increase safety and efficiency of neurosurgery and better outcomes to patients. Our contributions will be publicly available after acceptance in https://github.com/SurgicalDataScienceKCL/NeuralShift.

Jascha Achterberg, Danyal Akarca, Moataz Assem et al. · cambridge

There is a concerted effort to build domain-general artificial intelligence in the form of universal neural network models with sufficient computational flexibility to solve a wide variety of cognitive tasks but without requiring fine-tuning on individual problem spaces and domains. To do this, models need appropriate priors and inductive biases, such that trained models can generalise to out-of-distribution examples and new problem sets. Here we provide an overview of the hallmarks endowing biological neural networks with the functionality needed for flexible cognition, in order to establish which features might also be important to achieve similar functionality in artificial systems. We specifically discuss the role of system-level distribution of network communication and recurrence, in addition to the role of short-term topological changes for efficient local computation. As machine learning models become more complex, these principles may provide valuable directions in an otherwise vast space of possible architectures. In addition, testing these inductive biases within artificial systems may help us to understand the biological principles underlying domain-general cognition.

Oeslle Lucena, Sjoerd B. Vos, Vejay Vakharia et al.

Accurate local fiber orientation distribution (FOD) modeling based on diffusion magnetic resonance imaging (dMRI) capable of resolving complex fiber configurations benefits from specific acquisition protocols that sample a high number of gradient directions (b-vecs), a high maximum b-value(b-vals), and multiple b-values (multi-shell). However, acquisition time is limited in a clinical setting and commercial scanners may not provide such dMRI sequences. Therefore, dMRI is often acquired as single-shell (single b-value). In this work, we learn improved FODs for commercially acquired MRI. We evaluate patch-based 3D convolutional neural networks (CNNs)on their ability to regress multi-shell FOD representations from single-shell representations, where the representation is a spherical harmonics obtained from constrained spherical deconvolution (CSD) to model FODs. We evaluate U-Net and HighResNet 3D CNN architectures on data from the Human Connectome Project and an in-house dataset. We evaluate how well each CNN model can resolve local fiber orientation 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN models; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN models. Our approach may enable robust CSD model estimation on single-shell dMRI acquisition protocols with few gradient directions, reducing acquisition times, facilitating translation of improved FOD estimation to time-limited clinical environments.