Ramya Muthukrishnan

2026

1. ISMRM

   Towards real-time navigation in diagnostic fetal brain MRI

   Ramya Muthukrishnan, Paul Wighton, Robert Frost, and 6 more authors

   2026

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   We present a feedback loop system for automated prescription of imaging slices that account for the fetal head motion in real time. This work promises to significantly improve radiological assessments, reduce scan time, and alleviate pregnant patients’ discomfort.

2025

1. arXiV

   Equivariant symmetry-aware head pose estimation for fetal MRI

   Ramya Muthukrishnan, Borjan Gagoski, Aryn Lee, and 4 more authors

   2025

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   We present E(3)-Pose, a novel fast pose estimation method that jointly and explicitly models rotation equivariance and object symmetry. Our work is motivated by the challenging problem of accounting for fetal head motion during a diagnostic MRI scan. We aim to enable automatic adaptive prescription of 2D diagnostic MRI slices with 6-DoF head pose estimation, supported by 3D MRI volumes rapidly acquired before each 2D slice. Existing methods struggle to generalize to clinical volumes, due to pose ambiguities induced by inherent anatomical symmetries, as well as low resolution, noise, and artifacts. In contrast, E(3)-Pose captures anatomical symmetries and rigid pose equivariance by construction, and yields robust estimates of the fetal head pose. Our experiments on publicly available and representative clinical fetal MRI datasets demonstrate the superior robustness and generalization of our method across domains. Crucially, E(3)-Pose achieves state-of-the-art accuracy on clinical MRI volumes, supporting future clinical translation. Our implementation is publicly available at github.com/MedicalVisionGroup/E3-Pose.

2. MICCAI

   Spatial regularisation for improved accuracy and interpretability in keypoint-based registration

   Benjamin Billot, Ramya Muthukrishnan, Esra Abaci-Turk, and 4 more authors

   2025

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   Unsupervised registration strategies bypass requirements in ground truth transforms or segmentations by optimising similarity metrics between fixed and moved volumes. Among these methods, a recent subclass of approaches based on unsupervised keypoint detection stand out as very promising for interpretability. Specifically, these methods train a network to predict feature maps for fixed and moving images, from which explainable centres of mass are computed to obtain point clouds, that are then aligned in closed-form. However, the features returned by the network often yield spatially diffuse patterns that are hard to interpret, thus undermining the purpose of keypoint-based registration. Here, we propose a three-fold loss to regularise the spatial distribution of the features. First, we use the KL divergence to model features as point spread functions that we interpret as probabilistic keypoints. Then, we sharpen the spatial distributions of these features to increase the precision of the detected landmarks. Finally, we introduce a new repulsive loss across keypoints to encourage spatial diversity. Overall, our loss considerably improves the interpretability of the features, which now correspond to precise and anatomically meaningful landmarks. We demonstrate our three-fold loss in foetal rigid motion tracking and brain MRI affine registration tasks, where it not only outperforms state-of-the-art unsupervised strategies, but also bridges the gap with state-of-the-art supervised methods. Our code is available at https://github.com/BenBillot/spatial regularisation.

3. arXiV

   LPAC: Learnable perception-action-communication loops with applications to coverage control

   Saurav Agarwal, Ramya Muthukrishnan, Walker Gosrich, and 2 more authors

   2025

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   Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known a priori. The problem is challenging in decentralized settings with robots that have limited communication and sensing capabilities. We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem, wherein a convolution neural network (CNN) processes localized perception; a graph neural network (GNN) facilitates robot communications; finally, a shallow multi-layer perceptron (MLP) computes robot actions. The GNN enables collaboration in the robot swarm by computing what information to communicate with nearby robots and how to incorporate received information. Evaluations show that the LPAC models—trained using imitation learning—outperform standard decentralized and centralized coverage control algorithms. The learned policy generalizes to environments different from the training dataset, transfers to larger environments with more robots, and is robust to noisy position estimates. The results indicate the suitability of LPAC architectures for decentralized navigation in robot swarms to achieve collaborative behavior.

2023

1. AAAI

   InvRT: Solving Radar Inverse Problems with Transformers

   Ramya Muthukrishnan, Justin Goodwin, Adam Kern, and 2 more authors

   2023

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   A wide variety of applications rely on the characterization of objects from radar observations. Existing approaches in this space primarily focus on inferring object type. In this work, we focus on inferring the input parameters of a physics-based radar simulator from its output, a temporal sequence of radar observations. Sequential radar observations inherently have a complex spatio-temporal structure that is difficult to capture with many standard vision-based deep learning architectures. We model such complex phenomena as a sequence to sequence prediction problem and use a transformer architecture, taking advantage of its ability to capture contextual temporal dependencies. We demonstrate that our method, Inverse Radar Transformer (InvRT), outperforms baseline approaches in predicting object properties, for both high and low observability settings. Furthermore, its errors are highly correlated with the level of object observability, highlighting its potential to learn the geometric limitations of radar sensing.

2022

1. NeuroImage Clin

   Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI

   T. Campbell Arnold, Ramya Muthukrishnan, Akash R. Pattnaik, and 9 more authors

   NeuroImage: Clinical, 2022

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   Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection cavity segmentation algorithm is developed for analyzing postoperative MRI of epilepsy patients and deployed in an easy-to-use graphical user interface (GUI) that estimates remnant brain volumes, including postsurgical hippocampal remnant tissue. This retrospective study included postoperative T1-weighted MRI from 62 temporal lobe epilepsy (TLE) patients who underwent resective surgery. The resection site was manually segmented and reviewed by a neuroradiologist (JMS). A majority vote ensemble algorithm was used to segment surgical resections, using 3 U-Net convolutional neural networks trained on axial, coronal, and sagittal slices, respectively. The algorithm was trained using 5-fold cross validation, with data partitioned into training (N = 27) testing (N = 9), and validation (N = 9) sets, and evaluated on a separate held-out test set (N = 17). Algorithm performance was assessed using Dice-Sørensen coefficient (DSC), Hausdorff distance, and volume estimates. Additionally, we deploy a fully-automated, GUI-based pipeline that compares resection segmentations with preoperative imaging and reports estimates of resected brain structures. The cross-validation and held-out test median DSCs were 0.84 ± 0.08 and 0.74 ± 0.22 (median ± interquartile range) respectively, which approach inter-rater reliability between radiologists (0.84–0.86) as reported in the literature. Median 95% Hausdorff distances were 3.6 mm and 4.0 mm respectively, indicating high segmentation boundary confidence. Automated and manual resection volume estimates were highly correlated for both cross-validation (r = 0.94, p < 0.0001) and held-out test subjects (r = 0.87, p < 0.0001). Automated and manual segmentations overlapped in all 62 subjects, indicating a low false negative rate. In control subjects (N = 40), the classifier segmented no voxels (N = 33), <50 voxels (N = 5), or a small volumes<0.5 cm3 (N = 2), indicating a low false positive rate that can be controlled via thresholding. There was strong agreement between postoperative hippocampal remnant volumes determined using automated and manual resection segmentations (r = 0.90, p < 0.0001, mean absolute error = 6.3 %), indicating that automated resection segmentations can permit quantification of postoperative brain volumes after epilepsy surgery. Applications include quantification of postoperative remnant brain volumes, correction of deformable registration, and localization of removed brain regions for network modeling.