Magnetic resonance imaging (MRI) relies on physiological triggering for cardiac gating, typically using the R-peak of an electrocardiogram (ECG). However, ECG-based triggering faces limitations in MRI environments due to challenges in electrode placement, magnetic field interference, and RF-induced heating. Alternatively, contact photoplethysmography (PPG) offers a feasible solution; however, it suffers from reduced accuracy and requires the fixation of a probe on the finger tip. To overcome these limitations, this paper proposes an MRI-compatible system for contactless forehead PPG using bundled fiber-optic guides. The proposed approach eliminates electrical interference and ensures safety. A low-power sensor node is proposed to investigate the trade-offs among signal fidelity, energy efficiency, and system latency in on-device PPG. By combining programmable optical sources, an analog front-end, and BLE connectivity, the platform enables reproducible MRI experimentation. It fully processes the PPG data in just 2.8 ms onboard, utilizing a low-power ARM Cortex-M33 core running at 128 MHz. A feasibility study involving 8 subjects was conducted to evaluate the proposed system and demonstrate the effectiveness of the MRI-compatible contactless PPG using green/red light. Several fiducial points of the PPG waveform - foot, onset, and peak - were evaluated for trigger generation. Despite a physiological delay of ∼100-150 ms relative to the R-peak of a reference ECG, the PPG-based R-peak point is reliably estimated with a jitter of 5.14 ms. The sensor node demonstrated the efficiency of the proposed solution operating with a 520 mAh battery for over 23 hours and integrates custom adapters for 2 m optical guides, ensuring safe electronic placement outside the MRI bore. These results confirm that our system can enable prospective gating of MRI measurements without the practical challenges of securing ECG leads or a fingertip PPG. The proposed system paves the way for a safe, contactless, low-power, self-contained sensor node with onboard processing and an interference-free gating method, with the potential to redefine physiological monitoring workflows in MRI environments, enabling precise synchronization without electromagnetic interference.

We propose a differentiable volumetric mesh voxelization technique based on deformation of a shape-model, and demonstrate that it can be used to predict left-ventricular anatomies directly from magnetic resonance image slice data. The predicted anatomies are volumetric meshes suitable for direct inclusion in biophysical simulations. The proposed method can leverage existing (pixel-based) segmentation networks, and does not require any ground truth paired image and mesh training data. We demonstrate that this approach produces accurate predictions from few slices, and can combine information from images acquired in different views (e.g. fusing shape information from short axis and long axis slices). We demonstrate that the proposed method is several times faster than a state-of-the-art registration based method. Additionally, we show that our method can correct for slice misalignment, and is robust to incomplete and inaccurate input data. We further demonstrate that by fitting a mesh to every frame of 4D data we can determine ejection fraction, stroke volume and strain.

We present a parametric physics-informed neural network for the simulation of personalised left-ventricular biomechanics. The neural network is constrained to the biophysical problem in two ways: (i) the network output is restricted to a subspace built from radial basis functions capturing characteristic deformations of left ventricles and (ii) the cost function used for training is the energy potential functional specifically tailored for hyperelastic, anisotropic, nearly-incompressible active materials. The radial bases are generated from the results of a nonlinear Finite Element model coupled with an anatomical shape model derived from high-resolution cardiac images. We show that, by coupling the neural network with a simplified circulation model, we can efficiently generate computationally inexpensive estimations of cardiac mechanics. Our model is 30 times faster than the reference Finite Element model used, including training time, while yielding satisfactory average errors in the predictions of ejection fraction (-3%), peak systolic pressure (7%), stroke work (4%) and myocardial strains (14%). This physics-informed neural network is well suited to efficiently augment cardiac images with functional data and to generate large sets of synthetic cases for training deep network classifiers while it provides efficient personalization to the specific patient of interest with a high level of detail.

The intravoxel incoherent motion (IVIM) model allows to map diffusion (D) and perfusion-related parameters (F and D*). Parameter estimation is, however, error-prone due to the non-linearity of the signal model, the limited signal-to-noise ratio (SNR) and the small volume fraction of perfusion in the in-vivo brain. In the present work, the performance of Bayesian inference was examined in the presence of brain pathologies characterized by hypo- and hyperperfusion. In particular, a hierarchical and a spatial prior were combined. Performance was compared relative to conventional segmented least squares regression, hierarchical prior only (non-segmented and segmented data likelihoods) and a deep learning approach. Realistic numerical brain IVIM simulations were conducted to assess errors relative to ground truth. In-vivo, data of 11 central nervous system cancer patients and 9 patients with acute stroke were acquired. The proposed method yielded reduced error in simulations for both the cancer and acute stroke scenarios compared to other methods across the whole investigated SNR range. The contrast-to-noise ratio of the proposed method was better or on par compared to the other techniques in-vivo. The proposed Bayesian approach hence improves IVIM parameter estimation in brain cancer and acute stroke.

Cardiac myocyte aggregate orientation has a strong impact on cardiac electrophysiology and mechanics. Studying the link between structural characteristics, strain, and stresses over the cardiac cycle and cardiac function requires a full volumetric representation of the microstructure. In this work, we exploit the structural similarity across hearts to extract a low-rank representation of predominant myocyte orientation in the left ventricle from high-resolution magnetic resonance ex-vivo cardiac diffusion tensor imaging (cDTI) in porcine hearts. We compared two reduction methods, Proper Generalized Decomposition combined with Singular Value Decomposition and Proper Orthogonal Decomposition. We demonstrate the existence of a general set of basis functions of aggregated myocyte orientation which defines a data-driven, personalizable, parametric model featuring higher flexibility than existing atlas and rule-based approaches. A more detailed representation of microstructure matching the available patient data can improve the accuracy of personalized computational models. Additionally, we approximate the myocyte orientation of one ex-vivo human heart and demonstrate the feasibility of transferring the basis functions to humans.

As disease often alters structural and functional properties in tissue, the noninvasive measurement of material stiffness in vivo is desirable. Magnetic resonance elastography provides an approach to in vivo tissue characterization, using images of wave motion in tissue and biomechanical principles to reconstruct and quantify stiffness. Successful clinical translation of this technology requires stiffness reconstruction algorithms that are robust, easy to manage, and fast. In this paper, a reconstruction method is presented which addresses these issues by using a local compact divergence-free reconstruction kernel coupled with non-physical constraint elimination and inverse residual weighting to reliably reconstruct stiffness. The proposed technique is compared with local curl reconstructions and global stiffness-pressure reconstructions across two ground-truth phantoms as well as in vivo data sets. Sensitivity analysis is also performed, assessing the variability of reconstruction results and robustness to noise. It is shown that the proposed method can be robustly applied across data sets, is less sensitive to noise, attains comparable (or improved) accuracy, provides better correlation to anatomical features, and can be completed in short timescales.

Navigated 2-D multi-slice dynamic magnetic resonance imaging (MRI) acquisitions are essential for MR guided therapies. This technique yields time-resolved volumetric images during free-breathing, which are ideal for visualizing and quantifying breathing induced motion. To achieve this, navigated dynamic imaging requires acquiring multiple navigator slices. Reducing the number of navigator slices would allow for acquiring more data slices in the same time, and hence, increasing through-plane resolution or alternatively the overall acquisition time can be reduced while keeping resolution unchanged. To this end, we propose temporal interpolation of navigator slices using convolutional neural networks (CNNs). Our goal is to acquire fewer navigators and replace the missing ones with interpolation. We evaluate the proposed method on abdominal navigated dynamic MRI sequences acquired from 14 subjects. Investigations with several CNN architectures and training loss functions show favorable results for cost and a simple feed-forward network with no skip connections. When compared with interpolation by non-linear registration, the proposed method achieves higher interpolation accuracy on average as quantified in terms of root mean square error and residual motion. Analysis of the differences shows that the better performance is due to more accurate interpolation at peak exhalation and inhalation positions. Furthermore, the CNN-based approach requires substantially lower execution times than that of the registration-based method. At last, experiments on dynamic volume reconstruction reveal minimal differences between reconstructions with acquired and interpolated navigator slices.