CyberSec Research

Background: Ultrasound imaging plays a pivotal role in diagnosing carotid atherosclerosis, a significant precursor to cardiovascular and cerebrovascular diseases and events. This non-invasive modality provides real-time, high-resolution images, allowing clinicians to assess atherosclerotic plaques in the carotid arteries without invasive procedures. Purpose: In this study, we present the refinement of a Mask R-CNN model initially designed for carotid lumen detection to automatically generate bounding boxes enclosing atherosclerotic plaque for segmentation to assist in our ultrasound elastography workflow. Methods: We utilize a PyTorch torchvision implementation of the Mask R-CNN for carotid plaque detection. Our dataset consists of 118 severe stenotic carotid plaques from presenting patients, clinically indicated for a carotid endarterectomy. Due to the variability of plaque presentation in the dataset, a multitude of different R-CNN models were observed to have varying results based on the allowed number of prediction regions. An overview analysis looking at shared predictions from these models showed a slight improvement compared to the individual model results Results: Evaluation metrics such as Dice similarity coefficient and intersection over Union are employed. The model trained with 5 maximum prediction regions and tested with 2 maximum prediction regions produced the highest individual accuracy with a Dice score of 0.74 and intersection over union of 0.61. A filtered combined analysis of all the models demonstrated a slight increase in performance with scores of 0.76 and 0.61 respectively. Conclusion: Due to the significant variation in plaque presentation and types amongst presenting patients, the accuracy of the Plaque Mask R-CNN network would benefit from the incorporation of additional patient datasets to incorporate increased variation into the training dataset.

Objectives: Magnetic resonance elastography (MRE) is a noninvasive technique for assessing the viscoelastic properties of soft biological tissues in vivo, with potential relevance for pediatric tumor evaluation. This study aimed to evaluate the feasibility of multifrequency MRE in children with solid tumors and to report initial findings on stiffness and fluidity across rare pediatric tumor entities. Additionally, the potential of viscoelastic properties as biomarkers of tumor malignancy was explored. Materials and Methods: Ten pediatric patients (mean age, 5.7 +/- 4.8 years; four female) with extracranial solid tumors underwent multifrequency MRE. Shear waves at 30 - 70 Hz were subsequently generated and measured with a phase-sensitive single-shot spin-echo planar imaging sequence. The obtained shear wave fields were processed by wavenumber (k-)based multi-frequency inversion to reconstruct tumor stiffness and fluidity. The viscoelastic properties within the tumors were quantified and correlated with the apparent diffusion coefficient (ADC). In addition, differences in stiffness and fluidity were assessed across the histopathologically confirmed tumor entities, which were stratified into malignancy-based groups. Results: MRE was successfully performed in all patients in under five minutes. Differences in viscoelastic properties were observed among tumor entities: Stiffness, fluidity, and their spatial variability increased significantly with tumor malignancy. Furthermore, a significant inverse correlation was observed between stiffness and tumor ADC values. Conclusion: Multifrequency MRE was feasible in pediatric MRI and provided insight into tumor biomechanics. Preliminary data revealed differences in stiffness and fluidity across pediatric solid tumors correlating with malignancy. MRE holds promise for diagnosis and classification of pediatric tumor entities and their malignancy.

Background Brain tumours are the most common solid malignancies in children, encompassing diverse histological, molecular subtypes and imaging features and outcomes. Paediatric brain tumours (PBTs), including high- and low-grade gliomas (HGG, LGG), medulloblastomas (MB), ependymomas, and rarer forms, pose diagnostic and therapeutic challenges. Deep learning (DL)-based segmentation offers promising tools for tumour delineation, yet its performance across heterogeneous PBT subtypes and MRI protocols remains uncertain. Methods A retrospective single-centre cohort of 174 paediatric patients with HGG, LGG, medulloblastomas (MB), ependymomas, and other rarer subtypes was used. MRI sequences included T1, T1 post-contrast (T1-C), T2, and FLAIR. Manual annotations were provided for four tumour subregions: whole tumour (WT), T2-hyperintensity (T2H), enhancing tumour (ET), and cystic component (CC). A 3D nnU-Net model was trained and tested (121/53 split), with segmentation performance assessed using the Dice similarity coefficient (DSC) and compared against intra- and inter-rater variability. Results The model achieved robust performance for WT and T2H (mean DSC: 0.85), comparable to human annotator variability (mean DSC: 0.86). ET segmentation was moderately accurate (mean DSC: 0.75), while CC performance was poor. Segmentation accuracy varied by tumour type, MRI sequence combination, and location. Notably, T1, T1-C, and T2 alone produced results nearly equivalent to the full protocol. Conclusions DL is feasible for PBTs, particularly for T2H and WT. Challenges remain for ET and CC segmentation, highlighting the need for further refinement. These findings support the potential for protocol simplification and automation to enhance volumetric assessment and streamline paediatric neuro-oncology workflows.

Multi-layer diffuse correlation spectroscopy (DCS) models have been developed to reduce the contamination of superficial signals in cerebral blood flow index (CBFi) measurements. However, a systematic comparison of these models and clear guidance on model selection are still lacking. This study compares three DCS analytical models: semi-infinite, two-layer, and three-layer, focusing on their fitting strategies, performance, and suitability for CBFi and relative CBFi (rCBFi) estimation. We simulated DCS data using a four-layer slab head model with the Monte Carlo eXtreme (MCX) toolkit. Multiple fitting strategies were evaluated: early time lag range (ETLR) fitting with fixed or variable beta for the semi-infinite model, and single-distance (SD) and multi-distance (MD) fitting for the two- and three-layer models. Model performance was assessed based on CBFi sensitivity, accuracy of CBFi and rCBFi recovery, resistance to signal contamination from scalp and skull, sensitivity to assumed parameter errors, and computational efficiency across source-detector separations of 20 to 35 mm. Optimal fitting methods include ETLR with fixed beta for the semi-infinite model, SD with fixed beta for the two-layer model, and MD for the three-layer model. The multi-layer models achieved higher CBFi sensitivity (up to 100%) compared to 36.8% for the semi-infinite model. The two-layer model offered the best balance of accuracy and robustness, while the three-layer model enabled simultaneous recovery of CBFi, scalp BFi, and rCBFi. The semi-infinite model was the most computationally efficient, requiring only 0.38 seconds for 500 samples, supporting its use in real-time monitoring. This work offers a practical and systematic evaluation of DCS analytical models and provides guidance for selecting the most appropriate model based on application needs.

One of the fundamental challenges for non-Cartesian MRI is the need of designing time-optimal and hardware-compatible gradient waveforms for the provided $k$-space trajectory. Currently dominant methods either work only for certain trajectories or require significant computation time. In this paper, we aim to develop a fast general method that is able to generate time-optimal gradient waveforms for arbitrary non-Cartesian trajectories satisfying both slew rate and gradient constraints. In the proposed method, the gradient waveform is projected into a space defined by the gradients along the spatial directions, termed as $g$-space. In the constructed $g$-space, the problem of finding the next gradient vector given the current gradient vector under desired slew rate limit and with desired direction is simplified to finding the intersection between a line and a circle. To handle trajectories with increasing curvature, a Forward and Backward Sweep (FBS) strategy is introduced, which ensures the existence of the solution to the above mentioned geometry problem for arbitrary trajectories. Furthermore, trajectory reparameterization is proposed to ensure trajectory fidelity. We compare the proposed method with the previous optimal-control method in simulations and validate its feasibility for real MR acquisitions in phantom and human knee for a wide range of non-Cartesian trajectories. The proposed method enables accurate and fast gradient waveform design, achieving significant reduction in computation time and slew rate overshoot compared to the previous method. The source code will be publicly accessible upon publication of this study.

Purpose: This study explores the feasibility of dose-escalated proton beam therapy (dPBT) for Locally Advanced Pancreatic Cancer (LAPC) patients by modeling common patient scenarios using current clinically-adopted practices. Methods: Five patient datasets were used as simulation phantoms, each with six tumour sizes, to systematically simulate treatment scenarios typical in LAPC patients. Using the Raystation treatment planning system, robustly-optimised dPBT and stereotactic ablative radiotherapy (SABR) treatment plans were created with a 5 mm margin allowing for intra- and inter-fraction anatomical changes. following clinically-adopted protocols. Safe dose-escalation feasibility is assessed with dose metrics, tumour control (TCP) and normal tissue complication probabilities (NTCP) for average and worst-case intra-fraction motion scenarios. Significance testing was performed using a paired student's t-test. Results: Dose-escalation feasibility is largely dependent on tumour size and proximity to critical structures. Minimal therapeutic benefit was observed for patients with greater than 4.5 cm tumours, however for tumours less than or equal to 4.5 cm dPBT TCPs of 45-90% compared to SABR TCPs of 10-40% (p<0.05). The worst-case scenario dPBT TCP was comparable to SABR. Hypofractioned dPBT further improved this result to greater than 90% (p<0.05) for tumours less than or equal to 4.5 cm. Conclusion: Safe dPBT is feasible for patients with targets up to the median size and see a significant therapeutic benefit compared to the current standard of care in SABR. A patient-specific approach should be taken based on tumour size and surrounding anatomy.

Conventional methods for analysing cartilage microstructure under mechanical loading are largely destructive. In this work, we evaluate the efficacy of using depth-resolved polarisation sensitive optical coherence tomography (PS-OCT) to study the cartilage morphological response to compression. We show that depth-resolved PS-OCT reveals the microstructure of cartilage under load, and it can do so non-destructively, opening significant possibilities for enhanced clinical assessment of cartilage health by detecting deviance from normal load-bearing behaviour.

Magnetic resonance imaging (MRI) is a crucial medical imaging modality. However, long acquisition times remain a significant challenge, leading to increased costs, and reduced patient comfort. Recent studies have shown the potential of using deep learning models that incorporate information from prior subject-specific MRI scans to improve reconstruction quality of present scans. Integrating this prior information requires registration of the previous scan to the current image reconstruction, which can be time-consuming. We propose a novel deep-learning-based MRI reconstruction framework which consists of an initial reconstruction network, a deep registration model, and a transformer-based enhancement network. We validated our method on a longitudinal dataset of T1-weighted MRI scans with 2,808 images from 18 subjects at four acceleration factors (R5, R10, R15, R20). Quantitative metrics confirmed our approach's superiority over existing methods (p < 0.05, Wilcoxon signed-rank test). Furthermore, we analyzed the impact of our MRI reconstruction method on the downstream task of brain segmentation and observed improved accuracy and volumetric agreement with reference segmentations. Our approach also achieved a substantial reduction in total reconstruction time compared to methods that use traditional registration algorithms, making it more suitable for real-time clinical applications. The code associated with this work is publicly available at https://github.com/amirshamaei/longitudinal-mri-deep-recon.

Self-powered intracardiac implant devices show great promise for future clinical applications due to their extended operational lifespan and the potential to reduce the need for high-risk repeat surgeries. This study investigates the feasibility of harvesting energy from cardiac motion through in vivo testing of intracardiac devices. Comprehensive three-dimensional translational and rotational cardiac motions are captured in a porcine model using a miniaturized 9-degree-of-freedom motion sensor implanted at six strategic epicardial sites. Kinematic criteria are developed to evaluate the energy harvesting potential of each implant site based on the available kinetic energy, acceleration, and jerk factors. The recorded heart motion signals are analyzed and applied to a conceptual energy harvester proposed to identify the optimal implant site. The results reveal that the left ventricular apex emerges as a preferable site for energy harvesting, particularly at moderate heart rates. These findings offer valuable insights into optimizing self-powered intracardiac implants, reducing dependency on battery replacements, and enhancing long-term patient safety.

Magnetic resonance imaging (MRI) scanners have advanced significantly, with a growing use of highfield 3 T systems. This evolution gives rise to safety concerns for healthcare personnel working in proximity to MRI equipment. While manufacturers provide theoretical Gauss line projections, these are typically derived under ideal open-environment conditions and may not reflect real-world installations. For this reason, identical MRI models can produce markedly different fringe field distributions depending on shielding and room configurations. The present study proposes an experimental methodology for the mapping of the fringe magnetic field in the vicinity of three 3 T MRI scanners. Field measurements were interpolated to generate threedimensional magnetic field maps. A comparative analysis was conducted, which revealed notable differences among the scanners. These differences serve to highlight the influence of site-specific factors on magnetic field propagation.

This study presents a practical and dose-efficient strategy for resolution enhancement in planar radiography, based on mechanically supersampled acquisition with high-Z photon-counting detectors (PCDs). Unlike prior event-based or cluster methods, our approach operates in true photon-counting mode and supports clinical flux rates. Using detector trajectories spanning multiple pixels and image registration-based shift estimation, we achieve sub-pixel sampling without requiring mechanical precision, while also compensating for motion and geometric instabilities. An iterative reconstruction framework based on Maximum Likelihood Expectation Maximization (MLEM) with a distance-driven ray model further enhances resolution and noise robustness. Long-range supersampling additionally mitigates pixel defects and spectral inhomogeneities inherent to high-Z detectors. Phantom studies demonstrate substantial resolution improvement and image uniformity. In comparison with a clinical mammography system, the method reveals sharper detail and more homogeneous contrast at comparable or reduced dose. The resolution gain also reduces the need for geometric magnification, enabling smaller and more cost-effective PCDs. These results establish mechanically supersampled radiography as a clinically viable approach for micron-scale imaging, with strong potential for digital mammography and other high-resolution applications and with scan times compatible with clinical workflow.

Purpose: Low-field MRI systems operate at single MHz-range frequencies, where signal losses are primarily dominated by thermal noise from the radio-frequency (RF) receive coils. Achieving operation close to this limit is essential for maximizing imaging performance and signal-to-noise ratio (SNR). However, electromagnetic interference (EMI) from cabling, electronics, and patient loading often degrades system performance. Our goal is to develop and validate a practical protocol that guides users in identifying and suppressing electromagnetic noise in low-field MRI systems, enabling operation near the thermal noise limit. Methods: We present a systematic, stepwise methodology that includes diagnostic measurements, hardware isolation strategies, and good practices for cabling and shielding. Each step is validated with corresponding noise measurements under increasingly complex system configurations, both unloaded and with a human subject present. Results: Noise levels were monitored through the incremental assembly of a low-field MRI system, revealing key sources of EMI and quantifying their impact. Final configurations achieved noise within 1.5x the theoretical thermal bound with a subject in the scanner. Image reconstructions illustrate the direct relationship between system noise and image quality. Conclusion: The proposed protocol enables low-field MRI systems to operate close to fundamental noise limits in realistic conditions. The framework also provides actionable guidance for the integration of additional system components, such as gradient drivers and automatic tuning networks, without compromising SNR.

This paper presents an improved technique for solving the inverse problem in magnetic induction tomography (MIT) by considering skin and proximity effects in coils. MIT is a non-contact, noninvasive, and low-cost imaging modality for obtaining the distribution of conductivity inside an object. Reconstruction of low conductivity distribution by MIT requires more accurate techniques since measured signals are inherently weak and the reconstruction problem is highly nonlinear and ill-posed. Previous MIT inverse problem studies have ignored skin and proximity effects inside coils in the forward method. In this article, the improved technique incorporates these effects in the forward method. Furthermore, it employs the regularized Gauss-Newton algorithm to reconstruct the conductivity distribution. The regularization parameter is obtained by an adaptive method using the two input parameters: a coefficient and an initial conductivity distribution. The new Jacobian matrix is computed based on a standard technique. To compare the early and improved forward methods in possible medical and industrial applications with low conductivity regions, a 2D 8-coil MIT system is modeled, and image reconstruction is performed for synthetic phantoms. Results show that it is crucial to use the improved forward method for the reconstruction of the absolute conductivity values.

Assessment of muscle coordination during cycling may provide insight into motor control strategies and movement efficiency. This study evaluated muscle synergies and coactivation patterns as indicators of neuromuscular coordination in lower-limb across three power levels of cycling. Twenty recreational cyclists performed a graded cycling test on a stationary bicycle ergometer. Electromyography was recorded bilaterally from seven lower-limb muscles and muscle synergies were extracted using non-negative matrix factorization. The Coactivation Index (CI), Synergy Index (SI), and Synergy Coordination Index (SCI) were calculated to assess muscle coordination patterns. Four muscle synergies were identified consistently across power levels, with changes in synergy composition and activation timing correlated with increased muscular demands. As power level increased, the CI showed reduced muscle coactivation at the knee and greater muscle coactivation at the ankle. The SI revealed a greater contribution of the synergy weights of the extensor muscles than those of the flexor muscles at the knee. In contrast, the relative EMG contribution of hip extensor and flexor muscles remained consistent with increasing power levels. The SCI increased significantly with increasing power level, suggesting a reduction in the size of the synergy space and improved neuromuscular coordination. These findings provide insight into how the central nervous system modulates its response to increasing mechanical demands. Combining synergy and coactivation indices offers a promising approach to assess motor control, inform rehabilitation, and optimize performance in cycling tasks.

Serial Magnetic Resonance Imaging (MRI) exams are often performed in clinical practice, offering shared anatomical and motion information across imaging sessions. However, existing reconstruction methods process each session independently without leveraging this valuable longitudinal information. In this work, we propose a novel concept of longitudinal dynamic MRI, which incorporates patient-specific prior images to exploit temporal correlations across sessions. This framework enables progressive acceleration of data acquisition and reduction of scan time as more imaging sessions become available. The concept is demonstrated using the 4D Golden-angle RAdial Sparse Parallel (GRASP) MRI, a state-of-the-art dynamic imaging technique. Longitudinal reconstruction is performed by concatenating multi-session time-resolved 4D GRASP datasets into an extended dynamic series, followed by a low-rank subspace-based reconstruction algorithm. A series of experiments were conducted to evaluate the feasibility and performance of the proposed method. Results show that longitudinal 4D GRASP reconstruction consistently outperforms standard single-session reconstruction in image quality, while preserving inter-session variations. The approach demonstrated robustness to changes in anatomy, imaging intervals, and body contour, highlighting its potential for improving imaging efficiency and consistency in longitudinal MRI applications. More generally, this work suggests a new context-aware imaging paradigm in which the more we see a patient, the faster we can image.

Purpose: Accurate tumor segmentation is vital for adaptive radiation therapy (ART) but remains time-consuming and user-dependent. Segment Anything Model 2 (SAM2) shows promise for prompt-based segmentation but struggles with tumor accuracy. We propose prior knowledge-based augmentation strategies to enhance SAM2 for ART. Methods: Two strategies were introduced to improve SAM2: (1) using prior MR images and annotations as contextual inputs, and (2) improving prompt robustness via random bounding box expansion and mask erosion/dilation. The resulting model, SAM2-Aug, was fine-tuned and tested on the One-Seq-Liver dataset (115 MRIs from 31 liver cancer patients), and evaluated without retraining on Mix-Seq-Abdomen (88 MRIs, 28 patients) and Mix-Seq-Brain (86 MRIs, 37 patients). Results: SAM2-Aug outperformed convolutional, transformer-based, and prompt-driven models across all datasets, achieving Dice scores of 0.86(liver), 0.89(abdomen), and 0.90(brain). It demonstrated strong generalization across tumor types and imaging sequences, with improved performance in boundary-sensitive metrics. Conclusions: Incorporating prior images and enhancing prompt diversity significantly boosts segmentation accuracy and generalizability. SAM2-Aug offers a robust, efficient solution for tumor segmentation in ART. Code and models will be released at https://github.com/apple1986/SAM2-Aug.

The purpose of this research is to estimate sensitivity maps when imaging X-nuclei that may not have a significant presence throughout the field of view. We propose to estimate the coil's sensitivities by solving a least-squares problem where each row corresponds to an individual estimate of the sensitivity for a given voxel. Multiple estimates come from the multiple bins of the spectrum with spectroscopy, multiple times with dynamic imaging, or multiple frequencies when utilizing spectral excitation. The method presented in this manuscript, called the L2 optimal method, is compared to the commonly used RefPeak method which uses the spectral bin with the highest energy to estimate the sensitivity maps. The L2 optimal method yields more accurate sensitivity maps when imaging a numerical phantom and is shown to yield a higher signal-to-noise ratio when imaging the brain, pancreas, and heart with hyperpolarized pyruvate as the contrast agent with hyperpolarized MRI. The L2 optimal method is able to better estimate the sensitivity by extracting more information from the measurements.

This report details the successful construction of an ultrasound imaging platform and the design and fabrication of a novel ultrasound endoscope probe. The projects primary objective was to establish a functional system for acquiring and processing ultrasound signals, specifically targeting minimally invasive endoscopic applications. The ultrasound imaging platform was primarily designed and developed based on Texas Instruments (TI) Evaluation Modules (EVMs). It enables the transmission of 32-channel high-voltage signals and the reception of echo signals, with on-chip signal amplification and acquisition capabilities. Furthermore, the platform integrates a complete Time Gain Control (TGC) imaging path and a ContinuousWave Doppler (CWD) path. In conjunction with host computer software, it supports imaging with linear array, convex array, and phased array probes. Concurrently, a 64-element, 5MHz center frequency, phased array linear ultrasound endoscopic probe was designed, aiming for miniaturization and optimal imaging performance. The fabrication and assembly of its matching layer, backing layer, 2-2 piezoelectric composite material, and electrodes were completed.