ABSTRACT The influence of sex and age on cerebral perfusion is recognized, but the specific impacts on regional cerebral blood flow (CBF) and vascular risk remain to be fully characterized. In this study, 3D pseudo-continuous arterial spin labeling (pCASL) MRI was used to identify sex and age related CBF patterns, and a vascular risk score (VRS) was developed based on normative perfusion profiles. Perfusion data from 186 cognitively healthy participants (89 males, 97 females; aged 8 to 92 years), obtained from a publicly available dataset, were analyzed. An extension of the 3D Simple Linear Iterative Clustering (SLIC) supervoxel algorithm was applied to CBF maps to group neighboring voxels with similar intensities into anatomically meaningful regions. Regional CBF features were extracted and used to train a convolutional neural network (CNN) for sex classification and perfusion pattern analysis. Global, age related CBF changes were also assessed. Participant specific VRS was computed by comparing individual CBF profiles to age and sex specific normative data to quantify perfusion deficits. A 95 percent accuracy in sex classification was achieved using the proposed supervoxel based method, and distinct perfusion signatures were identified. Higher CBF was observed in females in medial Brod-mann areas 6 and 10, area V5, occipital polar cortex, and insular regions. A global decline in CBF with age was observed in both sexes. Individual perfusion deficits were quantified using VRS, providing a personalized biomarker for early hy-poperfusion. Sex and age specific CBF patterns were identified, and a personalized vascular risk biomarker was proposed, contributing to advancements in precision neurology. Keywords-- 3D pCASL MRI, CBF, age-and sex-specific perfusion patterns, vascular risk score, cognitively healthy 1. INTRODUCTION Arterial Spin Labeling (ASL) is a non-invasive Magnetic Resonance Imaging (MRI) technique designed to quantitatively assess cerebral blood flow (CBF) by magnetically labeling endogenous arterial blood water protons without the need for exogenous contrast agents or ionizing radiation [1]. The ASL technique involves three key steps: (i) magnetic labeling of arterial blood proximal to the imaging region, (ii) delivery of magnetically tagged blood to brain tissue altering the local MR signal, and (iii) acquisition of paired labeled and control images whose subtraction yields perfusion-weighted maps [1].

Accurate estimation of intravoxel incoherent motion (IVIM) parameters from diffusion-weighted MRI remains challenging due to the ill-posed nature of the inverse problem and high sensitivity to noise, particularly in the perfusion compartment. In this work, we propose a probabilistic deep learning framework based on Deep Ensembles (DE) of Mixture Density Networks (MDNs), enabling estimation of total predictive uncertainty and decomposition into aleatoric (AU) and epistemic (EU) components. The method was benchmarked against non probabilistic neural networks, a Bayesian fitting approach and a probabilistic network with single Gaussian parametrization. Supervised training was performed on synthetic data, and evaluation was conducted on both simulated and an in vivo dataset. The reliability of the quantified uncertainties was assessed using calibration curves, output distribution sharpness, and the Continuous Ranked Probability Score (CRPS). MDNs produced more calibrated and sharper predictive distributions for the diffusion coefficient D and fraction f parameters, although slight overconfidence was observed in pseudo-diffusion coefficient D*. The Robust Coefficient of Variation (RCV) indicated smoother in vivo estimates for D* with MDNs compared to Gaussian model. Despite the training data covering the expected physiological range, elevated EU in vivo suggests a mismatch with real acquisition conditions, highlighting the importance of incorporating EU, which was allowed by DE. Overall, we present a comprehensive framework for IVIM fitting with uncertainty quantification, which enables the identification and interpretation of unreliable estimates. The proposed approach can also be adopted for fitting other physical models through appropriate architectural and simulation adjustments.

Purpose: To develop biophysics-based method for estimating perfusion Q from arterial spin labeling (ASL) images using deep learning. Methods: A 3D U-Net (QTMnet) was trained to estimate perfusion from 4D tracer propagation images. The network was trained and tested on simulated 4D tracer concentration data based on artificial vasculature structure generated by constrained constructive optimization (CCO) method. The trained network was further tested in a synthetic brain ASL image based on vasculature network extracted from magnetic resonance (MR) angiography. The estimations from both trained network and a conventional kinetic model were compared in ASL images acquired from eight healthy volunteers. Results: QTMnet accurately reconstructed perfusion Q from concentration data. Relative error of the synthetic brain ASL image was 7.04% for perfusion Q, lower than the error using single-delay ASL model: 25.15% for Q, and multi-delay ASL model: 12.62% for perfusion Q. Conclusion: QTMnet provides accurate estimation on perfusion parameters and is a promising approach as a clinical ASL MRI image processing pipeline.

Compressed Sensing (CS) significantly speeds up Magnetic Resonance Image (MRI) processing and achieves accurate MRI reconstruction from under-sampled k-space data. According to the current research, there are still several problems with dynamic MRI k-space reconstruction based on CS. 1) There are differences between the Fourier domain and the Image domain, and the differences between MRI processing of different domains need to be considered. 2) As three-dimensional data, dynamic MRI has its spatial-temporal characteristics, which need to calculate the difference and consistency of surface textures while preserving structural integrity and uniqueness. 3) Dynamic MRI reconstruction is time-consuming and computationally resource-dependent. In this paper, we propose a novel robust low-rank dynamic MRI reconstruction optimization model via highly under-sampled and Discrete Fourier Transform (DFT) called the Robust Depth Linear Error Decomposition Model (RDLEDM). Our method mainly includes linear decomposition, double Total Variation (TV), and double Nuclear Norm (NN) regularizations. By adding linear image domain error analysis, the noise is reduced after under-sampled and DFT processing, and the anti-interference ability of the algorithm is enhanced. Double TV and NN regularizations can utilize both spatial-temporal characteristics and explore the complementary relationship between different dimensions in dynamic MRI sequences. In addition, Due to the non-smoothness and non-convexity of TV and NN terms, it is difficult to optimize the unified objective model. To address this issue, we utilize a fast algorithm by solving a primal-dual form of the original problem. Compared with five state-of-the-art methods, extensive experiments on dynamic MRI data demonstrate the superior performance of the proposed method in terms of both reconstruction accuracy and time complexity.

Text-to-image models (T2I) offer a new level of flexibility by allowing users to guide the creative process through natural language. However, personalizing these models to align with user-provided visual concepts remains a challenging problem. The task of T2I personalization poses multiple hard challenges, such as maintaining high visual fidelity while allowing creative control, combining multiple personalized concepts in a single image, and keeping a small model size. We present Perfusion, a T2I personalization method that addresses these challenges using dynamic rank-1 updates to the underlying T2I model. Perfusion avoids overfitting by introducing a new mechanism that "locks" new concepts' cross-attention Keys to their superordinate category. Additionally, we develop a gated rank-1 approach that enables us to control the influence of a learned concept during inference time and to combine multiple concepts. This allows runtime-efficient balancing of visual-fidelity and textual-alignment with a single 100KB trained model, which is five orders of magnitude smaller than the current state of the art. Moreover, it can span different operating points across the Pareto front without additional training. Finally, we show that Perfusion outperforms strong baselines in both qualitative and quantitative terms. Importantly, key-locking leads to novel results compared to traditional approaches, allowing to portray personalized object interactions in unprecedented ways, even in one-shot settings.

Boston-based Activ Surgical designed ActivSight to provide enhanced visualization and real-time on-demand surgical insights in the operating room. The easy-to-adapt model seamlessly attaches to laparoscopic and robotic systems. Activ Surgical received CE mark approval for ActivSight in December 2022. The FDA cleared the technology in April 2021. The company aims to transform the surgical experience using emerging technologies and data.

This paper presents an approach to assess the perfusion of visible human tissue from RGB video files. We propose metrics derived from remote photoplethysmography (rPPG) signals to detect whether a tissue is adequately supplied with blood. The perfusion analysis is done in three different scales, offering a flexible approach for different applications. We perform a plane-orthogonal-to-skin rPPG independently for locally defined regions of interest on each scale. From the extracted signals, we derive the signal-to-noise ratio, magnitude in the frequency domain, heart rate, perfusion index as well as correlation between specific rPPG signals in order to locally assess the perfusion of a specific region of human tissue. We show that locally resolved rPPG has a broad range of applications. As exemplary applications, we present results in intraoperative perfusion analysis and visualization during skin and organ transplantation as well as an application for liveliness assessment for the detection of presentation attacks to authentication systems.

Purpose: Arterial Spin Labeling (ASL) is a quantitative, non-invasive alternative to perfusion imaging with contrast agents. Fixing values of certain model parameters in traditional ASL, which actually vary from region to region, may introduce bias in perfusion estimates. Adopting Magnetic Resonance Fingerprinting (MRF) for ASL is an alternative where these parameters are estimated alongside perfusion, but multiparametric estimation can degrade precision. We aim to improve the sensitivity of ASL-MRF signals to underlying parameters to counter this problem, and provide precise estimates. We also propose a regression based estimation framework for MRF-ASL. Methods: To improve the sensitivity of MRF-ASL signals to underlying parameters, we optimize ASL labeling durations using the Cramer-Rao Lower Bound (CRLB). This paper also proposes a neural network regression based estimation framework trained using noisy synthetic signals generated from our ASL signal model. Results: We test our methods in silico and in vivo, and compare with multiple post labeling delay (multi-PLD) ASL and unoptimized MRF-ASL. We present comparisons of estimated maps for six parameters accounted for in our signal model. Conclusions: The scan design process facilitates precise estimates of multiple hemodynamic parameters and tissue properties from a single scan, in regions of gray and white matter, as well as regions with anomalous perfusion activity in the brain. The regression based estimation approach provides perfusion estimates rapidly, and bypasses problems with quantization error. Keywords: Arterial Spin Labeling, Magnetic Resonance Fingerprinting, Optimization, Cramer-Rao Bound, Scan Design, Regression, Neural Networks, Deep Learning, Precision, Estimation, Brain Hemodynamics.

Intravoxel incoherent motion (IVIM) imaging allows contrast-agent free in vivo perfusion quantification with magnetic resonance imaging (MRI). However, its use is limited by typically low accuracy due to low signal-to-noise ratio (SNR) at large gradient encoding magnitudes as well as dephasing artefacts caused by subject motion, which is particularly challenging in fetal MRI. To mitigate this problem, we propose an implicit IVIM signal acquisition model with which we learn full posterior distribution of perfusion parameters using artificial neural networks. This posterior then encapsulates the uncertainty of the inferred parameter estimates, which we validate herein via numerical experiments with rejection-based Bayesian sampling. Compared to state-of-the-art IVIM estimation method of segmented least-squares fitting, our proposed approach improves parameter estimation accuracy by 65% on synthetic anisotropic perfusion data. On paired rescans of in vivo fetal MRI, our method increases repeatability of parameter estimation in placenta by 46%.

The devastating neurodegenerative condition Alzheimer's disease is incurable, but with early detection, patients can seek treatments to slow the disease's progression, before some major symptoms appear. Now, by applying artificial intelligence algorithms to MRI brain scans, researchers have developed a way to automatically distinguish between patients with Alzheimer's and two early forms of dementia that can be precursors to the memory-robbing disease. The researchers, from the VU University Medical Center in Amsterdam, suggest the approach could eventually allow automated screening and assisted diagnosis of various forms of dementia, particularly in centers that lack experienced neuroradiologists. Additionally, the results, published online July 6 in the journal Radiology, show that the new system was able to classify the form of dementia that patients were suffering from, using previously unseen scans, with up to 90 percent accuracy. "The potential is the possibility of screening with these techniques so people at risk can be intercepted before the disease becomes apparent," said Alle Meije Wink, a senior investigator in the center's radiology and nuclear medicine department. "I think very few patients at the moment will trust an outcome predicted by a machine," Wink told Live Science.