arXiv subjects

Unay Dorken Gallastegi

Publications and source records attributed to Unay Dorken Gallastegi.

4 recordsLinked to original sources

arXiv2026

Vendor-Agnostic Joint Relaxometry and Myelin Water Fraction Mapping with B1 and Motion Correction

Obtaining consistent quantitative maps of myelin content and relaxation times across different sites and vendors is essential for advancing our understanding of brain development. Herein, we present a harmonized, vendor-agnostic magnetic resonance acquisition method designed for joint T1, T2, and myelin water fraction mapping, along with a method for rapid B1+ and B1- field estimation. We used our dictionary-based fitting and multi-compartment modeling for joint mapping of T1, T2 and myelin water fraction. Self-navigation-based retrospective motion correction was integrated with subspace reconstruction to track and correct rigid head motion during scanning, operating without the need for external hardware. Simulations, phantom and in vivo experiments confirmed the sensitivity and accuracy of the method, particularly for short T2 values corresponding to myelin, and demonstrated consistent performance across multiple scanner types. Coupled with the harmonized calibration scan, the proposed package offers a practical tool for multi-site, multi-vendor neuroimaging studies in both adult and pediatric populations.

physics.med-ph↗

arXiv2026

Ozone Cues Mitigate Reflected Downwelling Radiance in LWIR Absorption-Based Ranging

Passive long-wave infrared (LWIR) absorption-based ranging relies on atmospheric absorption to estimate distances to objects from their emitted thermal radiation. First demonstrated decades ago for objects much hotter than the air and recently extended to scenes with low temperature variations, this ranging has depended on reflected radiance being negligible. Downwelling radiance is especially problematic, sometimes causing large inaccuracies. In two new ranging methods, we use characteristic features from ozone absorption to estimate the contribution of reflected downwelling radiance. The quadspectral method gives a simple closed-form range estimate from four narrowband measurements, two at a water vapor absorption line and two at an ozone absorption line. The hyperspectral method uses a broader spectral range to improve accuracy while also providing estimates of temperature, emissivity profiles, and contributions of downwelling from a collection of zenith angles. Experimental results demonstrate improved ranging accuracy, in one case reducing error from over 100 m when reflected light is not modeled to 6.8 m with the quadspectral method and 1.2 m with the hyperspectral method.

cs.CV↗

arXiv2025

Omni-QALAS: Optimized Multiparametric Imaging for Simultaneous T1, T2 and Myelin Water Mapping

Purpose: To improve the accuracy of multiparametric estimation, including myelin water fraction (MWF) quantification, and reduce scan time in 3D-QALAS by optimizing sequence parameters, using a self-supervised multilayer perceptron network. Methods: We jointly optimize flip angles, T2 preparation durations, and sequence gaps for T1 recovery using a self-supervised MLP trained to minimize a Cramer-Rao bound-based loss function, with explicit constraints on total scan time. The optimization targets white matter, gray matter, and myelin water tissues, and its performance was validated through simulation, phantom, and in vivo experiments. Results: Building on our previously proposed MWF-QALAS method for simultaneous MWF, T1, and T2 mapping, the optimized sequence reduces the number of readouts from six to five and achieves a scan time nearly one minute shorter, while also yielding higher T1 and T2 accuracy and improved MWF maps. This sequence enables simultaneous multiparametric quantification, including MWF, at 1 mm isotropic resolution within 3 minutes and 30 seconds. Conclusion: This study demonstrated that optimizing sequence parameters using a self-supervised MLP network improved T1, T2 and MWF estimation accuracy, while reducing scan time.

q-bio.QM↗

arXiv2025

Absorption-Based, Passive Range Imaging from Hyperspectral Thermal Measurements

Passive hyperspectral longwave infrared measurements are remarkably informative about the surroundings. Remote object material and temperature determine the spectrum of thermal radiance, and range, air temperature, and gas concentrations determine how this spectrum is modified by propagation to the sensor. We introduce a passive range imaging method based on computationally separating these phenomena. Previous methods assume hot and highly emitting objects; ranging is more challenging when objects' temperatures do not deviate greatly from air temperature. Our method jointly estimates range and intrinsic object properties, with explicit consideration of air emission, though reflected light is assumed negligible. Inversion being underdetermined is mitigated by using a parametric model of atmospheric absorption and regularizing for smooth emissivity estimates. To assess where our estimate is likely accurate, we introduce a technique to detect which scene pixels are significantly influenced by reflected downwelling. Monte Carlo simulations demonstrate the importance of regularization, temperature differentials, and availability of many spectral bands. We apply our method to longwave infrared (8--13 $μ$m) hyperspectral image data acquired from natural scenes with no active illumination. Range features from 15m to 150m are recovered, with good qualitative match to lidar data for pixels classified as having negligible reflected downwelling.

cs.CV↗