631 research outputs found
Dependence of nuclear spin singlet lifetimes on RF spin-locking power
We measure the lifetime of long-lived nuclear spin singlet states as a
function of the strength of the RF spin-locking field and present a simple
theoretical model that agrees well with our measurements, including the
low-RF-power regime. We also measure the lifetime of a long-lived coherence
between singlet and triplet states that does not require a spin-locking field
for preservation. Our results indicate that for many molecules, singlet states
can be created using weak RF spin-locking fields: more than two orders of
magnitude lower RF power than in previous studies. Our findings suggest that in
many biomolecules, singlets and related states with enhanced lifetimes might be
achievable in vivo with safe levels of RF power
Uncertainty Estimation using the Local Lipschitz for Deep Learning Image Reconstruction Models
The use of supervised deep neural network approaches has been investigated to
solve inverse problems in all domains, especially radiology where imaging
technologies are at the heart of diagnostics. However, in deployment, these
models are exposed to input distributions that are widely shifted from training
data, due in part to data biases or drifts. It becomes crucial to know whether
a given input lies outside the training data distribution before relying on the
reconstruction for diagnosis. The goal of this work is three-fold: (i)
demonstrate use of the local Lipshitz value as an uncertainty estimation
threshold for determining suitable performance, (ii) provide method for
identifying out-of-distribution (OOD) images where the model may not have
generalized, and (iii) use the local Lipschitz values to guide proper data
augmentation through identifying false positives and decrease epistemic
uncertainty. We provide results for both MRI reconstruction and CT sparse view
to full view reconstruction using AUTOMAP and UNET architectures due to it
being pertinent in the medical domain that reconstructed images remain
diagnostically accurate
SE-Sync: a certifiably correct algorithm for synchronization over the special Euclidean group
Many important geometric estimation problems naturally take the form of synchronization over the special Euclidean group: estimate the values of a set of unknown group elements (Formula presented.) given noisy measurements of a subset of their pairwise relative transforms (Formula presented.). Examples of this class include the foundational problems of pose-graph simultaneous localization and mapping (SLAM) (in robotics), camera motion estimation (in computer vision), and sensor network localization (in distributed sensing), among others. This inference problem is typically formulated as a non-convex maximum-likelihood estimation that is computationally hard to solve in general. Nevertheless, in this paper we present an algorithm that is able to efficiently recover certifiably globally optimal solutions of the special Euclidean synchronization problem in a non-adversarial noise regime. The crux of our approach is the development of a semidefinite relaxation of the maximum-likelihood estimation (MLE) whose minimizer provides an exact maximum-likelihood estimate so long as the magnitude of the noise corrupting the available measurements falls below a certain critical threshold; furthermore, whenever exactness obtains, it is possible to verify this fact a posteriori, thereby certifying the optimality of the recovered estimate. We develop a specialized optimization scheme for solving large-scale instances of this semidefinite relaxation by exploiting its low-rank, geometric, and graph-theoretic structure to reduce it to an equivalent optimization problem defined on a low-dimensional Riemannian manifold, and then design a Riemannian truncated-Newton trust-region method to solve this reduction efficiently. Finally, we combine this fast optimization approach with a simple rounding procedure to produce our algorithm, SE-Sync. Experimental evaluation on a variety of simulated and real-world pose-graph SLAM datasets shows that SE-Sync is capable of recovering certifiably globally optimal solutions when the available measurements are corrupted by noise up to an order of magnitude greater than that typically encountered in robotics and computer vision applications, and does so significantly faster than the Gauss–Newton-based approach that forms the basis of current state-of-the-art techniques
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture
Low-field (LF) MRI scanners have the power to revolutionize medical imaging
by providing a portable and cheaper alternative to high-field MRI scanners.
However, such scanners are usually significantly noisier and lower quality than
their high-field counterparts. The aim of this paper is to improve the SNR and
overall image quality of low-field MRI scans to improve diagnostic capability.
To address this issue, we propose a Nested U-Net neural network architecture
super-resolution algorithm that outperforms previously suggested deep learning
methods with an average PSNR of 78.83 and SSIM of 0.9551. We tested our network
on artificial noisy downsampled synthetic data from a major T1 weighted MRI
image dataset called the T1-mix dataset. One board-certified radiologist scored
25 images on the Likert scale (1-5) assessing overall image quality, anatomical
structure, and diagnostic confidence across our architecture and other
published works (SR DenseNet, Generator Block, SRCNN, etc.). We also introduce
a new type of loss function called natural log mean squared error (NLMSE). In
conclusion, we present a more accurate deep learning method for single image
super-resolution applied to synthetic low-field MRI via a Nested U-Net
architecture
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