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Artificial intelligence in Nuclear Medicine Physics and Imaging.

Hellenic journal of nuclear medicine
No one can deny the significant impact of artificial intelligence (AI) on everyday life, especially in the health sector where it has emerged as a crucial and beneficial tool in Nuclear Medicine (NM) and molecular imaging. The objective of this revie...

Deep learning based unpaired image-to-image translation applications for medical physics: a systematic review.

Physics in medicine and biology
. There is a growing number of publications on the application of unpaired image-to-image (I2I) translation in medical imaging. However, a systematic review covering the current state of this topic for medical physicists is lacking. The aim of this a...

Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows.

Proceedings of the National Academy of Sciences of the United States of America
Quantifying the flow of cerebrospinal fluid (CSF) is crucial for understanding brain waste clearance and nutrient delivery, as well as edema in pathological conditions such as stroke. However, existing in vivo techniques are limited to sparse velocit...

Protein Design Using Physics Informed Neural Networks.

Biomolecules
The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been su...

Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity.

Neural networks : the official journal of the International Neural Network Society
The paper presents an efficient and robust data-driven deep learning (DL) computational framework developed for linear continuum elasticity problems. The methodology is based on the fundamentals of the Physics Informed Neural Networks (PINNs). For an...

Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies.

Current opinion in structural biology
Computational protein design facilitates the discovery of novel proteins with prescribed structure and functionality. Exciting designs were recently reported using novel data-driven methodologies that can be roughly divided into two categories: evolu...

Physics-informed neural networks for transcranial ultrasound wave propagation.

Ultrasonics
Transcranial ultrasound imaging has been playing an increasingly important role in the non-invasive treatment of brain disorders. However, the conventional mesh-based numerical wave solvers, which are an integral part of imaging algorithms, suffer fr...

Physics-Informed Deep-Learning For Elasticity: Forward, Inverse, and Mixed Problems.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Elastography is a medical imaging technique used to measure the elasticity of tissues by comparing ultrasound signals before and after a light compression. The lateral resolution of ultrasound is much inferior to the axial resolution. Current elastog...

Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks.

Communications biology
Tissue dynamics play critical roles in many physiological functions and provide important metrics for clinical diagnosis. Capturing real-time high-resolution 3D images of tissue dynamics, however, remains a challenge. This study presents a hybrid phy...

Machine learning based prediction of phase ordering dynamics.

Chaos (Woodbury, N.Y.)
Machine learning has proven exceptionally competent in numerous applications of studying dynamical systems. In this article, we demonstrate the effectiveness of reservoir computing, a famous machine learning architecture, in learning a high-dimension...