Recurrent inference machines as inverse problem solvers for MR relaxometry

In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T₁ and T₂ mapping. The RIM is a neural network framework that learns an iterative inference process based on the signal model, similar to conventional statistical methods for quantitative MRI (QMRI), such as the Maximum Likelihood...

Atom-counting in High Resolution Electron Microscopy: TEM or STEM – That's the question

In this work, a recently developed quantitative approach based on the principles of detection theory is used in order to determine the possibilities and limitations of High Resolution Scanning Transmission Electron Microscopy (HR STEM) and HR TEM for atom-counting. So far, HR STEM has been shown to be an appropriate imaging mode to count the...

Detecting and locating light atoms from high-resolution STEM images: The quest for a single optimal design

In the present paper, the optimal detector design is investigated for both detecting and locating light atoms from high resolution scanning transmission electron microscopy (HR STEM) images. The principles of detection theory are used to quantify the probability of error for the detection of light atoms from HR STEM images. To determine the...

Multi-voxel algorithm for quantitative bi-exponential MRI T₁ estimation

Quantification of the spin-lattice relaxation time, T₁, of tissues is important for characterization of tissues in clinical magnetic resonance imaging (MRI). In T₁ mapping, T₁ values are estimated from a set of T₁-weighted MRI images. Due to the limited spatial resolution of the T₁...

Optimal experimental design for the detection of light atoms from high-resolution scanning transmission electron microscopy images

We report an innovative method to explore the optimal experimental settings to detect light atoms from scanning transmission electron microscopy (STEM) images. Since light elements play a key role in many technologically important materials, such as lithium-battery devices or hydrogen storage applications, much effort has been made to optimize...

Likelihood-Based Hypothesis Tests for Brain Activation Detection From MRI Data Disturbed by Colored Noise: A Simulation Study

Functional magnetic resonance imaging (fMRI) data that are corrupted by temporally colored noise are generally preprocessed (i.e., prewhitened or precolored) prior to functional activation detection. In this paper, we propose likelihood-based hypothesis tests that account for colored noise directly within the framework of functional activation...

Automatic estimation of the noise variance from the histogram of a magnetic resonance image

Robust estimation of the noise variance from background MR data

In the literature, many methods are available for estimation of the variance of the noise in magnetic resonance (MR) images. A commonly used method, based on the maximum of the background mode of the histogram, is revisited and a new, robust, and easy to use method is presented based on maximum likelihood (ML) estimation. Both methods are...

Generalized likelihood ratio tests for complex fMRI data: A Simulation study

Generalized likelihood ratio tests for complex fMRI data

Functional magnetic resonance imaging (fMRI) intends to detect significant neural activity by means of statistical data processing. Commonly used statistical tests include the Student-t test, analysis of variance, and the generalized linear model test. A key assumption underlying these methods is that the data are Gaussian distributed. Moreover,...