Deep learning-based whole-brain B -mapping at 7T.

Purpose: This study investigates the feasibility of using complex-valued neural networks (NNs) to estimate quantitative transmit magnetic RF field (B ) maps from multi-slice localizer scans with different slice orientations in the human head at 7T, aiming to accelerate subject-specific B -calibration using parallel transmission (pTx).

Methods: Datasets containing channel-wise B -maps and corresponding multi-slice localizers were acquired in axial, sagittal, and coronal orientation in 15 healthy subjects utilizing an eight-channel pTx transceiver head coil. Training included five-fold cross-validation for four network configurations: used transversal, sagittal, coronal data, and was trained on all slice orientations.

Background phase induced steady-state effects in velocity quantification using phase-contrast MRI.

Purpose: Flow quantification using phase-contrast (PC) MRI is based on steady-state gradient echo (GRE) sequences and is hampered by spatially varying background phase offsets. The purpose of this work was to investigate the effect of steady-state disruptions during PC-MRI GRE sequences on these background phases. Based on these findings, a specific sequence and timing is suggested, and caution is expressed when using typical correction algorithms.

Robust Myocardial Perfusion MRI Quantification with DeepFermi.

Stress perfusion cardiac magnetic resonance is an important technique for examining and assessing the blood supply of the myocardium. Currently, the majority of clinical perfusion scans are evaluated based on visual assessment by experienced clinicians. This makes the process subjective, and to this end, quantitative methods have been proposed to offer a more user-independent assessment of perfusion.

B1-MRF: Large dynamic range MRF-based absolute mapping in the human body at 7T.

Purpose: This study aims to map the transmit magnetic field ( ) in the human body at 7T using MR fingerprinting (MRF), with a focus on achieving high accuracy and precision across a large dynamic range, particularly at low flip angles (FAs).

Methods: A FLASH-based MRF sequence (B1-MRF) with high sensitivity was developed. Phantom and in vivo abdominal imaging were performed at 7T, and the results were compared with established reference methods, including a slow but precise preparation-based method (PEX), saturated TurboFLASH (satTFL), actual flip angle imaging (AFI) and Bloch-Siegert shift (BSS).

Respiratory motion-corrected T1 mapping of the abdomen.

Objective: The purpose of this study was to investigate an approach for motion-corrected T1 mapping of the abdomen that allows for free breathing data acquisition with 100% scan efficiency.

Materials And Methods: Data were acquired using a continuous golden radial trajectory and multiple inversion pulses. For the correction of respiratory motion, motion estimation based on a surrogate was performed from the same data used for T1 mapping.

The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review.

The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is especially important in medicine due to the large implications for patients' lives. While trustworthiness concerns various aspects including ethical, transparency and safety requirements, we focus on the importance of data quality (training/test) in DL.

Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks.

Purpose: To provide a simulation framework for routine neuroimaging test data, which allows for "stress testing" of deep segmentation networks against acquisition shifts that commonly occur in clinical practice for T2 weighted (T2w) fluid-attenuated inversion recovery magnetic resonance imaging protocols.

Approach: The approach simulates "acquisition shift derivatives" of MR images based on MR signal equations. Experiments comprise the validation of the simulated images by real MR scans and example stress tests on state-of-the-art multiple sclerosis lesion segmentation networks to explore a generic model function to describe the F1 score in dependence of the contrast-affecting sequence parameters echo time (TE) and inversion time (TI).

3D whole heart k-space-based super-resolution cardiac T1 mapping using rotated stacks.

. To provide three-dimensional (3D) whole-heart high-resolution isotropic cardiac T1 maps using a k-space-based through-plane super-resolution reconstruction (SRR) with rotated multi-slice stacks..

Toward accurate and fast velocity quantification with 3D ultrashort TE phase-contrast imaging.

Purpose: Traditional phase-contrast MRI is affected by displacement artifacts caused by non-synchronized spatial- and velocity-encoding time points. The resulting inaccurate velocity maps can affect the accuracy of derived hemodynamic parameters. This study proposes and characterizes a 3D radial phase-contrast UTE (PC-UTE) sequence to reduce displacement artifacts.

Data-efficient Bayesian learning for radial dynamic MR reconstruction.

Background: Cardiac MRI has become the gold-standard imaging technique for assessing cardiovascular morphology and function. In spite of this, its slow data acquisition process presents imaging challenges due to the motion from heartbeats, respiration, and blood flow. In recent studies, deep learning (DL) algorithms have shown promising results for the task of image reconstruction.

Motion-corrected model-based reconstruction for 2D myocardial T1 mapping.

Purpose: To allow for T1 mapping of the myocardium within 2.3 s for a 2D slice utilizing cardiac motion-corrected, model-based image reconstruction.

Methods: Golden radial data acquisition is continuously carried out for 2.

PTB-XL+, a comprehensive electrocardiographic feature dataset.

Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists' decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public.

Germany's journey toward 14 Tesla human magnetic resonance.

Multiple sites within Germany operate human MRI systems with magnetic fields either at 7 Tesla or 9.4 Tesla. In 2013, these sites formed a network to facilitate and harmonize the research being conducted at the different sites and make this technology available to a larger community of researchers and clinicians not only within Germany, but also worldwide.

Ultra-wide band radar for prospective respiratory motion correction in the liver.

T1 mapping of the liver is time consuming and can be challenging due to respiratory motion. Here we present a prospective slice tracking approach, which utilizes an external ultra-wide band radar signal and allows for efficient T1 mapping during free-breathing.The fast radar signal is calibrated to an MR-based motion signal to create a motion model.

Deep supervised dictionary learning by algorithm unrolling-Application to fast 2D dynamic MR image reconstruction.

Background: Unrolled neural networks (NNs) have been extensively applied to different image reconstruction problems across all imaging modalities. A key component of the latter is that they allow for physics-informed learning of the regularization method, which is parametrized by the NN. However, due to the lack of understanding of deep NNs from a theoretical point of view, unrolled NNs are still black-boxes when the regularizers are given by deep NNs, for example, U-Nets.

Rapid estimation of 2D relative -maps from localizers in the human heart at 7T using deep learning.

Purpose: Subject-tailored parallel transmission pulses for ultra-high fields body applications are typically calculated based on subject-specific -maps of all transmit channels, which require lengthy adjustment times. This study investigates the feasibility of using deep learning to estimate complex, channel-wise, relative 2D -maps from a single gradient echo localizer to overcome long calibration times.

Methods: 126 channel-wise, complex, relative 2D -maps of the human heart from 44 subjects were acquired at 7T using a Cartesian, cardiac gradient-echo sequence obtained under breath-hold to create a library for network training and cross-validation.

3D model-based super-resolution motion-corrected cardiac T1 mapping.

. To provide 3D high-resolution cardiac T1 maps using model-based super-resolution reconstruction (SRR)..

Active Tracking-based cardiac triggering for MR-thermometry during radiofrequency ablation therapy in the left ventricle.

Cardiac MR thermometry shows promise for real-time guidance of radiofrequency ablation of cardiac arrhythmias. This technique uses ECG triggering, which can be unreliable in this situation. A prospective cardiac triggering method was developed for MR thermometry using the active tracking (AT) signal measured from catheter microcoils.

Pilot tone-based prospective correction of respiratory motion for free-breathing myocardial T1 mapping.

Objective: To provide respiratory motion correction for free-breathing myocardial T1 mapping using a pilot tone (PT) and a continuous golden-angle radial acquisition.

Materials And Methods: During a 45 s prescan the PT is acquired together with a dynamic sagittal image covering multiple respiratory cycles. From these images, the respiratory heart motion in head-feet and anterior-posterior direction is estimated and two linear models are derived between the PT and heart motion.

Fully automated background phase correction using M-estimate SAmple consensus (MSAC)-Application to 2D and 4D flow.

Purpose: Flow quantification by phase-contrast MRI is hampered by spatially varying background phase offsets. Correction performance by polynomial regression on stationary tissue may be affected by outliers such as wrap-around or constant flow. Therefore, we propose an alternative, M-estimate SAmple Consensus (MSAC) to reject outliers, and improve and fully automate background phase correction.

Cardio-respiratory motion-corrected 3D cardiac water-fat MRI using model-based image reconstruction.

Purpose: Myocardial fat infiltrations are associated with a range of cardiomyopathies. The purpose of this study was to perform cardio-respiratory motion-correction for model-based water-fat separation to image fatty infiltrations of the heart in a free-breathing, non-cardiac-triggered high-resolution 3D MRI acquisition.

Methods: Data were acquired in nine patients using a free-breathing, non-cardiac-triggered high-resolution 3D Dixon gradient-echo sequence and radial phase encoding trajectory.

Motion-compensated fat-water imaging for 3D cardiac MRI at ultra-high fields.

Purpose: Respiratory motion-compensated (MC) 3D cardiac fat-water imaging at 7T.

Methods: Free-breathing bipolar 3D triple-echo gradient-recalled-echo (GRE) data with radial phase-encoding (RPE) trajectory were acquired in 11 healthy volunteers (7M\4F, 21-35 years, mean: 30 years) with a wide range of body mass index (BMI; 19.9-34.

Calibration-free pTx of the human heart at 7T via 3D universal pulses.

Purpose: MRI at ultra-high fields in the human body is highly challenging and requires lengthy calibration times to compensate for spatially heterogeneous profiles. This study investigates the feasibility of using pre-computed universal pulses for calibration-free homogeneous 3D flip angle distribution in the human heart at 7T.

Methods: Twenty-two channel-wise 3D data sets were acquired under free-breathing in 19 subjects to generate a library for an offline universal pulse (UP) design (group 1: 12 males [M] and 7 females [F], 21-66 years, 19.

Respiratory motion correction for enhanced quantification of hepatic lesions in simultaneous PET and DCE-MR imaging.

Simultaneous positron-emission tomography (PET)-magnetic resonance (MR) imaging is a hybrid technique in oncological hepatic imaging combining soft-tissue and functional contrast of dynamic contrast enhanced MR (DCE-MR) with metabolic information from PET. In this context, respiratory motion represents a major challenge by introducing blurring, artifacts and misregistration in the liver. In this work, we propose a free-breathing 3D non-rigid respiratory motion correction framework for simultaneously acquired DCE-MR and PET data, which makes use of higher spatial resolution MR data to derive motion information used directly during image reconstruction to minimize image blurring and motion artifacts.