Hypothalamic atrophy in primary lateral sclerosis, assessed by convolutional neural network-based automatic segmentation.

Primary lateral sclerosis (PLS) is a motor neuron disease (MND) which mainly affects upper motor neurons. Within the MND spectrum, PLS is much more slowly progressive than amyotrophic laterals sclerosis (ALS). `Classical` ALS is characterized by catabolism and abnormal energy metabolism preceding onset of motor symptoms, and previous studies indicated that the disease progression of ALS involves hypothalamic atrophy.

AI-assisted automatic MRI-based tongue volume evaluation in motor neuron disease (MND).

Purpose: Motor neuron disease (MND) causes damage to the upper and lower motor neurons including the motor cranial nerves, the latter resulting in bulbar involvement with atrophy of the tongue muscle. To measure tongue atrophy, an operator independent automatic segmentation of the tongue is crucial. The aim of this study was to apply convolutional neural network (CNN) to MRI data in order to determine the volume of the tongue.

Multi-network approach for image segmentation in non-contrast enhanced cardiac 3D MRI of arrhythmic patients.

Left atrial appendage (LAA) is the source of thrombi formation in more than 90% of strokes in patients with nonvalvular atrial fibrillation. Catheter-based LAA occlusion is being increasingly applied as a treatment strategy to prevent stroke. Anatomical complexity of LAA makes percutaneous occlusion commonly performed under transesophageal echocardiography (TEE) and X-ray (XR) guidance especially challenging.

Impact of cardiac and respiratory motion on the 3D accuracy of image-guided interventions on monoplane systems.

Purpose: Image-guided intervention (IGI) systems have the potential to increase the efficiency in interventional cardiology but face limitations from motion. Even though motion compensation approaches have been proposed, the resulting accuracy has rarely been quantified using in vivo data. The purpose of this study is to investigate the potential benefit of motion-compensation in IGS systems.

Cardiac magnetic resonance imaging for preprocedural planning of percutaneous left atrial appendage closure.

Introduction: Percutaneous closure of the left atrial appendage (LAA) facilitates stroke prevention in patients with atrial fibrillation. Optimal device selection and positioning are often challenging due to highly variable LAA shape and dimension and thus require accurate assessment of the respective anatomy. Transesophageal echocardiography (TEE) and x-ray fluoroscopy (XR) represent the gold standard imaging techniques.

MRI-based training model for left atrial appendage closure.

Purpose: Percutaneous closure of the left atrial appendage (LAA) reduces the risk of embolic stroke in patients with atrial fibrillation. Thereby, the optimal transseptal puncture (TSP) site differs due to the highly variable anatomical shape of the LAA, which is rarely considered in existing training models. Based on non-contrast-enhanced magnetic resonance imaging (MRI) volumes, we propose a training model for LAA closure with interchangeable and patient-specific LAA enabling LAA-specific identification of the TSP site best suited.

Computed tomography angiography/magnetic resonance imaging-based preprocedural planning and guidance in the interventional treatment of structural heart disease.

Preprocedural planning and periprocedural guidance based on image fusion are widely established techniques supporting the interventional treatment of structural heart disease. However, these two techniques are typically used independently. Previous works have already demonstrated the benefits of integrating planning details into image fusion but are limited to a few applications and the availability of the proprietary tools used.

3D localization from 2D X-ray projection.

Purpose: Most cardiology procedures are guided using X-ray (XR) fluoroscopy. However, the projective nature of the XR fluoroscopy does not allow for true depth perception as required for safe and efficient intervention guidance in structural heart diseases. For improving guidance, different methods have been proposed often being radiation-intensive, time-consuming, or expensive.

Deep learning-based framework for motion-compensated image fusion in catheterization procedures.

Objective: Augmenting X-ray (XR) fluoroscopy with 3D anatomic overlays is an essential technique to improve the guidance of the catheterization procedures. Unfortunately, cardiac and respiratory motion compromises the augmented fluoroscopy. Motion compensation methods can be applied to update the overlay of a static model with regard to respiratory and cardiac motion.

Body fat compartment determination by encoder-decoder convolutional neural network: application to amyotrophic lateral sclerosis.

The objective of this study was to automate the discrimination and quantification of human abdominal body fat compartments into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) from T1-weighted MRI using encoder-decoder convolutional neural networks (CNN) and to apply the algorithm to a diseased patient sample, i.e., patients with amyotrophic lateral sclerosis (ALS).

Cryo-balloon catheter localization in X-Ray fluoroscopy using U-net.

Purpose: Automatic identification of interventional devices in X-ray (XR) fluoroscopy offers the potential of improved navigation during transcatheter endovascular procedures. This paper presents a prototype implementation of fully automatic 3D reconstruction of a cryo-balloon catheter during pulmonary vein isolation (PVI) procedures by deep learning approaches.

Methods: We employ convolutional neural networks (CNN) to automatically identify the cryo-balloon XR marker and catheter shaft in 2D fluoroscopy during PVI.

3D-XGuide: open-source X-ray navigation guidance system.

Purpose: With the growing availability and variety of imaging modalities, new methods of intraoperative support have become available for all kinds of interventions. The basic principles of image fusion and image guidance have been widely adopted and are commercialized through a number of platforms. Although multimodal systems have been found to be useful for guiding interventional procedures, they all have their limitations.

Quantitative F MRI of perfluoro-15-crown-5-ether using uniformity correction of the spin excitation and signal reception.

Objectives: A common limitation of all H contrast agents is that they only allow indirect visualization through modification of the intrinsic properties of the tissue, making quantification of this effect challenging. F compounds, on the contrary, are measured directly, without any background signal. There is a linear relationship between the amount of F spins and the intensity of the signal.

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation.

The fusion of 3D anatomical models derived from high-fidelity pre-interventional computed tomography angiography (CTA), and x-ray (XR) fluoroscopy to facilitate anatomical guidance is of huge interest for complex cardiac interventions like TAVI procedures with cerebral protection. Co-registration of CTA and XR has been introduced either based on additional intraoperative non-/contrast-enhanced cone-beam computed tomography (CBCT) or two separate aortograms. With the related increase of radiation exposure and/or contrast agent (CA) dose, a potential additional risk for the patient is introduced.

The CAM cancer xenograft as a model for initial evaluation of MR labelled compounds.

Non-invasive assessment of the biodistribution is of great importance during the development of new pharmaceutical compounds. In this contribution, the applicability of in ovo MRI for monitoring the biodistribution of MR contrast agent-labelled compounds was investigated in mamaria carcinomas xentotransplanted on the chorioallantoic membrane (CAM) exemplarily for Gd-DOTA and cHSA-PEO (2000)16-Gd after systemic injection of the compounds into a chorioallantoic capillary vein. MRI was performed directly prior and 30 min, 3 h, 5 h, 20 h, and 40 h after injection of the compound.

Cargo-influences on the biodistribution of hollow mesoporous silica nanoparticles as studied by quantitative F-magnetic resonance imaging.

Hypothesis: Biodistribution is a key issue when it comes to medical applications of nanomaterials. Hollow mesoporous silica nanoparticles (HMSNs) loaded with fluorine compounds can be applied as positive magnetic resonance imaging (MRI) contrast agents (CAs). These CAs exhibit an unusual biodistribution which is influenced by the cargo and which could be linked to their serum protein adsorption behaviour.

Diffusion tensor magnetic resonance imaging of the brain in APP transgenic mice: a cohort study.

Introduction: Fast in-vivo high resolution diffusion tensor imaging (DTI) of the mouse brain has recently been shown to enable cohort studies by the combination of appropriate pulse sequences and cryogenically cooled resonators (CCR). The objective of this study was to apply this DTI approach at the group level to β-amyloid precursor protein (APP) transgenic mice.

Methods: Twelve mice (5 wild type, 7 APP transgenic tg2576) underwent DTI examination at 156(2) × 250 µm(3) spatial resolution with a CCR at ultrahigh field (11.

Fast diffusion tensor magnetic resonance imaging of the mouse brain at ultrahigh-field: aiming at cohort studies.

Introduction: In-vivo high resolution diffusion tensor imaging (DTI) of the mouse brain is often limited by the low signal to noise ratio (SNR) resulting from the required small voxel sizes. Recently, cryogenically cooled resonators (CCR) have demonstrated significant increase of the effective SNR. It is the objective of this study to enable fast DTI of the mouse brain.