Synthetic Arterial Spin Labeling MRI of the Kidneys for Evaluation of Data Processing Pipeline.

Accurate quantification of perfusion is crucial for diagnosis and monitoring of kidney function. Arterial spin labeling (ASL), a completely non-invasive magnetic resonance imaging technique, is a promising method for this application. However, differences in acquisition (e.

Fast CBCT Reconstruction using Convolutional Neural Networks for Arbitrary Robotic C-arm Orbits.

Cone-beam CT (CBCT) with non-circular acquisition orbits has the potential to improve image quality, increase the field-of view, and facilitate minimal interference within an interventional imaging setting. Because time is of the essence in interventional imaging scenarios, rapid reconstruction methods are advantageous. Model-Based Iterative Reconstruction (MBIR) techniques implicitly handle arbitrary geometries; however, the computational burden for these approaches is particularly high.

Development of an abdominal phantom for the validation of an oligometastatic disease diagnosis workflow.

Purpose: The liver is a common site for metastatic disease, which is a challenging and life-threatening condition with a grim prognosis and outcome. We propose a standardized workflow for the diagnosis of oligometastatic disease (OMD), as a gold standard workflow has not been established yet. The envisioned workflow comprises the acquisition of a multimodal image data set, novel image processing techniques, and cone beam computed tomography (CBCT)-guided biopsy for subsequent molecular subtyping.

Automated Screening for Abdominal Aortic Aneurysm in CT Scans under Clinical Conditions Using Deep Learning.

Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment.

An anthropomorphic pelvis phantom for MR-guided prostate interventions.

Purpose: To design and manufacture a pelvis phantom for magnetic resonance (MR)-guided prostate interventions, such as MRGB (MR-guided biopsy) or brachytherapy seed placement.

Methods: The phantom was designed to mimic the human pelvis incorporating bones, bladder, prostate with four lesions, urethra, arteries, veins, and six lymph nodes embedded in ballistic gelatin. A hollow rectum enables transrectal access to the prostate.

[MRI-guided minimally invasive treatment of prostate cancer].

Clinical/methodological Issue: Multiparametric magnetic resonance imaging (mpMRI) of the prostate plays a crucial role in the diagnosis and local staging of primary prostate cancer.

Standard Radiological Methods: Image-guided biopsy techniques such as MRI-ultrasound fusion not only allow guidance for targeted tissue sampling of index lesions for diagnostic confirmation, but also improve the detection of clinically significant prostate cancer.

Methodological Innovations: Minimally invasive, focal therapies of localized prostate cancer complement the treatment spectrum, especially for low- and intermediate-risk patients.

Generation of annotated multimodal ground truth datasets for abdominal medical image registration.

Purpose: Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets.

Convolutional Neural Network Ensemble Segmentation With Ratio-Based Sampling for the Arteries and Veins in Abdominal CT Scans.

Objective: Three-dimensional (3D) blood vessel structure information is important for diagnosis and treatment in various clinical scenarios. We present a fully automatic method for the extraction and differentiation of the arterial and venous vessel trees from abdominal contrast enhanced computed tomography (CE-CT) volumes using convolutional neural networks (CNNs).

Methods: We used a novel ratio-based sampling method to train 2D and 3D versions of the U-Net, the V-Net and the DeepVesselNet.

Synthesis of CT images from digital body phantoms using CycleGAN.

Purpose: The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size.

Methods: We evaluated eleven CycleGAN for the synthesis of computed tomography (CT) images based on XCAT body phantoms.