Accuracy of Dose-Saving Artificial-Intelligence-Based 3D Angiography (3DA) for Grading of Intracranial Artery Stenoses: Preliminary Findings.

Background And Purpose: Based on artificial intelligence (AI), 3D angiography (3DA) is a novel postprocessing algorithm for "DSA-like" 3D imaging of cerebral vasculature. Because 3DA requires neither mask runs nor digital subtraction as the current standard 3D-DSA does, it has the potential to cut the patient dose by 50%. The object was to evaluate 3DA's diagnostic value for visualization of intracranial artery stenoses (IAS) compared to 3D-DSA.

Robust learning-based x-ray image denoising-potential pitfalls, their analysis and solutions.

Since guidance based on x-ray imaging is an integral part of interventional procedures, continuous efforts are taken towards reducing the exposure of patients and clinical staff to ionizing radiation. Even though a reduction in the x-ray dose may lower associated radiation risks, it is likely to impair the quality of the acquired images, potentially making it more difficult for physicians to carry out their procedures.We present a robust learning-based denoising strategy involving model-based simulations of low-dose x-ray images during the training phase.

Data-driven estimation of noise variance stabilization parameters for low-dose x-ray images.

Purpose: Denoising x-ray images corrupted by signal-dependent mixed noise is usually approached either by considering noise statistics directly or by using noise variance stabilization (NVS) techniques. An advantage of the latter is that the noise variance can be stabilized to a known constant throughout the image, facilitating the application of denoising algorithms designed for the removal of additive Gaussian noise. A well-performing NVS is the generalized Anscombe transform (GAT).

Evaluation of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Vasculature.

Purpose: The three-dimensional digital subtraction angiography (3D DSA) technique is the current standard and is based on both mask and fill runs to enable the subtraction technique. Artificial intelligence (AI)-based 3D angiography (3DA) was developed to reduce radiation dosage because only one contrast-enhanced run of the C‑arm system is required for reconstruction of DSA-like 3D volumes. The aim was the evaluation of this algorithm regarding its diagnostic information.

Magnetic particle imaging in vascular medicine.

Magnetic particle imaging (MPI) is a new medical imaging technique that enables three-dimensional real-time imaging of a magnetic tracer material. Although it is not yet in clinical use, it is highly promising, especially for vascular and interventional imaging. The advantages of MPI are that no ionizing radiation is necessary, its high sensitivity enables the detection of very small amounts of the tracer material, and its high temporal resolution enables real-time imaging, which makes MPI suitable as an interventional imaging technique.

Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system.

Purpose: 2D digital subtraction angiography (DSA) has become an important technique for interventional neuroradiology tasks, such as detection and subsequent treatment of aneurysms. In order to provide high-quality DSA images, usually undiluted contrast agent and a high X-ray dose are used. The iodinated contrast agent puts a burden on the patients' kidneys while the use of high-dose X-rays expose both patients and medical staff to a considerable amount of radiation.

An analytical approach for the simulation of realistic low-dose fluoroscopic images.

Purpose: The quality of X-ray images plays an important role in computer-assisted interventions. Although learning-based denoising techniques have been shown to be successful in improving the image quality, they often rely on pairs of associated low- and high-dose X-ray images that are usually not possible to acquire at different dose levels in a clinical scenario. Moreover, since data variation is an important requirement for learning-based methods, the use of phantom data alone may not be sufficient.

A photon recycling approach to the denoising of ultra-low dose X-ray sequences.

Purpose: Clinical procedures that make use of fluoroscopy may expose patients as well as the clinical staff (throughout their career) to non-negligible doses of radiation. The potential consequences of such exposures fall under two categories, namely stochastic (mostly cancer) and deterministic risks (skin injury). According to the "as low as reasonably achievable" principle, the radiation dose can be lowered only if the necessary image quality can be maintained.

Non-Equispaced System Matrix Acquisition for Magnetic Particle Imaging Based on Lissajous Node Points.

Magnetic Particle Imaging (MPI) is an emerging technology in the field of (pre)clinical imaging. The acquisition of a particle signal is realized along specific sampling trajectories covering a defined field of view (FOV). In a system matrix (SM) based reconstruction procedure, the commonly used acquisition path in MPI is a Lissajous trajectory.

Magnetic particle imaging: current developments and future directions.

Magnetic particle imaging (MPI) is a novel imaging method that was first proposed by Gleich and Weizenecker in 2005. Applying static and dynamic magnetic fields, MPI exploits the unique characteristics of superparamagnetic iron oxide nanoparticles (SPIONs). The SPIONs' response allows a three-dimensional visualization of their distribution in space with a superb contrast, a very high temporal and good spatial resolution.

Axially elongated field-free point data acquisition in magnetic particle imaging.

The magnetic particle imaging (MPI) technology is a new imaging technique featuring an excellent possibility to detect iron oxide based nanoparticle accumulations in vivo. The excitation of the particles and in turn the signal generation in MPI are achieved by using oscillating magnetic fields. In order to realize a spatial encoding, a field-free point (FFP) is steered through the field of view (FOV).

On the formulation of the image reconstruction problem in magnetic particle imaging.

In magnetic particle imaging (MPI), the spatial distribution of magnetic nanoparticles is determined by applying various static and dynamic magnetic fields. Due to the complex physical behavior of the nanoparticles, it is challenging to determine the MPI system matrix in practice. Since the first publication on MPI in 2005, different methods that rely on measurements or simulations for the determination of the MPI system matrix have been proposed.