Simultaneous imaging of widely differing particle concentrations in MPI: problem statement and algorithmic proposal for improvement.

Magnetic particle imaging (MPI) is a tomographic imaging technique for determining the spatial distribution of superparamagnetic nanoparticles. Current MPI systems are capable of imaging iron masses over a wide dynamic range of more than four orders of magnitude. In theory, this range could be further increased using adaptive amplifiers, which prevent signal clipping.

Suppression of Motion Artifacts Caused by Temporally Recurring Tracer Distributions in Multi-Patch Magnetic Particle Imaging.

Magnetic particle imaging is a tracer based imaging technique to determine the spatial distribution of superparamagnetic iron oxide nanoparticles with a high spatial and temporal resolution. Due to physiological constraints, the imaging volume is restricted in size and larger volumes are covered by shifting object and imaging volume relative to each other. This results in reduced temporal resolution, which can lead to motion artifacts when imaging dynamic tracer distributions.

Moving table magnetic particle imaging: a stepwise approach preserving high spatio-temporal resolution.

Magnetic particle imaging (MPI) is a highly sensitive imaging method that enables the visualization of magnetic tracer materials with a temporal resolution of more than 46 volumes per second. In MPI, the size of the field of view (FoV) scales with the strengths of the applied magnetic fields. In clinical applications, those strengths are limited by peripheral nerve stimulation, specific absorption rates, and the requirement to acquire images of high spatial resolution.

Efficient Joint Image Reconstruction of Multi-Patch Data Reusing a Single System Matrix in Magnetic Particle Imaging.

Due to peripheral nerve stimulation, the magnetic particle imaging (MPI) method is limited in the maximum applicable excitation-field amplitude. This in turn leads to a limitation of the size of the covered field of view (FoV) to few millimeters. In order to still capture a larger FoV, MPI is capable to rapidly acquire volumes in a multi-patch fashion.

Fast multiresolution data acquisition for magnetic particle imaging using adaptive feature detection.

Purpose: Magnetic particle imaging is a tomographic imaging modality capable of determining the distribution of magnetic nanoparticles with high temporal resolution. The spatial resolution of magnetic particle imaging is influenced by the gradient strength of the selection field used for spatial encoding. By increasing the gradient strength, the spatial resolution is improved, but at the same time the imaging volume decreases.

Magnetic Particle Imaging for Real-Time Perfusion Imaging in Acute Stroke.

The fast and accurate assessment of cerebral perfusion is fundamental for the diagnosis and successful treatment of stroke patients. Magnetic particle imaging (MPI) is a new radiation-free tomographic imaging method with a superior temporal resolution, compared to other conventional imaging methods. In addition, MPI scanners can be built as prehospital mobile devices, which require less complex infrastructure than computed tomography (CT) and magnetic resonance imaging (MRI).

Robust abdominal imaging with incomplete breath-holds.

Purpose: Breath-holding is an established strategy for reducing motion artifacts in abdominal imaging. However, the breath-holding capabilities of patients are often overstrained by scans with large coverage and high resolution. In this work, a new strategy for coping with resulting incomplete breath-holds in abdominal imaging is suggested.

Krylov-space approach to the equilibrium and nonequilibrium single-particle Green's function.

The zero-temperature single-particle Green's function of correlated fermion models with moderately large Hilbert-space dimensions can be calculated by means of Krylov-space techniques. The conventional Lanczos approach consists of finding the ground state in a first step, followed by an approximation for the resolvent of the Hamiltonian in a second step. We analyze the character of this approximation and discuss a numerically exact variant of the Lanczos method which is formulated in the time domain.