Search

Image reconstruction plays an important role for the tomographic imaging technique magnetic particle imaging (MPI) since the measured raw data cannot be directly interpreted. Instead, one needs to invert the image formation process, which involves the solution of an ill-conditioned linear system of equations. Currently, most MPI researchers have implemented custom reconstruction algorithms that cannot be directly compared since the source code is not openly available. The software package MPIReco.jl aims to change this situation by providing a reference implementation for a variety of reconstruction algorithms. With the recently proposed magnetic particle imaging data format and its reference implementation MPIFiles.jl we have taken the first steps towards standardised data exchange. With MPIReco.jl we complement these initiatives to standardise the reconstruction algorithms and to facilitate reproducible research. We chose to implement the algorithms in the programming language Julia, which provides a high level syntax making the software accessible even for non-professional software developers. On the other hand Julia code has a high run-time performance comparable to low-level C code. In the present paper, we outline some of the design principles of MPIReco.jl and give an overview of the software package. Int. J. Mag. Part. Imag. 5(1-2), 2019, Article ID: 1907001, DOI: 10.18416/IJMPI.2019.1907001, International Journal on Magnetic Particle Imaging, Vol 5 No 1-2 (2019)

Magnetic particle imaging visualizes the spatial distribution of superparamagnetic nanoparticles. Because of its key features of excellent sensitivity, high temporal and spatial resolution and biocompatibility of the tracer material it can be used in multiple medical imaging applications. The common reconstruction technique for Lissajous-type trajectories uses a system matrix that has to be previously acquired in a time-consuming calibration scan, leading to long downtimes of the scanning device. In this work, the system matrix is determined by a hybrid approach. Using the hybrid system matrix for reconstruction, the calibration downtime of the scanning device can be neglected. Furthermore, the signal to noise ratio of the hybrid system matrix is much higher, since the size of the required nanoparticle sample can be chosen independently of the desired voxel size. As the signal to noise ratio influences the reconstruction process, the resulting images have better resolution and are less affected by artefacts. Additionally, a new approach is introduced to address the background signal in image reconstruction. The common technique of subtraction of the background signal is replaced by extending the system matrix with an entry that represents the background. It is shown that this approach reduces artefacts in the reconstructed images.

Magnetic Particle Imaging (MPI) is a fast and highly sensitive tomographic imaging modality. When applying 3D Lissajous imaging sequences, the region of interest is rapidly sampled by moving a field-free point along a predefined trajectory. Since the field excitation is done using three orthogonal excitation coils, usually also the magnetization response is measured with three independent and orthogonal receive coils. In this work the influence of selecting a subset of receive channels during reconstruction on the resulting image quality is analyzed. It is shown that using a single receive channel a slight loss of spatial resolution in the order of 12 % to 22 % in the direction perpendicular to the receiving direction can be observed while in direction of the receive coil the resolution is preserved and partially even improved. Since the construction of decoupled 3D receive coil units is a major engineering effort, the findings can be used to simplify the construction of 3D Lissajous type scanners., International Journal on Magnetic Particle Imaging, Vol.3, No.1, Article ID 1703014

Objectives Fat quantification by dual-energy computed tomography (DECT) provides contrast-independent objective results, for example, on hepatic steatosis or muscle quality as parameters of prognostic relevance. To date, fat quantification has only been developed and used for source-based DECT techniques as fast kVp-switching CT or dual-source CT, which require a prospective selection of the dual-energy imaging mode. It was the purpose of this study to develop a material decomposition algorithm for fat quantification in phantoms and validate it in vivo for patient liver and skeletal muscle using a dual-layer detector-based spectral CT (dlsCT), which automatically generates spectral information with every scan. Materials and Methods For this feasibility study, phantoms were created with 0%, 5%, 10%, 25%, and 40% fat and 0, 4.9, and 7.0 mg/mL iodine, respectively. Phantom scans were performed with the IQon spectral CT (Philips, the Netherlands) at 120 kV and 140 kV and 3 T magnetic resonance (MR) (Philips, the Netherlands) chemical-shift relaxometry (MRR) and MR spectroscopy (MRS). Based on maps of the photoelectric effect and Compton scattering, 3-material decomposition was done for fat, iodine, and phantom material in the image space. After written consent, 10 patients (mean age, 55 ± 18 years; 6 men) in need of a CT staging were prospectively included. All patients received contrast-enhanced abdominal dlsCT scans at 120 kV and MR imaging scans for MRR. As reference tissue for the liver and the skeletal muscle, retrospectively available non-contrast-enhanced spectral CT data sets were used. Agreement between dlsCT and MR was evaluated for the phantoms, 3 hepatic and 2 muscular regions of interest per patient by intraclass correlation coefficients (ICCs) and Bland-Altman analyses. Results The ICC was excellent in the phantoms for both 120 kV and 140 kV (dlsCT vs MRR 0.98 [95% confidence interval (CI), 0.94-0.99]; dlsCT vs MRS 0.96 [95% CI, 0.87-0.99]) and in the skeletal muscle (0.96 [95% CI, 0.89-0.98]). For log-transformed liver fat values, the ICC was moderate (0.75 [95% CI, 0.48-0.88]). Bland-Altman analysis yielded a mean difference of -0.7% (95% CI, -4.5 to 3.1) for the liver and of 0.5% (95% CI, -4.3 to 5.3) for the skeletal muscle. Interobserver and intraobserver agreement were excellent (>0.9). Conclusions Fat quantification was developed for dlsCT and agreement with MR techniques demonstrated for patient liver and muscle. Hepatic steatosis and myosteatosis can be detected in dlsCT scans from clinical routine, which retrospectively provide spectral information independent of the imaging mode.