International Journal on Magnetic Particle Imaging (IJMPI)
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Advancing brain drug delivery: Focused magnetic hyperthermia and magnetic particle imaging for real-time BBB modulation
No full textAbstractThe blood-brain barrier (BBB) is crucial for brain protection but limits therapeutic delivery for neurological disorders. This study utilizes magnetic hyperthermia (MH) to transiently and reversibly open the BBB, with magnetic particle imaging (MPI) enabling real-time, high-sensitivity monitoring. Using field-free point (FFP)-based focused heating, MH facilitated magnetic nanoparticles (MNPs) penetration into the brain and prolonged retention in the target area. Fluorescence imaging was confirmed on Evans blue staining to analyze BBB permeability immediately after MH, while MPI quantification revealed significant MNPs accumulation at target sites in focused-heated groups compared to non-heated controls. Fluorescence images further showed that BBB permeability restored after 24 hours, though MNPs retention persisted in heated regions for more than 72 hours. Fluorescence imaging confirms BBB permeability immediately after MH, while MPI provides both qualitative imaging and quantitative data on MNPs distribution and retention. These findings indicate that MPI can detect particle retention and distribution patterns that are not visible with Fluorescence imaging
A Large Dataset for Model-Based Image Reconstruction and Operator Correction in MPI
No full textWithin the context of MPI, the accuracy of model-based system matrices has witnessed remarkable recent improvements. However, model-based reconstruction approaches still require substantial enhancement to compete with measured system matrix approaches, particularly including multi-dimensional Lissajous-type trajectories of the field-free point. The latter approach has demonstrated its ability to produce satisfactory reconstructions under the application of standard techniques. This makes it one of the preferable state-of-the-art methods for image reconstruction in MPI, despite its extended calibration time. Determining the source of reconstruction artifacts turns out as a challenging aspect for model-based reconstruction approaches. It is not straightforward to distinguish between errors caused by background noise and errors resulting from inaccurate assumptions on the chosen physical model, leading to deviations of the forward operator. We provide a dataset tailored for operator correction in the context of model-based reconstruction approaches in MPI, where we use most recent particle magnetization models for the simulation of system matrices. The integration of precisely calibrated components from the Bruker MPI system contributes to the enhancement of model accuracy. Our dataset comprises measurements from simulated system matrices, combined with corresponding ground truth phantoms. It covers a variation of physical models and diverse noise scenarios, using realistic noise data captured by the Bruker MPI system. Accessing the set of data tuples facilitates an analysis of model deviations, potentially serving as valuable prior information for model corrections. Moreover, we provide validations of standard reconstruction methods when applied to our model-based simulations of the system matrix
Magnetic particle imaging can be used to assess tumour associated macrophage density in vivo
No full textTumour Associated Macrophages (TAMs) play a crucial role in breast cancer prognosis. Here we propose Magnetic Particle Imaging (MPI) for non-invasive TAM assessment. By employing an advanced reconstruction algorithm and a small field of view (FOV) focused on the tumour, we demonstrate enhanced image quality and successful quantification of TAMs in mouse mammary tumours with different metastatic potentials (4T1 and E0771). Utilizing in vivo MPI, we did not see significant differences in the MPI signal for 4T1 tumours compared to E0771. These findings highlight the potential of MPI for in vivo TAM quantification despite dynamic range limitations, offering a promising avenue for broader applications in cancer research and potentially overcoming constraints in other in vivo imaging contexts.
Extension of the Kaczmarz algorithm with a deep plug-and-play regularizer
No full textThe Kaczmarz algorithm is widely used for image reconstruction in magnetic particle imaging (MPI) because it converges rapidly and often provides good image quality even after a few iterations. It is often combined with Tikhonov regularization to cope with noisy measurements and the ill-posed nature of the imaging problem. In this abstract, we propose to combine the Kaczmarz method with a plug-and-play (PnP) denoiser for regularization, which can provide more specific prior knowledge than handcrafted priors. Using measurement data of a spiral phantom, we show that Kaczmarz-PnP yields excellent image quality, while speeding up the already fast convergence. Since the PnP denoiser is not coupled to the imaging operator, the Kaczmarz-PnP method is very generic and can be used for image reconstruction independently of the measurement sequence and MPI tracer type
Power-optimized drive field coils for human brain magnetic particle imaging
No full textThe implementation of human-scale magnetic particle imaging is significantly restricted by the nonlinear growth in power with the size of the field-generating coils. To address this issue, we developed anatomically optimized shapes with a reduced internal volume for the head drive field system using a wide range of anatomical data as a reference. On the base of designed complex bodies, we synthesized windings for two orthogonal coils with the help of the stream functions approach. The resulting coil set was compared to the state-of-the-art solenoid/saddle coil pair and showed a reduction in power consumption by a factor of 1.58 in numerical simulations. We also built a prototype of the designed coils using additive manufacturing and used it to receive the first signals from the nanoparticles
Simulation of a single-sided MPI handheld device with offset magnetic field for spatial encoding
No full textSingle-sided MPI devices provide images from regions outside of an enclosed scanner but they are constrained by limited in detection depth and non-uniform magnetic field distribution, which restrict imaging quality and sensitivity. In this study, we propose a novel handheld single-sided MPI device that utilizes an offset magnetic field with varying amplitude, coupled with high-frequency dynamic magnetic fields, to excite magnetic nanoparticles and sensitively capture the harmonic variations in spatial distribution. By moving the device horizontally in a direction perpendicular to the depth, two-dimensional spatial distribution information of magnetic nanoparticles can be acquired without the need for classical static selection fields. When the depth distance is 0-15 mm, the best spatial resolution is 1 mm. This technology holds the promise of achieving real-time imaging of superficial human tissue layers with lower cost and a simpler device structure
Design of a Hybrid Magnetic Fluid Hyperthermia and Magnetic Particle Spectrometer Setup
No full textMagnetic particle imaging (MPI) enables cancer imaging via enhanced permeability and retention effect that causes magnetic nanoparticles (MNPs) to accumulate in cancerous tissue, or via determining the change in MNP signal due to increased viscosity in cancerous tissue. MPI can also enable localization of magnetic fluid hyperthermia (MFH) therapy to a targeted region to locally heat up the MNPs and cause the death of cancerous tissue. However, the heating should be kept under control, so that the nearby healthy tissue is spared. Hybrid MFH-MPI systems have the potential to enable real-time non-invasive temperature monitoring for hyperthermia therapy via the relaxation response of MNPs. Here, we present the design and simulation results of a hybrid MFH and magnetic particle spectrometer (MPS) setup. This hybrid design is composed of three coaxial coils with gradiometric windings to minimize the mutual inductances among all three coils
Motion-corrected image reconstruction from dynamic MPI data
No full textMagnetic Particle Imaging (MPI) is an emerging imaging modality that offers unique insights into the structures under examination. Typically, it is assumed that the studied specimen is stationary. In practical applications however, the 3D distribution of the magnetic nanoparticles might show a dynamic behavior, caused by e.g. breathing or movement of the blood. Neglecting those dynamics during reconstruction of the data might result in motion artifacts and compromised image quality.
This talk will focus on the reconstruction from simulated as well as real dynamic MPI data. We introduce and assess different methods designed to approximate high-quality images in the presence of motion. A promising technique provides the regularized sequential subspace optimization (RESESOP) algorithm, which does only need few a priori information and little additional computational resources for improved reconstructions. All results will be compared against established algorithms like regularized Kaczmarz
Deep-learning-based denoising network for reducing MPI multiple repetition measurements
No full textMagnetic particle imaging (MPI) is an emerging medical imaging technique with high temporal resolution, and has the potential for real-time imaging. However, current MPI systems usually require multi-repetition measurement averaging for signal denoising, which diminishes the temporal resolution of MPI. Therefore, in this study, we use the deep-learning method to resolve the problem. Specifically, MPI images with less repetition times are used to predict the high-quality images with more repetition times using the neural network (e.g., UNet). In such cases, high-quality images can still be obtained with high temporal resolution. Our method is evaluated on the simulation dataset and shows its superiority. We will further evaluate the method in the real-world dataset and discuss its applicability
Frequency components selected based on gravitational search algorithm for magnetic particle imaging reconstruction
No full text It often utilizes system matrix to reconstruct in Magnetic Particle Imaging (MPI), but it is time-consuming and memory-intensive. Therefore, use Signal-to-Noise Ratio (SNR) to reduce frequency components to speed up and reduce memory, but only use SNR values do not contain other information about the frequency and may lose crucial information required for reconstruction. To address this limitation, the frequency components selection based on gravitational search algorithm (GSA) method is proposed herein, this method leverages Newton\u27s Law of Universal Gravitation and the Law of Kinematics to intelligently select frequency components, potentially enhancing the reconstruction image quality in MPI. Experimental results demonstrate favorable quantitative indices for the reconstructed images