In situ theranostic platform combining highly localized magnetic fluid hyperthermia, magnetic particle imaging, and thermometry in 3D.

Theranostic platforms, combining diagnostic and therapeutic approaches within one system, have garnered interest in augmenting invasive surgical, chemical, and ionizing interventions. Magnetic particle imaging (MPI) offers a quite recent alternative to established radiation-based diagnostic modalities with its versatile tracer material (superparamagnetic iron oxide nanoparticles, SPION). It also offers a bimodal theranostic framework that can combine tomographic imaging with therapeutic techniques using the very same SPION. Methods: We show the interleaved combination of MPI-based imaging, therapy (highly localized magnetic fluid hyperthermia (MFH)) and therapy safety control (MPI-based thermometry) within one theranostic platform in all three spatial dimensions using a commercial MPI system and a custom-made heating insert. The heating characteristics as well as theranostic applications of the platform were demonstrated by various phantom experiments using commercial SPION. Results: We have shown the feasibility of an MPI-MFH-based theranostic platform by demonstrating high spatial control of the therapeutic target, adequate MPI-based thermometry, and successful in situ interleaved MPI-MFH application. Conclusions: MPI-MFH-based theranostic platforms serve as valuable tools that enable the synergistic integration of diagnostic and therapeutic approaches. The transition into in vivo studies will be essential to further validate their potential, and it holds promising prospects for future advancements.

Navigation of a magnetic micro-robot through a cerebral aneurysm phantom with magnetic particle imaging.

Cerebral aneurysms are potentially life threatening and nowadays treated by a catheter-guided coiling or by a neurosurgical clipping intervention. Here, we propose a helically shaped magnetic micro-robot, which can be steered by magnetic fields in an untethered manner and could be applied for a novel coiling procedure. This is shown by navigating the micro-robot through an additively manufactured phantom of a human cerebral aneurysm. The magnetic fields are applied with a magnetic particle imaging (MPI) scanner, which allows for the navigation and tomographic visualization by the same machine. With MPI the actuation process can be visualized with a localization accuracy of 0.68 mm and an angiogram can be acquired both without any radiation exposure. First in-vitro phantom experiments are presented, showing an idea of a robot conducted treatment of cerebral aneurysms.

A New Phantom for Individual Verification of the Dose Distribution in Precision Radiotherapy for Head-and-Neck Cancer.

BACKGROUND/AIM: Many patients with head-and-neck cancers receive radiotherapy. Treatment planning can be very complex in case of dental fillings or implants that cause metal artefacts. Verification of dose distributions may be performed using specific phantoms. This study aimed to develop a 3D-printed phantom that can be produced easily and cost-effectively. PATIENTS AND METHODS: The phantom was designed to allow fast adaption to a patient's individual situation with a particular focus on metal artefacts due to dental fillings. Bone and soft-tissue shells were 3D-printed and filled with tissue-equivalent materials. RESULTS: Attenuation properties of the tissue-equivalent structures in the phantom corresponded well to the structures of real human anatomy. In magnetic resonance (MR)-imaging, useful signals of the materials in the phantom were obtained. CONCLUSION: The phantom met the requirements including equivalence with human tissues and can be useful for highly individual treatment planning in precision-radiotherapy of head-and-neck cancers. It can be also used for scientific issues related to MR-imaging.

Improvement of dose calculation in radiation therapy due to metal artifact correction using the augmented likelihood image reconstruction.

BACKGROUND: Metal artifacts caused by high-density implants lead to incorrectly reconstructed Hounsfield units in computed tomography images. This can result in a loss of accuracy in dose calculation in radiation therapy. This study investigates the potential of the metal artifact reduction algorithms, Augmented Likelihood Image Reconstruction and linear interpolation, in improving dose calculation in the presence of metal artifacts. MATERIALS AND METHODS: In order to simulate a pelvis with a double-sided total endoprosthesis, a polymethylmethacrylate phantom was equipped with two steel bars. Artifacts were reduced by applying the Augmented Likelihood Image Reconstruction, a linear interpolation, and a manual correction approach. Using the treatment planning system Eclipse™, identical planning target volumes for an idealized prostate as well as structures for bladder and rectum were defined in corrected and noncorrected images. Volumetric modulated arc therapy plans have been created with double arc rotations with and without avoidance sectors that mask out the prosthesis. The irradiation plans were analyzed for variations in the dose distribution and their homogeneity. Dosimetric measurements were performed using isocentric positioned ionization chambers. RESULTS: Irradiation plans based on images containing artifacts lead to a dose error in the isocenter of up to 8.4%. Corrections with the Augmented Likelihood Image Reconstruction reduce this dose error to 2.7%, corrections with linear interpolation to 3.2%, and manual artifact correction to 4.1%. When applying artifact correction, the dose homogeneity was slightly improved for all investigated methods. Furthermore, the calculated mean doses are higher for rectum and bladder if avoidance sectors are applied. CONCLUSION: Streaking artifacts cause an imprecise dose calculation within irradiation plans. Using a metal artifact correction algorithm, the planning accuracy can be significantly improved. Best results were accomplished using the Augmented Likelihood Image Reconstruction algorithm.

Towards Picogram Detection of Superparamagnetic Iron-Oxide Particles Using a Gradiometric Receive Coil.

Superparamagnetic iron-oxide nanoparticles can be used in medical applications like vascular or targeted imaging. Magnetic particle imaging (MPI) is a promising tomographic imaging technique that allows visualizing the 3D nanoparticle distribution concentration in a non-invasive manner. The two main strengths of MPI are high temporal resolution and high sensitivity. While the first has been proven in the assessment of dynamic processes like cardiac imaging, it is unknown how far the detection limit of MPI can be lowered. Within this work, we will present a highly sensitive gradiometric receive-coil unit combined with a noise-matching network tailored for the imaging of mice. The setup is capable of detecting 5 ng of iron in-vitro with an acquisition time of 2.14 sec. In terms of iron concentration we are able to detect 156 µg/L marking the lowest value that has been reported for an MPI scanner so far. In-vivo MPI mouse images of a 512 ng bolus and a 21.5 ms acquisition time allow for capturing the flow of an intravenously injected tracer through the heart of a mouse. Since it has been rather difficult to compare detection limits across MPI publications we propose guidelines to improve the comparability of future MPI studies.

The effects of metal artifact reduction on the retrieval of attenuation values.

BACKGROUND: The quality of CT slices can be drastically reduced in the presence of high-density objects such as metal implants within the patients' body due to the occurrence of streaking artifacts. Consequently, a delineation of anatomical structures might not be possible, which strongly influences clinical examination. PURPOSE: The aim of the study is to clinically evaluate the retrieval of attenuation values and structures by the recently proposed Augmented Likelihood Image Reconstruction (ALIR) and linear interpolation in the presence of metal artifacts. MATERIAL AND METHODS: A commercially available phantom was equipped with two steel inserts. At a position between the metal rods, which shows severe streaking artifacts, different human tissue-equivalent inserts are alternately mounted. Using a single-source computer tomograph, raw data with and without metal rods are acquired for each insert. Images are reconstructed using the ALIR algorithm and a filtered back projection with and without linear interpolation. Mean and standard deviation are compared for a region of interest in the ALIR reconstructions, linear interpolation results, uncorrected images with metal rods, and the images without metal rods, which are used as a reference. Furthermore, the reconstructed shape of the inserts is analyzed by comparing different profiles of the image. RESULTS: The measured mean and standard deviation values show that for all tissue classes, the metal artifacts could be reduced using the ALIR algorithm and the linear interpolation. Furthermore, the HU values for the different classes could be retrieved with errors below the standard deviation in the reference image. An evaluation of the shape of the inserts shows that the reconstructed object fits the shape of the insert accurately after metal artifact correction. Moreover, the evaluation shows a drop in the standard deviation for the ALIR reconstructed images compared to the reference images while reducing artifacts and keeping the shape of the inserts, which indicates a noise reduction ability of the ALIR algorithm. CONCLUSION: HU values, which are distorted by metal artifacts, can be retrieved accurately with the ALIR algorithm and the linear interpolation approach. After metal artifact correction, structures, which are not perceptible in the original images due to streaking artifacts, are reconstructed correctly within the image using the ALIR algorithm. Furthermore, the ALIR produced images with a reduced noise level compared to reference images and artifact images. Linear interpolation results in a distortion of the investigated shapes and features remaining streaking artifacts.

Identification of crucial parameters in a mathematical multiscale model of glioblastoma growth.

Glioblastomas are highly malignant brain tumours. Mathematical models and their analysis provide a tool to support the understanding of the development of these tumours as well as the design of more effective treatment strategies. We have previously developed a multiscale model of glioblastoma progression that covers processes on the cellular and molecular scale. Here, we present a novel nutrient-dependent multiscale sensitivity analysis of this model that helps to identify those reaction parameters of the molecular interaction network that influence the tumour progression on the cellular scale the most. In particular, those parameters are identified that essentially determine tumour expansion and could be therefore used as potential therapy targets. As indicators for the success of a potential therapy target, a deceleration of the tumour expansion and a reduction of the tumour volume are employed. From the results, it can be concluded that no single parameter variation results in a less aggressive tumour. However, it can be shown that a few combined perturbations of two systematically selected parameters cause a slow-down of the tumour expansion velocity accompanied with a decrease of the tumour volume. Those parameters are primarily linked to the reactions that involve the microRNA-451 and the thereof regulated protein MO25.

Magnetic particle imaging: introduction to imaging and hardware realization.

Magnetic Particle Imaging (MPI) is a recently invented tomographic imaging method that quantitatively measures the spatial distribution of a tracer based on magnetic nanoparticles. The new modality promises a high sensitivity and high spatial as well as temporal resolution. There is a high potential of MPI to improve interventional and image-guided surgical procedures because, today, established medical imaging modalities typically excel in only one or two of these important imaging properties. MPI makes use of the non-linear magnetization characteristics of the magnetic nanoparticles. For this purpose, two magnetic fields are created and superimposed, a static selection field and an oscillatory drive field. If superparamagnetic iron-oxide nanoparticles (SPIOs) are subjected to the oscillatory magnetic field, the particles will react with a non-linear magnetization response, which can be measured with an appropriate pick-up coil arrangement. Due to the non-linearity of the particle magnetization, the received signal consists of the fundamental excitation frequency as well as of harmonics. After separation of the fundamental signal, the nanoparticle concentration can be reconstructed quantitatively based on the harmonics. The spatial coding is realized with the static selection field that produces a field-free point, which is moved through the field of view by the drive fields. This article focuses on the frequency-based image reconstruction approach and the corresponding imaging devices while alternative concepts like x-space MPI and field-free line imaging are described as well. The status quo in hardware realization is summarized in an overview of MPI scanners.

A novel method for simulating the extracellular matrix in models of tumour growth.

A novel hybrid continuum-discrete model to simulate tumour growth on a cellular scale is proposed. The lattice-based spatiotemporal model consists of reaction-diffusion equations that describe interactions between cancer cells and their microenvironment. The fundamental ingredients that are typically considered are the nutrient concentration, the extracellular matrix (ECM), and matrix degrading enzymes (MDEs). The in vivo processes are very complex and occur on different levels. This in turn leads to huge computational costs. The main contribution of the present work is therefore to describe the processes on the basis of simplified mathematical approaches, which, at the same time, depict realistic results to understand the biological processes. In this work, we discuss if we have to simulate the MDE or if the degraded matrix can be estimated directly with respect to the cancer cell distribution. Additionally, we compare the results for modelling tumour growth using the common and our simplified approach, thereby demonstrating the advantages of the proposed method. Therefore, we introduce variations of the positioning of the nutrient delivering blood vessels and use different initializations of the ECM. We conclude that the novel method, which does not explicitly model the matrix degrading enzymes, provides means for a straightforward and fast implementation for modelling tumour growth.

Biophysical modeling of brain tumor progression: from unconditionally stable explicit time integration to an inverse problem with parabolic PDE constraints for model calibration.

PURPOSE: A novel unconditionally stable, explicit numerical method is introduced to the field of modeling brain cancer progression on a tissue level together with an inverse problem (IP) based on optimal control theory that allows for automated model calibration with respect to observations in clinical imaging data. METHODS: Biophysical models of cancer progression on a tissue level are in general based on the assumption that the spatiotemporal spread of cancerous cells is determined by cell division and net migration. These processes are typically described in terms of a parabolic partial differential equation (PDE). In the present work a parallelized implementation of an unconditionally stable, explicit Euler (EE(⋆)) time integration method for the solution of this PDE is detailed. The key idea of the discussed EE(⋆) method is to relax the strong stability requirement on the spectral radius of the coefficient matrix by introducing a subdivision regime for a given outer time step. The performance is related to common implicit numerical methods. To quantify the numerical error, a simplified model that has a closed form solution is considered. To allow for a systematic, phenomenological validation a novel approach for automated model calibration on the basis of observations in medical imaging data is developed. The resulting IP is based on optimal control theory and manifests as a large scale, PDE constrained optimization problem. RESULTS: The numerical error of the EE(⋆) method is at the order of standard implicit numerical methods. The computing times are well below those obtained for implicit methods and by that demonstrate efficiency. Qualitative and quantitative analysis in 12 patients demonstrates that the obtained results are in strong agreement with observations in medical imaging data. Rating simulation success in terms of the mean overlap between model predictions and manual expert segmentations yields a success rate of 75% (9 out of 12 patients). CONCLUSIONS: The discussed EE(⋆) method provides desirable features for image-based model calibration or hybrid image registration algorithms in which the model serves as a biophysical prior. This is due to (i) ease of implementation, (ii) low memory requirements, (iii) efficiency, (iv) a straightforward interface for parameter updates, and (v) the fact that the method is inherently matrix-free. The explicit time integration method is confirmed via experiments for automated model calibration. Qualitative and quantitative analysis demonstrates that the proposed framework allows for recovering observations in medical imaging data and by that phenomenological model validity.

A computational multiscale model of glioblastoma growth: regulation of cell migration and proliferation via microRNA-451, LKB1 and AMPK.

A new computational multiscale model of glioblastoma growth is introduced. This model combines an agent-based model for representing processes on the cellular level with a molecular interaction network for each cell on the subcellular scale. The network is based on recently published work on the interaction of microRNA-451, LKB1 and AMPK in the regulation of glioblastoma cell migration and proliferation. We translated this network into a mathematical description by the use of 17 ordinary differential equations. In our model, we furthermore establish a link from the molecular interaction network of a single cell to cellular actions (e.g. chemotactic movement) on the microscopic level. First results demonstrate that the computational model reproduces a tumor cell development comparable to that observed in in vitro experiments.

In-silico oncology: an approximate model of brain tumor mass effect based on directly manipulated free form deformation.

PURPOSE: The present work introduces a novel method for approximating mass effect of primary brain tumors. METHODS: The spatio-temporal dynamics of cancerous cells are modeled by means of a deterministic reaction-diffusion equation. Diffusion tensor information obtained from a probabilistic diffusion tensor imaging atlas is incorporated into the model to simulate anisotropic diffusion of cancerous cells. To account for the expansive nature of the tumor, the computed net cell density of malignant cells is linked to a parametric deformation model. This mass effect model is based on the so-called directly manipulated free form deformation. Spatial correspondence between two successive simulation steps is established by tracking landmarks, which are attached to the boundary of the gross tumor volume. The movement of these landmarks is used to compute the new configuration of the control points and, hence, determines the resulting deformation. To prevent a deformation of rigid structures (i.e. the skull), fixed shielding landmarks are introduced. In a refinement step, an adaptive landmark scheme ensures a dense sampling of the tumor isosurface, which in turn allows for an appropriate representation of the tumor shape. RESULTS: The influence of different parameters on the model is demonstrated by a set of simulations. Additionally, simulation results are qualitatively compared to an exemplary set of clinical magnetic resonance images of patients diagnosed with high-grade glioma. CONCLUSIONS: Careful visual inspection of the results demonstrates the potential of the implemented model and provides first evidence that the computed approximation of tumor mass effect is sensible. The shape of diffusive brain tumors (glioblastoma multiforme) can be recovered and approximately matches the observations in real clinical data.

Functional infrared imaging for skin-cancer screening.

Annually 133.000 people world-wide get sick on malign melanoma, tendency increasing. The purpose of this study is the early diagnosis of malignant skin cancer. At the moment the dermatologists are screening for anomalies at the relevant lesion by examining the skin area with a microscope. To determine changes, another scan has to be taken in a follow-up session after a time period of about 15-20 weeks. Today's visual diagnostic decision is based on the pragmatic ABCD approach (Asymmetry, Border, Colour, and Diameter). However, there is no adequate and sound non-invasive way to find out, if a skin spot is either malign or benign. If the visual approach corroborates a suspicion of skin cancer, histology is needed to make explicit diagnosis. To avoid unnecessary surgeries (on false positive alarm) and to initiate necessary surgeries in early stages a new diagnostic screening approach is presented here. Based on the fact that malign melanoma have higher metabolism as well as increased blood flow, it has been conjectured that malign melanoma have slightly higher temperature compared to the healthy skin that can be measured by high resolution functional infrared imaging.

Development of a radiation therapy open-source platform.

Although several industrial radio-therapy planning solutions presently exist, most of them do not cover the customer needs sufficiently. The aim of this project is to develop an open source radio-therapy planning system that would offer the ground work for individual customer solutions. During the first stage of the project we concentrate on the development of a visualisation module, since it is required for other parts such as dose calculation and beam modelling. The visualisation module will feature 2D and 3D visualisation of CT-, MRI- and PET DICOM images, registration of the images using Normalised Mutual Information, 2D and 3D visualisation of the matched images as well as 2D and 3D user interaction. In this paper an outline for our system is presented as well as a registration module, which is the first part of the visualisation module.