Evaluation of tractogram filtering methods using human-like connectome phantoms.

Tractography algorithms are prone to reconstructing spurious connections. The set of streamlines generated with tractography can be post-processed to retain the streamlines that are most biologically plausible. Several microstructure-informed filtering algorithms are available for this purpose, however, the comparative performance of these methods has not been extensively evaluated. In this study, we aim to evaluate streamline filtering and post-processing algorithms using simulated connectome phantoms. We first establish a framework for generating connectome phantoms featuring brain-like white matter fiber architectures. We then use our phantoms to systematically evaluate the performance of a range of streamline filtering algorithms, including SIFT, COMMIT, and LiFE. We find that all filtering methods successfully improve connectome accuracy, although filter performance depends on the complexity of the underlying white matter fiber architecture. Filtering algorithms can markedly improve tractography accuracy for simple tubular fiber bundles (F-measure deterministic- unfiltered: 0.49 and best filter: 0.72; F-measure probabilistic- unfiltered: 0.37 and best filter: 0.81), but for more complex brain-like fiber architectures, the improvement is modest (F-measure deterministic- unfiltered: 0.53 and best filter: 0.54; F-measure probabilistic- unfiltered: 0.46 and best filter: 0.50). Overall, filtering algorithms have the potential to improve the accuracy of connectome mapping pipelines, particularly for weighted connectomes and pipelines using probabilistic tractography methods. Our results highlight the need for further advances tractography and streamline filtering to improve the accuracy of connectome mapping.

PhoenixMR: A GPU-based MRI simulation framework with runtime-dynamic code execution.

BACKGROUND: Simulations of physical processes and behavior can provide unique insights and understanding of real-world problems. Magnetic Resonance Imaging (MRI) is an imaging technique with several components of complexity. Several of these components have been characterized and simulated in the past. However, several computational challenges prevent simulations from being simultaneously fast, flexible, and accurate. PURPOSE: The simulation of MRI experiments is underutilized by medical physicists and researchers using currently available simulators due to reasons including speed, accuracy, and extensibility constraints. This paper introduces an innovative MRI simulation engine and framework that aims to overcome these issues making available realistic and fast MRI simulation. METHODS: Using the CUDA C/C++ programing language, an MRI simulation engine (PhoenixMR), incorporating a Turing-complete virtual machine (VM) to simulate abstract spatiotemporal complexities, was developed. This engine solves a set of time-discrete Bloch equations using the symmetric operator splitting technique. An extensible front-end framework package (written in Python) aids the use of PhoenixMR to simplify simulation development. RESULTS: The PhoenixMR library and front-end codes have been developed and tested. A set of example simulations were performed to demonstrate the ease of use and flexibility of simulation components such as geometrical setup, pulse sequence design, phantom design, and so forth. Initial validation of PhoenixMR is performed by comparing its accuracy and performance against a widely used MRI simulator using identical simulation parameters. Validation results show PhoenixMR simulations are three orders of magnitude faster. There is also strong agreement between models. CONCLUSIONS: A novel MRI simulation platform called PhoenixMR has been introduced. This research tool is designed to be usable by physicists and engineers interested in performing MRI simulations. Examples are shown demonstrating the accuracy, flexibility, and usability of PhoenixMR in several key areas of MRI simulation.

Fabrication of Customizable Intraplaque Hemorrhage Phantoms for Magnetic Resonance Imaging.

PURPOSE: Magnetic resonance (MR) imaging detection of methemoglobin, a molecular marker of intraplaque hemorrhage (IPH), in atherosclerotic plaque is a promising method of assessing stroke risk. However, the multicenter imaging studies required to further validate this technique necessitate the development of IPH phantoms to standardize images acquired across different scanners. This study developed a set of phantoms that modeled methemoglobin-laden IPH for use in MR image standardization. PROCEDURES: A time-stable material mimicking the MR properties of methemoglobin in IPH was created by doping agarose hydrogel with gadolinium and sodium alginate. This material was used to create a phantom that consisted of 9 cylindrical IPH sites (with sizes from 1 to 8 mm). Anatomical replicas of IPH-positive atherosclerosis were also created using 3D printed molds. These plaque replicas also modeled other common plaque components including a lipid core and atheroma cap. T1 mapping and a magnetization-prepared rapid acquisition gradient echo (MPRAGE) carotid imaging protocol were used to assess phantom realism and long-term stability. RESULTS: Cylindrical phantom IPH sites possessed a T1 time of 335 ± 51 ms and exhibited little change in size or MPRAGE signal intensity over 31 days; the mean (SD) magnitude of changes in size and signal were 6.4 % (2.7 %) and 7.3 % (6.7 %), respectively. IPH sites incorporated into complex anatomical plaque phantoms exhibited contrast comparable to clinical images. CONCLUSIONS: The cylindrical IPH phantom accurately modeled the short T1 time characteristic of methemoglobin-laden IPH, with the IPH sites exhibiting little variation in imaging properties over 31 days. Furthermore, MPRAGE images of the anatomical atherosclerosis replicas closely matched those of clinical plaques. In combination, these phantoms will allow for IPH imaging protocol standardization and thus facilitate future multicenter IPH imaging.

MRI phantom for tissue simulation with respect to diffusion coefficient and kurtosis - Validation with injection of liposomal theranostics.

This study presents gelatine-based and agar-based phantoms with an addition of glycerol, safflower oil, silicone oil and cellulose microcrystalline with a potential to cover the entire range of tissue diffusion coefficients and kurtosis values. Forty types of phantoms were prepared and examined for NMR relaxation times T1 and T2 and diffusional metrics D, K and ADC. Wide ranges of values of D (0.0003-0.0031 mm2s-1), K (0.00-7.24) and ADC (0.0002-0.0031 mm2s-1) were observed. Two of the phantoms closely mimic muscle and cortical gray matter with respect to water diffusion parameters. Although many of the presented phantoms display both D and K values within the range of human tissues, they match different tissues with respect to D and K. The imaging results for the gray matter simulating phantom injected with the liposomal solution, bear a resemblance to the particle size effect described in the literature. The phantoms presented in this work are simple in preparation and affordable tissue-simulating materials to be used primarily in development of diffusion kurtosis-based MRI methods and possibly in a preliminary assessment of MRI contrast agents. Further adjustments of the chemical compositions could potentially lead to development of new types of phantoms mimicking diffusional properties of more kinds of soft tissues.

Gellan gum gel tissue phantoms and gel dosimeters with tunable electrical, mechanical and dosimetric properties.

Gellan gum gels have been proposed as tissue- and water-mimicking materials (phantoms) applied in medical imaging and radiotherapy dosimetry. Phantoms often require ionic additives to induce desirable electrical conductivity, resistance to biological spoilage, and radical scavenging properties. However, gellan gum is strongly crosslinked by the typically used sodium salts, forming difficult-to-work with gels with reduced optical clarity. Herein we investigated lithium and tetramethylammonium chloride to induce the required electrical conductivity while maintaining optical clarity; lithium formate and methylparaben were used as a radical scavenger and antimicrobial additive, respectively. Using a multifactorial design of experiments, we studied and modeled the electrical and mechanical properties and liquid expulsion (syneresis) properties of the gels. Finally, by the addition of a radiation-sensitive tetrazolium salt, dosimeters with favorable properties were produced. The results described herein may be used to prepare tissue phantoms and dosimeters with tuned electrical, mechanical, and dosimetric properties.

Multicenter evaluation of MRI-based radiomic features: A phantom study.

INTRODUCTION: This work describes the development of a novel radiomics phantom designed for magnetic resonance imaging (MRI) that can be used in a multicenter setting. The purpose of this study is to assess the stability and reproducibility of MRI-based radiomic features using this phantom across different MRI scanners. METHODS & MATERIALS: A set of phantoms were three-dimensional (3D) printed using MRI visible materials. One set of phantoms were imaged on seven MRI scanners and one was imaged on one MRI scanner. Radiomics analysis of the phantoms, which included first-order features, shape and texture features was performed. Intraclass correlation coefficient (ICC) was used to assess the stability of radiomic features across eight scanners and the reproducibility of two printed models on one scanner. Coefficient of variation (COV) was used to assess the reproducibility of radiomics measurements in the phantom on a single scanner. RESULTS: The phantom models provide sufficient signal-to-noise and contrast in all the tumor models permitting robust automatic segmentation. During a 12-month period of monitoring, the phantom material was stable with T1 and T2 of 150.7 ± 6.7 ms and 56.1 ± 3.9 ms, respectively. Of all the radiomic features computed, 34 of 69 had COV < 10%. Features from first-order statistics were the most robust in stability across the eight scanners with eight of 12 (67%) having high stability. About 29 of 50 (58%) texture features had high stability and no shape features had high stability features across the eight scanners. CONCLUSION: A novel MRI radiomics phantom has been developed to assess the reproducibility and stability of MRI-based radiomic features across multiple institutions. The variation in radiomic feature stability demonstrates the need for caution when interpreting these features for clinical studies.

Characterization of magnetic viral complexes for targeted delivery in oncology.

Oncolytic viruses are promising new agents in cancer therapy. Success of tumor lysis is often hampered by low intra-tumoral titers due to a strong anti-viral host immune response and insufficient tumor targeting. Previous work on the co-assembly of oncolytic virus particles (VPs) with magnetic nanoparticles (MNPs) was shown to provide shielding from inactivating immune response and improve targeting by external field gradients. In addition, MNPs are detected by magnet resonance imaging (MRI) enabling non-invasive therapy monitoring. In this study two selected core-shell type iron oxide MNPs were assembled with adenovirus (Ad) or vesicular stomatitis virus (VSV). The selected MNPs were characterized by high r2 and r2(*) relaxivities and thus could be quantified non-invasively by 1.5 and 3.0 tesla MRI with a detection limit below 0.001 mM iron in tissue-mimicking phantoms. Assembly and cell internalization of MNP-VP complexes resulted in 81 - 97 % reduction of r2 and 35 - 82 % increase of r2(*) compared to free MNPs. The relaxivity changes could be attributed to the clusterization of particles and complexes shown by transmission electron microscopy (TEM). In a proof-of-principle study the non-invasive detection of MNP-VPs by MRI was shown in vivo in an orthotopic rat hepatocellular carcinoma model. In conclusion, MNP assembly and compartmentalization have a major impact on relaxivities, therefore calibration measurements are required for the correct quantification in biodistribution studies. Furthermore, our study provides first evidence of the in vivo applicability of selected MNP-VPs in cancer therapy.