PURPOSE:Focused ultrasound-induced blood-brain barrier (BBB) opening is a promising method for neurotherapeutic delivery. The standard for quantifying induced BBB permeability is the K trans $$ {K}^{\mathrm{trans}} $$ parameter, which reflects both permeability and plasma flow. The influence of plasma flow can be eliminated by estimating the PS parameter. However, this parameter has been largely unexplored in this application. This study aims to compare permeability estimates based on K trans $$ {K}^{\mathrm{trans}} $$ and PS in focused ultrasound-induced BBB opening experiments. METHODS:We used the extended Tofts model (ETM) and the two-compartment exchange model (2CXM) to estimate K trans $$ {K}^{\mathrm{trans}} $$ and PS parameters, respectively. Permeability estimates were compared using simulated concentration curves, simulated DCE-MRI data, and real datasets. We explored the influence of spatially-regularized model fitting on the results. RESULTS:For opened BBB, K trans $$ {K}^{\mathrm{trans}} $$ was minimally influenced by plasma flow under the tested conditions. However, fitting the ETM often introduced outliers in K trans $$ {K}^{\mathrm{trans}} $$ estimates in regions with closed BBB. The 2CXM outperformed the ETM at high signal-to-noise ratios, but its higher complexity led to lower precision at low signal-to-noise ratios. Both these issues were successfully compensated by spatially-regularized model fitting. CONCLUSION:Both K trans $$ {K}^{\mathrm{trans}} $$ and PS seem to be eligible options for the quantification of BBB opening, and the correct choice depends on the specifics of the acquired DCE-MRI data. Additionally, spatial regularization has demonstrated its importance in enhancing the accuracy and reproducibility of results for both models.

The paper aims at improved reliability of magnetic resonance perfusion imaging and estimation of an extended set of biomarkers using these techniques. Magnetic resonance perfusion imaging is an important experimental methodology with main applications in diagnostics and therapy monitoring in oncology. The main two methods are Dynamic Contrast-Enhanced (DCE) Magnetic Resonance Imaging (MRI) and Dynamic Susceptibility Contrast (DSC) MRI. We combine these two methods in a simultaneous acquisition and data processing approach. For simultaneous DCE-DSC-MRI data processing, we suggest a conventional non-linear least-squares method and a method based on convolutional neural networks. We evaluated the proposed methods on realistically simulated synthetic datasets and illustrated their performance on a real dataset. Compared to the standard approach, the methods of simultaneous DCE-DSC-MRI analysis were more reliable. The two proposed methods of simultaneous DCE-DSC-MRI analysis were comparable, with the neural-network approach being computationally far more effective.

PURPOSE:The aim of this study was to develop a simple, robust, and easy-to-use calibration procedure for correcting misalignments in rosette MRI k-space sampling, with the objective of producing images with minimal artifacts. METHODS:Quick automatic calibration scans were proposed for the beginning of the measurement to collect information on the time course of the rosette acquisition trajectory. A two-parameter model was devised to match the measured time-varying readout gradient delays and approximate the actual rosette sampling trajectory. The proposed calibration approach was implemented, and performance assessment was conducted on both phantoms and human subjects. RESULTS:The fidelity of phantom and in vivo images exhibited significant improvement compared with uncorrected rosette data. The two-parameter calibration approach also demonstrated enhanced precision and reliability, as evidenced by quantitative T 2 * $$ {\mathrm{T}}_2^{\ast } $$ relaxometry analyses. CONCLUSION:Adequate correction of data sampling is a crucial step in rosette MRI. The presented experimental results underscore the robustness, ease of implementation, and suitability for routine experimental use of the proposed two-parameter rosette trajectory calibration approach.

PurposeThe aim of this study is to design a method of myocardial T1 quantification in small laboratory animals and to investigate the effects of spatiotemporal regularization and the needed acquisition duration.MethodsWe propose a compressed-sensing approach to T1 quantification based on self-gated inversion-recovery radial two/three-dimensional (2D/3D) golden-angle stack-of-stars acquisition with image reconstruction performed using total-variation spatiotemporal regularization. The method was tested on a phantom and on a healthy rat, as well as on rats in a small myocardium-remodeling study.ResultsThe results showed a good match of the T1 estimates with the results obtained using the ground-truth method on a phantom and with the literature values for rats myocardium. The proposed 2D and 3D methods showed significant differences between normal and remodeling myocardium groups for acquisition lengths down to approximately 5 and 15 min, respectively.ConclusionsA new 2D and 3D method for quantification of myocardial T1 in rats was proposed. We have shown the capability of both techniques to distinguish between normal and remodeling myocardial tissue. We have shown the effects of image-reconstruction regularization weights and acquisition length on the T1 estimates.

In quantitative perfusion MRI, reliable estimation of tissue perfusion parameters remains a challenging task. Recently, the simultaneous DCE-DSC-MRI technique has attracted attention since it leads to more reliable estimates of perfusion-parameter maps than DCE- or DSC-MRI alone. In this paper, we propose a method of simultaneous DCE-DSC-MRI based on spatio-temporal locally low-rank regularization of the DCE and DSC image sequences. By comparison with the baseline DCE-DSC-MRI method, we demonstrate improved accuracy of the perfusion-parameter maps. The evaluations are performed on a realistic synthetic phantom of a rat head with several noise levels.

Background: Major depressive disorder (MDD) is a mental illness with a high worldwide prevalence and suboptimal pharmacological treatment, which necessitates the development of novel, more efficacious MDD medication. Nuclear magnetic resonance (NMR) can non-invasively provide insight into the neurochemical state of the brain using proton magnetic resonance spectroscopy (1H MRS), and an assessment of regional cerebral blood flow (rCBF) by perfusion imaging. These methods may provide valuable in vivo markers of the pathological processes underlying MDD. Methods: This study examined the effects of the chronic antidepressant medication, citalopram, in a well-validated MDD model induced by bilateral olfactory bulbectomy (OB) in rats. 1H MRS was utilized to assess key metabolite ratios in the dorsal hippocampus and sensorimotor cortex bilaterally, and arterial spin labelling was employed to estimate rCBF in several additional brain regions. Results: The 1H MRS data results suggest lower hippocampal Cho/tCr and lower cortical NAA/tCr levels as a characteristic of the OB phenotype. Spectroscopy revealed lower hippocampal Tau/tCr in citalopram-treated rats, indicating a potentially deleterious effect of the drug. However, the significant OB model–citalopram treatment interaction was observed using 1H MRS in hippocampal mI/tCr, Glx/tCr and Gln/tCr, indicating differential treatment effects in the OB and control groups. The perfusion data revealed higher rCBF in the whole brain, hippocampus and thalamus in the OB rats, while citalopram appeared to normalise it without affecting the control group. Conclusion: Collectively, 1H MRS and rCBF approaches demonstrated their capacity to capture an OB-induced phenotype and chronic antidepressant treatment effect in multiple brain regions.

This work is focused on estimation of an arterial input function (AIF) in DCE-MRI. We propose a new concept of blind deconvolution to use as much available information as possible. In contrast to multi-channel blind deconvolution based on processing of selected tissue signals, our all-channel blind deconvolution uses tissue signals of all voxels within the studied tissue to simultaneously estimate the AIF and perfusion maps. In addition, edge-preserving spatial regularization is included in the perfusion-map estimation scheme. The method is illustrated on DCE-MRI recordings of tumor-bearing mice.

OBJECTIVES:Pilot study validating the animal model of depression - the bilateral olfactory bulbectomy in rats - by two nuclear magnetic resonance methods, indirectly detecting the metabolic state of the brain. Furthermore, the study focussed on potential differences in brain laterality.METHODS:Arterial spin labelling assessed cerebral brain flow in prefrontal, sensorimotor, and piriform cortices, nucleus accumbens, hippocampus, thalamus, circle of Willis, and whole brain. Proton magnetic resonance spectroscopy provided information about relative metabolite concentrations in the cortex and hippocampus.RESULTS:Arterial spin labelling found no differences in cerebral perfusion in the group comparison but revealed lateralisation in the thalamus of the control group and the sensorimotor cortex of the bulbectomized rats. Lower Cho/tCr and Cho/NAA levels were found in the right hippocampus in bulbectomized rats. The differences in lateralisation were shown in the hippocampus: mI/tCr in the control group, Cho/NAA, NAA/tCr, Tau/tCr in the model group, and in the cortex: NAA/tCr, mI/tCr in the control group.CONCLUSION:Olfactory bulbectomy affects the neuronal and biochemical profile of the rat brain laterally and, as a model of depression, was validated by two nuclear magnetic resonance methods.

We propose to combine DCE and DSC-MRI in a simultaneous acquisition and processing scheme to provide more information for estimation of the arterial input function using multi-channel blind deconvolution. On simulated and flow-phantom data, we show that DCE-DSC blind deconvolution is superior to standard DCE blind deconvolution, especially for low signal-to-noise-ratio conditions.

PURPOSE:K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS:A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS:Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS:This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

The temporomandibular joint (TMJ) is typically involved in 45-87% of children with Juvenile Idiopathic Arthritis (JIA). Accurate diagnosis of JIA is difficult as various clinical tests, including MRI, disagree. The purpose of this study is to optimize the methodological aspects of Dynamic Contrast Enhanced (DCE) MRI of the TMJ in children. In this cross-sectional study, including data from 73 JIA affected children, aged 6-15 years, effects of motion correction, sampling rate and parametric modelling on DCE-MRI data is investigated. Consensus among three radiologists determined the regions of interest. Quantitative perfusion parameters were estimated using four perfusion models; the Adiabatic Approximation to Tissue Homogeneity (AATH), Distributed Capillary Adiabatic Tissue Homogeneity (DCATH), Gamma Capillary Transit Time (GCTT) and Two Compartment Exchange (2CXM) models. Effects of motion correction were evaluated by a sum of least squares between corrected raw data and the GCTT model. The effect of systematically down sampling the raw data was tested. The sum of least squares was computed across all pharmacokinetic models. Relative difference perfusion parameters between the left and right TMJ were used for an unsupervised k-means based stratification of the data based on a principal component analysis, as well as for a supervised random forest classification. Diagnostic sensitivity and specificity were computed relative to structural image scorings. Paired sample t-tests, as well as ANOVA tests, were used (significant threshold: p < 0.05) with Tukeys post hoc test. High-level elastic motion correction provides the best least square fit to the GCTT model (percental improvement: 72-84%). A 4 s sampling rate captures more of the potentially disease relevant signal variations. The various parametric models all leave comparable residues (relative standard deviation: 3.4%). In further evaluation of DCE-MRI as a potential diagnostic tool for JIA a high-level elastic motion correction scheme should be adopted, with a sampling rate of at least 4 s. Results suggest that DCE-MRI data can be a valuable part in JIA diagnostics in the TMJ.

PURPOSE:One of the main obstacles for reliable quantitative dynamic contrast-enhanced (DCE) MRI is the need for accurate knowledge of the arterial input function (AIF). This is a special challenge for preclinical small animal applications where it is very difficult to measure the AIF without partial volume and flow artifacts. Furthermore, using advanced pharmacokinetic models (allowing estimation of blood flow and permeability-surface area product in addition to the classical perfusion parameters) poses stricter requirements on the accuracy and precision of AIF estimation. This paper addresses small animal DCE-MRI with advanced pharmacokinetic models and presents a method for estimation of the AIF based on blind deconvolution.METHODS:A parametric AIF model designed for small animal physiology and use of advanced pharmacokinetic models is proposed. The parameters of the AIF are estimated using multichannel blind deconvolution.RESULTS:Evaluation on simulated data show that for realistic signal to noise ratios blind deconvolution AIF estimation leads to comparable results as the use of the true AIF. Evaluation on real data based on DCE-MRI with two contrast agents of different molecular weights showed a consistence with the known effects of the molecular weight.CONCLUSION:Multi-channel blind deconvolution using the proposed AIF model specific for small animal DCE-MRI provides reliable perfusion parameter estimates under realistic signal to noise conditions.

Dynamic contrast enhanced MRI (DCE-MRI) and dynamic susceptibility contrast MRI (DSC-MRI) are perfusion imaging techniques used mainly for clinical and preclinical measurement of vessel permeability and capillary blood flow, respectively. It is advantageous to apply both methods to exploit their complementary information about the perfusion status of the tissue. We propose a novel acquisition method that combines advantages of the current simultaneous and sequential acquisition. The proposed method consists of a DCE-MRI acquisition interrupted by DSC-MRI acquisition. A new method for processing of the DCE-MRI data is proposed which takes the interleaved acquisition into account. Analysis of both the DCE- and DSC-MRI data is reformulated so that they are approximated by the same pharmacokinetic model (constrained distributed capillary adiabatic tissue homogeneity model). This provides a straightforward evaluation of the methodology as some of the estimated DCE- and DSC-MRI perfusion parameters should be identical. Evaluation on synthetic data showed an acceptable precision and no apparent bias introduced by the interleaved character of the DCE-MRI acquisition. Intravascular perfusion parameters obtained from clinical glioma data showed a fairly high correlation of blood flow estimates from DCE- and DSC-MRI, however, an unknown scaling factor was still present mainly because of the tissue-specific r2*$r_2^*$ relaxivity. The results show validity of the proposed acquisition method. They also indicate that simultaneous processing of both DCE- and DSC-MRI data with joint estimation of some perfusion parameters (included in both DCE- and DSC-MRI) might be possible to increase the reliability of the DCE- and DSC-MRI methods alone.

A new non-optic method for the study of Liesegang patterns was tested. Liesegang patterns are periodically banded precipitates produced by the reaction–diffusion process, mostly occurring in geology. The Liesegang experiment (5.5 M NH4OH, gelatin matrix with 0.4 M MgSO4) was monitored using a 9.4 T magnetic resonance imaging (MRI) system, allowing the acquisition of sample images in any location and orientation without interfering with the process studied. The development of patterns in a gel matrix was observed for 7 days after starting the reaction. Qualitative data (T 2- and T 2*-weighted images) and quantitative T 2 maps were acquired. Preliminary experiments show that MRI is able to distinguish the regions between the precipitated bands and the structural changes in the gel matrix during the reaction. A damaged gel matrix was measured to reveal the effect of the initial reagent-gel contact area. MRI study of Liesegang patterns provides empirical data which can be used in theoretical model testing.

In DCE-MRI, tissue contrast-agent (CA) concentration curves are modeled as a convolution of the arterial input function (AIF) and the impulse residue function (IRF). The 2CXM and ATH models [1] are the most widely used advanced IRF models that provide separate estimates of blood flow, Fb, and permeability- surface area product, PS, contrary to the commonly applied Tofts models. No consensus exists on which advanced pharmacokinetic model is better. Simulation-based and blind-deconvolution-based preclinical model comparisons are published. This contribution presents a new model-evaluation method based on high- and low-molecular weight (MW) contrast-agents administered within one examination. Some perfusion parameters are expected to be MW-independent (namely Fb and blood volume—vb), while PS should decrease with increasing MW.

The aim of this study was to investigate Mag 102-6, a maghemite hybrid drug nanocarrier, as a potential T2 contrast agent (CA) for magnetic resonance imaging (MRI). Samples of agar gel mixed with different Fe concentrations of Mag 102-6 were measured at laboratory temperature for relaxivity r2 determination in 9.4 T. The results were compared with commercial CA FeraSpin XXL measured in the same arrangement. In addition, the r2 of Mag 102-6 in saline solution at physiological temperature 37.7 degree Celsius was measured. MRI was performed with four mice using a 9.4T NMR scanner before, and up to 24 h post-injection (p) of Mag 102-6 (dose 5 mg Fe kg-1).

This paper is focused on quantitative perfusion analysis using MRI and ultrasound. In both MRI and ultrasound, most approaches allow estimation of rate constants (Ktrans, kep for MRI) and indices (AUC, TTP) that are only related to the physiological perfusion parameters of a tissue (e.g. blood flow, vessel permeability) but do not allow their absolute quantification. Recent methods for quantification of these physiological perfusion parameters are shortly reviewed. The main problem of these methods is estimation of the arterial input function (AIF). This paper summarizes and extends the current blind-deconvolution approaches to AIF estimation. The feasibility of these methods is shown on a small preclinical study using both MRI and ultrasound.

Blind Multichannel Deconvolution for Estimation of a Parametric AIF in DCE-MRI of Mice Radovan Jirik, Karel Soucek, Eva Drazanova, Lucie Grossová, Michal Standara, Jirí Kratochvíla, Ondrej Macícek, Aneta Malá, Torfinn Taxt, and Zenon Starcuk, Jr. 1 Institute of Scientific Instruments of the ASCR, Brno, Czech Republic, Department of Cytokinetics, Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic, Center of Biomolecular and Cellular Engineering, International Clinical Research Center, Brno, Czech Republic, Dept. of Biomedical Engineering, Brno Univ. of Technology, Brno, Czech Republic, Dept. of Radiology, Masaryk Memorial Cancer Institute, Brno, Czech Republic, Dept. of Biomedicine, Univ. of Bergen, Bergen, Norway