MR Relaxometry Research Articles

Color-map recommendation for MR relaxometry maps.

To harmonize the use of color for MR relaxometry maps and therefore recommend the use of specific color-maps for representing , , and maps and their inverses. Perceptually linearized color-maps were chosen to have similar color settings as those proposed by Griswold et al. in 2018. A Delphi process, polling the opinion of a panel of 81 experts, was used to generate consensus on the suitability of these maps. Consensus was reached on the suitability of the logarithm-processed Lipari color-map for and the logarithm-processed Navia color-map for and . There was consensus on color bars being mandatory and on the use of a specific value indicating "invalidity." There was no consensus on whether the ranges should be fixed per anatomy. The authors recommend the use of the logarithm-processed Lipari color-map for displaying quantitative maps and maps; likewise, the authors recommend the logarithm-processed Navia color-map for displaying , , , and maps. This work originated with the Quantitative MR Study Group of the International Society of Magnetic Resonance in Medicine (ISMRM); it has the approval of the Publication Committee and of the Board of the ISMRM.

Highly accelerated parameter mapping using model-based alternating reconstruction coupling fitting

Objective. A model-based alternating reconstruction coupling fitting, termed Model-based Alternating Reconstruction COupling fitting (MARCO), is proposed for accurate and fast magnetic resonance parameter mapping. Approach. MARCO utilizes the signal model as a regularization by minimizing the bias between the image series and the signal produced by the suitable signal model based on iteratively updated parameter maps when reconstructing. The technique can incorporate prior knowledge of both image series and parameters by adding sparsity constraints. The optimization problem is decomposed into three subproblems and solved through three alternating steps involving reconstruction and nonlinear least-square fitting, which can produce both contrast-weighted images and parameter maps simultaneously. Main results. The algorithm is applied to T 2 mapping with extended phase graph algorithm integrated and validated on undersampled multi-echo spin-echo data from both phantom and in vivo sources. Compared with traditional compressed sensing and model-based methods, the proposed approach yields more accurate T 2 maps with more details at high acceleration factors. Significance. The proposed method provides a basic framework for quantitative MR relaxometry, theoretically applicable to all quantitative MR relaxometry. It has the potential to improve the diagnostic utility of quantitative imaging techniques.

In Situ Magnetometry of Iron in Human Dopaminergic Neurons Using Superresolution MRI and Ion-Beam Microscopy

Paramagnetic transition metals play a crucial role as cofactors in various cellular catalytic processes. However, their high concentrations in reactive oxidation states can induce oxidative stress, resulting in cell dysfunction or death. Hence, it is vital to have methods to monitor metal concentrations and paramagnetic properties in cells for medicine and cell biology. Here we present a novel multimodal method for in-cell magnetometry enabling direct measurement of metal magnetic properties within individual cells in tissue, without prior isolation and at room temperature. Individual cell magnetic moments are measured using superresolution magnetic resonance imaging (MRI) microscopy at 9.4 T by detecting microscopic magnetic-field perturbations around the cells. The cellular metal content is quantified using ion-beam microscopy or synchrotron micro-x-ray fluorescence for the same cells. The metal magnetic susceptibility at 9.4 T is then obtained from the slope of the cell magnetic moments’ dependence on cell metal content. To estimate the susceptibility at lower fields, multifield MR relaxometry and biophysical modeling are employed, extrapolating the 9.4-T susceptibility values to fields as low as 3 T. We apply the new method to determine the susceptibility of iron accumulated in human dopaminergic neurons inside neuromelanin, the by-product of dopamine synthesis. The susceptibility of iron in neuromelanin is measured to be χρ=(2.98±0.19)×10−6 m3/kg providing unique insights into the biochemistry of iron inside dopaminergic neurons. The obtained value reveals a predominant monoatomic low-affinity iron-binding site within neuromelanin, indicating a higher neurotoxicity of iron than previously suggested. Furthermore, the measured susceptibility value establishes a quantitative relationship between cellular iron concentration and iron-sensitive MRI parameters, which can be noninvasively measured . This breakthrough paves the way for the detection of dopaminergic neuron density and iron load, requiring a standard clinical MRI scanner only. It promises to facilitate early diagnosis of Parkinson’s disease. In conclusion, our presented novel method enables the direct measurements of magnetic properties of paramagnetic metals within single cells with high sensitivity and across large cell groups within a macroscopic volume, providing invaluable information about the cellular biology of metals. Published by the American Physical Society 2024

In vivo demonstration of globotriaosylceramide brain accumulation in Fabry Disease using MR Relaxometry

PurposeHow to measure brain globotriaosylceramide (Gb3) accumulation in Fabry Disease (FD) patients in-vivo is still an open challenge. The objective of this study is to provide a quantitative, non-invasive demonstration of this phenomenon using quantitative MRI (qMRI).MethodsIn this retrospective, monocentric cross-sectional study conducted from November 2015 to July 2018, FD patients and healthy controls (HC) underwent an MRI scan with a relaxometry protocol to compute longitudinal relaxation rate (R1) maps to evaluate gray (GM) and white matter (WM) lipid accumulation. In a subgroup of 22 FD patients, clinical (FAbry STabilization indEX -FASTEX- score) and biochemical (residual α-galactosidase activity) variables were correlated with MRI data. Quantitative maps were analyzed at both global (“bulk” analysis) and regional (“voxel-wise” analysis) levels.ResultsData were obtained from 42 FD patients (mean age = 42.4 ± 12.9, M/F = 16/26) and 49 HC (mean age = 42.3 ± 16.3, M/F = 28/21). Compared to HC, FD patients showed a widespread increase in R1 values encompassing both GM (pFWE = 0.02) and WM (pFWE = 0.02) structures. While no correlations were found between increased R1 values and FASTEX score, a significant negative correlation emerged between residual enzymatic activity levels and R1 values in GM (r = -0.57, p = 0.008) and WM (r = -0.49, p = 0.03).ConclusionsWe demonstrated the feasibility and clinical relevance of non-invasively assessing cerebral Gb3 accumulation in FD using MRI. R1 mapping might be used as an in-vivo quantitative neuroimaging biomarker in FD patients.

Kiosk 3R-TA-05 - 4D Flow in Fontan Circulation - Relation to MR Relaxometry, MR and US Liver Stiffness/ Viscosity and Lymphatic Stasis

Kiosk 3R-TA-05 - 4D Flow in Fontan Circulation - Relation to MR Relaxometry, MR and US Liver Stiffness/ Viscosity and Lymphatic Stasis

Abstract 17366: Effects of Acute Maternal Supplemental Oxygen on the Fetal Circulation in Transposition of the Great Arteries

Introduction: The presence of a restrictive atrial septum in the setting of transposition of the great arteries (TGA) is associated with a high risk of hypoxic ischemic injury upon transition to postnatal circulation. Fetal cardiovascular magnetic resonance (CMR) measures of vessel flow and oxygen saturation provide a unique opportunity to study fetal TGA hemodynamics at baseline and during acute maternal hyperoxygenation. Methods: Thirty-one pregnant mothers with fetuses diagnosed with TGA and an intact ventricular septum (n=23) or small ventricular septal defect (n=8) were recruited and underwent fetal CMR in room air and during administration of 10L/min of oxygen by face mask for up to 45 minutes. Blood flow and oxygen saturations were measured in the major fetal vessels using cine phase contrast MRI and MR relaxometry at baseline and during hyperoxygenation. Results: Two patients exhibited severe restriction at the atrial septum, one undergoing fetal atrial septostomy at 37 weeks and one requiring cannulation onto ECMO shortly after birth. Both revealed reversal of the usual relationship of oxygen saturation between the aorta and the main pulmonary artery at baseline and during hyperoxygenation, i.e. in TGA with restrictive atrial septum the saturations are higher in the aorta than pulmonary. Acute maternal supplemental oxygen was associated with increased pulmonary blood flow (p=0.008) and reduced foramen ovale (p=0.01) and ductus arteriosus shunting (p=0.01). There were no significant changes in umbilical vein, descending aorta and superior vena cava flow. Fetal oxygen saturations increased throughout fetal circulation (p<0.0001) with an associated increase in fetal oxygen delivery (p=0.007), but no increase in fetal cerebral oxygen delivery (p=0.58). Conclusions: This study characterized the effects of acute maternal supplemental oxygen on the fetal circulation in patients with TGA and confirmed the redistribution of flow toward the fetal pulmonary circulation. We observed higher oxygen saturations in the aorta than main pulmonary artery in fetuses with TGA with an intact or highly restrictive atrial septal communication. This finding could be helpful in selecting patients for prenatal or emergency neonatal balloon atrial septostomy.

A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology.

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

Volume-Assisted Estimation of Remnant Liver Function Based on Gd-EOB-DTPA Enhanced MR Relaxometry: A Prospective Observational Trial.

In the context of liver surgery, predicting postoperative liver dysfunction is essential. This study explored the potential of preoperative liver function assessment by MRI for predicting postoperative liver dysfunction and compared these results with the established indocyanine green (ICG) clearance test. This prospective study included patients undergoing liver resection with preoperative MRI planning. Liver function was quantified using T1 relaxometry and correlated with established liver function scores. The analysis revealed an improved model for predicting postoperative liver dysfunction, exhibiting an accuracy (ACC) of 0.79, surpassing the 0.70 of the preoperative ICG test, alongside a higher area under the curve (0.75). Notably, the proposed model also successfully predicted all cases of liver failure and showed potential in predicting liver synthesis dysfunction (ACC 0.78). This model showed promise in patient survival rates with a Hazard ratio of 0.87, underscoring its potential as a valuable tool for preoperative evaluation. The findings imply that MRI-based assessment of liver function can provide significant benefits in the early identification and management of patients at risk for postoperative liver dysfunction.

Deep learning-based detection and identification of brain tumor biomarkers in quantitative MR-images

The infiltrative nature of malignant gliomas results in active tumor spreading into the peritumoral edema, which is not visible in conventional magnetic resonance imaging (cMRI) even after contrast injection. MR relaxometry (qMRI) measures relaxation rates dependent on tissue properties and can offer additional contrast mechanisms to highlight the non-enhancing infiltrative tumor. To investigate if qMRI data provides additional information compared to cMRI sequences when considering deep learning-based brain tumor detection and segmentation, preoperative conventional (T1w per- and post-contrast, T2w and FLAIR) and quantitative (pre- and post-contrast R1, R2 and proton density) MR data was obtained from 23 patients with typical radiological findings suggestive of a high-grade glioma. 2D deep learning models were trained on transversal slices (n = 528) for tumor detection and segmentation using either cMRI or qMRI. Moreover, trends in quantitative R1 and R2 rates of regions identified as relevant for tumor detection by model explainability methods were qualitatively analyzed. Tumor detection and segmentation performance for models trained with a combination of qMRI pre- and post-contrast was the highest (detection Matthews correlation coefficient (MCC) = 0.72, segmentation dice similarity coefficient (DSC) = 0.90), however, the difference compared to cMRI was not statistically significant. Overall analysis of the relevant regions identified using model explainability showed no differences between models trained on cMRI or qMRI. When looking at the individual cases, relaxation rates of brain regions outside the annotation and identified as relevant for tumor detection exhibited changes after contrast injection similar to region inside the annotation in the majority of cases. In conclusion, models trained on qMRI data obtained similar detection and segmentation performance to those trained on cMRI data, with the advantage of quantitatively measuring brain tissue properties within a similar scan time. When considering individual patients, the analysis of relaxation rates of regions identified by model explainability suggests the presence of infiltrative tumor outside the cMRI-based tumor annotation.

MR microscopy to assess clot composition following mechanical thrombectomy predicts recanalization and clinical outcome

BackgroundMechanical thrombectomy (MT) is the standard of care for patients with a stroke and large vessel occlusion. Clot composition is not routinely assessed in clinical practice as no specific diagnostic...

Physico-chemical and MR relaxometry study of bovine serum albumin-coated magneto-plasmonic nanoparticles designed for potential use in cancer nanotheranostics

PurposeIn recent years, the use of nanoparticles has been developed to improve MRI contrast. To improve the contrast agents in image-guided therapy by Multifunctional nanoparticles, in this study, we synthesized a theranostic magneto-plasmonic nanocomplex based on magnetic iron oxide nanoparticles and bovine serum albumin-modified gold nanorod (Au@BSA-Fe3O4@CMD). The purpose of synthesizing these nanoparticles was to use them as MRI contrast agent and photothermal agents in in vitro and in vivo experiments. Materials and methodsInitially, the properties of the synthesized nanoparticles were investigated by methods such as DLS, TEM, FTIR. MTT assay was used to evaluate the toxicity of nanoparticles. Finally, to evaluate the contrast ability of nanoparticles, MRI images were taken in in vitro and in vivo conditions and then the images were analyzed. ResultsMTT test results on CT26 cell line showed no significant cytotoxicity for Au@BSA-Fe3O4@CMD nanoparticles at concentrations up to 20 ppm. The in vitro results demonstrated that the Au@BSA-Fe3O4@CMD nanocomplex has high T2 relaxation rate and great relaxivities (r2 = 140.14 mM−1 s−1, r1 = 2.066 mM−1 s−1, r2/r1 = 67.83). For in vivo conditions, a decrease in T2 signal of 9.64 and 11.01, respectively, was observed for intratumoral and intraperitoneal injection of nanoparticles. ConclusionThese in vitro and in vivo studies show that Au @ BSA-Fe3O4@CMD nanoparticles can significantly reduce the signal intensity of T2-weight MRI images, and therefore can offer significant potential as a theranostic platform for effective tumor MR imaging.

In vivo assessment of differences in fungal cell density in cerebral cryptococcomas of mice infected with Cryptococcus neoformans or Cryptococcus gattii

In cerebral cryptococcomas caused by Cryptococcus neoformans or Cryptococcus gattii, the density of fungal cells within lesions can contribute to the overall brain fungal burden. In cultures, cell density is inversely related to the size of the cryptococcal capsule, a dynamic polysaccharide layer surrounding the cell. Methods to investigate cell density or related capsule size within fungal lesions of a living host are currently unavailable, precluding in vivo studies on longitudinal changes. Here, we assessed whether intravital microscopy and quantitative magnetic resonance imaging techniques (diffusion MRI and MR relaxometry) would enable non-invasive investigation of fungal cell density in cerebral cryptococcomas in mice. We compared lesions caused by type strains C. neoformans H99 and C. gattii R265 and evaluated potential relations between observed imaging properties, fungal cell density, total cell and capsule size. The observed inverse correlation between apparent diffusion coefficient and cell density permitted longitudinal investigation of cell density changes. Using these imaging methods, we were able to study the multicellular organization and cell density within brain cryptococcomas in the intact host environment of living mice. Since the MRI techniques are also clinically available, the same approach could be used to assess fungal cell density in brain lesions of patients.

Magnetic resonance evidence of increased iron content in subcortical brain regions in asymptomatic Alzheimer's disease.

While iron over-accumulation has been reported in late stage Alzheimer's disease (AD), whether this occurs early in the asymptomatic stage of AD remains unknown. We aimed to assess brain iron levels in asymptomatic AD using quantitative MR relaxometry of effective transverse relaxation rate (R2*) and longitudinal relaxation rate (R1), and recruited 118 participants comprised of three groups including healthy young participants, and cognitively normal older individuals without or with positive AD biomarkers based on cerebrospinal fluid (CSF) proteomics analysis. Compared with the healthy young group, increased R2* was found in widespread cortical and subcortical regions in the older groups. Further, significantly higher levels of R2* were found in the cognitively normal older subjects with positive CSF AD biomarker (i.e., asymptomatic AD) compared with those with negative AD biomarker in subcortical regions including the left and right caudate, left and right putamen, and left and right globus pallidus (p < .05 for all regions), suggesting increased iron content in these regions. Subcortical R2* of some regions was found to significantly correlate with CSF AD biomarkers and neuropsychological assessments of visuospatial functions. In conclusion, R2* could be a valuable biomarker for studying early pathophysiological changes in AD.

Non-destructive classification of unlabeled cells: Combining an automated benchtop magnetic resonance scanner and artificial intelligence.

In order to treat degenerative diseases, the importance of advanced therapy medicinal products has increased in recent years. The newly developed treatment strategies require a rethinking of the appropriate analytical methods. Current standards are missing the complete and sterile analysis of the product of interest to make the drug manufacturing effort worthwhile. They only consider partial areas of the sample or product while also irreversibly damaging the investigated specimen. Two-dimensional T1 / T2 MR relaxometry meets these requirements and is therefore a promising in-process control during the manufacturing and classification process of cell-based treatments. In this study a tabletop MR scanner was used to perform two-dimensional MR relaxometry. Throughput was increased by developing an automation platform based on a low-cost robotic arm, resulting in the acquisition of a large dataset of cell-based measurements. Two-dimensional inverse Laplace transformation was used for post-processing, followed by data classification performed with support vector machines (SVM) as well as optimized artificial neural networks (ANN). The trained networks were able to distinguish non-differentiated from differentiated MSCs with a prediction accuracy of 85%. To increase versatility, an ANN was trained on 354 independent, biological replicates distributed across ten different cell lines, resulting in a prediction accuracy of up to 98% depending on data composition. The present study provides a proof of principle for the application of T1 / T2 relaxometry as a non-destructive cell classification method. It does not require labeling of cells and can perform whole mount analysis of each sample. Since all measurements can be performed under sterile conditions, it can be used as an in-process control for cellular differentiation. This distinguishes it from other characterization techniques, as most are destructive or require some type of cell labeling. These advantages highlight the technique's potential for preclinical screening of patient-specific cell-based transplants and drugs.

1.5-T MR relaxometry in quantifying splenic and pancreatic iron: retrospective comparison of a commercial 3D-Dixon sequence and an established 2D multi-gradient echo sequence

ObjectivesTo compare the quantitative measurement of splenic and pancreatic iron content using a commercial 3D-Dixon sequence (qDixon) versus an established fat-saturated R2* relaxometry method (ME-GRE).MethodsWe analyzed splenic and pancreatic iron levels in 143 MR examinations (1.5 T) using the qDixon and a ME-GRE sequence (108 patients: 65 males, 43 females, mean age 61.31 years). Splenic and pancreatic R2* values were compared between both methods using Bland–Altman plots, concordance correlation coefficients (CCC), and linear regression analyses. Iron overload (R2* > 50 1/s) was defined for both organs and compared using contingency tables, overall agreement, and Gwet’s AC1 coefficient.ResultsOf all analyzable examinations, the median splenic R2* using the qDixon sequence was 25.75 1/s (range: 5.6–433) and for the ME-GRE sequence 35.35 1/s (range: 10.9–400.8) respectively. Concerning the pancreas, a median R2* of 29.93 1/s (range: 14–111.45) for the qDixon and 31.25 1/s (range: 14–97) for the ME-GRE sequence was found. Bland–Altman analysis showed a mean R2* difference of 2.12 1/s with a CCC of 0.934 for the spleen and of 0.29 1/s with a CCC of 0.714 for the pancreas. Linear regression for the spleen/pancreas resulted in a correlation coefficient of 0.94 (p < 0.001)/0.725 (p < 0.001). Concerning iron overload, the proportion of overall agreement between the two methods was 91.43% for the spleen and 93.18% for the pancreas.ConclusionsOur data show good concordance between R2* values obtained with a commercial qDixon sequence and a validated ME-GRE relaxometry method. The 3D-qDixon sequence, originally intended for liver assessment, seems to be a reliable tool for non-invasive evaluation of iron content also in the spleen and the pancreas.Key Points• A 3D chemical shift imaging sequence and 2D multi-gradient echo sequence show good conformity quantifying splenic and pancreatic R2* values.• The 3D chemical shift imaging sequence allows a reliable analysis also of splenic and pancreatic iron status.• In addition to the liver, the analysis of the spleen and pancreas is often helpful for further differential diagnostic clarification and patient guidance regarding the iron status.

Single-Sided Magnet System for Quantitative MR Relaxometry and Preclinical In-Vivo Monitoring.

We have developed a single-sided magnet system that allows Magnetic Resonance relaxation and diffusion parameters to be measured. A single-sided magnet system has been developed, using an array of permanent magnets. The magnet positions are optimised to produce a B0 magnetic field with a spot that is relatively homogenous and can project into a sample. NMR relaxometry experiments are used to measure quantitative parameters such as T2, T1 and apparent diffusion coefficient (ADC) on samples on the benchtop. To explore preclinical application, we test whether it can detect changes during acute global cerebral hypoxia in an ovine model. The magnet produces a 0.2 T field projected into the sample. Measurements of benchtop samples show that it can measure T1, T2 and ADC, producing trends and values that are in line with literature measurements. In-vivo studies show a decrease in T2 during cerebral hypoxia that recovers following normoxia. The single-sided MR system has the potential to allow non-invasive measurements of the brain. We also demonstrate that it can operate in a pre-clinical environment, allowing T2 to be monitored during brain tissue hypoxia. MRI is a powerful technique for non-invasive diagnosis in the brain, but its application has been limited by the requirements for magnetic field strength and homogeneity that imaging methods have. The technology described in this study provides a portable alternative to acquiring clinically significant MR parameters without the need for traditional imaging equipment.

Predicting the Early Response to Neoadjuvant Therapy with Breast MR Morphological, Functional and Relaxometry Features-A Pilot Study.

Aim: To evaluate the role of MR relaxometry and derived proton density analysis in the prediction of early treatment response after two cycles of neoadjuvant therapy (NAT), in patients with breast cancer. Methods: This was a prospective study that included 59 patients with breast cancer, who underwent breast MRI prior (MRI1) and after two cycles of NAT (MRI2). The MRI1 included a sequential acquisition with five different TE’s (50, 100, 150, 200 and 250 ms) and a TR of 5000 ms. Post-processing was used to obtain the T2 relaxometry map from the MR acquisition. The tumor was delineated and seven relaxometry and proton density parameters were extracted. Additional histopathology data, T2 features and ADC were included. The response to NAT was reported based on the MRI2 as responders: partial response (>30% decreased size) and complete response (no visible tumor stable disease (SD); and non-responders: stable disease or progression (>20% increased size). Statistics was done using Medcalc software. Results: There were 50 (79.3%) patients with response and 13 (20.7%) non-responders to NAT. Age, histologic type, “in situ” component, tumor grade, estrogen and progesterone receptors, ki67% proliferation index and HER2 status were not associated with NAT response (all p > 0.05). The nodal status (N) 0 was associated with early response, while N2 was associated with non-response (p = 0.005). The tumor (T) and metastatic (M) stage were not statistically significant associated with response (p > 0.05). The margins, size and ADC values were not associated with NAT response (p-value > 0.05). The T2 min relaxometry value was associated with response (p = 0.017); a cut-off value of 53.58 obtained 86% sensitivity (95% CI 73.3−94.2), 69.23 specificity (95% CI 38.6−90.9), with an AUC = 0.715 (p = 0.038). The combined model (T2 min and N stage) achieved an AUC of 0.826 [95% CI: 0.66−0.90, p-value < 0.001]. Conclusions: MR relaxometry may be a useful tool in predicting early treatment response to NAT in breast cancer patients.

SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling.

To develop an ultrafast and robust MR parameter mapping network using deep learning. We design a deep learning framework called SuperMAP that directly converts a series of undersampled (both in k-space and parameter-space) parameter-weighted images into several quantitative maps, bypassing the conventional exponential fitting procedure. We also present a novel technique to simultaneously reconstruct T1rho and T2 relaxation maps within a single scan. Full data were acquired and retrospectively undersampled for training and testing using traditional and state-of-the-art techniques for comparison. Prospective data were also collected to evaluate the trained network. The performance of all methods is evaluated using the parameter qualification errors and other metrics in the segmented regions of interest. SuperMAP achieved accurate T1rho and T2 mapping with high acceleration factors (R=24 and R=32). It exploited both spatial and temporal information and yielded low error (normalized mean square error of 2.7% at R=24 and 2.8% at R=32) and high resemblance (structural similarity of 97% at R=24 and 96% at R=32) to the gold standard. The network trained with retrospectively undersampled data also works well for the prospective data (with a slightly lower acceleration factor). SuperMAP is also superior to conventional methods. Our results demonstrate the feasibility of generating superfast MR parameter maps through very few undersampled parameter-weighted images. SuperMAP can simultaneously generate T1rho and T2 relaxation maps in a short scan time.

Multiparametric MRI identifies subtle adaptations for demarcation of disease transition in murine aortic valve stenosis

Aortic valve stenosis (AS) is the most frequent valve disease with relevant prognostic impact. Experimental model systems for AS are scarce and comprehensive imaging techniques to simultaneously quantify function and morphology in disease progression are lacking. Therefore, we refined an acute murine AS model to closely mimic human disease characteristics and developed a high-resolution magnetic resonance imaging (MRI) approach for simultaneous in-depth analysis of valvular, myocardial as well as aortic morphology/pathophysiology to identify early changes in tissue texture and critical transition points in the adaptive process to AS. AS was induced by wire injury of the aortic valve. Four weeks after surgery, cine loops, velocity, and relaxometry maps were acquired at 9.4 T to monitor structural/functional alterations in valve, aorta, and left ventricle (LV). In vivo MRI data were subsequently validated by histology and compared to echocardiography. AS mice exhibited impaired valve opening accompanied by significant valve thickening due to fibrotic remodelling. While control mice showed bell-shaped flow profiles, AS resulted not only in higher peak flow velocities, but also in fragmented turbulent flow patterns associated with enhanced circumferential strain and an increase in wall thickness of the aortic root. AS mice presented with a mild hypertrophy but unaffected global LV function. Cardiac MR relaxometry revealed reduced values for both T1 and T2 in AS reflecting subtle myocardial tissue remodelling with early alterations in mitochondrial function in response to the enhanced afterload. Concomitantly, incipient impairments of coronary flow reserve and myocardial tissue integrity get apparent accompanied by early troponin release. With this, we identified a premature transition point with still compensated cardiac function but beginning textural changes. This will allow interventional studies to explore early disease pathophysiology and novel therapeutic targets.

344 Comparison of Magnetic Resonance Relaxometry and Apparent Diffusion Coefficient for Tissue Cell Density Quantification in Glioma Imaging: Exploratory Study via 11C-methionine PET and Validation via Stereotactic Tissue Sampling

INTRODUCTION: Visualization of non-contrast-enhancing tumor lesions in glioma is one of the most crucial yet challenging issues for patients with this pathology. METHODS: Participants comprised of patients with low- or high-grade glioma. Correlation between T1-, T2-relaxation time, ADC, and 11C-methionine uptake as measured by positron emission tomography was investigated, followed by comparing T1-, T2-relaxation time, ADC with tumor cell density as measured by stereotactic image-guided tissue sampling. MR relaxometry was achieved by converting Magnetization Prepared Rapid Gradient Echo (MP2RAGE) images and multi-echo T2-weighted images via Bayesian inference modeling. RESULTS: T1-relaxation time &gt;1850 ms but &lt;3200 ms or T2-relaxation time &gt;115 ms, but &lt;225 ms under 3T were indicative of high 11C-methionine uptake. Stereotactic tissue sampling study confirmed that only the T1-relaxation time of 1850-3200 ms was significantly indicative of higher tumor cell density (p = 0.02). ADC was unable to show a significant correlation with 11C-methionine uptake or with tumor cell density. Synthetic tumor load images were successfully reconstructed using T1- and T2-relaxation mapping. CONCLUSION: T1-relaxation time correlated well with tumor cell density in glioma tissues, while neither T2-relaxation time nor ADC showed significant correlation. The ideal ranges for identifying high tumor load tissues was 1850-3200 ms for T1-relaxation time measured at 3 T.