Quantitative Multiparametric Magnetic Resonance Imaging Research Articles

A three-dimensional Magnetic Resonance Spin Tomography in Time-domain protocol for high-resolution multiparametric quantitative magnetic resonance imaging.

Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) is a multiparametric quantitative MR framework, which allows for simultaneously acquiring quantitative tissue parameters such as T1, T2, and proton density from one single short scan. A typical two-dimensional (2D) MR-STAT acquisition uses a gradient-spoiled, gradient-echo sequence with a slowly varying RF flip-angle train and Cartesian readouts, and the quantitative tissue maps are reconstructed by an iterative, model-based optimization algorithm. In this work, we design a three-dimensional (3D) MR-STAT framework based on previous 2D work, in order to achieve better image signal-to-noise ratio, higher though-plane resolution, and better tissue characterization. Specifically, we design a 7-min, high-resolution 3D MR-STAT sequence, and the corresponding two-step reconstruction algorithm for the large-scale dataset. To reduce the long acquisition time, Cartesian undersampling strategies such as SENSE are adopted in our transient-state quantitative framework. To reduce the computational burden, a data-splitting scheme is designed for decoupling the 3D reconstruction problem into independent 2D reconstructions. The proposed 3D framework is validated by numerical simulations, phantom experiments, and in vivo experiments. High-quality knee quantitative maps with 0.8 × 0.8 × 1.5 mm3 resolution and bilateral lower leg maps with 1.6 mm isotropic resolution can be acquired using the proposed 7-min acquisition sequence and the 3-min-per-slice decoupled reconstruction algorithm. The proposed 3D MR-STAT framework could have wide clinical applications in the future.

Physical model-based cascaded generative adversarial networks for accelerating quantitative multi-parametric magnetic resonance imaging

To explore the feasibility and interpretation of physical model- based cascaded generative adversarial networks for accelerating quantitative multi-echo multi-parametric magnetic resonance imaging using raw multi-echo multicoil k-space data. A physical model-based cascaded generative adversarial network is proposed to enhance image feature information to obtain high-quality reconstructed images using joint training of multi-domain information and learning of key parameters required for image reconstruction through a system matrix and adaptively optimizing the k-space generator and image generator structures. Raw multi-echo multi-coil k-space data are used to accelerate multi-contrast multi-parametric magnetic resonance imaging. A physically driven deep learning reconstruction method is used to increase the generalization capability and improve the model performance by building a system matrix function instead of direct end-to-end training of the model. In terms of overall image quality, the proposed model achieved significant improvements compared to other methods. On an 80- case test set, the average PSNR value of the reconstructed images was 34.13, SSIM was 0.965, and NRMSE was 0.114. In terms of multi-contrast multi-parametric image reconstruction, the model achieved PSNR values of 38.87 for PDW, 35.62 for T1W, and 34.38 for T2* Map, which were significantly better than those of other methods for quantitative evaluation. The model also produced clearer features of the brain gray matter, white matter, and cerebrospinal fluid. Furthermore, compared with the existing methods with a reconstruction time difference of less than 10%, the proposed method achieved the highest improvement of up to 20% in the metrics of PSNR, SSIM, and NRMSE. Compared with other existing methods, the physical model-based cascaded generative adversarial networks can reconstruct more image details and features, thus improving the quality and accuracy of the reconstructed images.

Focal epilepsy without overt epileptogenic lesions: no evidence of microstructural brain tissue damage in multi-parametric quantitative MRI.

In patients with epilepsies of structural origin, brain atrophy and pathological alterations of the tissue microstructure extending beyond the putative epileptogenic lesion have been reported. However, in patients without any evidence of epileptogenic lesions on diagnostic magnetic resonance imaging (MRI), impairment of the brain microstructure has been scarcely elucidated. Using multiparametric quantitative (q) magnetic resonance imaging MRI, we aimed to investigate diffuse impairment of the microstructural tissue integrity in MRI-negative focal epilepsy patients. 27 MRI-negative patients with focal epilepsy (mean age 33.1 ± 14.2 years) and 27 matched healthy control subjects underwent multiparametric qMRI including T1, T2, and PD mapping at 3 T. After tissue segmentation based on synthetic anatomies, mean qMRI parameter values were extracted from the cerebral cortex, the white matter (WM) and the deep gray matter (GM) and compared between patients and control subjects. Apart from calculating mean values for the qMRI parameters across the respective compartments, voxel-wise analyses were performed for each tissue class. There were no significant differences for mean values of quantitative T1, T2, and PD obtained from the cortex, the WM and the deep GM between the groups. Furthermore, the voxel-wise analyses did not reveal any clusters indicating significant differences between patients and control subjects for the qMRI parameters in the respective compartments. Based on the employed methodology, no indication for an impairment of the cerebral microstructural tissue integrity in MRI-negative patients with focal epilepsy was found in this study. Further research will be necessary to identify relevant factors and mechanisms contributing to microstructural brain tissue damage in various subgroups of patients with epilepsy.

Multiparametric Quantitative MRI of Peripheral Nerves in the Leg: A Reliability Study.

Patients with polyneuropathies typically have demyelination and/or axonal degeneration in peripheral nerves. Currently, there is a lack of imaging biomarkers to track the changes in these pathologies. To develop and evaluate the reliability of a multiparametric quantitative magnetic resonance imaging (qMRI) method of peripheral nerves in the leg. Prospective. Seventeen healthy volunteers (36.2 ± 13.8 years old, 9 males) with 10 of them scanned twice for test-retest. 3 T, three-dimensional gradient echo and diffusion tensor imaging. A qMRI protocol and processing pipeline was established for quantifying the following nerve parameters that are sensitive to myelin and axonal pathologies: magnetization transfer (MT) ratio (MTR), MT saturation index (MTsat), T2 *, T1 , proton density (PD), fractional anisotropy (FA), and mean/axial/radial diffusivities (MD, AD, and RD). The qMRI protocol also measures the volume of nerve fascicles (fVOL) and the fat fraction (FF) of muscles. The intersession reproducibility and inter-rater reliability of each qMRI parameter were assessed by Bland-Altman analysis and intraclass correlation coefficient (ICC). Pairwise Pearson correlation analyses were performed to investigate the intrinsic association between qMRI parameters. Distal-to-proximal variations were evaluated by paired t-tests with Bonferroni-Holm multiple comparison corrections. P < 0.05 was considered statistically significant. The MTR, MTsat, T2 *, T1 , PD, FA, AD, and fVOL of the sciatic and tibial nerves, and the FF of leg muscles, had an overall good-to-excellent test-retest agreement (ICC varying from 0.78 to 0.99). All the qMRI parameters had good-to-excellent inter-rater reliability (ICC > 0.80). The data demonstrated a pattern of distal-to-proximal changes of an increased nerve MTsat and FA, and a decreased nerve T1 , PD, MD, and RD, as well as a significantly increased muscle FF. The proposed multiparametric qMRI method of the peripheral nerves is highly reproducible and provided healthy control data which will be used in developing monitoring biomarkers in patients with polyneuropathies. 1 TECHNICAL EFFICACY: Stage 2.

Feasibility of in vivo multi-parametric quantitative magnetic resonance imaging of the healthy sciatic nerve with a unified signal readout protocol

Magnetic resonance neurography (MRN) has been used successfully over the years to investigate the peripheral nervous system (PNS) because it allows early detection and precise localisation of neural tissue damage. However, studies demonstrating the feasibility of combining MRN with multi-parametric quantitative magnetic resonance imaging (qMRI) methods, which provide more specific information related to nerve tissue composition and microstructural organisation, can be invaluable. The translation of emerging qMRI methods previously validated in the central nervous system to the PNS offers real potential to characterise in patients in vivo the underlying pathophysiological mechanisms involved in a plethora of conditions of the PNS. The aim of this study was to assess the feasibility of combining MRN with qMRI to measure diffusion, magnetisation transfer and relaxation properties of the healthy sciatic nerve in vivo using a unified signal readout protocol. The reproducibility of the multi-parametric qMRI protocol as well as normative qMRI measures in the healthy sciatic nerve are reported. The findings presented herein pave the way to the practical implementation of joint MRN-qMRI in future studies of pathological conditions affecting the PNS.

Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images

The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is limited by the specificity of data for a particular medical condition. In this context, the most recent technologies, like generative adversarial networks (GANs), are being looked upon as a potential way to generate high-quality synthetic data that preserve the clinical variability of a condition. However, despite some success, GAN model usage remains largely minimal when depicting the heterogeneity of a disease such as prostate cancer. Previous studies from our group members have focused on automating the quantitative multi-parametric magnetic resonance imaging (mpMRI) using habitat risk scoring (HRS) maps on the prostate cancer patients in the BLaStM trial. In the current study, we aimed to use the images from the BLaStM trial and other sources to train the GAN models, generate synthetic images, and validate their quality. In this context, we used T2-weighted prostate MRI images as training data for Single Natural Image GANs (SinGANs) to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degrees of experience (more than ten years, one year, or no experience) to work with MRI images. Results showed that the most experienced participating group correctly identified conventional vs. synthetic images with 67% accuracy, the group with one year of experience correctly identified the images with 58% accuracy, and the group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional. Interestingly, in a blinded quality assessment, a board-certified radiologist did not significantly differentiate between conventional and synthetic images in the context of the mean quality of synthetic and conventional images. Furthermore, to validate the usability of the generated synthetic images from prostate cancer MRIs, we subjected these to anomaly detection along with the original images. Importantly, the success rate of anomaly detection for quality control-approved synthetic data in phase one corresponded to that of the conventional images. In sum, this study shows promise that high-quality synthetic images from MRIs can be generated using GANs. Such an AI model may contribute significantly to various clinical applications which involve supervised machine-learning approaches.

Multiparametric MR imaging with diffusion-weighted, intravoxel incoherent motion, diffusion tensor, and dynamic contrast-enhanced perfusion sequences to assess gallbladder wall thickening: a prospective study based on surgical histopathology.

To investigate the diagnostic performance of a multiparametric magnetic resonance imaging (MRI) protocol comprising quantitative MRI (diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), and dynamic contrast-enhanced (DCE) perfusionMRI) and conventional MRI in the characterization of gallbladder wall thickening (GWT). This prospective study comprised consecutive adults with GWT who underwent multiparametric MRI between July 2020 and April 2022. Two radiologistsevaluated the MRI independently. The final diagnosis was based on surgical histopathology. The association of MRI parameters with malignant GWT was evaluated. The area under the curve (AUC) for the quantitative MRI parameters and diagnostic performance of conventional, and multiparametric MRI were compared. The interobserver agreementbetween two radiologists was calculated. Thirty-five patients (mean age, 56years, 23 females) with GWT (25 benign and ten malignant) were evaluated. The quantitative MRI parameters significantly associated with malignant GWT were apparent diffusion coefficient on DWI (p = 0.007) and mean diffusivity (MD) on DTI (p = 0.013), perfusion fraction (f) on IVIM (p = 0.033), time to peak enhancement (TTP, p = 0.008), and wash in rate (p = 0.049) on DCE-MRI. TTP had the highest AUC of 0.790, followed by MD (0.782) and f (0.742) (p = 0.213) for predicting malignant GWT. Multiparametric MRI had significantly higher sensitivity (90% vs. 80%, p = 0.045) than conventional MRI for diagnosing malignant GWT. The two radiologists' reading had substantial to near-perfect agreement (kappa = 0.639-1) and moderate to strong correlation (interclass correlation coefficient = 0.5-0.88). Multiparametric protocol incorporating advanced sequences improved the diagnostic performance of MRI for differentiating benign and malignant GWT. • Multiparametric MRI had 90% sensitivity and 88% specificity for diagnosing malignant GWT, compared to 80% sensitivity and 88% specificity for conventional CE-MRI. • Among the quantitative MRI parameters, TTP (perfusion-MRI) had the highest AUC of 0.790, followed by MD (0.782) and IVIM-f (0.742). • For most quantitative MRI parameters, there was moderate to strong agreement (ICC = 0.5-0.88).

Phase-constrained reconstruction method with compressed sensing for multi-parametric quantitative magnetic resonance imaging

PurposeIn magnetic resonance imaging (MRI), STAGE method is a novel quantitative imaging method to generate multi-parametric quantitative images during single one scan. Due to the use of multiple echo sequences with double flip angles, current acquisition methods consume a lot of time, which limits the clinical application of these methods. To shorten the acquisition time of the STAGE method, a phase-constrained CS reconstruction method combined with parallel imaging (PI) is proposed in this paper. MethodsThe phase consistency between the acquisition data from different flip angles (FA) is exploited to reconstruct STAGE sequences. The phase from the first flip acquisition is used as the constraint term in the CS model to reconstruct the acquisition data from the second flip angle. To solve the optimization problem with multiple regularization in the CS model,the primal–dual algorithm is applied to the optimization problem. In vivo data from 10 volunteers are reconstructed, and the reconstruction results are compared with those of the conventional l1-iterative self-consistent parallel imaging reconstruction from arbitrary k-space (l1-SPIRiT) method. Also, quantitative comparisons are performed with multiple quantitative matrics including structural similarity index measure (SSIM), peak signal-to-noise ratio (pSNR) for the contrast image, normalized root-mean-square error (NRMS), and linear relationship value. ResultsThe experimental results demonstrate that the proposed method outperforms the conventional l1-SPIRiT method under different acceleration factors. Meanwhile, the proposed method achieves better SSIM and higher pSNR than the conventional l1-SPIRiT for T1 weighted (T1W) images. Besides, the quantitative mapping obtained by the proposed method under different acceleration factors have better consistency with the reference one. Moreover, the proposed method enables further acceleration of the image acquisition of STAGE sequences. ConclusionThe proposed phase-constrained reconstruction method with compressed sensing accelerates STAGE without compromising the image quality, which enables higher acceleration than the conventional method.

The Utility of Mean Apparent Diffusion Coefficient Value in Patients with Prostate Cancer Treated with Definitive Radiotherapy

<h3>Purpose/Objective(s)</h3> By providing more objective measurements, quantitative magnetic resonance imaging (MRI) has the potential to further improve prediction of treatment outcomes for prostate cancer patients. The purpose of this study was to determine whether quantitative multiparametric MRI performed prior to treatment can accurately predict the biochemical outcome of prostate cancer patients receiving definitive radiotherapy (RT). <h3>Materials/Methods</h3> The clinical data of 503 prostate cancer patients who received definitive radiation therapy (RT) between June 2008 and September 2021 were analyzed retrospectively. Prior to RT, all patients had a prostate multiparametric MRI on a 3T MRI scanner. A radiologist outlined the index lesion and measured the quantitative apparent diffusion coefficient (ADC). The Phoenix criteria were used to define biochemical failure. The primary endpoints were failure from biochemical failure (FFBF) and progression free survival (PFS). <h3>Results</h3> The median age of the population was 69 years (range: 47–87 years). According to D'Amico risk categories, there were 183 patients (36.4%) classified as low risk, 156 patients (31%) classified as intermediate risk, and 164 patients (32.6%) classified as high risk. The mean ADC values for tumor and normal prostate tissue were 0.712±0.158 µm²/ms and 1.365±0.227 µm²/ms, respectively (p<0.001). The ADC values were significantly lower in patients with higher PSA (≥20 ng/mL), advanced T stage (≥T3a), nodal metastasis, higher ISUP score (≥3) and high-risk group compared to their counterparts. The median duration of follow-up was 72.9 months (range, 4.3–170.6 months) for the entire cohort. The 5-year FFBF and PFS rates were 93.2% and 86.2%, respectively. Biochemical failure was observed in 42 patients (9.3%). After ‘Receiver Operating Characteristics' (ROC) curve analysis, the cut-off point of ADC for predicting high-risk progression was 0.653 µm²/ms with area under curve of 0.656 (95%CI 0.581 – 0.736; p<0.001). In univariate analysis, PSA, T stage, ISUP score, risk group were significant prognostic factors for FFBF and PFS. Additional prognostic factor for OS was N stage, and age was for PFS. The 5-year FFBF for lower and higher ADC values were 89.1% and 95.6%, respectively (p<0.001). The 5-year PFS was significantly less for patients with lower ADC compared to those with higher ADC (82.2% vs. 88.5%; p = 0.002). In multivariate analysis higher ISUP score and lower ADC values were independent predictors for worse FFBF and PFS rates. Additional predictive factor for dismal PFS was older age. <h3>Conclusion</h3> Our findings suggest that quantitative ADC values of the index lesion may be used to predict biochemical failure and disease progression in patients with prostate cancer following definitive RT. Inferior biochemical control was associated with lower ADC values and higher ISUP scores.

Single-shot multi-parametric mapping based on multiple overlapping-echo detachment (MOLED) imaging

Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the characterization of multiple tissue properties non-invasively and has shown great potential to enhance the sensitivity of MRI measurements. However, real-time mqMRI during dynamic physiological processes or general motions remains challenging. To overcome this bottleneck, we propose a novel mqMRI technique based on multiple overlapping-echo detachment (MOLED) imaging, termed MQMOLED, to enable mqMRI in a single shot. In the data acquisition of MQMOLED, multiple MR echo signals with different multi-parametric weightings and phase modulations are generated and acquired in the same k-space. The k-space data is Fourier transformed and fed into a well-trained neural network for the reconstruction of multi-parametric maps. We demonstrated the accuracy and repeatability of MQMOLED in simultaneous mapping apparent proton density (APD) and any two parameters among T2, T2*, and apparent diffusion coefficient (ADC) in 130–170 ms. The abundant information delivered by the multiple overlapping-echo signals in MQMOLED makes the technique potentially robust to system imperfections, such as inhomogeneity of static magnetic field or radiofrequency field. Benefitting from the single-shot feature, MQMOLED exhibits a strong motion tolerance to the continuous movements of subjects. For the first time, it captured the synchronous changes of ADC, T2, and T1-weighted APD in contrast-enhanced perfusion imaging on patients with brain tumors, providing additional information about vascular density to the hemodynamic parametric maps. We expect that MQMOLED would promote the development of mqMRI technology and greatly benefit the applications of mqMRI, including therapeutics and analysis of metabolic/functional processes.

Preoperative Multiparametric Quantitative Magnetic Resonance Imaging Correlates with Prognosis and Recurrence Patterns in Pancreatic Ductal Adenocarcinoma.

Simple SummaryMagnetic resonance imaging (MRI) has been considered a noninvasive prognostic biomarker in some cancers; however, the correlation with pancreatic ductal adenocarcinoma (PDAC) remains inconclusive. The aim of our study was to identify quantitative MRI parameters associated with prognosis and recurrence patterns. In an analysis of data from the 136 patients ultimately included in this study, we found that the value of the pure diffusion coefficient D in intravoxel incoherent MRI is an independent risk factor for overall survival (OS) and recurrence-free survival (RFS), while a low value of D is significantly associated with a higher risk of local recurrence. All the patients have been operated on with histopathology for further evaluation. Based on the results of our research, we believe that it is possible in clinical practice to stratify patients based on quantitative MRI data in order to guide treatment strategies, reduce the risk of local tumor recurrence, and improve patients’ prognosis.Magnetic resonance imaging (MRI) has been shown to be associated with prognosis in some tumors; however, the correlation in pancreatic ductal adenocarcinoma (PDAC) remains inconclusive. In this retrospective study, we ultimately included 136 patients and analyzed quantitative MRI parameters that are associated with prognosis and recurrence patterns in PDAC using survival analysis and competing risks models; all the patients have been operated on with histopathology and immunohistochemical staining for further evaluation. In intravoxel incoherent motion diffusion-weighted imaging (DWI), we found that pure-diffusion coefficient D value was an independent risk factor for overall survival (OS) (HR: 1.696, 95% CI: 1.003–2.869, p = 0.049) and recurrence-free survival (RFS) (HR: 2.066, 95% CI: 1.252–3.409, p = 0.005). A low D value (≤1.08 × 10−3 mm2/s) was significantly associated with a higher risk of local recurrence (SHR: 5.905, 95% CI: 2.107–16.458, p = 0.001). Subgroup analysis revealed that patients with high D and f values had significantly better outcomes with adjuvant chemotherapy. Distant recurrence patients in the high-D value group who received chemotherapy may significantly improve their OS and RFS. It was found that preoperative multiparametric quantitative MRI correlates with prognosis and recurrence patterns in PDAC. Diffusion coefficient D value can be used as a noninvasive biomarker for predicting prognosis and recurrence patterns in PDAC.

Non-Invasive Monitoring of Increased Fibrotic Tissue and Hyaluronan Deposition in the Tumor Microenvironment in the Advanced Stages of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinomas are characterized by a complex and robust tumor microenvironment (TME) consisting of fibrotic tissue, excessive levels of hyaluronan (HA), and immune cells. We utilized quantitative multi-parametric magnetic resonance imaging (mp-MRI) methods at 14 Tesla in a genetically engineered KPC (KrasLSL-G12D/+, Trp53LSL-R172H/+, Cre) mouse model to assess the complex TME in advanced stages of tumor development. The whole tumor, excluding cystic areas, was selected as the region of interest for data analysis and subsequent statistical analysis. Pearson correlation was used for statistical inference. There was a significant correlation between tumor volume and T2 (r = -0.66), magnetization transfer ratio (MTR) (r = 0.60), apparent diffusion coefficient (ADC) (r = 0.48), and Glycosaminoglycan-chemical exchange saturation transfer (GagCEST) (r = 0.51). A subset of mice was randomly selected for histological analysis. There were positive correlations between tumor volume and fibrosis (0.92), and HA (r = 0.76); GagCEST and HA (r = 0.81); and MTR and CD31 (r = 0.48). We found a negative correlation between ADC low-b (perfusion) and Ki67 (r = -0.82). Strong correlations between mp-MRI and histology results suggest that mp-MRI can be used as a non-invasive tool to monitor the tumor microenvironment.

Multi-parametric quantitative magnetic resonance imaging of the upper arm muscles of patients with spinal muscular atrophy.

Quantitative magnetic resonance imaging (qMRI) is frequently used to map the disease state and disease progression in the lower extremity muscles of patients with spinal muscular atrophy (SMA). This is in stark contrast to the almost complete lack of data on the upper extremity muscles, which are essential for carrying out daily activities. The aim of this study was therefore to assess the disease state in the upper arm muscles of patients with SMA in comparison with healthy controls by quantitative assessment of fat fraction, diffusion indices, and water T2 relaxation times, and to relate these measures to muscle force. We evaluated 13 patients with SMA and 15 healthy controls with a 3‐T MRI protocol consisting of DIXON, diffusion tensor imaging, and T2 sequences. qMRI measures were compared between groups and related to muscle force measured with quantitative myometry. Fat fraction was significantly increased in all upper arm muscles of the patients with SMA compared with healthy controls and correlated negatively with muscle force. Additionally, fat fraction was heterogeneously distributed within the triceps brachii (TB) and brachialis muscle, but not in the biceps brachii muscle. Diffusion indices and water T2 relaxation times were similar between patients with SMA and healthy controls, but we did find a slightly reduced mean diffusivity (MD), λ1, and λ3 in the TB of patients with SMA. Furthermore, MD was positively correlated with muscle force in the TB of patients with SMA. The variation in fat fraction further substantiates the selective vulnerability of muscles. The reduced diffusion tensor imaging indices, along with the positive correlation of MD with muscle force, point to myofiber atrophy. Our results show the feasibility of qMRI to map the disease state in the upper arm muscles of patients with SMA. Longitudinal data in a larger cohort are needed to further explore qMRI to map disease progression and to capture the possible effects of therapeutic interventions.

Phase-Constrained Reconstruction Method with Compressed Sensing for Multi-Parametric Quantitative Magnetic Resonance Imaging

Phase-Constrained Reconstruction Method with Compressed Sensing for Multi-Parametric Quantitative Magnetic Resonance Imaging

Quantitative magnetic resonance imaging of chronic kidney disease: an experimental in vivo study using rat chronic kidney disease models.

Recent advances in magnetic resonance imaging (MRI) may allow it to be an alternative emerging tool for the non-invasive evaluation of renal parenchymal disease. To validate the usefulness of quantitative multiparametric MRI protocols and suggest the suitable quantitative MR sequence protocol to evaluate parenchymal fibrosis using an animal model of chronic kidney disease (CKD) by long-term adenine intake. In this prospective animal study, 16 male Wistar rats were analyzed and categorized into three groups. Rats in the CKD groups underwent 0.25% adenine administration for three or six weeks. Quantitative MRI protocols, including diffusion-weighted imaging (DWI), T1ρ (T1 rho), and T2* mapping were performed using a 9.4-T animal MR scanner. A semi-quantitative histopathologic analysis for renal fibrosis was conducted. Quantitative MR values measured from anatomic regions of kidneys underwent intergroup comparative analyses. The apparent diffusion coefficient (ADC) and T1 (T1 rho) values were significantly increased in all CKD groups. Values measured from the cortex and outer medulla showed significant intergroup differences. Total ADC values tended to increase according to periods, and T1ρ values increased in three weeks and decreased in six weeks. Quantitative MRI protocols could be a non-invasive assessment modality in the diagnosis and evaluation of CKD. Particularly, T1ρ may be a suitable MR sequence to quantitatively assess renal parenchymal fibrosis.

Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting

BackgroundThe purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer.MethodsPatients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves.ResultsTumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR.ConclusionsQuantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.

Toward magnetic resonance fingerprinting for low-field MR-guided radiation therapy.

The acquisition of multiparametric quantitative magnetic resonance imaging (qMRI) is becoming increasingly important for functional characterization of cancer prior to- and throughout the course of radiation therapy. The feasibility of a qMRI method known as magnetic resonance fingerprinting (MRF) for rapid T1 and T2 mapping was assessed on a low-field MR-linac system. A three-dimensional MRF sequence was implemented on a 0.35T MR-guided radiotherapy system. MRF-derived measurements of T1 and T2 were compared to those obtained with gold standard single spin echo methods, and the impacts of the radiofrequency field homogeneity and scan times ranging between 6 and 48 min were analyzed by acquiring between 1 and 8 spokes per time point in a standard quantitative system phantom. The short-term repeatability of MRF was assessed over three measurements taken over a 10-h period. To evaluate transferability, MRF measurements were acquired on two additional MR-guided radiotherapy systems. Preliminary human volunteer studies were performed. The phantom benchmarking studies showed that MRF is capable of mapping T1 and T2 values within 8% and 10% of gold standard measures, respectively, at 0.35T. The coefficient of variation of T1 and T2 estimates over three repeated scans was<5% over a broad range of relaxation times. The T1 and T2 times derived using a single-spoke MRF acquisition across three scanners were near unity and mean percent errors in T1 and T2 estimates using the same phantom were<3%. The mean percent differences in T1 and T2 as a result of truncating the scan time to 6 min over the large range of relaxation times in the system phantom were 0.65% and 4.05%, respectively. The technical feasibility and accuracy of MRF on a low-field MR-guided radiation therapy device has been demonstrated. MRF can be used to measure accurate T1 and T2 maps in three dimensions from a brief 6-min scan, offering strong potential for efficient and reproducible qMRI for future clinical trials in functional plan adaptation and tumor/normal tissue response assessment.

Utility of multi-parametric quantitative magnetic resonance imaging of the lacrimal gland for diagnosing and staging Graves’ ophthalmopathy

PurposeTo explore radiological changes of the lacrimal gland (LG) in Graves’ ophthalmopathy (GO) based on multi-parametric quantitative MRI and its clinical utility in LG diagnosis and activity in GO. MethodsWe enrolled 99 consecutive patients with GO (198 eyes) and 12 Graves’ Disease (GD) patients (24 eyes) from July 2018 to June 2020. Clinical, laboratory, and MRI data were collected at the first visit. Based on clinical activity scores, eyes with GO were subdivided into active and inactive groups. T2-relaxation time (T2) and the absolute reduction in T1-relaxation time (ΔT1) were determined. After MRI and processing, we performed descriptive data analysis and group comparisons. Novel logistic regression predictive models were developed for diagnosing and staging GO. Diagnostic performance of MRI parameters and models was assessed by receiver operating characteristic curve analysis. ResultsLG in GO group had significantly higher T2 and ΔT1 values than the GD group [106.25(95.30,120.21) vs. 83.35(78.15,91.45), P<0.001, and 662.62(539.33,810.95) vs. 547.35(458.62,585.57), P = 0.002, respectively]. The GO group had higher T2 of LG indicating higher disease activity [110.93(102.54,127.67) vs. 93.29(87.06,101.96), P < 0.001]. Combining T2 and ΔT1 values of LG, Model I had higher diagnostic value for distinguishing GO from GD (AUC=0.94, 95 %CI: 0.89,0.99, P<0.001). Meanwhile, T2 of LG had higher diagnostic value for grading GO activity (AUC = 0.84, 95 %CI: 0.76,0.92, P<0.001). ConclusionsMulti-parametric quantitative MRI parameters of the LG in GO were significantly altered. Novel models combining LG T2 and ΔT1 values showed excellent predictive performances in diagnosing GO. Furthermore, T2 of LG showed practical utility for staging GO.

Evaluation of Pancreatic Fibrosis Grading by Multiparametric Quantitative Magnetic Resonance Imaging.

Early detection and grading of pancreatic fibrosis (PF) are important and challenging clinical goals. To determine main pancreatic duct (MPD) diameter, pancreatic thickness, and grades of PF via magnetic resonance elastography (MRE), T1 mapping, and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), assessing respective diagnostic performances. Prospective. Histopathologic and imaging records (MRE, T1 mapping, and IVIM-DWI) generated by 144 patients between December 2018 and May 2020 were collected for analysis. Grades of PF were distributed as follows: F0, 82; F1, 22; F2, 22; and F3, 18. 3 T pancreatic MRI, encompassing MRE, T1 mapping, and IVIM-DWI. In all patients, T1 relaxation times, pancreatic stiffness values, IVIM-DWI parameters, MPD diameter, and pancreatic thickness were measured. Receiver operating characteristic (ROC) analysis served to assess imaging parameters useful in diagnosing PF. To identify relations between specific parameters and grades of PF, logistic regression analysis was invoked. Both pancreatic stiffness (r=0.754; P < 0.001) and T1 relaxation time (r=0.433; P < 0.001) correlated significantly with PF (%). To determine PF grades ≥F1, a combined model (area under the curve [AUC]=0.906) performed significantly better than pancreatic stiffness (AUC=0.855; P < 0.001) or T1 relaxation time (AUC=0.754; P < 0.001) alone. For PF grades ≥F2 or grade F3, both the combined model (≥F2: AUC=0.910; F3: AUC=0.939) and pancreatic stiffness (≥F2: AUC=0.906; F3: AUC=0.929) outperformed T1 relaxation time (≥F2: AUC=0.768 [P=0.005 and P=0.004, respectively]; F3: AUC=0.816 [both P < 0.005]). All IVIM-DWI parameters generated AUC values <0.700. A combination of MRE and T1 mapping seems promising in diagnosing various grades of PF, particularly at an early stage. 1 TECHNICAL EFFICACY: Stage 2.

Multiparametric Quantitative MRI in Neurological Diseases.

Magnetic resonance imaging (MRI) is the gold standard imaging technique for diagnosis and monitoring of many neurological diseases. However, the application of conventional MRI in clinical routine is mainly limited to the visual detection of macroscopic tissue pathology since mixed tissue contrasts depending on hardware and protocol parameters hamper its application for the assessment of subtle or diffuse impairment of the structural tissue integrity. Multiparametric quantitative (q)MRI determines tissue parameters quantitatively, enabling the detection of microstructural processes related to tissue remodeling in aging and neurological diseases. In contrast to measuring tissue atrophy via structural imaging, multiparametric qMRI allows for investigating biologically distinct microstructural processes, which precede changes of the tissue volume. This facilitates a more comprehensive characterization of tissue alterations by revealing early impairment of the microstructural integrity and specific disease-related patterns. So far, qMRI techniques have been employed in a wide range of neurological diseases, including in particular conditions with inflammatory, cerebrovascular and neurodegenerative pathology. Numerous studies suggest that qMRI might add valuable information, including the detection of microstructural tissue damage in areas appearing normal on conventional MRI and unveiling the microstructural correlates of clinical manifestations. This review will give an overview of current qMRI techniques, the most relevant tissue parameters and potential applications in neurological diseases, such as early (differential) diagnosis, monitoring of disease progression, and evaluating effects of therapeutic interventions.