MR Parameters Research Articles

Quantification of disease progression in spinal muscular atrophy with muscle MRI-a pilot study.

ObjectivesQuantitative MRI (qMRI) of muscles is a promising tool to measure disease progression or to assess therapeutic effects in neuromuscular diseases. Longitudinal imaging studies are needed to show sensitivity of qMRI in detecting disease progression in spinal muscular atrophy (SMA). In this pilot study we therefore studied one‐year changes in quantitative MR parameters in relation to clinical scores.MethodsWe repeated quantitative 3 T MR analysis of thigh muscles and clinical testing one year after baseline in 10 treatment‐naïve patients with SMA, 5 with Type 2 (21.6 ± 7.0 years) and 5 with Type 3 (33.4 ± 11.9 years). MR protocol consisted of Dixon, T 2 mapping and diffusion tensor imaging (DTI). The temporal relation of parameters was examined with a mixed model.ResultsWe detected a significant increase in fat fraction (baseline, 38.2% SE 0.6; follow‐up, 39.5% SE 0.6; +1.3%, p = 0.001) in all muscles. Muscles with moderate to high fat infiltration at baseline show a larger increase over time (+1.6%, p < 0.001). We did not find any changes in DTI parameters except for low fat‐infiltration muscles (m. adductor longus and m. biceps femoris (short head)). The T 2 of muscles decreased from 28.2 ms to 28.0 ms (p = 0.07). Muscle strength and motor function scores were not significantly different between follow‐up and baseline.ConclusionLongitudinal imaging data show slow disease progression in skeletal muscle of the thigh of (young‐) adult patients with SMA despite stable strength and motor function scores. Quantitative muscle imaging demonstrates potential as a biomarker for disease activity and monitoring of therapy response.

Impact of Prematurity on the Tissue Properties of the Neonatal Brain Stem: A Quantitative MR Approach.

Preterm birth interferes with regular brain development. The aim of this study was to investigate the impact of prematurity on the physical tissue properties of the neonatal brain stem using a quantitative MR imaging approach. A total of 55 neonates (extremely preterm [n = 30]: <28 + 0 weeks gestational age; preterm [n = 10]: 28 + 0-36 + 6 weeks gestational age; term [n = 15]: ≥37 + 0 weeks gestational age) were included in this retrospective study. In most cases, imaging was performed at approximately term-equivalent age using a standard MR protocol. MR data postprocessing software SyMRI was used to perform multidynamic multiecho sequence (acquisition time: 5 minutes, 24 seconds)-based MR postprocessing to determine T1 relaxation time, T2 relaxation time, and proton density. Mixed-model ANCOVA (covariate: gestational age at MR imaging) and the post hoc Bonferroni test were used to compare the groups. There were significant differences between premature and term infants for T1 relaxation time (midbrain: P < .001; pons: P < .001; basis pontis: P = .005; tegmentum pontis: P < .001; medulla oblongata: P < .001), T2 relaxation time (midbrain: P < .001; tegmentum pontis: P < .001), and proton density (tegmentum pontis: P = .004). The post hoc Bonferroni test revealed that T1 relaxation time/T2 relaxation time in the midbrain differed significantly between extremely preterm and preterm (T1 relaxation time: P < .001/T2 relaxation time: P = .02), extremely preterm and term (T1 relaxation time/T2 relaxation time: P < .001), and preterm and term infants (T1 relaxation time: P < .001/T2 relaxation time: P = .006). Quantitative MR parameters allow preterm and term neonates to be differentiated. T1 and T2 relaxation time metrics of the midbrain allow differentiation between the different stages of prematurity. SyMRI allows for a quantitative assessment of incomplete brain maturation by providing tissue-specific properties while not exceeding a clinically acceptable imaging time.

Magnetic resonance parameter mapping using model-guided self-supervised deep learning.

To develop a model-guided self-supervised deep learning MRI reconstruction framework called reference-free latent map extraction (RELAX) for rapid quantitative MR parameter mapping. Two physical models are incorporated for network training in RELAX, including the inherent MR imaging model and a quantitative model that is used to fit parameters in quantitative MRI. By enforcing these physical model constraints, RELAX eliminates the need for full sampled reference data sets that are required in standard supervised learning. Meanwhile, RELAX also enables direct reconstruction of corresponding MR parameter maps from undersampled k-space. Generic sparsity constraints used in conventional iterative reconstruction, such as the total variation constraint, can be additionally included in the RELAX framework to improve reconstruction quality. The performance of RELAX was tested for accelerated T1 and T2 mapping in both simulated and actually acquired MRI data sets and was compared with supervised learning and conventional constrained reconstruction for suppressing noise and/or undersampling-induced artifacts. In the simulated data sets, RELAX generated good T1 /T2 maps in the presence of noise and/or undersampling artifacts, comparable to artifact/noise-free ground truth. The inclusion of a spatial total variation constraint helps improve image quality. For the in vivo T1 /T2 mapping data sets, RELAX achieved superior reconstruction quality compared with conventional iterative reconstruction, and similar reconstruction performance to supervised deep learning reconstruction. This work has demonstrated the initial feasibility of rapid quantitative MR parameter mapping based on self-supervised deep learning. The RELAX framework may also be further extended to other quantitative MRI applications by incorporating corresponding quantitative imaging models.

Experimental Protocols for MRI Mapping of Renal T1.

The water proton longitudinal relaxation time, T1, is a common and useful MR parameter in nephrology research. Here we provide three step-by-step T1-mapping protocols suitable for different types of nephrology research. Firstly, we provide a single-slice 2D saturation recovery protocol suitable for studies of global pathology, where whole-kidney coverage is unnecessary. Secondly, we provide an inversion recovery type imaging protocol that may be optimized for specific kidney disease applications. Finally, we also provide imaging protocol for small animal kidney imaging in a clinical scanner.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.

Experimental Protocol for MRI Mapping of the Blood Oxygenation-Sensitive Parameters T2* and T2 in the Kidney.

Renal hypoxia is generally accepted as a key pathophysiologic event in acute kidney injury of various origins, and has also been suggested to play a role in the development of chronic kidney disease. Here we describe a step-by-step experimental protocol for indirect monitoring of renal blood oxygenation in rodents via the deoxyhemoglobin sensitive MR parameters T2* and T2-a contrast mechanism known as the blood oxygenation level dependent (BOLD) effect. Since an absolute quantification of renal oxygenation from T2*/T2 remains challenging, the effects of controlled and standardized variations in the fraction of inspired oxygen are used for bench marking. This MRI method may be useful for investigating renal blood oxygenation of small rodents in vivo under various experimental (patho)physiological conditions.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This experimental protocol chapter is complemented by two separate chapters describing the basic concept and data analysis.

Analysis Protocols for MRI Mapping of the Blood Oxygenation-Sensitive Parameters T2* and T2 in the Kidney.

Renal hypoxia is generally accepted as a key pathophysiologic event in acute kidney injury of various origins and has also been suggested to play a role in the development of chronic kidney disease. Here we describe step-by-step data analysis protocols for MRI monitoring of renal oxygenation in rodents via the deoxyhemoglobin concentration sensitive MR parameters T2* and T2-a contrast mechanism known as the blood oxygenation level dependent (BOLD) effect.This chapter describes how to use the analysis tools provided by vendors of animal and clinical MR systems, as well as how to develop an analysis software. Aspects covered are: data quality checks, data exclusion, model fitting, fitting algorithm, starting values, effects of multiecho imaging, and result validation.This chapter is based upon work from the PARENCHIMA COST Action, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This experimental protocol chapter is complemented by two separate chapters describing the basic concept and data analysis.

“Aerobic glycolytic imaging” of human gliomas using combined pH-, oxygen-, and perfusion-weighted magnetic resonance imaging

PurposeTo quantify abnormal metabolism of diffuse gliomas using “aerobic glycolytic imaging” and investigate its biological correlation. MethodsAll subjects underwent a pH-weighted amine chemical exchange saturation transfer spin-and-gradient-echo echoplanar imaging (CEST-SAGE-EPI) and dynamic susceptibility contrast perfusion MRI. Relative oxygen extraction fraction (rOEF) was estimated as the ratio of reversible transverse relaxation rate R2′ to normalized relative cerebral blood volume. An aerobic glycolytic index (AGI) was derived by the ratio of pH-weighted image contrast (MTRasym at 3.0 ppm) to rOEF. AGI was compared between different tumor types (N = 51, 30 IDH mutant and 21 IDH wild type). Metabolic MR parameters were correlated with 18F-FDG uptake (N = 8, IDH wild-type glioblastoma), expression of key glycolytic proteins using immunohistochemistry (N = 38 samples, 21 from IDH mutant and 17 from IDH wild type), and bioenergetics analysis on purified tumor cells (N = 7, IDH wild-type high grade). ResultsAGI was significantly lower in IDH mutant than wild-type gliomas (0.48 ± 0.48 vs. 0.70 ± 0.48; P = 0.03). AGI was strongly correlated with 18F-FDG uptake both in non-enhancing tumor (Spearman, ρ = 0.81; P = 0.01) and enhancing tumor (ρ = 0.81; P = 0.01). AGI was significantly correlated with glucose transporter 3 (ρ = 0.71; P = 0.004) and hexokinase 2 (ρ = 0.73; P = 0.003) in IDH wild-type glioma, and monocarboxylate transporter 1 (ρ = 0.59; P = 0.009) in IDH mutant glioma. Additionally, a significant correlation was found between AGI derived from bioenergetics analysis and that estimated from MRI (ρ = 0.79; P = 0.04). ConclusionAGI derived from molecular MRI was correlated with glucose uptake (18F-FDG and glucose transporter 3/hexokinase 2) and cellular AGI in IDH wild-type gliomas, whereas AGI in IDH mutant gliomas appeared associated with monocarboxylate transporter density.

Subsegmentation of the Kidney in Experimental MR Images Using Morphology-Based Regions-of-Interest or Multiple-Layer Concentric Objects.

Functional renal MRI promises access to a wide range of physiologically relevant parameters such as blood oxygenation, perfusion, tissue microstructure, pH, and sodium concentration. For quantitative comparison of results, representative values must be extracted from the parametric maps obtained with these different MRI techniques. To improve reproducibility of results this should be done based on regions-of-interest (ROIs) that are clearly and objectively defined.Semiautomated subsegmentation of the kidney in magnetic resonance images represents a simple but very valuable approach for the quantitative analysis of imaging parameters in multiple ROIs that are associated with specific anatomic locations. Thereby, it facilitates comparing MR parameters between different kidney regions, as well as tracking changes over time.Here we provide detailed step-by-step instructions for two recently developed subsegmentation techniques that are suitable for kidneys of small rodents: i) the placement of ROIs in cortex, outer and the inner medulla based on typical kidney morphology and ii) the division of the kidney into concentrically oriented layers.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers.

Bayesian non-linear regression with spatial priors for noise reduction and error estimation in quantitative MRI with an application in T1 estimation

Purpose. To develop a method that can reduce and estimate uncertainty in quantitative MR parameter maps without the need for hand-tuning of any hyperparameters. Methods. We present an estimation method where uncertainties are reduced by incorporating information on spatial correlations between neighbouring voxels. The method is based on a Bayesian hierarchical non-linear regression model, where the parameters of interest are sampled, using Markov chain Monte Carlo (MCMC), from a high-dimensional posterior distribution with a spatial prior. The degree to which the prior affects the model is determined by an automatic hyperparameter search using an information criterion and is, therefore, free from manual user-dependent tuning. The samples obtained further provide a convenient means to obtain uncertainties in both voxels and regions. The developed method was evaluated on T 1 estimations based on the variable flip angle method. Results. The proposed method delivers noise-reduced T 1 parameter maps with associated error estimates by combining MCMC sampling, the widely applicable information criterion, and total variation-based denoising. The proposed method results in an overall decrease in estimation error when compared to conventional voxel-wise maximum likelihood estimation. However, this comes with an increased bias in some regions, predominately at tissue interfaces, as well as an increase in computational time. Conclusions. This study provides a method that generates more precise estimates compared to the conventional method, without incorporating user subjectivity, and with the added benefit of uncertainty estimation.

High-performance rapid MR parameter mapping using model-based deep adversarial learning

PurposeTo develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping. MethodsThe proposed method provides an image reconstruction framework by combining the end-to-end convolutional neural network (CNN) mapping, adversarial learning, and MR physical models. The CNN performs direct image-to-parameter mapping by transforming a series of undersampled images directly into MR parameter maps. Adversarial learning is used to improve image sharpness and enable better texture restoration during the image-to-parameter conversion. An additional pathway concerning the MR signal model is added between the estimated parameter maps and undersampled k-space data to ensure the data consistency during network training. The proposed framework was evaluated on T2 mapping of the brain and the knee at an acceleration rate R = 8 and was compared with other state-of-the-art reconstruction methods. Global and regional quantitative assessments were performed to demonstrate the reconstruction performance of the proposed method. ResultsThe proposed adversarial learning approach achieved accurate T2 mapping up to R = 8 in brain and knee joint image datasets. Compared to conventional reconstruction approaches that exploit image sparsity and low-rankness, the proposed method yielded lower errors and higher similarity to the reference and better image sharpness in the T2 estimation. The quantitative metrics were normalized root mean square error of 3.6% for brain and 7.3% for knee, structural similarity index of 85.1% for brain and 83.2% for knee, and tenengrad measures of 9.2% for brain and 10.1% for the knee. The adversarial approach also achieved better performance for maintaining greater image texture and sharpness in comparison to the CNN approach without adversarial learning. ConclusionThe proposed framework by incorporating the efficient end-to-end CNN mapping, adversarial learning, and physical model enforced data consistency is a promising approach for rapid and efficient reconstruction of quantitative MR parameters.

A multi-scale residual network for accelerated radial MR parameter mapping

A deep learning MR parameter mapping framework which combines accelerated radial data acquisition with a multi-scale residual network (MS-ResNet) for image reconstruction is proposed. The proposed supervised learning strategy uses input image patches from multi-contrast images with radial undersampling artifacts and target image patches from artifact-free multi-contrast images. Subspace filtering is used during pre-processing to denoise input patches. For each anatomy and relaxation parameter, an individual network is trained. in vivo T1 mapping results are obtained on brain and abdomen datasets and in vivo T2 mapping results are obtained on brain and knee datasets. Quantitative results for the T2 mapping of the knee show that MS-ResNet trained using either fully sampled or undersampled data outperforms conventional model-based compressed sensing methods. This is significant because obtaining fully sampled training data is not possible in many applications. in vivo brain and abdomen results for T1 mapping and in vivo brain results for T2 mapping demonstrate that MS-ResNet yields contrast-weighted images and parameter maps that are comparable to those achieved by model-based iterative methods while offering two orders of magnitude reduction in reconstruction times. The proposed approach enables recovery of high-quality contrast-weighted images and parameter maps from highly accelerated radial data acquisitions. The rapid image reconstructions enabled by the proposed approach makes it a good candidate for routine clinical use.

Value of Diffusion-Weighted and Perfusion-Weighted MR Imaging in Differentiation of Recurrent Tongue Carcinoma from Post-Treatment Changes

Abstract Background: Tongue carcinoma is characterized by high recurrence rate. Thus, post-treatment follow-up imaging is critically important. Advanced Magnetic Resonance Imaging (MRI) techniques can be employed for this purpose due to the limited accuracy of conventional MRI. Aim of Study: Evaluation of the value of Diffusion Weight-ed (DW) and perfusion weighted MRI implementation in the post-treatment follow-up of tongue carcinoma. Patients and Methods: This study was conducted on 23 patients on post-treatment follow-up of tongue carcinoma. They underwent DW-MRI and Dynamic Contrast Enhanced (DCE) perfusion T1-weighted MRI to differentiate between recurrent tumor and post-treatment changes. Apparent Diffu-sion Coefficient (ADC) was estimated, perfusion MR quali-tative and semiquantitative assessment was performed. Re-sulting data were compared to histopathologic characterization (n=18) and further clinical and radiological follow-up (n=5) which were considered as the reference standards. Results: The mean ADC value of recurrent tongue carci-noma (1.029±0.207 X 10–3mm2/s) was significantly lower (p < 0.001) than the mean ADC value of post-treatment changes (1.425±0.238 X 10–3mm2/s). The ADC threshold used for differentiating recurrent tumor from post-treatment changes was 1.175 X 10–3mm2/s with Area Under the Curve (AUC) of 0.938 and diagnostic accuracy of 82.6%. Among the DCE perfusion weighted MR parameters, the wash in rate and Area Under Gadolinium Curve (AUGC) displayed the highest diagnostic accuracy (73.9%) with thresholds of 18.65a.u/s and 126826.7m.M.s respectively. The combined use of both DW-MRI and perfusion weighted MRI showed the highest diagnostic accuracy. Conclusion: The DW MRI is a non-invasive technique providing accurate post-treatment follow-up assessment of tongue carcinoma. The perfusion weighted MRI provides lower diagnostic accuracy than DWI. The combined use of both techniques provides superior differentiation of tumor recurrence from post-treatment changes.

Determination of optimum pixel size and slice thickness for tractography and ulnar nerve diffusion tensor imaging at the cubital tunnel using 3T MRI.

Small peripheral nerve tractography is challenging because of the trade-off among resolution, image acquisition time, and signal-to-noise ratio. To optimize pixel size and slice thickness parameters for fiber tractography and diffusion tensor imaging (DTI) of the ulnar nerve at the cubital tunnel using 3T magnetic resonance imaging (MRI). Fifteen healthy volunteers (mean age 30 ± 6.8 years) were recruited prospectively. Axial T2-weighted and DTI scans were acquired, covering the cubital tunnel, using different pixel sizes and slice thicknesses. Three-dimensional (3D) nerve tractography was evaluated for the median number and length of the reconstructed fiber tracts and visual score from 0 to 5. Two-dimensional (2D) cross-sectional DTI was evaluated for fractional anisotropy (FA) values throughout the length of the ulnar nerve. A pixel size of 1.3 mm2 revealed the highest number of reconstructed nerve fibers compared to that of 1.1 mm2 (P = 0.048), with a good visual score. A slice thickness of 4 mm had the highest number of reconstructed nerve fibers and visual score compared with other thicknesses (all P < 0.05). In 2D cross-sectional images, the median FA values were in the range of 0.40-0.63 at the proximal, central, and distal portions of the cubital tunnel. Inter-observer agreement for all parameters was good to excellent. For fiber tractography and DTI of the ulnar nerve at the cubital tunnel, optimal image quality was obtained using a 1.3-mm2 pixel size and 4-mm slice thickness under MR parameters of this study at 3T.

Multiparameter quantitative histological MRI values in high-grade gliomas: a potential biomarker of tumor progression.

BackgroundConventional MRI poorly distinguishes brain parenchyma microscopically invaded by high-grade gliomas (HGGs) from the normal brain. By contrast, quantitative histological MRI (hMRI) measures brain microstructure in terms of physical MR parameters influenced by histochemical tissue composition. We aimed to determine the relationship between hMRI parameters in the area surrounding the surgical cavity and the presence of HGG recurrence.MethodsPatients were scanned after surgery with an hMRI multiparameter protocol that allowed for estimations of longitudinal relaxation rate (R1) = 1/T1, effective transverse relaxation rate (R2)*=1/T2*, magnetization transfer saturation (MTsat), and proton density. The initial perioperative zone (IPZ) was segmented on the postoperative MRI. Once recurrence appeared on conventional MRI, the area of relapsing disease was delineated (extension zone, EZ). Conventional MRI showing recurrence and hMRI were coregistered, allowing for the extraction of parameters R1, R2*, MTsat, and PD in 3 areas: the overlap area between the IPZ and EZ (OZ), the peritumoral brain zone, PBZ (PBZ = IPZ – OZ), and the area of recurrence (RZ = EZ – OZ).ResultsThirty-one patients with HGG who underwent gross-total resection were enrolled. MTsat and R1 were the most strongly associated with tumor progression. MTsat was significantly lower in the OZ and RZ, compared to PBZ. R1 was significantly lower in RZ compared to PBZ. PD was significantly higher in OZ compared to PBZ, and R2* was higher in OZ compared to PBZ or RZ. These changes were detected 4 to 120 weeks before recurrence recognition on conventional MRI.ConclusionsHGG recurrence was associated with hMRI parameters’ variation after initial surgery, weeks to months before overt recurrence.

Perfusion recovery on TTP maps after endovascular stroke treatment might predict favorable neurological outcomes.

Early recanalization and adequate collateral blood flow are surrogates for functional recovery in endovascular stroke treatment (EVT). We evaluated the prognostic value of pre- and immediate post-thrombectomy perfusion-weighted magnetic resonance imaging (PWI) parameters. Consecutive patients with acute ischemic stroke who underwent EVT were enrolled. Lesion volumes and their corresponding changes on diffusion-weighted (DWI) and PWI were assessed. Outcome was measured with modified Rankin Scale (mRS) at 90days, and early neurological improvement (> 8 points improvement on National Institutes of Health Stroke Scale [NIHSS] or 0 to 1) at 7days. Fifty-two patients were enrolled. After control of initial NIHSS and recanalization status, post-thrombectomy time-to-peak (TTP) hypoperfused volume and TTP hypoperfused volume change remained independent predictors of favorable functional outcome (odds ratio [OR] = 0.13, 95% confidence interval [CI] = 0.03-0.54, p = 0.005; OR = 1.018, 95% CI = 1.00-1.03, p = 0.017), and early neurological improvement (OR = 0.20, 95% CI 0.07-0.58, p = 0.003; OR = 1.02, 95% CI = 1.00-1.03, p = 0.010). The areas under the curve of post-thrombectomy TTP hypoperfused volume and TTP hypoperfused volume change were 0.90 and 0.82 (cutoff 68mL and 56mL) for favorable outcome and 0.86 and 0.82 (cutoff 76mL and 58mL) for early neurological improvement, which had better prognostic values than other MR parameters and recanalization grades. These results suggest a large amount of perfusion recovery on TTP is associated with favorable outcome as well as early neurological improvement after EVT, and may be a useful prognostic marker. • A large amount of perfusion recovery on TTP map is associated with favorable outcome and early neurological improvement after EVT. • The best cutoff value for favorable functional outcome was 68mL for post-EVT TTP hypoperfused volume and 56mL decrease for TTP hypoperfused volume. • Amount of perfusion recovery on TTP map has better performance on the prediction of favorable functional recovery and early neurological improvement than other diffusion- and perfusion-weighted MRI parameters and recanalization grades.

Diffusion tensor imaging quantifying the severity of chronic hepatitis in rats

BackgroundDiffusion tensor imaging (DTI) is mainly used for detecting white matter fiber in the brain. DTI was applied to assess fiber in liver disorders in previous studies. However, the data obtained have been insufficient in determining if DTI can be used to exactly stage chronic hepatitis. This study assessed the value of DTI for staging of liver fibrosis (F), necroinflammatory activity (A) and steatosis (S) with chronic hepatitis in rats.MethodsSeventy male Sprague-Dawley rats were divided into a control group(n = 10) and an experimental group(n = 60). The rat models of chronic hepatitis were established by abdominal subcutaneous injections of 40% CCl4. All of the rats underwent 3.0 T MRI. Regions of interest (ROIs) were subjected to DTI to estimate the MR parameters (rADC value and FA value). Histopathology was used as the reference standard. Multiple linear regression was used to analyze the associations between the MR parameters and pathology. The differences in the MR parameters among the pathological stages were evaluated by MANOVA or ANOVA. The LSD test was used to test for differences between each pair of groups. ROC analysis was also performed.ResultsThe count of each pathology was as follows: F0(n = 15), F1(n = 11), F2(n = 6), F3(n = 9), F4(n = 6); A0(n = 8), A1(n = 16), A2(n = 16), A3(n = 7); S0(n = 10), S1(n = 7), S2(n = 3), S3(n = 11), S4(n = 16). The rADC value had a negative correlation with liver fibrosis (r = − 0.392, P = 0.008) and inflammation (r = − 0.359, P = 0.015). The FA value had a positive correlation with fibrosis (r = 0.409, P = 0.005). Significant differences were found in the FA values between F4 and F0 ~ F3 (P = 0.03), while no significant differences among F0 ~ F3 were found (P > 0.05). The AUC of the FA value differentiating F4 from F0 ~ F3 was 0.909 (p < 0.001) with an 83.3% sensitivity and an 85.4% specificity when the FA value was at the cut-off of 588.089 (× 10− 6 mm2/s).ConclusionThe FA value for DTI can distinguish early cirrhosis from normal, mild and moderate liver fibrosis, but the rADC value lacked the ability to differentiate among the fibrotic grades. Both the FA and rADC values were unable to discriminate the stages of necroinflammatory activity and steatosis.

Multimode tumor ablation therapy induced different diffusion and microvasculature related parameters change on functional magnetic resonance imaging compared to radiofrequency ablation in liver tumor: An observational study.

To explore different posttreatment changes between multimode tumor ablation therapy (MTAT) and radiofrequency ablation (RFA) using intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and diffusion kurtosis imaging (DKI) in patients with hepatic malignancies.Eighty - seven patients with one hundred and twenty eight hepatic lesions receiving MTAT or RFA underwent IVIM-DWI and DKI before and after treatment. The mean value of apparent diffusion coefficient (ADC), IVIM-DWI parameters, including true diffusion coefficient (D), pseudo-diffusion coefficient (DP), perfusion fraction (f), and DKI parameters including diffusion coefficient (DK), apparent diffusional kurtosis (K) were retrospectively compared prior to and following treatment as well as between treatment groups. The degree of parameters change after ablation was compared between 2 treatment modalities.The mean value of ADC, D, and DK increased while f, and K decreased significantly in MTAT group. In RFA group, just ADC and K showed significantly change following treatment. The ADC and D value were higher in MTAT group than in RFA group 1 month after treatment. While f was lower in MTAT group after treatment compared with RFA group. The ADC, D and DK increased (21.89 ± 24.95% versus 8.76 ± 19.72%, P = .04 for ADC, 33.78 ± 54.01% versus 7.91 ± 25.16%, P = .03 for D, 25.91 ± 36.28% versus 1.75 ± 46.42%, P = .01 for DK) while f declined (-32.62 ± 41.48% versus 6.51 ± 44.16%, P < .001) more in MTAT group.The MTAT induced different posttreatment changes on water molecule diffusion and microvasculature related functional MR parameters compared to RFA in patients with liver tumors.

MRI features predict microvascular invasion in intrahepatic cholangiocarcinoma

BackgroundThe presence of microvascular invasion (MVI) in intrahepatic cholangiocarcinoma (ICC) is a significant adverse prognostic factor. This study sought to investigate the correlation between preoperative imaging parameters and MVI in ICC.MethodsA total of 108 patients with surgically resected single ICC tumors (34 MVI-positive and 74 MVI-negative lesions) who underwent MRI examination, including T1WI, T2WI, DWI, and dynamic enhancement imaging, were enrolled in this retrospective study. The following qualitative and quantitative characteristics were evaluated: tumor morphology, signal features on T1WI and T2WI, intrahepatic duct dilatation, hepatic capsule retraction, target sign on DWI, dynamic enhancement pattern, arterial phase enhancement pattern, dot−/band-like enhancement inside the tumor, visible vessel penetration inside the tumor (hepatic artery, portal vein, or hepatic vein), integrity of the enhancement edge of the arterial phase, peripheral hepatic enhancement, tumor size, maximum enhancement edge thickness, arterial edge enhancement ratio, and delayed phase enhancement ratio. Other clinicopathological features were also used to predict and evaluate MVI in ICC. Chi-square test, Fisher’s exact test, and independent t-test were used for univariate analysis to determine the relationships among the presence of MVI and these MR parameters. Logistic regression analysis was used to identify predictors of MVI among these MR parameters.ResultsAmong MRI characteristics, tumor morphology, intrahepatic duct dilatation, arterial phase enhancement pattern, visible hepatic artery penetration sign, maximum diameter of the tumor and the arterial phase edge enhancement ratio were correlated with MVI (P = 0.007, 0.003, 0.008, 0.000, 0.003, and 0.002, respectively). Furthermore, higher CA19–9 levels (≥37 U/ml) and pathological tumor grade III were also related to MVI (P = 0.014 and 0.004, respectively). However, multivariate logistic regression analysis demonstrated that none of the parameters were independent risk factors for the diagnosis of MVI in ICCs.ConclusionFor the preoperative prediction of MVI in ICC, six qualitative and quantitative data obtained on preoperative MRI, as well as one tumorigenic marker and the pathological tumor grade, were statistically significant. More research is needed to identify MR characteristics that can be used as independent risk factors.

Spatially regularized parametric map reconstruction for fast magnetic resonance fingerprinting.

Magnetic resonance fingerprinting (MRF) provides a unique concept for simultaneous and fast acquisition of multiple quantitative MR parameters. Despite acquisition efficiency, adoption of MRF into the clinics is hindered by its dictionary matching-based reconstruction, which is computationally demanding and lacks scalability. Here, we propose a convolutional neural network-based reconstruction, which enables both accurate and fast reconstruction of parametric maps, and is adaptable based on the needs of spatial regularization and the capacity for the reconstruction. We evaluated the method using MRF T1-FF, an MRF sequence for T1 relaxation time of water (T1H2O) and fat fraction (FF) mapping. We demonstrate the method's performance on a highly heterogeneous dataset consisting of 164 patients with various neuromuscular diseases imaged at thighs and legs. We empirically show the benefit of incorporating spatial regularization during the reconstruction and demonstrate that the method learns meaningful features from MR physics perspective. Further, we investigate the ability of the method to handle highly heterogeneous morphometric variations and its generalization to anatomical regions unseen during training. The obtained results outperform the state-of-the-art in deep learning-based MRF reconstruction. The method achieved normalized root mean squared errors of 0.048 ± 0.011 for T1H2O maps and 0.027 ± 0.004 for FF maps when compared to the dictionary matching in a test set of 50 patients. Coupled with fast MRF sequences, the proposed method has the potential of enabling multiparametric MR imaging in clinically feasible time.

Increased tumour burden alters skeletal muscle properties in the KPC mouse model of pancreatic cancer.

Cancer cachexia is a multifactorial wasting syndrome that is characterized by the loss of skeletal muscle mass and weakness, which compromises physical function, reduces quality of life, and ultimately can lead to mortality. Experimental models of cancer cachexia have recapitulated this skeletal muscle atrophy and consequent decline in muscle force generating capacity. We address these issues in a novel transgenic mouse model Kras, Trp53 and Pdx-1-Cre (KPC) of pancreatic ductal adenocarcinoma (PDA) using multi-parametric magnetic resonance (mp-MR) measures. KPC mice (n = 10) were divided equally into two groups (n = 5/group) depending on the size of the tumor i.e. tumor size <250 mm3 and >250 mm3. Using mp-MR measures, we demonstrated the changes in the gastrocnemius muscle at the microstructural level. In addition, we evaluated skeletal muscle contractile function in KPC mice using an in vivo approach. Increase in tumor size resulted in decrease in gastrocnemius maximum cross sectional area, decrease in T2 relaxation time, increase in magnetization transfer ratio, decrease in mean diffusivity, and decrease in radial diffusivity of water across the muscle fibers. Finally, we detected significant decrease in absolute and specific force production of gastrocnemius muscle with increase in tumor size. Our findings indicate that increase in tumor size may cause alterations in structural and functional parameters of skeletal muscles and that MR parameters may be used as sensitive biomarkers to noninvasively detect structural changes in cachectic muscles.