Signatures of microstructure in gradient-echo and spin-echo signals.

PURPOSE: To determine whether the spatial scale and magnetic susceptibility of microstructure can be evaluated robustly from the decay of gradient-echo and spin-echo signals. THEORY AND METHODS: Gradient-echo and spin-echo images were acquired from suspensions of spherical polystyrene microbeads of 10, 20, and 40 µm nominal diameter. The sizes of the beads and their magnetic susceptibility relative to the medium were estimated from the signal decay curves, using a lookup table generated from Monte Carlo simulations and an analytic model based on the Gaussian phase approximation. RESULTS: Fitting Monte Carlo predictions to spin-echo data yielded acceptable estimates of microstructural parameters for the 20 and 40 µm microbeads. Using gradient-echo data, the Monte Carlo lookup table provided satisfactory parameter estimates for the 20 µm beads but unstable results for the diameter of the largest beads. Neither spin-echo nor gradient-echo data allowed accurate parameter estimation for the smallest beads. The analytic model performed poorly over all bead sizes. CONCLUSIONS: Microstructural sources of magnetic susceptibility produce distinctive non-exponential signatures in the decay of gradient-echo and spin-echo signals. However, inverting the problem to extract microstructural parameters from the signals is nontrivial and, in certain regimes, ill-conditioned. For microstructure with small characteristic length scales, parameter estimation is hampered by the difficulty of acquiring accurate data at very short echo times. For microstructure with large characteristic lengths, the gradient-echo signal approaches the static-dephasing regime, where it becomes insensitive to size. Applicability of the analytic model was further limited by failure of the Gaussian phase approximation for all but the smallest beads.

Multiparametric assessment of microvascular invasion in hepatocellular carcinoma using gadoxetic acid-enhanced MRI.

PURPOSE: To elucidate how precisely microvascular invasion (MVI) in hepatocellular carcinoma (HCC) can be predicted using multiparametric assessment of gadoxetic acid-enhanced MRI. METHODS: In this retrospective single-center study, patients who underwent liver resection or transplantation of HCC were evaluated. Data obtained in patients who underwent liver resection were used as the training set. Nine kinds of MR findings for predicting MVI were compared between HCCs with and without MVI by univariate analysis, followed by multiple logistic regression analysis. Using significant findings, a predictive formula for diagnosing MVI was obtained. The diagnostic performance of the formula was investigated in patients who underwent liver resection (validation set 1) and in patients who underwent liver transplantation (validation set 2) using a receiver operating characteristic curve analysis. The area under the curves (AUCs) of these three groups were compared. RESULTS: A total of 345 patients with 356 HCCs were selected for analysis. Tumor diameter (D) (P = 0.021), tumor washout (TW) (P < 0.01), and peritumoral hypointensity in the hepatobiliary phase (PHH) (P < 0.01) were significantly associated with MVI after multivariate analysis. The AUCs for predicting MVI of the predictive formula were as follows: training set, 0.88 (95% confidence interval (CI) 0.82,0.93); validation set 1, 0.81 (95% CI 0.73,0.87); validation set 2, 0.67 (95% CI 0.51,0.80). The AUCs were not significantly different among three groups (training set vs validation set 1; P = 0.15, training set vs validation set 2; P = 0.09, validation set 1 vs validation set 2; P = 0.29, respectively). CONCLUSION: Our multiparametric assessment of gadoxetic acid-enhanced MRI performed quite precisely and with good reproducibility for predicting MVI.

Contrast-Enhanced Ultrasound of the Chest in Children and Adolescents: A Pilot Study for Assessment of Added Diagnostic Value.

OBJECTIVES: To determine the added diagnostic value of contrast-enhanced ultrasound (CEUS) in pediatric chest abnormalities by comparing interpretation of CEUS studies and confidence level to conventional US studies. METHODS: CEUS studies in patients with a variety of clinically suspected chest abnormalities performed between 2016 and 2020 were reviewed and compared to same-day conventional US studies. Examinations were independently interpreted by 4 radiologists blinded to clinical and other imaging data. Rater confidence was classified as low, moderate, or high. Diagnostic accuracy was determined by comparing image interpretation to patient outcome as the ground truth. Interobserver agreement was also assessed. RESULTS: Sixteen patients (10 male) with 18 CEUS studies were included. Median rater agreement with ground truth was significantly higher for CEUS (100%) than conventional US (50%; P = .004). Median rater confidence was high (3.0) for CEUS, and low-moderate (1.5) for conventional US (P < .001). CEUS sensitivity (54.6-81.8%) and specificity (63.4-100.0%) were greater than conventional US (45.5-72.7% and 12.5-63.5%, respectively). CEUS false positives (0-4) and false negatives (2-5) were fewer than conventional US (4-7 and 3-6, respectively). Except for one rater pair where agreement was substantial (κ = .78, P < .01), inter-rater agreement for CEUS for all other rater pairs was nonsignificant (κ = .25-0.51, P ≥ .07). Agreement for conventional US was moderate and statistically significant for 3 rater pairs (κ = .55-0.78) and nonsignificant for the remaining 3 rater pairs (P ≥ .06). CONCLUSIONS: CEUS adds diagnostic value to the assessment of a variety of chest abnormalities. The data support further evaluation of the role of CEUS as a non-invasive, problem-solving technique in children.

Texture and color enhancement imaging improves the visibility of gastric neoplasms: clinical trial with image catalogue assessment using conventional and newly developed endoscopes.

BACKGROUND: Texture and color enhancement imaging (TXI) enhances the changes in endoscopic features caused by gastric neoplasms, such as redness/whiteness and elevation/depression. This study aimed to demonstrate the effectiveness of TXI in improving the visibility of gastric neoplasms compared with white light imaging (WLI) using conventional (CE) and newly developed endoscopes (NE). METHODS: We recruited patients who were histologically diagnosed with gastric neoplasms; endoscopy was performed, and gastric neoplasms photographed using three imaging modalities, including WLI, TXI mode 1 (TXI-1) and TXI mode 2 (TXI-2). Two different endoscopes (CE and NE) were used for the same patients. Six endoscopists provided the visibility scale scores ranging from 1 (poor) to 4 (excellent) for gastric neoplasms. The primary outcome was the visibility scale scores based on each modality and endoscope. The secondary outcome was the identification of factors including H. pylori infection, atrophy, location, size, morphology, histological diagnosis and intestinal metaplasia that affect the differences in visibility scale scores between TXI-1/TXI-2 and WLI. RESULTS: Fifty-two gastric neoplasms were analyzed. The mean visibility scale scores with the NE were 2.79 ± 1.07, 3.23 ± 0.96 and 3.14 ± 0.92 for WLI, TXI-1 and TXI-2, respectively. The mean visibility scales with the CE were 2.53 ± 1.10, 3.04 ± 1.05 and 2.96 ± 1.92 for WLI, TXI-1 and TXI-2, respectively. For both endoscopes, significant differences were observed in visibility scale scores between WLI and TXI-1 (p < 0.001) and between WLI and TXI-2 (p < 0.001). The visibility scale scores of NE were superior to those of CE in all modalities. In the secondary outcome, there was no factor affected the differences of visibility scale scores between TXI-1/TXI-2 and WLI. CONCLUSIONS: This study demonstrated that TXI-1 and TXI-2 enhanced the visibility scale scores of gastric neoplasms compared with that of WLI. Moreover, newly developed endoscope has the potential to improve visibility compared to conventional endoscope. TRIAL REGISTRATION: This study was registered with the University Hospital Medical Information Network (UMIN000042429, 16/11/2020).

Assessment of Early Response to Lung Cancer Chemotherapy by Semiquantitative Analysis of Dynamic Contrast-Enhanced MRI.

Objective: To evaluate the early chemotherapy response in patients with lung cancer using semiquantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). Methods: Twenty-two patients with lung cancer treated with chemotherapy were subjected to DCE-MRI at two time points: before starting treatment and after one week of therapy. The image data were collected by DCE-MRI, and the semiquantitative parameters including positive enhancement integral (PEI), signal enhancement ratio (SER), maximum slope of increase (MSI), and time to peak (TTP) were calculated. After chemotherapy, the parameters and relevant variations between the responders and nonresponders were compared with Mann-Whitney U tests. Student's t-test for paired samples was used to evaluate the temporal changes between pre- and posttreatment images. Results: The patients were categorized as 13 responders and 9 nonresponders based on the tumor response evaluation. After chemotherapy, the PEI, SER, and MSI were significantly increased in responders compared with the pretreatment values (P < 0.05), while no obvious decrease in TTP was observed (P > 0.05). However, 9 nonresponders showed no significant changes in PEI, SER, MSI, and TTP values, as compared with those of pretreatment (P > 0.05). Moreover, the increase of PEI was more dramatically in responders than in nonresponders (P < 0.05), but no significantly differences were observed in SER, MSI, and TTP (P > 0.05). Conclusion: Semiquantitative analysis of DCE-MRI could provide a reliable noninvasive method for assessing early chemotherapy response in lung cancer patients.

Exploring Wavelet Transform-Based Image Enhancement Algorithm for Image Restoration of Long March National Cultural Park.

China has taken the lead in exploring the construction of large-scale public cultural parks. By restoring the relics of the Long March, we can further promote the spirit of the Red Army, inherit the red gene, contribute to national pride, and contribute to local economic development. A new community will be built with culture, education, tourism, and leisure as the main contents. System aberration, human interference, motion, and system noise can all lead to image quality degradation. Wavelet enhancement technique is to use Wiener filtering in the Fourier region, using other properties of wavelets for filtering. The wavelet analysis method was used to recover the images within the Long March National Park, and the corresponding processing was carried out to achieve a better recovery effect. Through comparative tests, it was found that the algorithm had an average reduction of 223 iterations and an average increase of 11.264 in signal-to-noise ratio when iterating, and the number of iterations was also greatly improved. The higher the wavelet coefficients are, the higher the noise is. This paper introduces a new wavelet transform-based image restoration technique for national parks.

Development and quantitative assessment of deep learning-based image enhancement for optical coherence tomography.

PURPOSE: To develop a deep learning-based framework to improve the image quality of optical coherence tomography (OCT) and evaluate its image enhancement effect with the traditional image averaging method from a clinical perspective. METHODS: 359 normal eyes and 456 eyes with various retinal conditions were included. A deep learning framework with high-resolution representation was developed to achieve image quality enhancement for OCT images. The quantitative comparisons, including expert subjective scores from ophthalmologists and three objective metrics of image quality (structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR) and contrast-to-noise ratio (CNR)), were performed between deep learning method and traditional image averaging. RESULTS: With the increase of frame count from 1 to 20, our deep learning method always obtained higher SSIM and PSNR values than the image averaging method while importing the same number of frames. When we selected 5 frames as inputs, the local objective assessment with CNR illustrated that the deep learning method had more obvious tissue contrast enhancement than averaging method. The subjective scores of image quality were all highest in our deep learning method, both for normal retinal structure and various retinal lesions. All the objective and subjective indicators had significant statistical differences (P < 0.05). CONCLUSION: Compared to traditional image averaging methods, our proposed deep learning enhancement framework can achieve a reasonable trade-off between image quality and scanning times, reducing the number of repeated scans.

Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses.

The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.

Use of dynamic contrast-enhanced MRI for the early assessment of outcome of CyberKnife stereotactic radiosurgery for patients with spinal metastases.

AIM: To explore the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for evaluating early outcomes of CyberKnife radiosurgery for spinal metastases. MATERIALS AND METHODS: Patients with spinal metastases who were treated with CyberKnife radiosurgery from July 2018 to December 2020 were enrolled. Conventional MRI and DCE-MRI were performed before treatment and at 3 months after treatment. Patients showing disease progression were defined as the progressive disease (PD) group and those showing complete response, partial response, and stable disease were defined as the non-PD group. The haemodynamic parameters (volume transfer constant [Ktrans], rate constant [Kep], and extravascular space [Ve]) before and after treatment between the groups were analysed. Area under the curve (AUC) values were calculated. RESULTS: A total of 27 patients with 39 independent spinal lesions were included. The median follow-up time was 18.6 months (6.2-36.4 months). There were 27 lesions in the non-PD group and 12 lesions in the PD group. Post-treatment Kep, ΔKtrans and ΔKep in the non-PD group (0.959/min, - 32.6% and -41.1%, respectively) were significantly lower than the corresponding values in PD group (1.429/min, 20.4% and -6%; p<0.05). Post-treatment Ve and ΔVe (0.223 and 27.8%, respectively) in the non-PD group were significantly higher than that of the PD group (0.165 and -13.5%, p<0.05). ΔKtrans showed the highest diagnostic efficiency, with an AUC of 0.821. CONCLUSIONS: DCE-MRI parameters change significantly at an early stage after CyberKnife stereotactic radiosurgery for spinal metastases. DCE-MRI may be of value in determining the early treatment response.

High-Spatial-Resolution 3D Whole-Heart MRI T2 Mapping for Assessment of Myocarditis.

Background Clinical guidelines recommend the use of established T2 mapping sequences to detect and quantify myocarditis and edema, but T2 mapping is performed in two dimensions with limited coverage and repetitive breath holds. Purpose To assess the reproducibility of an accelerated free-breathing three-dimensional (3D) whole-heart T2 MRI mapping sequence in phantoms and participants without a history of cardiac disease and to investigate its clinical performance in participants with suspected myocarditis. Materials and Methods Eight participants (three women, mean age, 31 years ± 4 [standard deviation]; cohort 1) without a history of cardiac disease and 25 participants (nine women, mean age, 45 years ± 17; cohort 2) with clinically suspected myocarditis underwent accelerated free-breathing 3D whole-heart T2 mapping with 100% respiratory scanning efficiency at 1.5 T. The participants were enrolled from November 2018 to August 2020. Three repeated scans were performed on 2 separate days in cohort 1. Segmental variations in T2 relaxation times of the left ventricular myocardium were assessed, and intrasession and intersession reproducibility were measured. In cohort 2, segmental myocardial T2 values, detection of focal inflammation, and map quality were compared with those obtained from clinical breath-hold two-dimensional (2D) T2 mapping. Statistical differences were assessed using the nonparametric Mann-Whitney and Kruskal-Wallis tests, whereas the paired Wilcoxon signed-rank test was used to assess subjective scores. Results Whole-heart T2 maps were acquired in a mean time of 6 minutes 53 seconds ± 1 minute 5 seconds at 1.5 mm3 resolution. Breath-hold 2D and free-breathing 3D T2 mapping had similar intrasession (mean T2 change of 3.2% and 2.3% for 2D and 3D, respectively) and intersession (4.8% and 4.9%, respectively) reproducibility. The two T2 mapping sequences showed similar map quality (P = .23, cohort 2). Abnormal myocardial segments were identified with confidence (score 3) in 14 of 25 participants (56%) with 3D T2 mapping and only in 10 of 25 participants (40%) with 2D T2 mapping. Conclusion High-spatial-resolution three-dimensional (3D) whole-heart T2 mapping shows high intrasession and intersession reproducibility and helps provide T2 myocardial characterization in agreement with clinical two-dimensional reference, while enabling 3D assessment of focal disease with higher confidence. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Friedrich in this issue.

Magnetic resonance assessment of sinusoidal obstruction syndrome after neoadjuvant chemotherapy for colorectal liver metastases is not reproducible.

BACKGROUND: Sinusoidal obstruction syndrome (SOS) due to chemotherapy can cause severe hepatotoxicity, leading to impaired outcome in patients with colorectal cancer. A previous study introduced gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI) to diagnose SOS. PURPOSE: To assess the reproducibility of Gd-EOB-MRI-based SOS diagnosis and its relationship with response to chemotherapy and long-term outcome. MATERIAL AND METHODS: Twenty-six Gd-EOB-MRI scans of patients undergoing chemotherapy for colorectal liver metastases (CRLM) were retrospectively analyzed. Three radiologists, blinded to clinical data, independently scored presence and severity of SOS on a 5-point scale (0, definitely not present to 4, definitely present). Patients with a score ≥3 were considered SOS+. Inter-observer agreement between readers was assessed with kappa statistics. Response (RECIST 1.1.), occurrence of new CRLM during follow-up (hepatic progression) and overall survival (OS) were compared between patients with and without SOS. RESULTS: The inter-observer agreement of SOS scores was poor, with quadratic kappas of 0.17-0.40. For the binary outcome of SOS+ (confidence level [CL] 3-4) vs. SOS- (CL 0-2) agreement was poor, with kappas of 0.03-0.37. Median follow-up was 24 months (range 4-44 months). Response and OS between patients with and without SOS did not differ significantly for any of the readers. CONCLUSION: Inter-observer agreement for the diagnosis of SOS on Gd-EOB-MRI is poor. No significant correlation with relevant outcomes was found for any of the readers. Therefore, MRI for SOS diagnosis might be less useful than previously reported. Other techniques should be explored to accurately diagnose SOS in absence of histological confirmation.

The diagnostic value of postcontrast susceptibility-weighted imaging in the assessment of intracranial brain neoplasm at 3T.

BACKGROUND: Susceptibility-weighted imaging (SWI) is occasionally performed with intravenous gadolinium (Gd). It was reported that SWI can be performed after Gd injection without information loss or signal change. PURPOSE: To investigate the diagnostic value of contrast-enhanced SWI (CE-SWI) in the assessment of intracranial brain neoplasm. MATERIAL AND METHODS: After obtaining the approval of the local ethics committee, 35 brain neoplasm patients (24 with metastasis and 11 with glioblastoma multiforme [GBM]) were enrolled. In order to investigate the value of using CE-SWI, two neuroradiologists performed an evaluation of the frequency of the intratumoral susceptibility signals (ITSS) in SWI and CE-SWI with visual assessment using 5-grade scales. We evaluated the visibility of the tumor margins and the internal architecture of tumors on T1-weighted imaging (T1WI), contrast-enhanced T1 (CE-T1), SWI, and CE-SWI. RESULTS: The contrast-enhanced scans (CE-T1 and CE-SWI) showed statistically significant higher scores compared to non-enhanced scans (T1WI and SWI) for the analysis of the tumor margin in GBM and metastasis (P < 0.05, Wilcoxon signed rank test). Statistically significant higher scores are noted in GBMs compared to metastases in the visibility of the internal architecture of tumors on CE-SWI and the visibility of the tumor margin on CE-T1 (P < 0.05, Mann-Whitney test). CONCLUSION: Based on our results, SWI can be performed after gadolinium injection without information loss or signal change. CE-SWI is useful in evaluating intracranial neoplasm due to its ability to simultaneously demonstrate both ITSS that are not visible with conventional magnetic resonance sequences and contrast enhancement.

Improving subspace constrained radial fast spin echo MRI using block matching driven non-local low rank regularization.

Subspace-constrained reconstruction methods restrict the relaxation signals (of size M) in the scene to a pre-determined subspace (of size K≪M) and allow multi-contrast imaging and parameter mapping from accelerated acquisitions. However, these constraints yield poor image quality at some imaging contrasts, which can impact the parameter mapping performance. Additional regularization such as the use of joint-sparse (JS) or locally-low-rank (LLR) constraints can help improve the recovery of these images but are not sufficient when operating at high acceleration rates. We propose a method, non-local rank 3D (NLR3D), that is built on block matching and transform domain low rank constraints to allow high quality recovery of subspace-coefficient images (SCI) and subsequent multi-contrast imaging and parameter mapping. The performance of NLR3D was evaluated using Monte-Carlo (MC) simulations and compared against the JS and LLR methods. In vivo T 2 mapping results are presented on brain and knee datasets. MC results demonstrate improved bias, variance, and MSE behavior in both the multi-contrast images and parameter maps when compared to the JS and LLR methods. In vivo brain and knee results at moderate and high acceleration rates demonstrate improved recovery of high SNR early TE images as well as parameter maps. No significant difference was found in the T2 values measured in ROIs between the NLR3D reconstructions and the reference images (Wilcoxon signed rank test). The proposed method, NLR3D, enables recovery of high-quality SCI and, consequently, the associated multi-contrast images and parameter maps.

The Value of Quantitative Musculoskeletal Imaging.

Musculoskeletal imaging is mainly based on the subjective and qualitative analysis of imaging examinations. However, integration of quantitative assessment of imaging data could increase the value of imaging in both research and clinical practice. Some imaging modalities, such as perfusion magnetic resonance imaging (MRI), diffusion MRI, or T2 mapping, are intrinsically quantitative. But conventional morphological imaging can also be analyzed through the quantification of various parameters. The quantitative data retrieved from imaging examinations can serve as biomarkers and be used to support diagnosis, determine patient prognosis, or monitor therapy.We focus on the value, or clinical utility, of quantitative imaging in the musculoskeletal field. There is currently a trend to move from volume- to value-based payments. This review contains definitions and examines the role that quantitative imaging may play in the implementation of value-based health care. The influence of artificial intelligence on the value of quantitative musculoskeletal imaging is also discussed.

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.

Assessment of MRI-based anomalous diffusion changes in brain ischemic stroke with a fractional motion model.

The actual diffusion process in human brain has been shown to be anomalous comparing to that predicted with traditional diffusion MRI (dMRI) theory. Recently, dMRI based on fractional motion (FM) model has demonstrated the potential to accurately describe anomalous diffusion in vivo. In this work, we explored the potential value of FM model-based dMRI in quantificational identification of ischemic stroke and compared that with the traditional apparent diffusion coefficient (ADC). We included 23 acute stroke patients, 8 of whom finished a follow-up scan, and 22 matched healthy controls. The dMRI images were acquired by using a Stejskal-Tanner single-shot spin-echo echo-planar-imaging sequence (diffusion gradients were applied in three orthogonal directions with 25 non-zero b values ranging from 248 to 4474 s/mm2) at 3.0 T MRI. We calculated the coefficient of variation (CV) for FM-related parameters in stroke lesions, and compared the mean values for FM-related parameters and ADC by using two-sample t-tests. Correlation analysis was achieved using Pearson correlation coefficient test. In acute stroke lesions, CV for FM-related parameters showed significant increase compared with normal tissues (P < 0.01), while those of ADC didn't appear statistical difference. Mean values for FM-related parameters showed significant decrease in acute lesion (P < 0.01) and their changing pattern during follow-up was positively correlated with ADC (P < 0.005). Our results initially verified the utility of the FM-model in detecting ischemic stroke compared with traditional dMRI.

Preoperative vascular heterogeneity and aggressiveness assessment of pituitary macroadenoma based on dynamic contrast-enhanced MRI texture analysis.

PURPOSE: To assess the vascular heterogeneity and aggressiveness of pituitary macroadenomas (PM) using texture analysis based on Dynamic Contrast-Enhanced MRI (DCE-MRI). METHOD: Fifty patients with pathologically confirmed PM, including 32 patients with aggressive PM (aggressive group) and 18 patients with non-aggressive PM (non-aggressive group), were included in this study. The preoperative DCE-MRI and clinical data were collected from all patients. The features based on Ktrans, Ve, and Kep were generated using Omni-Kinetics software. Independent-samples t-test and Mann-Whitney U test were used for comparison between two groups. Logistic regression analysis was used to determine the optimal model for distinguishing aggressive and non-aggressive PM. RESULTS: Six features related to tumor morphology, 24 features in Ktrans, 20 features in Ve, and 3 features in Kep were significantly different between the aggressive and non-aggressive groups. Volume count, gray-level non-uniformity in Ktrans, voxel value sum in Ve and run-length non-uniformity in Kep (AUC = 0.816, 0.903, 0.785, 0.813) were considered the best feature for tumor diagnosis. After modeling, the diagnosis efficiency of mean model and total model was desirable (AUC = 0.859 and 0.957), and the diagnostic efficiency of morphological, Ktrans, Ve and Kep features model was improved (AUC = 0.845, 0.951, 0.847, 0.804). CONCLUSIONS: Texture analysis based on DCE-MRI elucidates the vascular heterogeneity and aggressiveness of pituitary adenoma. The total model could be used as a new noninvasive method for predicting the aggressiveness of pituitary macroadenoma.

Ultrafast Dynamic Contrast-Enhanced Breast MRI: Lesion Conspicuity and Size Assessment according to Background Parenchymal Enhancement.

OBJECTIVE: To evaluate the clinical utility of ultrafast dynamic contrast-enhanced (DCE)-MRI compared to conventional DCE-MRI by studying lesion conspicuity and size according to the level of background parenchymal enhancement (BPE). MATERIALS AND METHODS: This study included 360 women (median age, 54 years; range, 26-82 years) with 361 who had undergone breast MRI, including both ultrafast and conventional DCE-MRI before surgery, between January and December 2017. Conspicuity was evaluated using a five-point score. Size was measured as the single maximal diameter. The Wilcoxon signed-rank test was used to compare median conspicuity score. To identify factors associated with conspicuity, multivariable logistic regression was performed. Absolute agreement between size at MRI and histopathologic examination was assessed using the intraclass correlation coefficient (ICC). RESULTS: The median conspicuity scores were 5 at both scans, but the interquartile ranges were significantly different (5-5 at ultrafast vs. 4-5 at conventional, p < 0.001). Premenopausal status (odds ratio [OR] = 2.2, p = 0.048), non-mass enhancement (OR = 4.1, p = 0.001), moderate to marked BPE (OR = 7.5, p < 0.001), and shorter time to enhancement (OR = 0.9, p = 0.043) were independently associated with better conspicuity at ultrafast scans. Tumor size agreement between MRI and histopathologic examination was similar for both scans (ICC = 0.66 for ultrafast vs. 0.63 for conventional). CONCLUSION: Ultrafast DCE-MRI could improve lesion conspicuity compared to conventional DCE-MRI, especially in women with premenopausal status, non-mass enhancement, moderate to marked BPE or short time to enhancement.

An MRI-based decision tree to distinguish lipomas and lipoma variants from well-differentiated liposarcoma of the extremity and superficial trunk: Classification and regression tree (CART) analysis.

PURPOSE: To build and validate a decision tree model using classification and regression tree (CART) analysis to distinguish lipoma and lipoma variants from well-differentiated liposarcoma of the extremities and superficial trunk. METHODS: This retrospective study included patients who underwent surgical resection and preoperative contrast-enhanced MR imaging for lipoma, lipoma variants, and well-differentiated liposarcoma in two tertiary referral centers. Six MRI findings (tumor size, anatomical location, tumor depth, shape, enhancement pattern, and presence of intermingled muscle fibers) and two demographic factors (patient age and sex) were assessed to build a classification tree using CART analysis with minimal error cross-validation pruning based on a complexity parameter. RESULTS: The model building cohort consisted of 231 patients (186 lipoma and lipoma variants and 45 well-differentiated liposarcoma) from one center, while the validation cohort consisted of 157 patients (136 lipoma and lipoma variants and 21 well-differentiated liposarcoma) from another center. In the CART analysis, the contrast enhancement pattern (no enhancement or thin septal enhancement versus thick septal, nodular, confluent hazy, or solid enhancement) was the first partitioning predictor, followed by a maximal tumor size of 12.75 cm. The tree model allowed distinction of lipoma and lipoma variants from well-differentiated liposarcoma in both the model building cohort (C-statistics, 0.955; sensitivity 80 %, specificity 94.62 %, accuracy 91.77 %) and the external validation cohort (C-statistics, 0.917; sensitivity 66.67 %, specificity 95.59 %, accuracy 91.72 %). CONCLUSION: The distinction of lipoma and lipoma variants from well-differentiated liposarcoma can be achieved with the simple classification tree model.