Synthetic MRI for Stroke: A Qualitative and Quantitative Pilot Study

PurposeSynthetic MR provides quantitative multiparametric data about tissue properties in a fast single-acquisition. We compared synthetic and conventional image quality and investigated synthetic relaxometry of acute and chronic ischemic lesions to support its interest in stroke imaging. MethodsFor this pilot study, we prospectively acquired synthetic and conventional brain MR of 43 consecutive adult patients with suspected stroke. We studied a total of 136 lesions, of which 46 DWI-positive with restricted ADC (DWI+/rADC), 90 white matter T2/FLAIR hyperintensities (WMH), and 430 normal brain regions (NBR). We assessed image quality for lesion definition according to a 3-level score by two readers of different experiences. We compared relaxometry of lesions and regions of interest.Results Synthetic images were superior to their paired conventional images for lesion definition except for sFLAIR (sT1 or sPSIR vs. cT1 and sT2 vs. cT2 for DWI+/rADC and WMH definition; p-values <.001) with substantial to almost perfect inter-rater reliability (κ ranging from 0.711 to 0.932, p-values <.001). We found significant differences in relaxometry between lesions and NBR and between acute and chronic lesions (T1, T2, and PD of DWI+/rADC or WMH vs. mirror NBR; p-values <.001; T1 and PD of DWI+/rADC vs. WMH; p-values of 0.034 and 0.008).Conclusion Synthetic MR may contribute to stroke imaging by fast acquiring consistent relaxometry data and accessible derived images of interest for the study of ischemic lesions.

Generation of quantification maps and weighted images from synthetic magnetic resonance imaging using deep learning network

Objective: The generation of quantification maps and weighted images in synthetic MRI techniques is based on complex fitting equations. This process requires longer image generation times. The objective of this study is to evaluate the feasibility of deep learning method for fast reconstruction of synthetic MRI. Approach: A total of 44 healthy subjects were recruited and random divided into a training set (30 subjects) and a testing set (14 subjects). A multiple-dynamic, multiple-echo (MDME) sequence was used to acquire synthetic MRI images. Quantification maps (T1, T2, and proton density (PD) maps) and weighted (T1W, T2W, and T2W FLAIR) images were created with MAGiC software and then used as the ground truth images in the deep learning (DL) model. An improved multichannel U-Net structure network was trained to generate quantification maps and weighted images from raw synthetic MRI imaging data (8 module images). Quantitative evaluation was performed on quantification maps. Quantitative evaluation metrics, as well as qualitative evaluation were used in weighted image evaluation. Nonparametric Wilcoxon signed-rank tests were performed in this study. Main results: The results of quantitative evaluation show that the error between the generated quantification images and the reference images is small. For weighted images, no significant difference in overall image quality or SNR was identified between DL images and synthetic images. Notably, the DL images achieved improved image contrast with T2W images, and fewer artifacts were present on DL images than synthetic images acquired by T2W FLAIR. Significance: The DL algorithm provides a promising method for image generation in synthetic MRI techniques, in which every step of the calculation can be optimized and faster, thereby simplifying the workflow of synthetic MRI techniques.

A pilot study of the association between leukoaraiosis and cerebral atherosclerosis using synthetic magnetic resonance imaging

Background Leukoaraiosis is a type of lesion characterized by tissue rarefaction or myelin pallor resulting from axons loss and gliosis. Synthetic magnetic resonance imaging (MRI) could yield quantitative T1, T2, proton density (PD) values of leukoaraiosis in addition to information on the volume of the lesion. Purpose To investigate the feasibility of quantifying leukoaraiosis using synthetic MRI and to explore the association between leukoaraiosis and cerebral small vascular diseases and cerebral atherosclerosis. Material and Methods Patients with acute ischemic stroke were enrolled in this study. All participants underwent a conventional T2-weighted image, brain volume, CUBE fluid attenuated inversion recovery, and synthetic MRI acquisition using a 3.0-T MR system. A time-of-flight magnetic resonance angiography was also obtained. We evaluated the T1, T2, PD values and leukoaraiosis volume. Results Analysis of the leukoaraiosis volume ratios demonstrated a positive association with T2 values, a negative association with T1 values, and no association with PD values. Leukoaraiosis volume ratios were independently correlated with age ( P < 0.001), lacunes ( P = 0.022), and cerebral microbleeds ( P = 0.010). A statistical association was found between both age ( P < 0.001) and lacunes ( P = 0.047) and leukoaraiosis T2 values. Conclusion Synthetic MRI may enhance the evaluation of leukoaraiosis, in addition to providing information on its volume. Leukoaraiosis may represent a type of cerebral small vascular disease rather than cerebral atherosclerosis and may share the same pathological mechanism as lacunes and cerebral microbleeds.

NIMG-19. SYNTHETIC ISOCITRATE DEHYDROGENASE-MUTANT GLIOBLASTOMAS FROM GENERATIVE ADVERSARIAL NETWORK PROVIDE MORPHOLOGIC VARIABILITY AND DIAGNOSTIC PERFORMANCE SIMILAR TO REAL DATA: DEVELOPMENT AND VALIDATION

BACKGROUND Generative adversarial network (GAN) creates synthetic MRI data that may provide morphologic variability to assess molecular characteristics of glioblastomas. PURPOSE To investigate the ability of GAN-based generation of isocitrate dehydrogenase (IDH)-mutant glioblastomas to provide morphologic variability and improve molecular prediction. METHODS GAN was retrospectively trained on 110 IDH-mutant high-grade gliomas. Paired contrast-enhanced T1-weighted and FLAIR synthetic MRI data were generated. Diagnostic models were developed from 80 IDH-wild type glioblastomas and 38 IDH-mutant patients, (real model), 38 IDH-mutant GAN-generated synthetic data (synthetic model), or both combined (augmented model). Two neuroradiologists independently assessed real and morphologic characteristics of contrast-enhancement patterns, the presence of necrosis, and margins and type of non-enhancing region. Significant predictors of IDH mutation were selected from multivariable logistic regression, and diagnostic performance was validated in 44 separate patients, 33 with IDH-wild type and 11 with IDH-mutant glioblastomas. RESULTS Synthetic IDH-mutant glioblastomas were similar to real tumors on Turing tests, with an area under the curve (AUC) of 0.67–0.71. Significant predictors of a more frontal or insular location (odds ratio [OR], 1.34 vs. 1.52; highest P = .04) and distinct non-enhancing tumor margins (OR, 2.68 vs. 3.88; P &lt; .001) were similar for the real and synthetic models, with the synthetic (AUC, 0.958) and augmented (AUC, 0.899) models having higher diagnostic performance than the real model (AUC, 0.864) in the training set. The diagnostic accuracy was higher for the synthetic and augmented models (90.9% [40/44] each for both observers) than for the real model (84.1% [37/44] for one observer and 86.4% [38/44] for the other). CONCLUSIONS The GAN-based synthetic images yield morphologically variable IDH-mutant glioblastomas and may be useful as realistic training sets.

Quantitative Values from Synthetic MRI Correlate with Breast Cancer Subtypes

Purpose To correlate quantitative T1, T2, and proton density (PD) values from synthetic MRI with breast cancer subtypes. Methods Twenty-eight breast cancer patients underwent MRI of the breast including synthetic MRI. T1, T2, and PD values were correlated with Ki-67. T1, T2, and PD values were compared between estrogen receptor (ER) positive and ER negative cancers, and between Luminal A and Luminal B cancers. The effectiveness of T1, T2, and PD in differentiating the ER-negative from the ER-positive group and Luminal A from Luminal B cancers was evaluated using receiver operating characteristic analysis.Results Mean T2 relaxation of ER-negative cancers was significantly higher than that of ER-positive cancers (p < .05). The T1, T2, and PD values exhibited a strong positive correlation with Ki-67 (Pearson's r = 0.75, 0.69, and 0.60 respectively; p < .001). Among ER-positive cancers (n=23), T1, T2, and PD values of Luminal A cancers were significantly lower than those of Luminal B cancers (p < .05). The areas under the curve (AUCs) of T1, T2, and PD for discriminating ER-negative from ER-positive cancers were 0.74 (95% confidence interval (CI): 0.54-0.88), 0.87 (95% CI: 0.69-0.97), and 0.62 (95% CI: 0.42-0.79), respectively. The AUCs of T1, T2, and PD values for discriminating Luminal A from Luminal B cancers were 0.83 (95% CI: 0.61-0.95), 0.75 (95% CI: 0.52-0.90), and 0.75 (95% CI: 0.53-0.91), respectively.Conclusion Quantitative values from synthetic MRI significantly correlate with subtypes of invasive breast cancers and may classify subtypes with reasonably good accuracy.

Simple parameters of synthetic MRI for assessment of bone density in patients with spinal degenerative disease

OBJECTIVE Good bone quality is the key to avoiding osteoporotic fragility fractures and poor outcomes after lumbar instrumentation and fusion surgery. Although dual-energy x-ray absorptiometry (DEXA) screening is the current standard for evaluating osteoporosis, many patients lack DEXA measurements before undergoing lumbar spine surgery. The present study aimed to investigate the utility of using simple quantitative parameters generated with novel synthetic MRI to evaluate bone quality, as well as the correlations of these parameters with DEXA measurements. METHODS This prospective study enrolled patients with symptomatic lumbar degenerative disease who underwent DEXA and conventional and synthetic MRI. The quantitative parameters generated with synthetic MRI were T1 map, T2 map, T1 intensity, proton density (PD), and vertebral bone quality (VBQ) score, and these parameters were correlated with T-score of the lumbar spine. RESULTS There were 62 patients and 238 lumbar segments eligible for analysis. PD and VBQ score moderately correlated with T-score of the lumbar spine (r = −0.565 and −0.651, respectively; both p < 0.001). T1 intensity correlated fairly well with T-score (r = −0.411, p < 0.001). T1 and T2 correlated poorly with T-score. Receiver operating characteristic curve analysis demonstrated area under the curve values of 0.808 and 0.794 for detecting osteopenia/osteoporosis (T-score ≤ −1.0) and osteoporosis (T-score ≤ −2.5) with PD (both p < 0.001). CONCLUSIONS PD and T1 intensity values generated with synthetic MRI demonstrated significant correlation with T-score. PD has excellent ability for predicting osteoporosis and osteopenia.

The Effect of Intraventricular Hemorrhage on Brain Development in Premature Infants: A Synthetic MRI Study

Objectives: Synthetic MRI can obtain multiple parameters in one scan, including T1 and T2 relaxation time, proton density (PD), brain volume, etc. This study aimed to investigate the parameter values T1 and T2 relaxation time, PD, and volume characteristics of intraventricular hemorrhage (IVH) newborn brain, and the ability of synthetic MRI parameters T1 and T2 relaxation time and PD to diagnose IVH.Materials and methods: The study included 50 premature babies scanned with conventional and synthetic MRI. Premature infants were allocated to the case group (n = 15) and NON IVH (n = 35). The T1, T2, PD values, and brain volume were obtained by synthetic MRI. Then we assessed the impact of IVH on these parameters.Results: In the posterior limbs of the internal capsule (PLIC), genu of the corpus callosum (GCC), central white matter (CWM), frontal white matter (FWM), and cerebellum (each p &lt; 0.05), the T1 and T2 relaxation times of the IVH group were significantly prolonged. There were significant differences also in PD. The brain volume in many parts were also significantly reduced, which was best illustrated in gray matter (GM), cerebrospinal fluid and intracranial volume, and brain parenchymal fraction (BPF) (each p &lt; 0.001, t = −5.232 to 4.596). The differential diagnosis ability of these quantitative values was found to be excellent in PLIC, CWM, and cerebellum (AUC 0.700–0.837, p &lt; 0.05).Conclusion: The quantitative parameters of synthetic MRI show well the brain tissue characteristic values and brain volume changes of IVH premature infants. T1 and T2 relaxation times and PD contribute to the diagnosis and evaluation of IVH.