Pushing MP2RAGE boundaries: Ultimate time-efficient parameterization combined with exhaustive T1 synthetic contrasts.

PURPOSE: MP2RAGE parameter optimization is redefined to allow more time-efficient MR acquisitions, whereas the T1 -based synthetic imaging framework is used to obtain on-demand T1 -weighted contrasts. Our aim was to validate this concept on healthy volunteers and patients with multiple sclerosis, using plug-and-play parallel-transmission brain imaging at 7 T. METHODS: A "time-efficient" MP2RAGE sequence was designed with optimized parameters including TI and TR set as small as possible. Extended phase graph formalism was used to set flip-angle values to maximize the gray-to-white-matter contrast-to-noise ratio (CNR). Several synthetic contrasts (UNI, EDGE, FGATIR, FLAWSMIN , FLAWSHCO ) were generated online based on the acquired T1 maps. Experimental validation was performed on 4 healthy volunteers at various spatial resolutions. Clinical applicability was evaluated on 6 patients with multiple sclerosis, scanned with both time-efficient and conventional MP2RAGE parameterizations. RESULTS: The proposed time-efficient MP2RAGE protocols reduced acquisition time by 40%, 30%, and 19% for brain imaging at (1 mm)3 , (0.80 mm)3 and (0.65 mm)3 , respectively, when compared with conventional parameterizations. They also provided all synthetic contrasts and comparable contrast-to-noise ratio on UNI images. The flexibility in parameter selection allowed us to obtain a whole-brain (0.45 mm)3 acquisition in 19 min 56 s. On patients with multiple sclerosis, a (0.67 mm)3 time-efficient acquisition enhanced cortical lesion visualization compared with a conventional (0.80 mm)3 protocol, while decreasing the scan time by 15%. CONCLUSION: The proposed optimization, associated with T1 -based synthetic contrasts, enabled substantial decrease of the acquisition time or higher spatial resolution scans for a given time budget, while generating all typical brain contrasts derived from MP2RAGE.

Comparison of diagnostic performance of radiologist- and AI-based assessments of T2-FLAIR mismatch sign and quantitative assessment using synthetic MRI in the differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, IDH-mutant and 1p/19q-codeleted.

PURPOSE: This study aimed to compare assessments by radiologists, artificial intelligence (AI), and quantitative measurement using synthetic MRI (SyMRI) for differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, and IDH-mutant and 1p/19q-codeleted and to identify the superior method. METHODS: Thirty-three cases (men, 14; women, 19) comprising 19 astrocytomas and 14 oligodendrogliomas were evaluated. Four radiologists independently evaluated the presence of the T2-FLAIR mismatch sign. A 3D convolutional neural network (CNN) model was trained using 50 patients outside the test group (28 astrocytomas and 22 oligodendrogliomas) and transferred to evaluate the T2-FLAIR mismatch lesions in the test group. If the CNN labeled more than 50% of the T2-prolonged lesion area, the result was considered positive. The T1/T2-relaxation times and proton density (PD) derived from SyMRI were measured in both gliomas. Each quantitative parameter (T1, T2, and PD) was compared between gliomas using the Mann-Whitney U-test. Receiver-operating characteristic analysis was used to evaluate the diagnostic performance. RESULTS: The mean sensitivity, specificity, and area under the curve (AUC) of radiologists vs. AI were 76.3% vs. 94.7%; 100% vs. 92.9%; and 0.880 vs. 0.938, respectively. The two types of diffuse gliomas could be differentiated using a cutoff value of 2290/128 ms for a combined 90th percentile of T1 and 10th percentile of T2 relaxation times with 94.4/100% sensitivity/specificity with an AUC of 0.981. CONCLUSION: Compared to the radiologists' assessment using the T2-FLAIR mismatch sign, the AI and the SyMRI assessments increased both sensitivity and objectivity, resulting in improved diagnostic performance in differentiating gliomas.

Value of synthetic MRI quantitative parameters in preprocedural evaluation for TRUS/MRI fusion-guided biopsy of the prostate.

BACKGROUND: Transrectal ultrasonography (TRUS)/magnetic resonance imaging (MRI) fusion-guided biopsy has a high clinical application value. However, this technique has some limitations, which limit its use in routine clinical practice. Therefore, the selection of suitable proatate lesions for this technique is worthy of our attention. Synthetic MRI (SyMRI) is capable of quantifying multiple relaxation parameters, which might have potential value in preprocedural evaluation for TRUS/MRI fusion-guided biopsy of the prostate. The aim of our study is to examine the value of SyMRI quantitative parameters in preprocedural evaluation for TRUS/MRI fusion-guided biopsy of the prostate. METHODS: We prospectively selected 148 lesions in 137 patients who underwent prostate biopsy in our hospital. Next, 2-4 needles of TRUS/MRI fusion-guided biopsy combined with 10 needles of system biopsy (SB) were used as the protocol for prostate biopsy. Before biopsy, the MAGiC sequences of the MRI images of the enrolled patients underwent post-processing, and the longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) were extracted. The biopsy pathology results were used as a gold standard to compare the differences in SyMRI quantitative parameters between benign and malignant prostate lesions in the peripheral and transitional zones. The receiver operating characteristic (ROC) curves were plotted to confirm the optimal SyMRI quantitative parameter for prostate lesion benignancy/malignancy performance, and the cutoff values of these parameters were used for grouping the lesions. The single-needle biopsy prostate cancer (PCa)-positivity rates (number of positive biopsy needles/total biopsy needles) and PCa overall detection rates by TRUS/MRI fusion-guided biopsy and SB were compared in different subgroups. RESULTS: The T1 and T2 values can determine the benignancy/malignancy of prostate transition lesions(p < 0.01), and the T2 value has a greater diagnostic performance (p = 0.0376). The T2 value can determine the benignancy/malignancy of prostate peripheral lesions. The optimal diagnostic cutoff values for T2 were 77 and 81 ms, respectively. The single-needle PCa positivity rate of TRUS/MRI fusion-guided biopsy was higher than SB for any prostate lesions in different subgroups (p < 0.01). However, only in the subgroup of transition zone lesions with T2 ≤ 77 ms, the PCa overall detection rate of TRUS/MRI fusion-guided biopsy was significantly higher than that of SB (p = 0.031). CONCLUSION: SyMRI-T2 value can provide a theoretical basis for the selection of suitable lesions for TRUS/MRI fusion-guided biopsy.

Personalized synthetic MR imaging with deep learning enhancements.

PURPOSE: Personalized synthetic MRI (syn-MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric ( ρ , T 1 , T 2 ) $$ \left(\rho, {\mathrm{T}}_1,{\mathrm{T}}_2\right) $$ maps, from where MR images of that individual at other design parameter settings are synthesized. However, classical methods that use least-squares (LS) or maximum likelihood estimators (MLE) are unsatisfactory at higher noise levels because the underlying inverse problem is ill-posed. This article provides a pipeline to enhance the synthesis of such images in three-dimensional (3D) using a deep learning (DL) neural network architecture for spatial regularization in a personalized setting where having more than a few training images is impractical. METHODS: Our DL enhancements employ a Deep Image Prior (DIP) with a U-net type denoising architecture that includes situations with minimal training data, such as personalized syn-MRI. We provide a general workflow for syn-MRI from three or more training images. Our workflow, called DIPsyn-MRI, uses DIP to enhance training images, then obtains parametric images using LS or MLE before synthesizing images at desired design parameter settings. DIPsyn-MRI is implemented in our publicly available Python package DeepSynMRI available at: https://github.com/StatPal/DeepSynMRI. RESULTS: We demonstrate feasibility and improved performance of DIPsyn-MRI on 3D datasets acquired using the Brainweb interface for spin-echo and FLASH imaging sequences, at different noise levels. Our DL enhancements improve syn-MRI in the presence of different intensity nonuniformity levels of the magnetic field, for all but very low noise levels. CONCLUSION: This article provides recipes and software to realistically facilitate DL-enhanced personalized syn-MRI.

Deep-Learning-Based Contrast Synthesis From MRF Parameter Maps in the Knee Joint.

BACKGROUND: Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast-weighted images that are required by radiologists, limiting reachable total scan time improvement. Contrast synthesis from MRF could significantly decrease the imaging time. PURPOSE: To improve clinical utility of MRF by synthesizing contrast-weighted MR images from the quantitative data provided by MRF, using U-nets that were trained for the synthesis task utilizing L1- and perceptual loss functions, and their combinations. STUDY TYPE: Retrospective. POPULATION: Knee joint MRI data from 184 subjects from Northern Finland 1986 Birth Cohort (ages 33-35, gender distribution not available). FIELD STRENGTH AND SEQUENCE: A 3 T, multislice-MRF, proton density (PD)-weighted 3D-SPACE (sampling perfection with application optimized contrasts using different flip angle evolution), fat-saturated T2-weighted 3D-space, water-excited double echo steady state (DESS). ASSESSMENT: Data were divided into training, validation, test, and radiologist's assessment sets in the following way: 136 subjects to training, 3 for validation, 3 for testing, and 42 for radiologist's assessment. The synthetic and target images were evaluated using 5-point Likert scale by two musculoskeletal radiologists blinded and with quantitative error metrics. STATISTICAL TESTS: Friedman's test accompanied with post hoc Wilcoxon signed-rank test and intraclass correlation coefficient. The statistical cutoff P <0.05 adjusted by Bonferroni correction as necessary was utilized. RESULTS: The networks trained in the study could synthesize conventional images with high image quality (Likert scores 3-4 on a 5-point scale). Qualitatively, the best synthetic images were produced with combination of L1- and perceptual loss functions and perceptual loss alone, while L1-loss alone led to significantly poorer image quality (Likert scores below 3). The interreader and intrareader agreement were high (0.80 and 0.92, respectively) and significant. However, quantitative image quality metrics indicated best performance for the pure L1-loss. DATA CONCLUSION: Synthesizing high-quality contrast-weighted images from MRF data using deep learning is feasible. However, more studies are needed to validate the diagnostic accuracy of these synthetic images. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Synthetic MRI with Magnetic Resonance Spin TomogrAphy in Time-Domain (MR-STAT): Results from a Prospective Cross-Sectional Clinical Trial.

BACKGROUND: Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) can reconstruct whole-brain multi-parametric quantitative maps (eg, T1 , T2 ) from a 5-minute MR acquisition. These quantitative maps can be leveraged for synthetization of clinical image contrasts. PURPOSE: The objective was to assess image quality and overall diagnostic accuracy of synthetic MR-STAT contrasts compared to conventional contrast-weighted images. STUDY TYPE: Prospective cross-sectional clinical trial. POPULATION: Fifty participants with a median age of 45 years (range: 21-79 years) consisting of 10 healthy participants and 40 patients with neurological diseases (brain tumor, epilepsy, multiple sclerosis or stroke). FIELD STRENGTH/SEQUENCE: 3T/Conventional contrast-weighted imaging (T1 /T2 weighted, proton density [PD] weighted, and fluid-attenuated inversion recovery [FLAIR]) and a MR-STAT acquisition (2D Cartesian spoiled gradient echo with varying flip angle preceded by a non-selective inversion pulse). ASSESSMENT: Quantitative T1 , T2 , and PD maps were computed from the MR-STAT acquisition, from which synthetic contrasts were generated. Three neuroradiologists blinded for image type and disease randomly and independently evaluated synthetic and conventional datasets for image quality and diagnostic accuracy, which was assessed by comparison with the clinically confirmed diagnosis. STATISTICAL TESTS: Image quality and consequent acceptability for diagnostic use was assessed with a McNemar's test (one-sided α = 0.025). Wilcoxon signed rank test with a one-sided α = 0.025 and a margin of Δ = 0.5 on the 5-level Likert scale was used to assess non-inferiority. RESULTS: All data sets were similar in acceptability for diagnostic use (≥3 Likert-scale) between techniques (T1 w:P = 0.105, PDw:P = 1.000, FLAIR:P = 0.564). However, only the synthetic MR-STAT T2 weighted images were significantly non-inferior to their conventional counterpart; all other synthetic datasets were inferior (T1 w:P = 0.260, PDw:P = 1.000, FLAIR:P = 1.000). Moreover, true positive/negative rates were similar between techniques (conventional: 88%, MR-STAT: 84%). DATA CONCLUSION: MR-STAT is a quantitative technique that may provide radiologists with clinically useful synthetic contrast images within substantially reduced scan time. EVIDENCE LEVEL: 1 Technical Efficacy: Stage 2.

Time Course Changes of Synthetic Relaxation Time During Neoadjuvant Chemotherapy in Breast Cancer: The Optimal Parameter for Treatment Response Evaluation.

BACKGROUND: Synthetic MRI (syMRI) has enabled quantification of multiple relaxation parameters (T1/T2 relaxation time [T1/T2], proton density [PD]), and their longitudinal change during neoadjuvant chemotherapy (NAC) promises to be valuable parameters for treatment response evaluation in breast cancer. PURPOSE: To investigate the time course changes of syMRI parameters during NAC and evaluate their value as predictors for pathological complete response (pCR) in breast cancer. STUDY TYPE: Retrospective, longitudinal. POPULATION: A total of 129 women (median age, 50 years; range, 28-69 years) with locally advanced breast cancer who underwent NAC; all performed multiple conventional breast MRI examinations with added syMRI during NAC. FIELD STRENGTH/SEQUENCE: A 3.0 T, T1-weighted dynamic contrast enhanced and syMRI acquired by a multiple-dynamic, multiple-echo sequence. ASSESSMENT: Breast MRI was set at four time-points: baseline, after one cycle, after three or four cycles of NAC and preoperation. SyMRI parameters and tumor diameters were measured and their changes from baseline were calculated. All parameters were compared between pCR and non-pCR. Interaction between syMRI parameters and clinicopathological features was analyzed. STATISTICAL TESTS: Mann-Whitney U tests, random effects model of repeated measurement, receiver operating characteristic (ROC) analysis, interaction analysis. RESULTS: Median synthetic T1/T2/PD and tumor diameter generally decreased throughout NAC. Absolute T1 at early-NAC, T1, and PD at mid-NAC were significantly lower in the pCR group. After early-NAC, the T1 change was significantly higher in the pCR (median ± IQR, 18.17 ± 11.33) than the non-pCR group (median ± IQR, 10.90 ± 10.03), with the highest area under the ROC curves (AUC) of 0.769 (95% CI, 0.684-0.838). Interaction analysis showed that histological grade III patients had higher odds ratio (OR) (OR = 1.206) compared to grade II patients (OR = 1.067). DATA CONCLUSION: Synthetic T1 changes after one cycle of NAC maybe useful for early evaluating NAC response in breast cancer during whole treatment cycles. However, its discriminative ability is significantly affected by histological grade. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Quantification of myelination in children with attention-deficit/hyperactivity disorder: a comparative assessment with synthetic MRI and DTI.

Evaluation of myelin content is crucial for attention-deficit/hyperactivity disorder (ADHD). To estimate myelin content in ADHD based on synthetic MRI-based method and compare it with established diffusion tensor imaging (DTI) method. Fifth-nine ADHD and fifty typically developing (TD) children were recruited. Global and regional myelin content (myelin volume fraction [MVF] and myelin volume [MYV]) were assessed using SyMRI and compared with DTI metrics (fractional anisotropy and mean/radial/axial diffusivity). The relationship between significant MRI parameters and clinical variables were assessed in ADHD. No between-group differences of whole-brain myelin content were found. Compared to TDs, ADHD showed higher mean MVF in bilateral internal capsule, external capsule, corona radiata, and corpus callosum, as well as in left tapetum, left superior fronto-occipital fascicular, and right cingulum (all PFDR-corrected < 0.05). Increased MYV were found in similar regions. Abnormalities of DTI metrics were mainly in bilateral corticospinal tract. Besides, MVF in right retro lenticular part of internal capsule was negatively correlated with cancellation test scores (r = - 0.41, P = 0.002), and MYV in right posterior limb of internal capsule (r = 0.377, P = 0.040) and left superior corona radiata (r = 0.375, P = 0.041) were positively correlated with cancellation test scores in ADHD. Increased myelin content underscored the important pathway of frontostriatal tract, posterior thalamic radiation, and corpus callosum underlying ADHD, which reinforced the insights into myelin quantification and its potential role in pathophysiological mechanism and disease diagnosis. Prospectively registered trials number: ChiCTR2100048109; date: 2021-07.

Synthetic MRI improves radiomics-based glioblastoma survival prediction.

Glioblastoma is an aggressive and fast-growing brain tumor with poor prognosis. Predicting the expected survival of patients with glioblastoma is a key task for efficient treatment and surgery planning. Survival predictions could be enhanced by means of a radiomic system. However, these systems demand high numbers of multicontrast images, the acquisitions of which are time consuming, giving rise to patient discomfort and low healthcare system efficiency. Synthetic MRI could favor deployment of radiomic systems in the clinic by allowing practitioners not only to reduce acquisition time, but also to retrospectively complete databases or to replace artifacted images. In this work we analyze the replacement of an actually acquired MR weighted image by a synthesized version to predict survival of glioblastoma patients with a radiomic system. Each synthesized version was realistically generated from two acquired images with a deep learning synthetic MRI approach based on a convolutional neural network. Specifically, two weighted images were considered for the replacement one at a time, a T2w and a FLAIR, which were synthesized from the pairs T1w and FLAIR, and T1w and T2w, respectively. Furthermore, a radiomic system for survival prediction, which can classify patients into two groups (survival >480 days and ≤ 480 days), was built. Results show that the radiomic system fed with the synthesized image achieves similar performance compared with using the acquired one, and better performance than a model that does not include this image. Hence, our results confirm that synthetic MRI does add to glioblastoma survival prediction within a radiomics-based approach.

Synthetic MRI: Technologies and Applications in Neuroradiology.

Synthetic MRI is a technique that synthesizes contrast-weighted images from multicontrast MRI data. There have been advances in synthetic MRI since the technique was introduced. Although a number of synthetic MRI methods have been developed for quantifying one or more relaxometric parameters and for generating multiple contrast-weighted images, this review focuses on several methods that quantify all three relaxometric parameters (T1 , T2 , and proton density) and produce multiple contrast-weighted images. Acquisition, quantification, and image synthesis techniques are discussed for each method. We discuss the image quality and diagnostic accuracy of synthetic MRI methods and their clinical applications in neuroradiology. Based on this analysis, we highlight areas that need to be addressed for synthetic MRI to be widely implemented in the clinic. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.

Assessment of 2D conventional and synthetic MRI in multiple sclerosis.

PURPOSE: To qualitatively and quantitatively compare synthetic and conventional MRI sequences acquired on a 1.5-T system for patients with multiple sclerosis (MS). METHODS: Prospective study that involved twenty-seven consecutive relapsing-remitting MS patients scanned on a 1.5-T MRI scanner. The MRI protocol included 2D transverse conventional spin-echo sequences: proton density-weighted (PD), T2-weighted, T2-FLAIR, and T1-weighted. Synthetic images were generated using 2D transverse QRAPMASTER and SyMRI software with the same voxel size, repetition, echo, and inversion times as the conventional sequences. Four raters performed a crosstab qualitative analysis that involved evaluating global image quality, contrast, flow artefacts, and confidence in lesion assessment introducing the concepts of predominance, agreement, and disagreement. A quantitative analysis was also performed and included evaluating the number of lesions (periventricular, juxtacortical, brainstem, and cerebellum) and the contrast-to-noise ratio between regions (CSF, white matter, grey matter, lesions). RESULTS: The global image quality assessment showed predominance for better scores for conventional sequences over synthetic sequences, whereas contrast, confidence in lesion assessment, and flow artefacts showed predominance for agreement between sequences. There was predominance for disagreement between all pairs of raters in most of the evaluated qualitative parameters. Synthetic PD and T2-FLAIR images showed higher contrast-to-noise ratios than the corresponding conventional images for most comparison between regions. There were no significant differences in the number of lesions detected for most of the study regions between conventional and synthetic images. CONCLUSION: Synthetic MRI can be potentially used as an alternative to conventional brain MRI sequences in the assessment of MS.

Clinical adaptation of synthetic MRI-based whole brain volume segmentation in children at 3 T: comparison with modified SPM segmentation methods.

PURPOSE: To validate the use of synthetic magnetic resonance imaging (SyMRI) volumetry by comparing with child-optimized SPM 12 volumetry in 3 T pediatric neuroimaging. METHODS: In total, 106 children aged 4.7-18.7 years who underwent both synthetic and 3D T1-weighted imaging and had no abnormal imaging/neurologic findings were included for the SyMRI vs. SPM T1-only segmentation (SPM T1). Forty of the 106 children who underwent an additional 3D T2-weighted imaging were included for the SyMRI vs. SPM multispectral segmentation (SPM multi). SPM segmentation using an age-appropriate atlas and inverse-transforming template-space intracranial mask was compared with SyMRI segmentation. Volume differences between SyMRI and SPM T1 were plotted against age to evaluate the influence of age on volume difference. RESULTS: Measurements derived from SyMRI and two SPM methods showed excellent agreements and strong correlations except for the CSF volume (CSFV) (intraclass correlation coefficients = 0.87-0.98; r = 0.78-0.96; relative volume difference other than CSFV = 6.8-18.5% [SyMRI vs. SPM T1] and 11.3-22.7% [SyMRI vs. SPM multi]). Dice coefficients of all brain tissues (except CSF) were in the range 0.78-0.91. The Bland-Altman plot and age-related volume difference change suggested that the volume differences between the two methods were influenced by the volume of each brain tissue and subject's age (p < 0.05). CONCLUSION: SyMRI and SPM segmentation results were consistent except for CSFV, which supports routine clinical use of SyMRI-based volumetry in pediatric neuroimaging. However, caution should be taken in the interpretation of the CSF segmentation results.

Reduced myelin in patients with isolated hippocampal sclerosis as assessed by SyMRI.

PURPOSE: Synthetic MRI (SyMRI) enables to quantify brain tissue and morphometry. We aimed to investigate the WM and myelin alterations in patients with unilateral hippocampal sclerosis (HS) with SyMRI. METHODS: Adult patients with isolated unilateral HS and age-matched control subjects (CSs) were included in this study. The SyMRI sequence QRAPMASTER in the coronal plane perpendicular to the hippocampi was obtained from the whole brain. Automatic segmentation of the whole brain was processed by SyMRI Diagnostic software (Version 11.2). Two neuroradiologists also performed quantitative analyses independently from symmetrical 14 ROIs placed in temporal and extratemporal WM, hippocampi, and amygdalae in both hemispheres. RESULTS: Sixteen patients (F/M = 6/10, mean age = 32.5 ± 11.3 years; right/left HS: 8/8) and 10 CSs (F/M = 5/5, mean age = 30.7 ± 7 years) were included. Left HS patients had significantly lower myelin and WM volumes than CSs (p < .05). Myelin was reduced significantly in the ipsilateral temporal lobe of patients than CSs, greater in left HS (p < .05). Histopathological examination including luxol fast blue stain also revealed myelin pallor in all of 6 patients who were operated. Ipsilateral temporal pole and sub-insular WM had significantly reduced myelin than the corresponding contralateral regions in patients (p < .05). No significant difference was found in WM values. GM values were significantly lower in hippocampi in patients than CSs (p < .05). CONCLUSION: SyMRI revealed myelin reduction in the ipsilateral temporal lobe and sub-insular WM of patients with HS. Whether this finding correlates with electrophysiological features and SyMRI could serve as lateralization of temporal lobe epilepsy need to be investigated.

Investigation of Synthetic Magnetic Resonance Imaging Applied in the Evaluation of the Tumor Grade of Bladder Cancer.

BACKGROUND: The histological grade of bladder cancer (BCa) is an important factor associated with the treatment and prognosis. However, accurate determination of the preoperative histological grade of BCa remains a challenge. PURPOSE: To investigate the diagnostic potential of synthetic MRI (SyMRI) in evaluating the histological grade of BCa. STUDY TYPE: Prospective. SUBJECTS: Sixty patients (48 men and 12 women; mean age, 65 ± 11 years) with pathologically confirmed BCa (33 with high-grade BCa and 27 with low-grade BCa) were enrolled. FIELD STRENGTH/SEQUENCE: Diffusion-weighted imaging (DWI) acquired by a single-shot echo-planar sequence and SyMRI acquired by a multidynamic multiecho (MDME) sequence at 3.0 T. ASSESSMENT: Preoperative quantitative longitudinal relaxation time (T1 ), transverse relaxation time (T2 ), proton density (PD), and apparent diffusion coefficient (ADC) values of BCa were independently measured by two radiologists. STATISTICAL TESTS: Interclass correlation coefficient (ICC), independent sample t-test, Mann-Whitney U test, Delong test, and receiver operating characteristic curve (ROC) analysis were used. RESULTS: Significant differences were found in the mean of all the T1 , T2 , PD, and ADC values between high- and low-grade BCa. The best diagnostic performance was found for the mean ADC value with an area under the ROC curve (AUC) of 0.869, while the AUC values of the mean PD, T1 , and T2 values were 0.755, 0.740, and 0.723, respectively. DATA CONCLUSION: SyMRI may be a potential noninvasive technique for evaluating the histological grade of BCa. However, the overall diagnostic performance of SyMRI-derived parameters was inferior to the ADC value. LEVELS OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 2.

Enhanced Masses on Contrast-Enhanced Breast: Differentiation Using a Combination of Dynamic Contrast-Enhanced MRI and Quantitative Evaluation with Synthetic MRI.

BACKGROUND: The addition of synthetic MRI might improve the diagnostic performance of dynamic contrast-enhanced MRI (DCE-MRI) in patients with breast cancer. PURPOSE: To evaluate the diagnostic value of a combination of DCE-MRI and quantitative evaluation using synthetic MRI for differentiation between benign and malignant breast masses. STUDY TYPE: Retrospective, observational. POPULATION: In all, 121 patients with 131 breast masses who underwent DCE-MRI with additional synthetic MRI were enrolled. FIELD STRENGTH/SEQUENCE: 3.0 Tesla, T1 -weighted DCE-MRI and synthetic MRI acquired by a multiple-dynamic, multiple-echo sequence. ASSESSMENT: All lesions were differentiated as benign or malignant using the following three diagnostic methods: DCE-MRI type based on the Breast Imaging-Reporting and Data System; synthetic MRI type using quantitative evaluation values calculated by synthetic MRI; and a combination of the DCE-MRI + Synthetic MRI types. The diagnostic performance of the three methods were compared. STATISTICAL TESTS: Univariate (Mann-Whitney U-test) and multivariate (binomial logistic regression) analyses were performed, followed by receiver-operating characteristic curve (AUC) analysis. RESULTS: Univariate and multivariate analyses showed that the mean T1 relaxation time in a breast mass obtained by synthetic MRI prior to injection of contrast agent (pre-T1 ) was the only significant quantitative value acquired by synthetic MRI that could independently differentiate between malignant and benign breast masses. The AUC for all enrolled breast masses assessed by DCE-MRI + Synthetic MRI type (0.83) was significantly greater than that for the DCE-MRI type (0.70, P < 0.05) or synthetic MRI type (0.73, P < 0.05). The AUC for category 4 masses assessed by the DCE-MRI + Synthetic MRI type was significantly greater than that for those assessed by the DCE-MRI type (0.74 vs. 0.50, P < 0.05). DATA CONCLUSION: A combination of synthetic MRI and DCE-MRI improves the accuracy of diagnosis of benign and malignant breast masses, especially category 4 masses. Level of Evidence 4 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:381-391.

Investigation of Synthetic Relaxometry and Diffusion Measures in the Differentiation of Benign and Malignant Breast Lesions as Compared to BI-RADS.

BACKGROUND: Breast cancer is the most common malignant tumor in women and a quantitative contrast-free method is highly desirable for its diagnosis. PURPOSE: To investigate the performance of quantitative MRI in differentiating malignant from benign breast lesions and to compare with the Breast Imaging Reporting and Data System (BI-RADS). STUDY TYPE: Retrospective. SUBJECTS: Eighty patients (56 with malignant lesions and 24 with benign lesions). FIELD STRENGTH/SEQUENCE: Diffusion-weighted imaging (DWI) with a single-shot echo planar sequence and synthetic MRI with magnetic resonance image compilation (MAGiC) were performed at 3T. ASSESSMENT: T1 relaxation time (T1 ), T2 relaxation time (T2 ), and proton density (PD) from synthetic MRI and apparent diffusion coefficient (ADC) from DWI were analyzed by two radiologists (Reader A, Reader B). Univariable and multivariable models were developed to optimize differentiation between malignant and benign lesions and their performances compared to BI-RADS. STATISTICAL TESTS: The diagnostic performance was evaluated using multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curves (AUC). RESULTS: T2 , PD, and ADC values for malignant lesions were significantly lower than those in benign breast lesions for both radiologists (all P < 0.05). The combined T2 , PD, and ADC model had the best performance for differentiating malignant and benign lesions with AUC, sensitivity, specificity, positive predictive value, and negative predictive values of 0.904, 94.6%, 87.5%, 94.6%, and 87.5%, respectively. The corresponding results for BI-RADS were no AUC, 94.6%, 75.0%, 89.8%, and 85.7%, respectively. DATA CONCLUSION: The approach that combined synthetic MRI and DWI outperformed BI-RADS in the differential diagnosis of malignant and benign breast lesions and was achieved without contrast agents. This approach may serve as an alternative and effective strategy for the improvement of breast lesion differentiation. LEVEL OF EVIDENCE: 3. TECHNICAL EFFICACY STAGE: 3.

Rapid Musculoskeletal MRI in 2021: Clinical Application of Advanced Accelerated Techniques.

OBJECTIVE. The purpose of this article is to provide a practice-focused review of the clinical application of advanced acceleration techniques for rapid musculoskeletal MRI examinations. CONCLUSION. Parallel imaging, simultaneous multislice acquisition, compressed sensing-based sampling, and synthetic MRI techniques provide unprecedented opportunities for rapid musculoskeletal MRI examinations. For 2D and 3D fast spin-echo and turbo spin-echo pulse sequences, acceleration factors between 3 and 8 can be realized in clinical practice, amounting to a time savings of 66-85% when compared with unaccelerated acquisitions.

Diagnosis and Grading of Prostate Cancer by Relaxation Maps From Synthetic MRI.

BACKGROUND: The interpretation system for prostate MRI is largely based on qualitative image contrast of different tissue types. Therefore, a fast, standardized, and robust quantitative technique is necessary. Synthetic MRI is capable of quantifying multiple relaxation parameters, which might have potential applications in prostate cancer (PCa). PURPOSE: To investigate the use of quantitative relaxation maps derived from synthetic MRI for the diagnosis and grading of PCa. STUDY TYPE: Prospective. SUBJECTS: In all, 94 men with pathologically confirmed PCa or benign pathological changes. FIELD STRENGTH/SEQUENCE: T1 -weighted imaging, T2 -weighted imaging, diffusion-weighted imaging, and synthetic MRI at 3.0T. ASSESSMENT: Four kinds of tissue types were identified on pathology, including PCa, stromal hyperplasia (SH), glandular hyperplasia (GH), and noncancerous peripheral zone (PZ). PCa foci were grouped as low-grade (LG, Gleason score ≤6) and intermediate/high-grade (HG, Gleason score ≥7). Regions of interest were manually drawn by two radiologists in consensus on parametric maps according to the pathological results. STATISTICAL TESTS: Independent sample t-test, Mann-Whitney U-test, and receiver operating characteristic curve analysis. RESULTS: T1 and T2 values of PCa were significantly lower than SH (P = 0.015 and 0.002). The differences of T1 and T2 values between PCa and noncancerous PZ were also significant (P ≤ 0.006). The area under the curve (AUC) of the apparent diffusion coefficient (ADC) value was significantly higher than T1 , T2 , and proton density (PD) values in discriminating PCa from SH and noncancerous PZ (P ≤ 0.025). T2 , PD, and ADC values demonstrated similar diagnostic performance in discriminating LG from HG PCa (AUC = 0.806 [0.640-0.918], 0.717 [0.542-0.854], and 0.817 [0.652-0.925], respectively; P ≥ 0.535). DATA CONCLUSION: Relaxation maps derived from synthetic MRI were helpful for discriminating PCa from other benign pathologies. But the overall diagnostic performance was inferior to the ADC values. T2 , PD, and ADC values performed similarly in discriminating LG from HG PCa lesions. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:552-564.

Initial clinical experience of synthetic MRI as a routine neuroimaging protocol in daily practice: A single-center study.

BACKGROUND AND PURPOSE: We investigated the clinical feasibility of synthetic MRI with a 4-min single scan using a 48-channel head coil as a routine neuroimaging protocol in daily practice by assessing its diagnostic image quality. METHODS: We retrospectively reviewed the imaging data of 89 patients who underwent routine brain MRI using synthetic MRI acquisition between February 2017 and April 2017. Image quality assessments were performed by two independent readers on synthetic T1 fluid-attenuated inversion recovery (FLAIR), T2-weighted, T2 FLAIR, and phase-sensitive inversion recovery sequences acquired using multiple-dynamic multiple-echo imaging. Interobserver reliability between the two readers was assessed using kappa (κ) statistics. RESULTS: On a 4-point assessment scale, the overall image quality and anatomical delineation provided by synthetic brain MRI were found to be good with scores of more than 3 points for all sequences except for the T2 FLAIR sequence. The synthetic T2 FLAIR sequence provided sufficient image quality but showed more pronounced artifacts, especially the CSF pulsation artifact and linear hyperintensity along the brain surface. Interobserver agreement for evaluating image quality of all synthetic sequences was good to excellent (κ, 0.61-0.99; P < 0.001). CONCLUSION: Synthetic MRI can be acceptable as a routine clinical neuroimaging protocol with a short scan time. It can be helpful to design customized and flexible neuroimaging protocols for each institution.

Data-driven synthetic MRI FLAIR artifact correction via deep neural network.

BACKGROUND: FLAIR (fluid attenuated inversion recovery) imaging via synthetic MRI methods leads to artifacts in the brain, which can cause diagnostic limitations. The main sources of the artifacts are attributed to the partial volume effect and flow, which are difficult to correct by analytical modeling. In this study, a deep learning (DL)-based synthetic FLAIR method was developed, which does not require analytical modeling of the signal. PURPOSE: To correct artifacts in synthetic FLAIR using a DL method. STUDY TYPE: Retrospective. SUBJECTS: A total of 80 subjects with clinical indications (60.6 ± 16.7 years, 38 males, 42 females) were divided into three groups: a training set (56 subjects, 62.1 ± 14.8 years, 25 males, 31 females), a validation set (1 subject, 62 years, male), and the testing set (23 subjects, 57.3 ± 20.4 years, 13 males, 10 females). FIELD STRENGTH/SEQUENCE: 3 T MRI using a multiple-dynamic multiple-echo acquisition (MDME) sequence for synthetic MRI and a conventional FLAIR sequence. ASSESSMENT: Normalized root mean square (NRMSE) and structural similarity (SSIM) were computed for uncorrected synthetic FLAIR and DL-corrected FLAIR. In addition, three neuroradiologists scored the three FLAIR datasets blindly, evaluating image quality and artifacts for sulci/periventricular and intraventricular/cistern space regions. STATISTICAL TESTS: Pairwise Student's t-tests and a Wilcoxon test were performed. RESULTS: For quantitative assessment, NRMSE improved from 4.2% to 2.9% (P < 0.0001) and SSIM improved from 0.85 to 0.93 (P < 0.0001). Additionally, NRMSE values significantly improved from 1.58% to 1.26% (P < 0.001), 3.1% to 1.5% (P < 0.0001), and 2.7% to 1.4% (P < 0.0001) in white matter, gray matter, and cerebral spinal fluid (CSF) regions, respectively, when using DL-corrected FLAIR. For qualitative assessment, DL correction achieved improved overall quality, fewer artifacts in sulci and periventricular regions, and in intraventricular and cistern space regions. DATA CONCLUSION: The DL approach provides a promising method to correct artifacts in synthetic FLAIR. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1413-1423.