Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI.

Synthesizing 7T Susceptibility Weighted Imaging (SWI) from 3T SWI could offer significant clinical benefits by combining the high sensitivity of 7T SWI for neurological disorders with the widespread availability of 3T SWI in diagnostic routines. Although methods exist for synthesizing 7T Magnetic Resonance Imaging (MRI), they primarily focus on traditional MRI modalities like T1-weighted imaging, rather than SWI. SWI poses unique challenges, including limited data availability and the invisibility of certain tissues in individual 3T SWI slices. To address these challenges, we propose a Self-supervised Anatomical Continuity Enhancement (SACE) network to synthesize 7T SWI from 3T SWI using plentiful 3T SWI data and limited 3T-7T paired data. The SACE employs two specifically designed pretext tasks to utilize low-level representations from abundant 3T SWI data for assisting 7T SWI synthesis in a downstream task with limited paired data. One pretext task emphasizes input-specific morphology by balancing the elimination of redundant patterns with the preservation of essential morphology, preventing the blurring of synthetic 7T SWI images. The other task improves the synthesis of tissues that are invisible in a single 3T SWI slice by aligning adjacent slices with the current slice and predicting their difference fields. The downstream task innovatively combines clinical knowledge with brain substructure diagrams to selectively enhance clinically relevant features. When evaluated on a dataset comprising 97 cases (5495 slices), the proposed method achieved a Peak Signal-to-Noise Ratio (PSNR) of 23.05 dB and a Structural Similarity Index (SSIM) of 0.688. Due to the absence of specific methods for 7T SWI, our method was compared with existing enhancement techniques for general 7T MRI synthesis, outperforming these techniques in the context of 7T SWI synthesis. Clinical evaluations have shown that our synthetic 7T SWI is clinically effective, demonstrating its potential as a clinical tool.

Impact of the Alberta Stroke Program CT Score subregions on long-term functional outcomes in acute ischemic stroke: Results from two multicenter studies in China.

Background and Objectives: The Alberta Stroke Program CT Score (ASPECTS) is a widely used rating system for assessing infarct extent and location. We aimed to investigate the prognostic value of ASPECTS subregions' involvement in the long-term functional outcomes of acute ischemic stroke (AIS). Materials and Methods: Consecutive patients with AIS and anterior circulation large-vessel stenosis and occlusion between January 2019 and December 2020 were included. The ASPECTS score and subregion involvement for each patient was assessed using posttreatment magnetic resonance diffusion-weighted imaging. Univariate and multivariable regression analyses were conducted to identify subregions related to 3-month poor functional outcome (modified Rankin Scale scores, 3-6) in the reperfusion and medical therapy cohorts, respectively. In addition, prognostic efficiency between the region-based ASPECTS and ASPECTS score methods were compared using receiver operating characteristic curves and DeLong's test. Results: A total of 365 patients (median age, 64 years; 70% men) were included, of whom 169 had poor outcomes. In the reperfusion therapy cohort, multivariable regression analyses revealed that the involvement of the left M4 cortical region in left-hemisphere stroke (adjusted odds ratio [aOR] 5.39, 95% confidence interval [CI] 1.53-19.02) and the involvement of the right M3 cortical region in right-hemisphere stroke (aOR 4.21, 95% CI 1.05-16.78) were independently associated with poor functional outcomes. In the medical therapy cohort, left-hemisphere stroke with left M5 cortical region (aOR 2.87, 95% CI 1.08-7.59) and caudate nucleus (aOR 3.14, 95% CI 1.00-9.85) involved and right-hemisphere stroke with right M3 cortical region (aOR 4.15, 95% CI 1.29-8.18) and internal capsule (aOR 3.94, 95% CI 1.22-12.78) affected were related to the increased risks of poststroke disability. In addition, region-based ASPECTS significantly improved the prognostic efficiency compared with the conventional ASPECTS score method. Conclusion: The involvement of specific ASPECTS subregions depending on the affected hemisphere was associated with worse functional outcomes 3 months after stroke, and the critical subregion distribution varied by clinical management. Therefore, region-based ASPECTS could provide additional value in guiding individual decision making and neurological recovery in patients with AIS.

Deep medullary vein damage correlates with small vessel disease in small vessel occlusion acute ischemic stroke.

OBJECTIVES: We aim to investigate whether cerebral small vessel disease (cSVD) imaging markers correlate with deep medullary vein (DMV) damage in small vessel occlusion acute ischemic stroke (SVO-AIS) patients. METHODS: The DMV was divided into six segments according to the regional anatomy. The total DMV score (0-18) was calculated based on segmental continuity and visibility. The damage of DMV was grouped according to the quartiles of the total DMV score. Neuroimaging biomarkers of cSVD including white matter hyperintensity (WMH), cerebral microbleed (CMB), perivascular space (PVS), and lacune were identified. The cSVD score were further analyzed. RESULTS: We included 229 SVO-AIS patients, the mean age was 63.7 ± 23.1 years, the median NIHSS score was 3 (IQR, 2-6). In the severe DMV burden group (the 4th quartile), the NIHSS score grade (6 (3-9)) was significantly higher than other groups (p < 0.01). The grade scores for basal ganglia PVS (BG-PVS) were positively correlated with the degree of DMV (R = 0.67, p < 0.01), rather than centrum semivole PVS (CS-PVS) (R = 0.17, p = 0.1). In multivariate analysis, high CMB burden (adjusted odds ratio [aOR], 25.38; 95% confidence interval [CI], 1.87-345.23) was associated with severe DMV scores. In addition, BG-PVS was related to severe DMV burden in a dose-dependent manner: when BG-PVS score was 3 and 4, the aORs of severe DMV burden were 18.5 and 12.19, respectively. CONCLUSION: The DMV impairment was associated with the severity of cSVD, which suggests that DMV burden may be used for risk stratification in SVO-AIS patients. CLINICAL RELEVANCE STATEMENT: The DMV damage score, based on the association between small vessel disease and the deep medullary veins impairment, is a potential new imaging biomarker for the prognosis of small vessel occlusion acute ischemic stroke, with clinical management implications. KEY POINTS: • The damage to the deep medullary vein may be one mechanism of cerebral small vessel disease. • Severe burden of the basal ganglia perivascular space and cerebral microbleed is closely associated with significant impairment to the deep medullary vein. • The deep medullary vein damage score may reflect a risk of added vascular damage in small vessel occlusion acute ischemic stroke patients.

Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI.

Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated great potential for accelerating MRI by reconstructing images from undersampled data. However, most existing deep convolutional neural networks (CNN) directly apply square convolution to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. In this work, we propose an encoding enhanced (EN2) complex CNN for highly undersampled pulmonary MRI reconstruction. EN2 complex CNN employs convolution along either the frequency or phase-encoding direction, resembling the mechanisms of k-space sampling, to maximize the utilization of the encoding correlation and integrity within a row or column of k-space. We also employ complex convolution to learn rich representations from the complex k-space data. In addition, we develop a feature-strengthened modularized unit to further boost the reconstruction performance. Experiments demonstrate that our approach can accurately reconstruct hyperpolarized 129Xe and 1H lung MRI from 6-fold undersampled k-space data and provide lung function measurements with minimal biases compared with fully sampled images. These results demonstrate the effectiveness of the proposed algorithmic components and indicate that the proposed approach could be used for accelerated pulmonary MRI in research and clinical lung disease patient care.

Alteration of White Matter Connectivity for MR-Guided Focused Ultrasound in the Treatment of Essential Tremor.

BACKGROUND: Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy has been implemented as a therapeutic alternative for the treatment of drug-refractory essential tremor (ET). However, its impact on the brain structural network is still unclear. PURPOSE: To investigate both global and local alterations of the white matter (WM) connectivity network in ET after MRgFUS thalamotomy. STUDY TYPE: Retrospective. SUBJECTS: Twenty-seven ET patients (61 ± 11 years, 19 males) with MRgFUS thalamotomy and 28 healthy controls (HC) (61 ± 11 years, 20 males) were recruited for comparison. FIELD STRENGTH/SEQUENCE: A 3 T/single shell diffusion tensor imaging by using spin-echo-based echo-planar imaging, three-dimensional T1 weighted imaging by using gradient-echo-based sequence. ASSESSMENT: Patients were undergoing MRgFUS thalamotomy and their clinical data were collected from pre-operation to 6-month post-operation. Network topological metrics, including rich-club organization, small-world, and efficiency properties were calculated. Correlation between the topological metrics and tremor scores in ET groups was also calculated to assess the role of neural remodeling in the brain. STATISTICAL TESTS: Two-sample independent t-tests, chi-squared test, ANOVA, Bonferroni test, and Spearman's correlation. Statistical significance was set at P < 0.05. RESULTS: For ET patients, the strength of rich-club connection and clustering coefficient significantly increased vs. characteristic path length decreased at 6-month post-operation compared with pre-operation. The distribution pattern of rich-club regions was different in ET groups. Specifically, the order of the rich-club regions was changed according to the network degree value after MRgFUS thalamotomy. Moreover, the altered nodal efficiency in the right temporal pole of the superior temporal gyrus (R = 0.434-0.596) and right putamen (R = 0.413-0.436) was positively correlated with different tremor improvement. DATA CONCLUSION: These findings might improve understanding of treatment-induced modulation from a network perspective and may work as an objective marker in the assessment of ET tremor control with MRgFUS thalamotomy. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 4.

Synthesized 7T MPRAGE From 3T MPRAGE Using Generative Adversarial Network and Validation in Clinical Brain Imaging: A Feasibility Study.

BACKGROUND: Ultra-high field 7T MRI can provide excellent tissue contrast and anatomical details, but is often cost prohibitive, and is not widely accessible in clinical practice. PURPOSE: To generate synthetic 7T images from widely acquired 3T images with deep learning and to evaluate the feasibility of this approach for brain imaging. STUDY TYPE: Prospective. POPULATION: 33 healthy volunteers and 89 patients with brain diseases, divided into training, and evaluation datasets in the ratio 4:1. SEQUENCE AND FIELD STRENGTH: T1-weighted nonenhanced or contrast-enhanced magnetization-prepared rapid acquisition gradient-echo sequence at both 3T and 7T. ASSESSMENT: A generative adversarial network (SynGAN) was developed to produce synthetic 7T images from 3T images as input. SynGAN training and evaluation were performed separately for nonenhanced and contrast-enhanced paired acquisitions. Qualitative image quality of acquired 3T and 7T images and of synthesized 7T images was evaluated by three radiologists in terms of overall image quality, artifacts, sharpness, contrast, and visualization of vessel using 5-point Likert scales. STATISTICAL TESTS: Wilcoxon signed rank tests to compare synthetic 7T images with acquired 7T and 3T images and intraclass correlation coefficients to evaluate interobserver variability. P < 0.05 was considered significant. RESULTS: Of the 122 paired 3T and 7T MRI scans, 66 were acquired without contrast agent and 56 with contrast agent. The average time to generate synthetic images was ~11.4 msec per slice (2.95 sec per participant). The synthetic 7T images achieved significantly improved tissue contrast and sharpness in comparison to 3T images in both nonenhanced and contrast-enhanced subgroups. Meanwhile, there was no significant difference between acquired 7T and synthetic 7T images in terms of all the evaluation criteria for both nonenhanced and contrast-enhanced subgroups (P ≥ 0.180). DATA CONCLUSION: The deep learning model has potential to generate synthetic 7T images with similar image quality to acquired 7T images. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

MRI Assessment of Brain Frailty and Clinical Outcome in Patients With Acute Posterior Perforating Artery Infarction.

BACKGROUND: Global brain health has gained increasing attention recently. Imaging markers of brain frailty have been related to functional outcomes in previous studies on anterior circulation; however, little data are available on imaging markers and posterior circulation. PURPOSE: To investigate the impact of brain frailty on functional outcomes in patients with acute perforating artery infarction (PAI) of the posterior circulation. STUDY TYPE: Prospective. POPULATION: One hundred patients (60.78 ± 9.51 years, 72% men) with acute posterior circulation PAI (determined by diffusion-weighted magnetic resonance imaging (MRI)/time-of-flight MR angiography). FIELD STRENGTH/SEQUENCE: T1- and T2-weighted fast spin echo, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted echo planar, gradient echo (susceptibility-weight imaging), and 3D time-of-flight MR angiography sequences at 3.0 T. ASSESSMENT: Periventricular and deep white matter hyperintensities (WMH), enlarged perivascular spaces (EPVS) in the basal ganglia and centrum semiovale area, lacunes, cerebral microbleeds (CMB), and total brain frailty score by calculating the above imaging characters were rated visually by three radiologists with 9, 10, and 11 years of experience and one neuroradiologist with 12. Infarction volume was assessed using baseline diffusion-weighted imaging (DWI) data obtained within 24 hours of symptom onset. A modified Rankin Scale (mRS) score >1 on day 90 defined an adverse functional outcome. Associations between the imaging markers of brain frailty and functional outcomes were assessed. STATISTICAL TESTS: Fisher's exact test, Mann-Whitney U test, and multivariable binary logistic regression. A P value <0.05 was considered statistically significant. RESULTS: Adverse prognoses (mRS > 1) were observed in 34 (34%) patients. Infarction volume, periventricular WMH, deep WMH, basal ganglia EPVS, CMB, and the brain frailty score were significantly associated with adverse functional outcomes. An increased brain frailty score was significantly associated with unfavorable mRS score on day 90 (odds ratio 1.773, 95% confidence interval 1.237-2.541). DATA CONCLUSION: Advanced MRI imaging markers of brain frailty, individually or combined as a total brain frailty score, were associated with worse functional outcomes after acute posterior circulation PAI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Generative Adversarial Network-based Noncontrast CT Angiography for Aorta and Carotid Arteries.

Background Iodinated contrast agents (ICAs), which are widely used in CT angiography (CTA), may cause adverse effects in humans, and their use is time-consuming and costly. Purpose To develop an ICA-free deep learning imaging model for synthesizing CTA-like images and to assess quantitative and qualitative image quality as well as the diagnostic accuracy of synthetic CTA (Syn-CTA) images. Materials and Methods A generative adversarial network (GAN)-based CTA imaging model was trained, validated, and tested on retrospectively collected pairs of noncontrast CT and CTA images of the neck and abdomen from January 2017 to June 2022, and further validated on an external data set. Syn-CTA image quality was evaluated using quantitative metrics. In addition, two senior radiologists scored the visual quality on a three-point scale (3 = good) and determined the vascular diagnosis. The validity of Syn-CTA images was evaluated by comparing the visual quality scores and diagnostic accuracy of aortic and carotid artery disease between Syn-CTA and real CTA scans. Results CT scans from 1749 patients (median age, 60 years [IQR, 50-68 years]; 1057 male patients) were included in the internal data set: 1137 for training, 400 for validation, and 212 for testing. The external validation set comprised CT scans from 42 patients (median age, 67 years [IQR, 59-74 years]; 37 male patients). Syn-CTA images had high similarity to real CTA images (normalized mean absolute error, 0.011 and 0.013 for internal and external test set, respectively; peak signal-to-noise ratio, 32.07 dB and 31.58 dB; structural similarity, 0.919 and 0.906). The visual quality of Syn-CTA and real CTA images was comparable (internal test set, P = .35; external validation set, P > .99). Syn-CTA showed reasonable to good diagnostic accuracy for vascular diseases (internal test set: accuracy = 94%, macro F1 score = 91%; external validation set: accuracy = 86%, macro F1 score = 83%). Conclusion A GAN-based model that synthesizes neck and abdominal CTA-like images without the use of ICAs shows promise in vascular diagnosis compared with real CTA images. Clinical trial registration no. NCT05471869 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Zhang and Turkbey in this issue.

Apparent Diffusion Coefficient as Imaging Biomarker for Identifying IDH Mutation, 1p19q Codeletion, and MGMT Promoter Methylation Status in Patients With Glioma.

BACKGROUND: Glioma genotypes are of importance for clinical decision-making. This data can only be acquired through histopathological analysis based on resection or biopsy. Consequently, there is a need for alternative biomarkers that noninvasively provide reliable information for preoperatively identifying molecular characteristics. PURPOSE: To investigate apparent diffusion coefficient (ADC) as imaging biomarker for preoperatively identifying glioma genotypes based on the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors. STUDY TYPE: Retrospective. SUBJECTS: One hundred and fifty-nine patients (47.6 ± 14.4 years) diagnosed with WHO grade 2-4 glioma including 93 males and 66 females. FIELD STRENGTH/SEQUENCE: A 3 T/spin echo echo planner imaging. ASSESSMENT: The ADC measurements were assessed by two neuroradiologists (both with 6 years of experience). Three different lowest portions inside the tumors without overlap were manually drawn on the ADC maps as regions of interest (ROIs). The mean ADC value of the three ROIs was defined as the minimum ADC value (ADCmin ). An ROI was placed in the contralateral normal appearing white matter (NAWM) to obtain the ADC value (ADCNAWM ). The ADCmin to ADCNAWM ratio (ADCratio ) was calculated. Genetics results were retrospectively recorded from pathologic and genetic test reports. STATISTICAL TESTS: Two-sample independent t-tests, receiver operating characteristic curve analysis, and intraclass correlation coefficient analysis were used. Statistical significance was set at P < 0.05. RESULTS: Isocitrate dehydrogenase (IDH)-mutated glioma showed higher ADCmin and ADCratio than IDH wild-type glioma. Among IDH-mutated glioma, higher ADCmin and ADCratio were found in 1p19q intact glioma than in 1p19q codeletion glioma. ADC parameters enabled differentiation of IDH mutation status with area under the curve (AUC) of 0.84 and 0.86. DATA CONCLUSION: ADC has potential discriminative value for IDH mutation and 1p19q codeletion status. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Small vessel disease burden predicts functional outcomes in patients with acute ischemic stroke using machine learning.

AIMS: Our purpose is to assess the role of cerebral small vessel disease (SVD) in prediction models in patients with different subtypes of acute ischemic stroke (AIS). METHODS: We enrolled 398 small-vessel occlusion (SVO) and 175 large artery atherosclerosis (LAA) AIS patients. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. MRI was performed to assess white matter hyperintensity (WMH), perivascular space (PVS), lacune, and cerebral microbleed (CMB). Logistic regression (LR) and machine learning (ML) were used to develop predictive models to assess the influences of SVD on the prognosis. RESULTS: In the feature evaluation of SVO-AIS for different outcomes, the modified total SVD score (Gain: 0.38, 0.28) has the maximum weight, and periventricular WMH (Gain: 0.07, 0.09) was more important than deep WMH (Gain: 0.01, 0.01) in prognosis. In SVO-AIS, SVD performed better than regular clinical data, which is the opposite of LAA-AIS. Among all models, eXtreme gradient boosting (XGBoost) method with optimal index (OI) has the best performance to predict excellent outcome in SVO-AIS. [0.91 (0.84-0.97)]. CONCLUSIONS: Our results revealed that different SVD markers had distinct prognostic weights in AIS patients, and SVD burden alone may accurately predict the SVO-AIS patients' prognosis.

Arterial Spin Labeling-Based MRI Estimation of Penumbral Tissue in Acute Ischemic Stroke.

BACKGROUND: Arterial spin labeling (ASL) has shown potential for the assessment of penumbral tissue in patients with acute ischemic stroke (AIS). The postlabeling delay (PLD) parameter is sensitive to arterial transit delays and influences cerebral blood flow measurements. PURPOSE: To assess the impact of ASL acquisition at different PLDs for penumbral tissue quantification and to compare their performance regarding assisting patient selection for endovascular treatment with dynamic susceptibility contrast MRI (DSC-MRI) as the reference method. STUDY TYPE: Retrospective. POPULATION: A total of 53 patients (59.98 ± 12.60 years, 32% women) with AIS caused by internal carotid or middle cerebral artery occlusion. FIELD STRENGTH/SEQUENCE: A 3-T, three-dimensional pseudo-continuous ASL with fast-spin echo readout. ASSESSMENT: Hypoperfusion volume was measured using DSC-MRI and ASL with PLDs of 1.500 msec and 2.500 msec, respectively. Eligibility for endovascular treatment was retrospectively determined according to the imaging criteria of the Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke trial (DEFUSE 3). STATISTICAL TESTS: Kruskal-Wallis tests, Bland-Altman plots, Cohen's kappa, and receiver operating characteristic analyses were used. The threshold for statistical significance was set at P Ë‚ 0.05. RESULTS: Hypoperfusion volume for ASL with a PLD of 1.500 msec was significantly larger than that for DSC-MRI, while the hypoperfusion volume for a PLD of 2.500 msec was not significantly different from that of DSC-MRI (P = 0.435). Bland-Altman plots showed that the mean volumetric error between the hypoperfusion volume measured by DSC-MRI and ASL with PLDs of 1.500/2.500 msec was -107.0 mL vs. 4.49 mL. Cohen's kappa was 0.679 vs. 0.773 for DSC-MRI and ASL, respectively, with a PLD of 1.500/2.500 msec. The sensitivity and specificity for ASL with a PLD of 1.500/2.500 msec in identifying patients eligible for treatment were 89.74% vs. 97.44% and 92.86% vs. 64.29%, respectively. DATA CONCLUSION: In AIS, PLDs for ASL acquisition may have a considerable impact on the quantification of the hypoperfusion volume. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Evaluation of postcontrast images of intracranial tumors at 7T and 3T MRI: An intra-individual comparison study.

AIM: This study aimed to evaluate the diagnostic value of ultrahigh-field magnetic resonance imaging (MRI) for brain tumors in clinical practice. METHODS: Thirty patients with brain tumors underwent 7- and 3-T MRI. The performance and diagnostic confidence of 7- and 3-T MRI in the visualization of tumor details such as internal structure and feeding artery were evaluated by radiologists. Contrast-enhanced region performance and tumor detail diagnostic confidence score (DCS) were calculated and compared between 7 and 3T using Wilcoxon rank sum test. RESULTS: In 19 with obvious enhancement and 11 cases without obvious enhancement, 7- and 3-T MRI showed similar performance. The tumors' internal structure and feeding artery were more clearly depicted by 7-T MRI (62.2% and 54.4%, respectively) than by 3-T MRI (2.2% and 6.7%, respectively). Furthermore, the mean DCSs of both internal structure and feeding artery were higher at 7T than at 3T (internal structure: 16.29 ± 9.67 vs. -5.79 ± 4.12, p = 0.028; feeding artery: 21.96 ± 6.93 vs. 4.46 ± 7.07, p = 0.028). The DCS was more significantly improved in the senior radiologist group. CONCLUSION: Better visualization of brain tumor details and higher tumor detail diagnostic confidence can be obtained with 7-T MRI.

Collateral-Core Ratio as a Novel Predictor of Clinical Outcomes in Acute Ischemic Stroke.

The interaction effect between collateral circulation and ischemic core size on stroke outcomes has been highlighted in acute ischemic stroke (AIS). However, biomarkers that assess the magnitude of this interaction are still lacking. We aimed to present a new imaging marker, the collateral-core ratio (CCR), to quantify the interaction effect between these factors and evaluate its ability to predict functional outcomes using machine learning (ML) in AIS. Patients with AIS caused by anterior circulation large vessel occlusion (LVO) were recruited from a prospective multicenter study. CCR was calculated as collateral perfusion volume/ischemic core volume. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. An ML model was built and tested with a tenfold cross-validation using nine clinical and four imaging variables with mRS score 3-6 as unfavorable outcomes. Among 129 patients, CCR was identified as the most important variable. The prediction model incorporating clinical factors, ischemic core volume, collateral perfusion volume, and CCR showed better discriminatory power in predicting unfavorable outcomes than the model without CCR (mean C index 0.853 ± 0.108 versus 0.793 ± 0.133, P = 0.70; mean net reclassification index 52.7% ± 32.7%, P < 0.05). When patients were divided into two groups based on their CCR value with a threshold of 0.73, unfavorable outcomes were significantly more prevalent in patients with CCR ≤ 0.73 than in those with CCR > 0.73. CCR is a robust predictor of functional outcomes, as identified by ML, in patients with acute LVO. The prediction model that incorporated CCR improved the model's ability to identify unfavorable outcomes. ClinicalTrials.gov Identifier: NCT02580097.

FLAIR vessel hyperintensities predict functional outcomes in patients with acute ischemic stroke treated with medical therapy.

OBJECTIVES: The prognostic value of fluid-attenuated inversion recovery vessel hyperintensity (FVH) remains controversial in acute ischemic stroke (AIS). The objective was to investigate whether the presence of FVH could predict long-term functional outcomes in patients with AIS receiving medical therapy. METHODS: Consecutive AIS patients with anterior circulation large vessel stenosis (LVS) in multiple centers between January 2019 and December 2020 were studied. Presence of FVH was identified and evaluated as FVH (+). Quantification of FVH was performed using an FVH-Alberta Stroke Program Early CT Score (ASPECTS) system and divided into grades: FVH-ASPECTS of 0 = grade 0; 1-2 = grade 1; 3-7 = grade 2. Poor functional outcome was defined as modified Rankin scale > 2 at 3 months. RESULTS: Overall, 175 patients were analyzed (age, 64.31 ± 13.47 years; men, 65.1%), and 78.9% patients presented with FVH. Larger infarct volume (19.90 mL vs. 5.50 mL, p < 0.001), higher rates of FVH (+) (92.0% vs. 65.9%, p < 0.001), and higher FVH grades (grade 2, 34.5% vs. 10.2%, p < 0.001) were more prone to be observed in patients with poor functional outcomes. FVH (+) with infarct volume larger than 6.265 mL (adjusted odds ratio [aOR] 6.03, 95% confidence interval [CI] 1.82-19.98) and FVH grade (grade 1, aOR 3.07, 95% CI 1.12-8.43; grade 2, aOR 5.80, 95% CI 1.59-21.11) were independently associated with poor functional outcomes. CONCLUSION: FVH (+) combined with large infarct volume and high FVH grade can predict poor long-term functional outcomes in patients with LVS who receive medical therapy. KEY POINTS: • FVH is expected to be a contrast agent-independent alternative for assessing hemodynamic status in the acute stage of stroke. • FVH (+) and high FVH grade, quantified by FVH-ASPECTS rating system and grades, are associated with large infarct volume. • The combination of FVH and DWI-based infarct volume has independent predictive value for long-term functional outcomes in AIS patients with large artery stenosis treated with medical therapy.

Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging.

OBJECTIVES: Susceptibility-weighted imaging (SWI) is crucial for the characterization of intracranial hemorrhage and mineralization, but has the drawback of long acquisition times. We aimed to propose a deep learning model to accelerate SWI, and evaluate the clinical feasibility of this approach. METHODS: A complex-valued convolutional neural network (ComplexNet) was developed to reconstruct high-quality SWI from highly accelerated k-space data. ComplexNet can leverage the inherently complex-valued nature of SWI data and learn richer representations by using complex-valued network. SWI data were acquired from 117 participants who underwent clinical brain MRI examination between 2019 and 2021, including patients with tumor, stroke, hemorrhage, traumatic brain injury, etc. Reconstruction quality was evaluated using quantitative image metrics and image quality scores, including overall image quality, signal-to-noise ratio, sharpness, and artifacts. RESULTS: The average reconstruction time of ComplexNet was 19 ms per section (1.33 s per participant). ComplexNet achieved significantly improved quantitative image metrics compared to a conventional compressed sensing method and a real-valued network with acceleration rates of 5 and 8 (p < 0.001). Meanwhile, there was no significant difference between fully sampled and ComplexNet approaches in terms of overall image quality and artifacts (p > 0.05) at both acceleration rates. Furthermore, ComplexNet showed comparable diagnostic performance to the fully sampled SWI for visualizing a wide range of pathology, including hemorrhage, cerebral microbleeds, and brain tumor. CONCLUSIONS: ComplexNet can effectively accelerate SWI while providing superior performance in terms of overall image quality and visualization of pathology for routine clinical brain imaging. KEY POINTS: • The complex-valued convolutional neural network (ComplexNet) allowed fast and high-quality reconstruction of highly accelerated SWI data, with an average reconstruction time of 19 ms per section. • ComplexNet achieved significantly improved quantitative image metrics compared to a conventional compressed sensing method and a real-valued network with acceleration rates of 5 and 8 (p < 0.001). • ComplexNet showed comparable diagnostic performance to the fully sampled SWI for visualizing a wide range of pathology, including hemorrhage, cerebral microbleeds, and brain tumor.

Pretherapeutic functional connectivity of tractography-based targeting of the ventral intermediate nucleus for predicting tremor response in patients with Parkinson's disease after thalamotomy with MRI-guided focused ultrasound.

OBJECTIVE: Tractography-based direct targeting of the ventral intermediate nucleus (T-VIM) is a novel method that provides patient-specific VIM coordinates. This study aimed to explore the accuracy and predictive value of using T-VIM in combination with tractography and resting-state functional connectivity techniques to perform magnetic resonance imaging-guided focused ultrasound (MRgFUS) thalamotomy as a treatment of Parkinson's disease (PD). METHODS: PD patients underwent MRgFUS thalamotomy and were recruited for functional MRI scanning. A subscore of the Clinical Rating Scale for Tremor was used to evaluate tremor improvement. T-VIM and surgical VIM (S-VIM) were defined on preoperative diffusion tensor MRI and 24-hour postoperative T1-weighted imaging, respectively. The overlapping volume and center distance between S-VIM and T-VIM were measured to determine their correlations with 12-month postoperative tremor improvement. Moreover, pretherapeutic functional connectivity of T-VIM or S-VIM, based on region-of-interest connectivity and whole-brain seed-to-voxel connectivity, was measured with the resting-state functional connectivity technique to investigate their correlations with tremor improvement. RESULTS: All patients had excellent tremor improvement (mean [range] tremor improvement 74.82% [50.00%-94.44%]). The authors found that both overlapping volume and center distance between T-VIM and S-VIM were significantly correlated with tremor improvement (r = 0.788 and p = 0.012 for overlapping volume; r = -0.696 and p = 0.037 for center distance). Pretherapeutic functional connectivity of T-VIM with the ipsilateral sensorimotor cortex (r = 0.876 and p = 0.002), subthalamic nucleus (r = 0.700 and p = 0.036), and visual area (r = 0.911 and p = 0.001) was significantly and positively correlated with tremor improvement. CONCLUSIONS: T-VIM may improve the clinical application of MRgFUS thalamotomy as a treatment of PD. Pretherapeutic functional connectivity of T-VIM with the ipsilateral sensorimotor cortex, subthalamic nucleus, and visual area may predict PD tremor responses after MRgFUS thalamotomy.

Correlation of visual area with tremor improvement after MRgFUS thalamotomy in Parkinson's disease.

OBJECTIVE: MRI-guided focused ultrasound (MRgFUS) thalamotomy is a novel and minimally invasive alternative for medication-refractory tremor in Parkinson's disease (PD). However, the impact of MRgFUS thalamotomy on spontaneous neuronal activity in PD remains unclear. The purpose of the current study was to evaluate the effects of MRgFUS thalamotomy on local fluctuations in neuronal activity as measured by the fractional amplitude of low-frequency fluctuations (fALFF) in patients with PD. METHODS: Participants with PD undergoing MRgFUS thalamotomy were recruited. Tremor scores were assessed before and 3 and 12 months after treatment using the Clinical Rating Scale for Tremor. MRI data were collected before and 1 day, 1 week, 1 month, 3 months, and 12 months after thalamotomy. The fALFF was calculated. A whole-brain voxel-wise paired t-test was used to identify significant changes in fALFF at 12 months after treatment compared to baseline. Then fALFF in the regions with significant differences were extracted from fALFF maps of patients for further one-way repeated-measures ANOVA to investigate its dynamic alterations. The association between fALFF changes induced by thalamotomy and tremor improvement were evaluated using the nonparametric Spearman rank test. RESULTS: Nine participants with PD (mean age ± SD 64.7 ± 6.1 years, 8 males) were evaluated. Voxel-based analysis showed that fALFF in the left occipital cortex (Brodmann area 17 [BA17]) significantly decreased at 12 months after thalamotomy compared to baseline (voxel p < 0.001, cluster p < 0.05 family-wise error [FWE] corrected). At baseline, fALFF in the left occipital BA17 in patients was elevated compared with that in 9 age- and gender-matched healthy subjects (p < 0.05). Longitudinal analysis displayed the dynamic changes of fALFF in this region (F (5,40) = 3.61, p = 0.009). There was a significant positive correlation between the falling trend in fALFF in the left occipital BA17 and hand tremor improvement after treatment over 3 time points (Spearman's rho = 0.44, p = 0.02). CONCLUSIONS: The present study investigated the impact of MRgFUS ventral intermediate nucleus thalamotomy on spontaneous neural activity in medication-refractory tremor-dominant PD. The visual area is, for the first time, reported as relevant to tremor improvement in PD after MRgFUS thalamotomy, suggesting a distant effect of MRgFUS thalamotomy and the involvement of specific visuomotor networks in tremor control in PD.

Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning.

OBJECTIVES: Multiple b-value gas diffusion-weighted MRI (DW-MRI) enables non-invasive and quantitative assessment of lung morphometry, but its long acquisition time is not well-tolerated by patients. We aimed to accelerate multiple b-value gas DW-MRI for lung morphometry using deep learning. METHODS: A deep cascade of residual dense network (DC-RDN) was developed to reconstruct high-quality DW images from highly undersampled k-space data. Hyperpolarized 129Xe lung ventilation images were acquired from 101 participants and were retrospectively collected to generate synthetic DW-MRI data to train the DC-RDN. Afterwards, the performance of the DC-RDN was evaluated on retrospectively and prospectively undersampled multiple b-value 129Xe MRI datasets. RESULTS: Each slice with size of 64 × 64 × 5 could be reconstructed within 7.2 ms. For the retrospective test data, the DC-RDN showed significant improvement on all quantitative metrics compared with the conventional reconstruction methods (p < 0.05). The apparent diffusion coefficient (ADC) and morphometry parameters were not significantly different between the fully sampled and DC-RDN reconstructed images (p > 0.05). For the prospectively accelerated acquisition, the required breath-holding time was reduced from 17.8 to 4.7 s with an acceleration factor of 4. Meanwhile, the prospectively reconstructed results showed good agreement with the fully sampled images, with a mean difference of -0.72% and -0.74% regarding global mean ADC and mean linear intercept (Lm) values. CONCLUSIONS: DC-RDN is effective in accelerating multiple b-value gas DW-MRI while maintaining accurate estimation of lung microstructural morphometry, facilitating the clinical potential of studying lung diseases with hyperpolarized DW-MRI. KEY POINTS: • The deep cascade of residual dense network allowed fast and high-quality reconstruction of multiple b-value gas diffusion-weighted MRI at an acceleration factor of 4. • The apparent diffusion coefficient and morphometry parameters were not significantly different between the fully sampled images and the reconstructed results (p > 0.05). • The required breath-holding time was reduced from 17.8 to 4.7 s and each slice with size of 64 × 64 × 5 could be reconstructed within 7.2 ms.

Severity of Intracranial Large Artery Disease Correlates With Cerebral Small Vessel Disease.

BACKGROUND: Small vessel disease (SVD) shares common vascular risk factors with large artery disease (LAD). However, little is known about the relationship between intracranial artery stenosis and SVD burden. PURPOSE: To investigate whether SVD burden correlates with severity of intracranial LAD. STUDY TYPE: Retrospective. POPULATION: Five hundred and sixteen patients with LAD of arterial circulation were enrolled from one hospital, including 384 males (59 ± 11 years) and 132 females (60 ± 12 years). FIELD STRENGTH/SEQUENCE: 3 T. T1 -weighted fast spin echo (T1 W FSE), T2 W FSE, T2 fluid attenuated inversion recovery, diffusion-weighted imaging, susceptibility-weight imaging, and time-of-flight magnetic resonance angiography. ASSESSMENT: The LAD was divided into mild stenosis (<30%), moderate stenosis (30%-69%), and severe stenosis (≥70%). The Standard for Reporting Vascular Changes on Neuroimaging criteria was used to rate the SVD burden according to the level of white matter hyperintensity (WMH), perivascular space (PVS), cerebral microbleed (CMB), and lacunes. STATISTICAL TESTS: Lilliefors test, ANOVA, chi-squared test, Mann-Whitney U test, Wilcoxon signed rank test, Bonferroni test, Spearman's correlation, logistic regression, and Cohen's kappa test. RESULTS: The grade scores for centrum semiovale PVS (CS-PVS) were positively correlated with the degree of stenosis (R = 0.413), whereas the presence of severe basal ganglia PVS (BG-PVS) was associated with CMB (R = 0.508), lacunes (R = 0.365), and severe WMH (R = 0.478). In multivariate analysis, severe CS-PVS (adjusted odds ratio [aOR], 3.1; 95% confidence interval [CI], 1.9-4.8) and lacunes (aOR, 2.1; 95% CI, 1.3-3.4) were associated with severe stenosis of LAD. In addition, CS-PVS was related to severe stenosis in a dose-dependent manner: when CS-PVS score was 3 and 4, the aORs of severe stenosis were 1.9 and 7.7, respectively. DATA CONCLUSION: The severity of LAD in anterior circulation is associated with SVD burden, which suggests that different SVD burden may be used for risk stratification in LAD. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Fast and accurate reconstruction of human lung gas MRI with deep learning.

PURPOSE: To fast and accurately reconstruct human lung gas MRI from highly undersampled k-space using deep learning. METHODS: The scheme was comprised of coarse-to-fine nets (C-net and F-net). Zero-filling images from retrospectively undersampled k-space at an acceleration factor of 4 were used as input for C-net, and then output intermediate results which were fed into F-net. During training, a L2 loss function was adopted in C-net, while a function that united L2 loss with proton prior knowledge was used in F-net. The 871 hyperpolarized 129 Xe pulmonary ventilation images from 72 volunteers were randomly arranged as training (90%) and testing (10%) data. Ventilation defect percentage comparisons were implemented using a paired 2-tailed Student's t-test and correlation analysis. Furthermore, prospective acquisitions were demonstrated in 5 healthy subjects and 5 asymptomatic smokers. RESULTS: Each image with size of 96 × 84 could be reconstructed within 31 ms (mean absolute error was 4.35% and structural similarity was 0.7558). Compared with conventional compressed sensing MRI, the mean absolute error decreased by 17.92%, but the structural similarity increased by 6.33%. For ventilation defect percentage, there were no significant differences between the fully sampled and reconstructed images through the proposed algorithm (P = 0.932), but had significant correlations (r = 0.975; P < 0.001). The prospectively undersampled results validated a good agreement with fully sampled images, with no significant differences in ventilation defect percentage but significantly higher signal-to-noise ratio values. CONCLUSION: The proposed algorithm outperformed classical undersampling methods, paving the way for future use of deep learning in real-time and accurate reconstruction of gas MRI.