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Magnetic resonance electrical properties tomography can extract the electrical properties of in-vivo tissue. To estimate tissue electrical properties, various reconstruction algorithms have been proposed. However, physics-based reconstructions are prone to various artifacts such as noise amplification and boundary artifact. Deep learning-based approaches are robust to these artifacts but need extensive training datasets and suffer from generalization to unseen data. To address these issues, we introduce a joint three-plane physics-constrained deep learning framework for polynomial fitting MR-EPT by merging physics-based weighted polynomial fitting with deep learning. Within this framework, deep learning is used to discern the optimal polynomial fitting weights for a physics based polynomial fitting reconstruction on the complex B 1 + data. For the prediction of optimal fitting coefficients, three neural networks were separately trained on simulated heterogeneous brain models to predict optimal polynomial weighting parameters in three orthogonal planes. Then, the network weights were jointly optimized to estimate the polynomial weights in each plane for a combined conductivity reconstruction. Based on this physics-constrained deep learning approach, we achieved an improvement of conductivity estimation accuracy in comparison to a single plane estimation and a reduction of computational load. The results demonstrate that the proposed method based on 3D data exhibits superior performance in comparison to conventional polynomial fitting methods in terms of capturing anatomical detail and homogeneity. Crucially, in-vivo application of the proposed method showed that the method generalizes well to in-vivo data, without introducing significant errors or artifacts. This generalization makes the presented method a promising candidate for use in clinical applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025. Published by Elsevier Inc.)

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality., Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast&#95;A) and a DL-based algorithm (Fast&#95;DL). Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists' ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity., Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols., Conclusion: Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging., Competing Interests: Young Hun Choi, who holds respective positions on the Section Editor of the Korean Journal of Radiology, was not involved in the editorial evaluation or decision to publish this article. The remaining authors have declared no conflicts of interest., (Copyright © 2025 The Korean Society of Radiology.)

Magnetic resonance imaging (MRI) can estimate tissue conductivity values using phase-based magnetic resonance electrical properties tomography (MR-EPT). However, this method is prone to noise amplification due to the Laplacian operator's sensitivity. To address this issue, we propose a novel unsupervised preprocessing denoiser for MRI transceive phase images. Our approach draws inspiration from the deep image prior (DIP) technique, utilizing the random initialization of a convolutional neural network (CNN) to enforce implicit regularization. Additionally, we incorporate Stein.s unbiased risk estimator (SURE) to optimize the network, which serves as an unbiased estimator of mean square error, thereby eliminating the need for labeled data. This modification mitigates the overfitting commonly associated with the DIP approach, enabling a fully unsupervised framework. Furthermore, we process real and imaginary images instead of phase images, aligning more closely with the theoretical basis of the risk estimator. Our generative model does not require pre-training or extensive training datasets, maintaining adaptability across different resolutions and signal-to-noise ratio levels. In our evaluations, the proposed method significantly reduced residual noise in phase maps, improving both quantitative and qualitative outcomes in phantom and simulated brain data. It also outperformed existing denoising techniques by reducing noise amplification and boundary errors. Applied to data from healthy volunteers and patients, our method yielded conductivity maps with reduced errors and values consistent with established literature. To our knowledge, this is the first blind, fully unsupervised approach capable of implementing a 2D phase-based MR-EPT reconstruction algorithm.

Background and Objectives : This study investigated associated meniscus and ligament injuries in tibial plateau fractures using magnetic resonance imaging (MRI) and assessed soft tissue injuries in relation to the Schatzker classification and Tscherne classification. Materials and Methods : The data of 185 patients who sustained tibial plateau fractures from January 2010 to April 2021 were retrospectively reviewed. Fractures were classified according to the Schatzker classification system. Soft-tissue injuries were assessed using the Tscherne classification. Menisci and ligaments were evaluated using preoperative MRI. Nerve injuries, compartment syndrome and wound problems were also assessed. The incidence of soft tissue injuries, as well as the relationship between the Schatzker and Tscherne classification systems, were analyzed. Results : Evidence of derangement of meniscus and ligament around the knee was found in 183 (98.9%) patients. The most common injury was a medial collateral ligament injury. The incidence of lateral collateral ligament injury, nerve injury, compartment syndrome and wound problem was higher in high-energy tibial plateau fractures. A tendency was observed between the Schatzker and the Tscherne classifications ( p value < 0.001). Higher Tscherne grade was also associated with the incidence of posterior cruciate ligament injury, nerve injury and compartment syndrome. Conclusions : In tibial plateau fractures, soft tissue injuries were highly prevalent. High-energy fractures tended to exhibit higher Tscherne classification grades and showed an increased incidence of meniscus and ligament injuries. The Tscherne classification appears to be a helpful system for predicting soft tissue injuries in tibial plateau fractures. And preoperative MRI can be a helpful tool.

Background and Purpose: To date, only a few small studies have attempted deep learning-based automatic segmentation of white matter hyperintensity (WMH) lesions in patients with cerebral infarction; this issue is complicated because stroke-related lesions can obscure WMH borders. We developed and validated deep learning algorithms to segment WMH lesions accurately in patients with cerebral infarction using multisite data sets involving 8421 patients with acute ischemic stroke., Materials and Methods: We included 8421 patients with stroke from 9 centers in Korea. 2D UNet and squeeze-and-excitation (SE)-UNet models were trained using 2408 FLAIR MRIs from 3 hospitals and validated using 6013 FLAIR MRIs from 6 hospitals. WMH segmentation performance was assessed by calculating the Dice similarity coefficient (DSC), the correlation coefficient, and the concordance correlation coefficient compared with a human-segmented criterion standard. In addition, we obtained an uncertainty index that represents overall ambiguity in the voxel classification for WMH segmentation in each patient based on the Kullback-Leibler divergence., Results: In the training data set, the mean age was 67.4 (SD, 13.0) years, and 60.4% were men. The mean (95% CI) DSCs for UNet in internal testing and external validation were, respectively, 0.659 (0.649-0.669) and 0.710 (0.707-0.714), which were slightly lower than the reliability between humans (DSC = 0.744; 95% CI, 0.738-0.751; P = .031). Compared with the UNet, the SE-UNet demonstrated better performance, achieving a mean DSC of 0.675 (95% CI, 0.666-0.685; P < .001) in the internal testing and 0.722 (95% CI, 0.719-0.726; P < .001) in the external validation; moreover, it achieved high DSC values (ranging from 0.672 to 0.744) across multiple validation data sets. We observed a significant correlation between WMH volumes that were segmented automatically and manually for the UNet ( r = 0.917, P < .001), and it was even stronger for the SE-UNet ( r = 0.933, P < .001). The SE-UNet also attained a high concordance correlation coefficient (ranging from 0.841 to 0.956) in the external test data sets. In addition, the uncertainty indices in most patients (86%) in the external data sets were <0.35, with an average DSC of 0.744 in these patients., Conclusions: We developed and validated deep learning algorithms to segment WMH in patients with acute cerebral infarction using the largest-ever MRI data sets. In addition, we showed that the uncertainty index can be used to identify cases in which automatic WMH segmentation is less accurate and requires human review., (© 2024 by American Journal of Neuroradiology.)

Background and Objectives : Despite extensive studies of the role of quadriceps and quadriceps/hamstring balance in knee osteoarthritis (OA), the roles of the vastus intermedius, medialis, and lateralis in OA remain unclear. The purpose of this study was to investigate the relationship of lower limb alignment and the ratio of the quadriceps femoris muscle to the knee extensor muscle. Materials and Methods : This study included 50 patients with advanced knee OA (Kellgren/Lawrence grade of 3 or 4) and 25 healthy control persons between June 2021 and May 2022. The osteoarthritis grade and anatomical tibiofemoral angle were measured based on plain radiography and scanography. All participants were divided into normal (0~5°), mild varus (5°~10°), and severe varus (>10°) groups. Using MRI, muscle size was determined by calculating the cross-sectional area (CSA) of the total quadriceps (rectus femoris, vastus intermedius, vastus medialis, and vastus lateralis) and its components. Results : The CSA ratio of the vastus lateralis was significantly smaller in the severe varus group than in the normal or mild varus groups. There was a significant positive correlation between the mechanical tibiofemoral angle and vastus lateralis CSA ( ρ = 0.282, p = 0.014) and between the anatomical tibiofemoral angle and vastus lateralis CSA ( ρ = 0.294, p = 0.011). There was a significant negative correlation between the mechanical tibiofemoral angle and vastus intermedius CSA ( ρ = -0.263, p = 0.023) and between the anatomical tibiofemoral angle and vastus intermedius CSA ( ρ = -0.243, p = 0.036). Conclusions : Patients with severe varus alignment exhibited vastus lateralis atrophy. This study highlights vastus lateralis atrophy in severe varus alignment, though causality between atrophy and varus knee OA remains uncertain. We think that patients with severe varus may require strengthening exercises focused on the vastus lateralis before and after surgery for alignment correction.

This bicenter retrospective analysis included 162 patients who had undergone prostate biopsy following prebiopsy MRI, excluding those with PCa identified only in the peripheral zone (PZ). DLR T2WI achieved a 69% reduction in scan time relative to TSE T2WI. The intermethod agreement between the two T2WI sets in terms of the Prostate Imaging Reporting and Data System (PI-RADS) classification and extraprostatic extension (EPE) grade was measured using the intraclass correlation coefficient (ICC) and diagnostic performance was assessed with the area under the receiver operating characteristic curve (AUC). Clinically significant PCa (csPCa) was found in 74 (45.7%) patients. Both T2WI methods showed high intermethod agreement for the overall PI-RADS classification (ICC: 0.907-0.949), EPE assessment (ICC: 0.925-0.957) and lesion size measurement (ICC: 0.980-0.996). DLR T2WI and TSE T2WI showed similar AUCs (0.666-0.814 versus 0.684-0.832) for predicting EPE. The AUCs for detecting csPCa with DLR T2WI (0.834-0.935) and TSE T2WI (0.891-0.935) were comparable in 139 patients with TZ lesions with no significant differences (P > 0.05). The findings suggest that DLR T2WI is an efficient alternative for TZ lesion assessment, offering reduced scan times without compromising diagnostic accuracy., Competing Interests: Declarations. Competing interests: Marcel Dominik Nickel, Hyun-Soo Lee and Dongyeob Han are employees of Siemens Healthineers, but they did not influence the results of this study. Moon Hyung Choi is currently receiving a research grant from Company Siemens Healthineers and the research is not related to prostate MRI. Other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical approval: This retrospective study involving human participants was performed in accordance with the ethical standards of the institutional and national research committees and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The Institutional Review Board of the Catholic Medical Center approved this study (XC23WIDI0064). Informed consent: Informed consent was waived due to the retrospective design of the study., (© 2024. The Author(s).)

Introduction/aims: Hourglass-like constriction (HGC) may occur in several peripheral nerves. However, data on the prognosis of motor weakness in patients with HGC of the suprascapular nerve (SSN) are limited compared with other nerves. Here, we aimed to describe the clinical and imaging features of HGC of the SSN., Methods: We retrospectively reviewed patients diagnosed with suprascapular neuropathy using magnetic resonance imaging (MRI) or electrodiagnostic studies over 16 years. After excluding extrinsic causes, patients with HGC of the SSN detected using MRI were included., Results: Fourteen patients with HGC of the SSN were identified. MRI revealed that all HGCs were located between the origin of the SSN from the upper trunk of the brachial plexus and the suprascapular notch. Seven patients exhibited HGC precisely at the origin of the SSN from the brachial plexus. Four patients showed T2 hyperintensity of the SSN extending to the upper trunk of the brachial plexus or the extraforaminal cervical root. The initial treatments included observation (n = 1), steroid therapy (n = 12), suprascapular notch release (n = 1). Of the 12 patients with a sufficient follow-up period, nine fully recovered from motor weakness of the SSN with non-operative treatments. Six of the nine patients who recovered fully experienced their first clinical improvement more than 6 months after onset., Discussion: Treatment strategies for HGC differ depending on the affected nerve. For HGC of the SSN, due to the high spontaneous recovery rate observed in our study, conservative management for at least 6 months should be initially considered., (© 2024 Wiley Periodicals LLC.)

It is uncertain whether the prognostic power of white matter hyperintensity (WMH) on post-stroke outcomes is modulated as a function of initial neurological severity, a critical determinant of outcome after stroke. This multi-center MRI study tested if higher WMH quintiles were associated with 3-month poor functional outcome (modified Rankin Scale ≥ 3) for mild versus moderate-to-severe ischemic stroke. Mild and moderate-to-severe stroke were defined as admission National Institute of Health Stroke Scale scores of 1-4 and ≥ 5, respectively. Mean age of the enrolled patients (n = 8918) was 67.2 ± 12.6 years and 60.1% male. The association between WMH quintiles and poor functional outcome was modified by stroke severity (p-for-interaction = 0.008). In mild stroke (n = 4994), WMH quintiles associated with the 3-month outcome in a dose-dependent manner for the 2nd to 5th quintile versus the 1st quintile, with adjusted-odds-ratios (aOR [95% confidence interval]) being 1.29 [0.96-1.73], 1.37 [1.02-1.82], 1.60 [1.19-2.13], and 1.89 [1.41-2.53], respectively. In moderate-to-severe stroke (n = 3924), however, there seemed to be a threshold effect: only the highest versus the lowest WMH quintile was significantly associated with poor functional outcome (aOR 1.69 [1.29-2.21]). WMH burden aggravates 3-month functional outcome after mild stroke, but has a lesser modulatory effect for moderate-to-severe stroke, likely due to saturation effects., (© 2024. The Author(s).)

Understanding brain function is essential for advancing our comprehension of human cognition, behavior, and neurological disorders. Magnetic resonance imaging (MRI) stands out as a powerful tool for exploring brain function, providing detailed insights into its structure and physiology. Combining MRI technology with electrophysiological recording system can enhance the comprehension of brain functionality through synergistic effects. However, the integration of neural implants with MRI technology presents challenges because of its strong electromagnetic (EM) energy during MRI scans. Therefore, MRI-compatible neural implants should facilitate detailed investigation of neural activities and brain functions in real-time in high resolution, without compromising patient safety and imaging quality. Here, we introduce the fully MRI-compatible monolayer open-mesh pristine PEDOT:PSS neural interface. This approach addresses the challenges encountered while using traditional metal-based electrodes in the MRI environment such as induced heat or imaging artifacts. PEDOT:PSS has a diamagnetic property with low electrical conductivity and negative magnetic susceptibility similar to human tissues. Furthermore, by adopting the optimized open-mesh structure, the induced currents generated by EM energy are significantly diminished, leading to optimized MRI compatibility. Through simulations and experiments, our PEDOT:PSS-based open-mesh electrodes showed improved performance in reducing heat generation and eliminating imaging artifacts in an MRI environment. The electrophysiological recording capability was also validated by measuring the local field potential (LFP) from the somatosensory cortex with an in vivo experiment. The development of neural implants with maximized MRI compatibility indicates the possibility of potential tools for future neural diagnostics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: The primary purpose of this study was to assess perianchor fluid collection (PFC) severity of medial anchor and rotator cuff integrity association after arthroscopic double-row suture-bridge rotator cuff repair (RCR) and the secondary purpose was to identify the demographic and radiologic risk factors for high-grade PFC., Hypothesis: Re-tear rate would be significant higher in patients with high-grade PFC., Methods: We retrospectively reviewed patients with arthroscopic double-row suture-bridge RCR for full-thickness rotator cuff tear (RCT) at our institution between February 2012 and May 2018. Based on the PFC severity, they were divided into the no-fluid (no fluid collection), low-grade (minimal or local fluid collection around the anchor), and high-grade (fluid collection beyond the entire length of the anchor) groups. Magnetic resonance imaging was performed 6 months postoperatively for assessing PFC severity and repaired rotator cuff integrity association, besides evaluating correlation between severity and various demographic and radiologic factors, including work level (low/medium/high) and RCT size., Results: This study included 312 patients, 181 (58%) in the no fluid group, 82 (26.3%) in the low-grade PFC group, and 49 (15.7%) in the high-grade PFC group. Re-tear occurred in 73 (23.4%) patients and was more frequent in the high-grade group (26/49 [53.1%]) than in the no-fluid (27/181 [14.9%]; p<0.001) and low-grade (20/82 [24.4%]; p=0.001) groups, without statistically significant differences between the latter two (p=0.082). Among the demographic factors, work level (low/medium/high) differed significantly between the no-fluid (22.1%/58.0%/19.9%), low-grade (25.7%/46.3%/28.0%), and high-grade (26.5%/34.7%/38.8%) groups (p=0.026). Among the radiologic factors, the mediolateral tear size differed significantly between the three groups (no-fluid group: 1.7±0.8cm, low-grade group: 1.8±0.6cm, high-grade group: 2.2±1.0cm; p=0.003). Multivariate regression analysis showed that mediolateral tear size (odds ratio: 1.821; 95% confidence interval: 1.258-2.636; p=0.001) was an independent risk factor for high-grade PFC., Conclusions: After arthroscopic double-row suture-bridge RCR, the highest re-tear rate was observed in patients with high-grade PFC, while there was no significant difference in rates between no-fluid and low-grade PFC groups. As PFC severity increased, the risk of re-tear increased. In particular, larger mediolateral tear size was associated with high-grade PFC., Level of Evidence: III; case-control study., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Background & Aims: Ultrasound (US) is recommended for HCC surveillance in high-risk patients but has limited performance in detecting early-stage HCC. We aimed to compare the diagnostic performance of biannual US and annual non-contrast abbreviated magnetic resonance imaging (NC-AMRI) as HCC surveillance modalities in high-risk patients., Methods: This prospective, multicenter cohort study enrolled participants with an estimated annual risk of HCC greater than 5% between October 2015 and April 2017. Participants underwent six rounds of HCC surveillance at 6-month intervals, with both US and NC-AMRI at rounds 1, 3, and 5, and only US at rounds 2, 4, and 6. The sensitivity, diagnostic yield (DY), and false referral rate (FRR) for HCC detection by US and NC-AMRI were compared., Results: In total, 208 participants underwent 980 US and 516 NC-AMRI examinations during 30 months of follow-up. Among them, 34 HCCs were diagnosed in 31 participants, with 20 (64.5%) classified as very early-stage and 11 (35.5%) as early-stage HCC. The sensitivity of annual NC-AMRI (71.0%, 22/31) was marginally higher than that of biannual US (45.2%, 14/31; p = 0.077). NC-AMRI showed a significantly higher DY than US (4.26% vs. 1.43%, p <0.001), with a similar FRR (2.91% vs. 3.06%, p = 0.885). A simulation of alternating US and NC-AMRI at 6-month intervals yielded a sensitivity of 83.9% (26/31), significantly exceeding that of biannual US (p = 0.006)., Conclusions: Annual NC-AMRI showed a marginally higher sensitivity than biannual US for HCC detection in high-risk patients. The DY of annual NC-AMRI was significantly higher than that of biannual US, without increasing the FRR. Thus, alternating US and NC-AMRI at 6-month intervals could be an optimal surveillance strategy for high-risk patients., Impact and Implications: Current guidelines permit the use of magnetic resonance imaging (MRI) as a surveillance tool for hepatocellular carcinoma in patients in whom ultrasonography (US) is inadequate. However, the specific indications, imaging sequences, and intervals for MRI surveillance remain unclear. In our study, we found that annual non-contrast abbreviated MRI exhibited marginally higher sensitivity and significantly better diagnostic yield than biannual US in patients at high risk of hepatocellular carcinoma. Alternating US and non-contrast abbreviated MRI at 6-month intervals led to significantly improved sensitivity compared to biannual US, making it a potentially optimal surveillance strategy for high-risk patients., Gov Identifier: NCT02551250., (Copyright © 2024 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

Purpose: We aimed to develop a preoperative prediction model for extraprostatic extension (EPE) in prostate cancer (PCa) patients following radical prostatectomy (RP) using MRI and clinical factors., Methods: This retrospective study enrolled 266 consecutive patients who underwent RP for PCa in 2022. These patients were divided into a training set (n = 187) and a test set (n = 79) through random assignment. The evaluated variables included age, prostate-specific antigen (PSA) level, prostate volume, PSA density (PSAD), index tumor length on MRI, Prostate Imaging-Reporting and Data System (PI-RADS) category, and EPE-related MRI features as defined by PI-RADS v2.1. A predictive model was constructed through multivariable logistic regression and subsequently translated into a scoring system. The performance of this scoring system in terms of prediction and calibration was assessed using C statistics and the Hosmer‒Lemeshow test., Results: Among patients in the training and test cohorts, 74 (39.6%) and 25 (31.6%), respectively, exhibited EPE after RP. The formulated scoring system incorporated the following factors: PSAD, index tumor length, bulging prostatic contour, and tumor-capsule interface > 10 mm as identified on MRI. This scoring system demonstrated strong prediction performance for EPE in both the training (C statistic, 0.87 [95% confidence interval, 0.86-0.87]) and test cohorts (C statistic, 0.85 [0.83-0.89]). Furthermore, the scoring system exhibited good calibration in both cohorts (P = 0.988 and 0.402, respectively)., Conclusion: Our scoring system, built upon MRI features defined by the PI-RADS, offers valuable assistance in assessing the likelihood of EPE after RP., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objectives: To analyze the characteristics of spinal metastasis in CT scans across diverse cancers for effective diagnosis and treatment, using MRI as the gold standard., Methods: A retrospective study of 309 patients from four centers, who underwent concurrent CT and spinal MRI, revealing spinal metastasis, was conducted. Data on metastasis including total number, volume, visibility on CT (visible, indeterminate, or invisible), and type of bone change were collected. Through chi-square and Mann-Whitney U tests, we characterized the metastasis across diverse cancers and investigated the variation in the intra-individual ratio representing the percentage of lesions within each category for each patient., Results: Out of 3333 spinal metastases from 309 patients, 55% were visible, 21% indeterminate, and 24% invisible. Sclerotic and lytic lesions made up 47% and 43% of the visible and indeterminate categories, respectively. Renal cell carcinoma (RCC), prostate cancer, and hepatocellular carcinoma (HCC) had the highest visibility at 86%, 73%, and 67% (p < 0.0001, p < 0.0001, and p = 0.003), while pancreatic cancer was lowest at 29% (p < 0.0001). RCC and HCC had significantly high lytic metastasis ratios (interquartile range (IQR) 0.96-1.0 and 0.31-1.0, p < 0.001 and p = 0.005). Prostate cancer exhibited a high sclerotic lesion ratio (IQR 0.52-0.97, p < 0.001). About 39% of individuals had invisible or indeterminate lesions, even with a single visible lesion on CT. The intra-individual ratio for indeterminate and invisible metastases surpassed 18%, regardless of the maximal size of the visible metastasis., Conclusions: This study highlights the variability in characteristics of spinal metastasis based on the primary cancer type through unique lesion-centric analysis., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

Objectives: This study aimed to identify predictors for the recurrence of spontaneous intracranial hypotension (SIH) after epidural blood patch (EBP)., Background: Epidural blood patch is the main treatment option for SIH; however, the characteristics of patients who experience relapse after successful EBP treatment for SIH remain understudied., Methods: In this exploratory, retrospective, case-control study, we included 19 patients with SIH recurrence after EBP and 36 age- and sex-matched patients without recurrence from a single tertiary medical institution. We analyzed clinical characteristics, neuroimaging findings, and volume changes in intracranial structures after EBP treatment. Machine learning methods were utilized to predict the recurrence of SIH after EBP treatment., Results: There were no significant differences in clinical features between the recurrence and no-recurrence groups. Among brain magnetic resonance imaging signs, diffuse pachymeningeal enhancement and cerebral venous dilatation were more prominent in the recurrence group than no-recurrence group after EBP (14/19 [73%] vs. eight of 36 [22%] patients, p = 0.001; 11/19 [57%] vs. seven of 36 [19%] patients, p = 0.010, respectively). The midbrain-pons angle decreased in the recurrence group compared to the no-recurrence group after EBP, at a mean (standard deviation [SD]) of -12.0 [16.7] vs. +1.8[18.3]° (p = 0.048). In volumetric analysis, volume changes after EBP were smaller in the recurrence group than in the no-recurrence group in intracranial cerebrospinal fluid (mean [SD] -11.6 [15.3] vs. +4.8 [17.1] mL, p = 0.001) and ventricles (mean [SD] +1.0 [2.0] vs. +2.0 [2.5] mL, p = 0.003). Notably, the random forest classifier indicated that the model constructed with brain volumetry was more accurate in discriminating SIH recurrence (area under the curve = 0.80 vs. 0.52)., Conclusion: Our study suggests that volumetric analysis of intracranial structures may aid in predicting recurrence after EBP treatment in patients with SIH., (© 2024 The Authors. Headache: The Journal of Head and Face Pain published by Wiley Periodicals LLC on behalf of American Headache Society.)

Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI., Materials and Methods: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set., Results: The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test., Conclusion: The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2024 The Korean Society of Radiology.)

Quantitative susceptibility mapping (QSM) is frequently employed in investigating brain iron related to brain development and diseases within deep gray matter (DGM). Nonetheless, the acquisition of whole-brain QSM data is time-intensive. An alternative approach, focusing the QSM specifically on areas of interest such as the DGM by reducing the field-of-view (FOV), can significantly decrease scan times. However, severe susceptibility value underestimations have been reported during QSM reconstruction with a limited FOV, largely attributable to artifacts from incorrect background field removal in the boundary region. This presents a considerable barrier to the clinical use of QSM with small spatial coverages using conventional methods alone. To mitigate the propagation of these errors, we proposed a harmonic field extension method based on a physics-informed generative adversarial network. Both quantitative and qualitative results demonstrate that our method outperforms conventional methods and delivers results comparable to those obtained with full FOV. Furthermore, we demonstrate the versatility of our method by applying it to data acquired prospectively with limited FOV and to data from patients with Parkinson's disease. The method has shown significant improvements in local field results, with QSM outcomes. In a clear illustration of its feasibility and effectiveness in real clinical environments, our proposed method addresses the prevalent issue of susceptibility underestimation in QSM with small spatial coverage., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Magnetic resonance electrical properties tomography (MR-EPT) is a non-invasive measurement technique that derives the electrical properties (EPs, e.g., conductivity or permittivity) of tissues in the radiofrequency range (64 MHz for 1.5 T and 128 MHz for 3 T MR systems). Clinical studies have shown the potential of tissue conductivity as a biomarker. To date, model-based conductivity reconstructions rely on numerical assumptions and approximations, leading to inaccuracies in the reconstructed maps. To address such limitations, we propose an artificial neural network (ANN)-based non-linear conductivity estimator trained on simulated data for conductivity brain imaging. Network training was performed on 201 synthesized T2-weighted spin-echo (SE) data obtained from the finite-difference time-domain (FDTD) electromagnetic (EM) simulation. The dataset was composed of an approximated T2-w SE magnitude and transceive phase information. The proposed method was tested three in-silico and in-vivo on two volunteers and three patients' data. For comparison purposes, various conventional phase-based EPT reconstruction methods were used that ignore B 1 + magnitude information, such as Savitzky-Golay kernel combined with Gaussian filter (S-G Kernel), phase-based convection-reaction EPT (cr-EPT), magnitude-weighted polynomial-fitting phase-based EPT (Poly-Fit), and integral-based phase-based EPT (Integral-based). From the in-silico experiments, quantitative analysis showed that the proposed method provides more accurate and improved quality (e.g., high structural preservation) conductivity maps compared to conventional reconstruction methods. Representatively, in the healthy brain in-silico phantom experiment, the proposed method yielded mean conductivity values of 1.97 ± 0.20 S/m for CSF, 0.33 ± 0.04 S/m for WM, and 0.52 ± 0.08 S/m for GM, which were closer to the ground-truth conductivity (2.00, 0.30, 0.50 S/m) than the integral-based method (2.56 ± 2.31, 0.39 ± 0.12, 0.68 ± 0.33 S/m). In-vivo ANN-based conductivity reconstructions were also of improved quality compared to conventional reconstructions and demonstrated network generalizability and robustness to in-vivo data and pathologies. The reported in-vivo brain conductivity values were in agreement with literatures. In addition, the proposed method was observed for various SNR levels (SNR levels = 10, 20, 40, and 58) and repeatability conditions (the eight acquisitions with the number of signal averages = 1). The preliminary investigations on brain tumor patient datasets suggest that the network trained on simulated dataset can generalize to unforeseen in-vivo pathologies, thus demonstrating its potential for clinical applications., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background: Cerebral microbleeds (CMBs) are microscopic brain hemorrhages with implications for various diseases. Automated detection of CMBs is a challenging task due to their wide distribution throughout the brain, small size, and visual similarity to their mimics. For this reason, most of the previously proposed methods have been accomplished through two distinct stages, which may lead to difficulties in integrating them into clinical workflows., Purpose: To develop a clinically feasible end-to-end CMBs detection network with a single-stage structure utilizing 3D information. This study proposes triplanar ensemble detection network (TPE-Det), ensembling 2D convolutional neural networks (CNNs) based detection networks on axial, sagittal, and coronal planes., Study Type: Retrospective., Subjects: Two datasets (DS1 and DS2) were used: 1) 116 patients with 367 CMBs and 12 patients without CMBs for training, validation, and testing (70.39 ± 9.30 years, 68 women, 60 men, DS1); 2) 58 subjects with 148 microbleeds and 21 subjects without CMBs only for testing (76.13 ± 7.89 years, 47 women, 32 men, DS2)., Field Strength/sequence: A 3 T field strength and 3D GRE sequence scan for SWI reconstructions., Assessment: The sensitivity, FP avg (false-positive per subject), and precision measures were computed and analyzed with statistical analysis., Statistical Tests: A paired t-test was performed to investigate the improvement of detection performance by the suggested ensembling technique in this study. A P value < 0.05 was considered significant., Results: The proposed TPE-Det detected CMBs on the DS1 testing set with a sensitivity of 96.05% and an FP avg of 0.88, presenting statistically significant improvement. Even when the testing on DS2 was performed without retraining, the proposed model provided a sensitivity of 85.03% and an FP avg of 0.55. The precision was significantly higher than the other models., Data Conclusion: The ensembling of multidimensional networks significantly improves precision, suggesting that this new approach could increase the benefits of detecting lesions in the clinic., Evidence Level: 1 TECHNICAL EFFICACY: Stage 2., (© 2022 International Society for Magnetic Resonance in Medicine.)

Patient movement during Magnetic Resonance Imaging (MRI) scan can cause severe degradation of image quality. In Susceptibility Weighted Imaging (SWI), several echoes are typically measured during a single repetition period, where the earliest echoes show less contrast between various tissues, while the later echoes are more susceptible to artifacts and signal dropout. In this paper, we propose a knowledge interaction paradigm that jointly learns feature details from multiple distorted echoes by sharing their knowledge with unified training parameters, thereby simultaneously reducing motion artifacts of all echoes. This is accomplished by developing a new scheme that boosts a Single Encoder with Multiple Decoders (SEMD), which assures that the generated features not only get fused but also learned together. We called the proposed method Knowledge Interaction Learning between Multi-Echo data (KIL-ME-based SEMD). The proposed KIL-ME-based SEMD allows to share information and gain an understanding of the correlations between the multiple echoes. The main purpose of this work is to correct the motion artifacts and maintain image quality and structure details of all motion-corrupted echoes towards generating high-resolution susceptibility enhanced contrast images, i.e., SWI, using a weighted average of multi-echo motion-corrected acquisitions. We also compare various potential strategies that might be used to address the problem of reducing artifacts in multi-echoes data. The experimental results demonstrate the feasibility and effectiveness of the proposed method, reducing the severity of motion artifacts and improving the overall clinical image quality of all echoes with their associated SWI maps. Significant improvement of image quality is observed using both motion-simulated test data and actual volunteer data with various motion severity strengths. Eventually, by enhancing the overall image quality, the proposed network can increase the effectiveness of the physicians' capability to evaluate and correctly diagnose brain MR images., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Magnetic Resonance Imaging (MRI) is sensitive to motion caused by patient movement due to the relatively long data acquisition time. This could cause severe degradation of image quality and therefore affect the overall diagnosis. In this paper, we develop an efficient retrospective 2D deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in 3D brain MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns the missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving the spatial image details and improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. The proposed network is optimized by minimizing the loss of structural similarity (SSIM) using the synthesized motion-corrupted images from 83 real motion-free subjects. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans. The overall image quality of the motion-corrected images via the proposed motion correction network significantly improves SSIM from 71.66% to 95.03% and declines the mean square error from 99.25 to 29.76. These results indicate the high similarity of the brain's anatomical structure in the corrected images compared to the motion-free data. The motion-corrected results of both the simulated and real motion data showed the potential of the proposed motion correction network to be feasible and applicable in clinical practices., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To enable acceleration in 3D multi-echo gradient echo (mGRE) acquisition for myelin water imaging (MWI) by combining joint parallel imaging (JPI) and joint deep learning (JDL)., Methods: We implemented a multistep reconstruction process using both advanced parallel imaging and deep learning network which can utilize joint spatiotemporal components between the multi-echo images to further accelerate 3D mGRE acquisition for MWI. In the first step, JPI was performed to estimate missing k-space lines. Next, JDL was implemented to reduce residual artifacts and produce high-fidelity reconstruction by using variable splitting optimization consisting of spatiotemporal denoiser block, data consistency block, and weighted average block. The proposed method was evaluated for MWI with 2D Cartesian uniform under-sampling for each echo, enabling scan times of up to approximately 2 min for 2 mm × 2 mm × 2 mm $2\ {\rm mm} \times 2\ {\rm mm} \times 2\ {\rm mm}$ 3D coverage., Results: The proposed method showed acceptable MWI quality with improved quantitative values compared to both JPI and JDL methods individually. The improved performance of the proposed method was demonstrated by the low normalized mean-square error and high-frequency error norm values of the reconstruction with high similarity to the fully sampled MWI., Conclusion: Joint spatiotemporal reconstruction approach by combining JPI and JDL can achieve high acceleration factors for 3D mGRE-based MWI., (© 2022 American Association of Physicists in Medicine.)

BACKGROUND. Isotropic 3D T1-weighted imaging has long acquisition times, potentially leading to motion artifact and altered brain volume measurements. Acquisition times may be greatly shortened using an isotropic ultrafast 3D echo-planar imaging (EPI) T1-weighted sequence. OBJECTIVE. The purpose of this article was to compare automated brain volume measurements between conventional 3D T1-weighted imaging and ultrafast 3D EPI T1-weighted imaging. METHODS. This retrospective study included 36 patients (25 women, 11 men; mean age, 68.4 years) with memory impairment who underwent 3-T brain MRI. Examinations included both conventional 3D T1-weighted imaging using inversion recovery gradient-recalled echo sequence (section thickness, 1.0 mm; acquisition time, 3 minutes 4 seconds) and, in patients exhibiting motion, an isotropic ultrafast 3D EPI T1-weighted sequence (section thickness, 1.2 mm; acquisition time, 30 seconds). The 36-patient sample excluded five patients in whom severe motion artifact rendered the conventional sequence of insufficient quality for volume measurements. Automated brain volumetry was performed using NeuroQuant (version 3.0, CorTechs Laboratories) and FreeSurfer (version 7.1.1, Harvard University) software. Volume measurements were compared between sequences for nine regions in each hemisphere. RESULTS. Volumes showed substantial to almost perfect agreement between the two sequences for most regions bilaterally. However, most regions showed significant mean differences between sequences, and Bland-Altman analyses showed consistent systematic biases and wide limits of agreement (LOA). For example, for the left hemisphere using NeuroQuant, volume was significantly greater for the ultrafast sequence in four regions and significantly greater for the conventional sequence in three regions, whereas standardized effect size between sequences was moderate for four regions and large for one region. Using NeuroQuant, mean bias (ultrafast minus conventional) and 95% LOA were greatest in cortical gray matter bilaterally (-50.61 cm 3 [-56.27 cm 3 , -44.94 cm 3 ] for the left hemisphere; -50.02 cm 3 [-54.88 cm 3 , -45.16 cm 3 ] for the right hemisphere). The variation between the two sequences was observed in subset analyses of 16 patients with and 20 patients without Alzheimer disease. CONCLUSION. Brain volume measurements show significant differences and systematic biases between the conventional and ultrafast sequences. CLINICAL IMPACT. In patients in whom severe motion artifact precludes use of the conventional sequence, the ultrafast sequence may be useful to enable brain volume-try. However, the current conventional 3D T1-weighted sequence remains preferred in patients who can tolerate the standard examination.

Objectives: Magnetic resonance fingerprinting (MRF) allows the simultaneous measurement of multiple tissue properties in a single acquisition. Three-dimensional (3D) MRF with high spatial resolution can be used for neonatal brain imaging. The aim of this study is to apply 3D MRF to neonates and show regional differences and maturation in the brain., Materials and Methods: In this prospective study, 3D MRF using hybrid radial-interleaved acquisition was performed on phantoms and neonates from December 2019 to October 2020. For the reconstruction of 3D MRF, singular value decomposition was applied to reduce reconstruction time, and the iterative reconstruction technique was applied to improve image quality. The accuracies of T1 and T2 values derived from 3D MRF were evaluated in a phantom experiment. Regional T1 and T2 values were obtained from neonates' brain T1 and T2 maps derived from 3D MRF. Regional T1 and T2 values were compared, and their changes according to corrected gestational age were evaluated., Results: The acquisition time for 3D MRF with a spatial resolution of 0.7 × 0.7 × 2 mm3 was less than 5 minutes. The phantom study showed high correlation between T1 and T2 values derived from 3D MRF and those from conventional spin echo sequences (T1, R2 = 0.998, P < 0.001; T2, R2 = 0.998, P < 0.001). Three-dimensional MRF was performed in 25 neonates (15 boys, 10 girls; median corrected gestational age, 263 days; interquartile range, 10 days). In neonates, T1 and T2 values differed in the frontal (median [interquartile range], 2785 [2684-2888] milliseconds and 189.8 [176.7-222.9] milliseconds), parietal (2849 [2741-2950] milliseconds and 191.6 [167.5-232.9] milliseconds), and occipital white matter (2621 [2513-2722] milliseconds and 162.9 [143.5-186.1] milliseconds), showing lower values in occipital white matter (P < 0.001). Regional T1 values showed a negative relationship with corrected gestational age (coefficient, -0.775 to -0.480; P < 0.05)., Conclusions: Fast and high spatial resolution 3D MRF was applied to neonates. T1 and T2 maps derived from 3D MRF enabled the quantification of regional differences and maturation in the neonatal brain., Competing Interests: Conflicts of interest and sources of funding: H.G.K. has received funding from National Research Foundation of Korea (NRF-2021R1A2C1007831) and from a Radiological Research Fund of Department of Radiology, The Catholic University of Korea for 2020. For the remaining authors, none were declared., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: To evaluate the feasibility of a new three-dimensional (3D) MR fingerprinting (MRF) technique for the prostate gland by conducting phantom and clinical studies., Materials and Methods: The new 3D MRF technique used in this study enables quick data acquisition and has a high resolution. For the phantom study, the MRF T1 and T2 values in an in-house phantom were compared with those of gold-standard mapping methods using linear regression analysis. For the clinical study, we evaluated 90 patients who underwent prostate imaging with MRF for suspected prostate cancer between September 2019 and February 2020. The mean T1 and T2 values were compared in the peripheral zone, transition zone, and focal lesions using paired t tests. The differences in the T1 and T2 values according to cancer aggressiveness were evaluated using one-way analysis of variance., Results: In the phantom study, the MRF T1 and T2 values showed a perfect correlation with the gold-standard T1 and T2 values (R > 0.99). In the clinical study, the T1 and T2 values in the peripheral zone were significantly higher than those in the transitional zone ( p < 0.001, both). The T1 and T2 values in prostate cancer were significantly lower than those in the peripheral and transitional zones. The higher the grade of cancer, the lower the T2 values., Conclusion: The T1 and T2 values obtained from the 3D MRF showed a perfect correlation with the gold standard values in the phantom study. Differences in the T1 and T2 values among the different zones of the prostate gland were identified using 3D MRF in patients., Competing Interests: Dongyeob Han. Activities related to the present article: an employee of Siemens Healtineers, Ltd. Other relationship: nothing to declare. Moon Hyung Choi, Young Joon Lee, and Dong-Hyun Kim. Nothing to declare., (Copyright © 2021 The Korean Society of Radiology.)

An increase in hyperpolarized (HP) [1- 13 C]lactate production has been suggested as a biomarker for cancer occurrence as well as for response monitoring of cancer treatment. Recently, the use of metformin has been suggested as an anticancer or adjuvant treatment. By regulating the cytosolic NAD + /NADH redox state, metformin stimulates lactate production and increases the HP [1- 13 C]lactate conversion rate in the kidney, liver, and heart. In general, increased HP [1- 13 C]lactate is regarded as a sign of cancer occurrence or tumor growth. Thus, the relationship between the tumor suppression effect of metformin and the change in metabolism monitored by HP [1- 13 C]pyruvate MRS in cancer treatment needs to be investigated. The present study was performed using a brain metastasis animal model with MDA-MB-231(BR)-Luc breast cancer cells. HP [1- 13 C]pyruvate MRS, T 2 -weighted MRI, and bioluminescence imaging were performed in groups treated with metformin or adjuvant metformin and radiation therapy. Metformin treatment alone did not display a tumor suppression effect, and the HP [1- 13 C]lactate conversion rate increased. In radiation therapy, the HP [1- 13 C]lactate conversion rate decreased with tumor suppression, with a p-value of 0.028. In the adjuvant metformin and radiation treatment, the tumor suppression effect increased, with a p-value of 0.001. However, the apparent HP [1- 13 C]lactate conversion rate (K pl ) was observed to be offset by two opposite effects: a decrease on radiation therapy and an increase caused by metformin treatment. Although HP [1- 13 C]pyruvate MRS could not evaluate the tumor suppression effect of adjuvant metformin and radiation therapy due to the offset phenomenon, metabolic changes following only metformin pre-treatment could be monitored. Therefore, our results indicate that the interpretation of HP [1- 13 C]pyruvate MRS for response monitoring of cancer treatment should be carried out with caution when metformin is used as an adjuvant cancer therapy., (© 2021 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.)

Objective: To demonstrate the utility of deep learning enhancement (DLE) to achieve diagnostic quality low-dose positron emission tomography (PET)/magnetic resonance (MR) imaging., Methods: Twenty subjects with known Crohn disease underwent simultaneous PET/MR imaging after intravenous administration of approximately 185 MBq of 18F-fluorodeoxyglucose (FDG). Five image sets were generated: (1) standard-of-care (reference), (2) low-dose (ie, using 20% of PET counts), (3) DLE-enhanced low-dose using PET data as input, (4) DLE-enhanced low-dose using PET and MR data as input, and (5) DLE-enhanced using no PET data input. Image sets were evaluated by both quantitative metrics and qualitatively by expert readers., Results: Although low-dose images (series 2) and images with no PET data input (series 5) were nondiagnostic, DLE of the low-dose images (series 3 and 4) achieved diagnostic quality images that scored more favorably than reference (series 1), both qualitatively and quantitatively., Conclusions: Deep learning enhancement has the potential to enable a 90% reduction of radiotracer while achieving diagnostic quality images., Competing Interests: A.P. has received payments as consultant from Sanofi Genzyme. D.H.K. is a shareholder for Elucent and Cellectar. The University of Wisconsin-Madison Department of Radiology receives research support from GE Healthcare. The remaining authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

In animal models of stroke, behavioral assessments could be complemented by a variety of neuroimaging studies to correlate them with recovery and better understand mechanisms of improvement after stem cell therapy. We evaluated morphological and connectivity changes after treatment with human mesenchymal stem cells (hMSCs) in a rat stroke model, through quantitative measurement of T2-weighted images and diffusion tensor imaging (DTI). Transient middle cerebral artery occlusion rats randomly received PBS (PBS-only), FBS cultured hMSCs (FBS-hMSCs), or stroke patients' serum cultured hMSCs (SS-hMSCs). Functional improvement was assessed using a modified neurological severity score (mNSS). Quantitative analyses of T2-weighted ischemic lesion and ventricular volume changes were performed. Brain microstructure/connectivity changes were evaluated in the ischemic recovery area by DTI-derived microstructural indices such as relative fractional anisotropy (rFA), relative axial diffusivity (rAD), and relative radial diffusivity (rRD), and relative fiber density (rFD) analyses. According to mNSS results, the SS-hMSCs group showed the most prominent functional improvement. Infarct lesion volume of the SS-hMSCs group was significantly decreased at 2 weeks when compared to the PBS-only groups, but there were no differences between the FBS-hMSCs and SS-hMSCs groups. Brain atrophy was significantly decreased in the SS-hMSCs group compared to the other groups. In DTI, rFA and rFD values were significantly higher and rRD value was significant lower in the SS-hMSCs group and these microstructure/connectivity changes were correlated with T2-weighted morphological changes. T2-weighted volume alterations (ischemic lesion and brain atrophy), and DTI microstructural indices and rFD changes, were well matched with the results of behavioral assessment. These quantitative MRI measurements could be potential outcome predictors of functional recovery after treatment with stem cells for stroke., Competing Interests: The authors have read the journal’s policy and the authors of this manuscript have the following competing interests: JHS, EHK, and OYB are paid employees of S&E bio, Inc. There are no patents, products in development or marketing products to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Objective: To determine whether changes in the transiting nerve rootlet or its surroundings, as seen on MRI performed after lumbar hemilaminectomy, are associated with persistent postoperative pain (PPP), commonly known as the failed back surgery syndrome., Materials and Methods: Seventy-three patients (mean age, 61 years; 43 males and 30 females) who underwent single-level partial hemilaminectomy of the lumbar spine without postoperative complications or other level spinal abnormalities between January 2010 and December 2018 were enrolled. Two musculoskeletal radiologists evaluated transiting nerve rootlet abnormalities (thickening, signal alteration, distinction, and displacement), epidural fibrosis, and intrathecal arachnoiditis on MRI obtained one year after the operations. A spine surgeon blinded to the radiologic findings evaluated each patient for PPP. Univariable and multivariable analyses were used to evaluate the association between the MRI findings and PPP., Results: The presence of transiting nerve rootlet thickening, signal alteration, and ill-distinction was significantly different between the patients with PPP and those without, for both readers ( p ≤ 0.020). Conversely, the presence of transiting nerve rootlet displacement, epidural fibrosis, and intrathecal arachnoiditis was not significantly different between the two groups ( p ≥ 0.128). Among the above radiologic findings, transiting nerve rootlet thickening and signal alteration were the most significant findings in the multivariable analyses ( p ≤ 0.009)., Conclusion: On MRI, PPP was associated with transiting nerve rootlet abnormalities, including thickening, signal alterations, and ill-distinction, but was not associated with epidural fibrosis or intrathecal arachnoiditis. The most relevant findings were the nerve rootlet thickening and signal alteration., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2021 The Korean Society of Radiology.)

Purpose: To develop a set of artificial neural networks, collectively termed qMTNet, to accelerate data acquisition and fitting for quantitative magnetization transfer (qMT) imaging., Methods: Conventional and interslice qMT data were acquired with two flip angles at six offset frequencies from seven subjects for developing the networks and from four young and four older subjects for testing the generalizability. Two subnetworks, qMTNet-acq and qMTNet-fit, were developed and trained to accelerate data acquisition and fitting, respectively. qMTNet-2 is the sequential application of qMTNet-acq and qMTNet-fit to produce qMT parameters (exchange rate, pool fraction) from undersampled qMT data (two offset frequencies rather than six). qMTNet-1 is one single integrated network having the same functionality as qMTNet-2. qMTNet-fit was compared with a Gaussian kernel-based fitting. qMT parameters generated by the networks were compared with those from ground truth fitted with a dictionary-driven approach., Results: The proposed networks achieved high peak signal-to-noise ratio (>30) and structural similarity index (>97) in reference to the ground truth. qMTNet-fit produced qMT parameters in concordance with the ground truth with better performance than the Gaussian kernel-based fitting. qMTNet-2 and qMTNet-1 could accelerate data acquisition at threefold and accelerate fitting at 5800- and 4218-fold, respectively. qMTNet-1 showed slightly better performance than qMTNet-2, whereas qMTNet-2 was more flexible for applications., Conclusion: The proposed single (qMTNet-1) and two joint neural networks (qMTNet-2) can accelerate qMT workflow for both data acquisition and fitting significantly. qMTNet has the potential to accelerate qMT imaging for clinical applications, which warrants further investigation., (© 2020 International Society for Magnetic Resonance in Medicine.)

Background: Coronal and sagittal views of magnetic resonance imaging (MRI) were used to determine rotator cuff tear size and fatty infiltration, but these images were not enough to identify the tear shape., Purpose: To correlate the preoperative axial MRI views and arthroscopic surgical findings to identify the two-dimensional shapes in rotator cuff tears., Material and Methods: This study included 166 patients who underwent arthroscopic repair between 2015 and 2018. Preoperative coronal, sagittal, and axial MRI views were evaluated for tear size and geographic configuration in axial sections, and the length and the width were measured and were matched with arthroscopic surgical views by lateral portals., Results: The agreement of axial MRI views with the arthroscopic view was 88.0% in crescent, 97.2% in longitudinal, 78.6% in massive, and 100% in rotator cuff tear arthropathy. The mean agreement rate of axial MRI views with arthroscopic view was 81.9%. Mean mediolateral and anteroposterior tear sizes on axial MRI were 16.68 mm and 19.33 mm, respectively. Mean mediolateral and anteroposterior tear sizes by arthroscopic view were 21.49 mm and 21.04 mm, respectively. Tear sizes by MRI axial images were 71.3% of arthroscopic view. SST/IST degenerative changes were noted in most patients with massive tears and rotator cuff arthropathy ( P  = 0.001)., Conclusion: Rotator cuff tear shape on preoperative axial MRI view had close agreement (81.9%) with arthroscopic findings by lateral portal, and tear size by preoperative axial MRI views was 71.3% of that of arthroscopic view. Axial MRI views helped to predict the geometric tear shape of rotator cuff tears.

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. In this paper, we propose a fully automated two-stage integrated deep learning approach for efficient CMBs detection, which combines a regional-based You Only Look Once (YOLO) stage for potential CMBs candidate detection and three-dimensional convolutional neural networks (3D-CNN) stage for false positives reduction. Both stages are conducted using the 3D contextual information of microbleeds from the MR susceptibility-weighted imaging (SWI) and phase images. However, we average the adjacent slices of SWI and complement the phase images independently and utilize them as a two- channel input for the regional-based YOLO method. The results in the first stage show that the proposed regional-based YOLO efficiently detected the CMBs with an overall sensitivity of 93.62% and an average number of false positives per subject (FP avg ) of 52.18 throughout the five-folds cross-validation. The 3D-CNN based second stage further improved the detection performance by reducing the FP avg to 1.42. The outcomes of this work might provide useful guidelines towards applying deep learning algorithms for automatic CMBs detection.

Background: Carpal tunnel syndrome is diagnosed based on history, physical examination, and nerve conduction testing; however, there are no clear criteria for the diagnosis of carpal tunnel syndrome. Recently, studies have aimed to diagnose carpal tunnel syndrome through ultrasound or MRI. The purpose of this study was to compare and analyze the cross-sectional area of the median nerve between patients with carpal tunnel syndrome and a control group., Methods: From July 2015 to August 2017, we retrospectively analyzed fishery and white-collar workers (164 people, 37 men, 127 women). Carpal tunnel syndrome was diagnosed on the basis of both physical examination and nerve conduction testing. A negative result in either test led to exclusion from the study., Results: In total, 164 wrist MRI were retrieved, with 67 patients diagnosed with carpal tunnel syndrome and 97 patients allocated to the control group. The mean value of cross-sectional area at the pisiform was 18.8 mm 2 in the MRI of the carpal tunnel syndrome patients and 12.1 mm 2 (p-value <0.05) in the control group. The mean value of cross-sectional area at the hook of hamate was 11.70 mm 2 and that at the control group was 11.67 mm 2 (p-value 0.055)., Conclusion: Cross-sectional area at pisiform in MRI is a valuable factor in the diagnosis of carpal tunnel syndrome and in predicting the duration of pain., (Copyright © 2019. Published by Elsevier Taiwan LLC.)

Objective: Recent innovations in biology are boosting gene and cell therapy, but monitoring the response to these treatments is difficult. The purpose of this study was to find an MRI-reporter gene that can be used to monitor gene or cell therapy and that can be delivered without a viral vector, as viral vector delivery methods can result in long-term complications., Materials and Methods: CMV promoter-human organic anion transporting polypeptide 1B3 ( CMV-hOATP1B3 ) cDNA or CMV-blank DNA (control) was transfected into HEK293 cells using Lipofectamine. OATP1B3 expression was confirmed by western blotting and confocal microscopy. In vitro cell phantoms were made using transfected HEK293 cells cultured in various concentrations of gadoxetic acid for 24 hours, and images of the phantoms were made with a 9.4T micro-MRI. In vivo xenograft tumors were made by implanting HEK293 cells transfected with CMV-hOATP1B3 (n = 4) or CMV-blank (n = 4) in 8-week-old male nude mice, and MRI was performed before and after intravenous injection of gadoxetic acid (1.2 μL/g)., Results: Western blot and confocal microscopy after immunofluorescence staining revealed that only CMV-hOATP1B3 -transfected HEK293 cells produced abundant OATP1B3, which localized at the cell membrane. OATP1B3 expression levels remained high through the 25th subculture cycle, but decreased substantially by the 50th subculture cycle. MRI of cell phantoms showed that only the CMV-hOATP1B3 -transfected cells produced a significant contrast enhancement effect. In vivo MRI of xenograft tumors revealed that only CMV-hOATP1B3 -transfected HEK293 tumors demonstrated a T1 contrast effect, which lasted for at least 5 hours., Conclusion: The human endogenous OATP1B3 gene can be non-virally delivered into cells to induce transient OATP1B3 expression, leading to gadoxetic acid-mediated enhancement on MRI. These results indicate that hOATP1B3 can serve as an MRI-reporter gene while minimizing the risk of long-term complications., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2020 The Korean Society of Radiology.)

There has been a recent increase in the diagnosis of diseases through radiographic images such as x-rays, magnetic resonance imaging, and computed tomography. The outcome of a radiological diagnostic test is often in the form of discrete ordinal data, and we usually summarize the performance of the diagnostic test using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The ROC curve will be concave and called proper when the outcomes of the diagnostic test in the actually positive subjects are higher than in the actually negative subjects. The diagnostic test for disease detection is clinically useful when a ROC curve is proper. In this study, we develop a hierarchical Bayesian model to estimate the proper ROC curve and AUC using stochastic ordering in several domains when the outcome of the diagnostic test is discrete ordinal data and compare it with the model without stochastic ordering. The model without stochastic ordering can estimate the improper ROC curve with a nonconcave shape or a hook when the true ROC curve of the population is a proper ROC curve. Therefore, the model with stochastic ordering is preferable over the model without stochastic ordering to estimate the proper ROC curve with clinical usefulness for ordinal data., (© 2020 John Wiley & Sons, Ltd.)

Objectives: To determine the strength of association with hepatocellular carcinoma (HCC) of each ancillary feature (AF) in LI-RADS version 2018, and to develop an appropriate strategy for applying AFs to improve the diagnosis of HCC ≤ 3 cm on gadoxetate-enhanced MRI., Methods: A total of 385 nodules (283 HCCs, 18 non-HCC malignancies, 84 benign nodules) of ≤ 3 cm in 266 patients at risk for HCC who underwent gadoxetate-enhanced MRI in 2016 were retrospectively evaluated. Two radiologists independently evaluated the presence/absence of AFs, and assigned a LI-RADS category to each nodule. Diagnostic odds ratio (DOR) of each AF was assessed. To improve the diagnostic performance for HCC, various criteria were developed based on the number of AFs favoring malignancy in general or HCC in particular. Generalized estimating equation models were used to compare the diagnostic performance of each criterion with that of the major features (MFs) only., Result: All AFs favoring HCC in particular and malignancy in general were more common in the HCC group than in the non-HCC group. Of these AFs, hepatobiliary-phase hypointensity had the strongest association with HCC (DOR, 21.82; 95% confidence interval, 5.59-85.20). When we applied AFs in addition to MFs, the new criterion (with a number of AFs ≥ 4) had significantly higher sensitivity (80.6% vs. 70.0%; p < 0.001) than MFs only, without significant lower specificity (85.3% vs. 90.2%; p = 0.060)., Conclusions: The AFs varied in the strengths of association with HCC. More strict application of AFs (AFs ≥ 4) in LR-3 may improve the diagnostic performance for probable HCC ≤ 3 cm., Key Points: • The ancillary features (AFs) in the Liver Imaging Reporting and Data System version 2018 showed variable frequencies of occurrence and strengths of association with hepatocellular carcinoma (HCC). • Of the various AFs, hepatobiliary-phase hypointensity had the highest frequency and strongest association with HCC on gadoxetate disodium-enhanced MRI. • When applying AFs in addition to major features, a criterion of four or more AFs significantly increased the sensitivity for diagnosing HCC, without a significantly decreased specificity, especially in LR-3 observations.

Background: Identifying acute ischemic stroke (AIS) among potential stroke cases is crucial for stroke research based on claims data. However, the accuracy of using the diagnostic codes of the International Classification of Diseases 10th revision was less than expected., Methods: From the National Health Insurance Service (NHIS) claims data, stroke cases admitted to the hospitals participating in the multicenter stroke registry (Clinical Research Collaboration for Stroke in Korea, CRCS-K) during the study period with principal or additional diagnosis codes of I60-I64 on the 10th revision of International Classification of Diseases were extracted. The datasets were randomly divided into development and validation sets with a ratio of 7:3. A stroke identification algorithm using the claims data was developed and validated through the linkage between the extracted datasets and the registry database., Results: Altogether, 40,443 potential cases were extracted from the NHIS claims data, of which 31.7% were certified as AIS through linkage with the CRCS-K database. We selected 17 key identifiers from the claims data and developed 37 conditions through combinations of those key identifiers. The key identifiers comprised brain CT, MRI, use of tissue plasminogen activator, endovascular treatment, carotid endarterectomy or stenting, antithrombotics, anticoagulants, etc. The sensitivity, specificity, and diagnostic accuracy of the algorithm were 81.2%, 82.9%, and 82.4% in the development set, and 80.2%, 82.0%, and 81.4% in the validation set, respectively., Conclusions: Our stroke identification algorithm may be useful to grasp stroke burden in Korea. However, further efforts to refine the algorithm are necessary., Competing Interests: The authors have declared that no competing interests exist.

Herein, ferumoxytol (Fer) capped antiprogrammed cell death-ligand 1 (PD-L1) antibodies (aPD-L1) loaded ultralarge pore mesoporous silica nanoparticles (Fer-ICB-UPMSNPs) are formulated for a sequential magnetic resonance (MR) image guided local immunotherapy after cabazitaxel (Cbz) chemotherapy for the treatment of prostate cancer (PC). The highly porous framework of UPMSNP provides a large capacity for aPD-L1. Fer capping of the pores extends the period of aPD-L1 release and provides MR visibility of the aPD-L1 loaded UPMSNP. As-chosen Cbz chemotherapy prior to the local immunotherapy induces strong immunogenic cell death, dendritic cell maturation, and upregulation of PD-L1 of tumor cells. Finally, tumor growth inhibition of sequential MR image-guided local delivery of Fer-ICB-UPMSNPs and a tumor specific adoptive immune reaction are demonstrated in the pretreated Tramp C1 PC mouse model with Cbz chemotherapy. The tumor suppression is superior to those obtained with systemic ICB treatment after Cbz, only Fer-ICB-UPMSNP or only Cbz. As a proof-of concept, MR image-guided local ICB immunotherapy using Fer-ICB-UPMSNPs after chemotherapy suggests a new perspective of translational local immunotherapy for patients who are treated with standard chemotherapies., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

For quantitative neuroimaging studies using multi-echo gradient echo (mGRE) images, additional T 1 -weighted magnetization prepared rapid gradient echo (MPRAGE) images are often acquired to supplement the insufficient morphometric information of mGRE for tissue segmentation which require lengthened scan time and additional processing such as image registration. This study investigated the feasibility of generating synthetic MPRAGE images from mGRE images using a deep convolutional neural network. Tissue segmentation results derived from the synthetic MPRAGE showed good agreement with those from actual MPRAGE (DSC = 0.882 ± 0.017). There was no statistically significant difference between the mean susceptibility values obtained with the regions of interest from synthetic and actual MPRAGEs and high correlation between the two measurements., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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., (© 2019 International Society for Magnetic Resonance in Medicine.)

In recent years, rectal MRI has become a central diagnostic tool in rectal cancer staging. Indeed, rectal MR has the ability to accurately evaluate a number of important findings that may impact patient management, including distance of the tumor to the mesorectal fascia, presence of extramural vascular invasion (EMVI), presence of lymph nodes, and involvement of the peritoneum/anterior peritoneal reflection. Many of these findings are difficult to assess in nonexpert hands. In this review, we present a practical approach for radiologists to provide high-quality interpretations at initial baseline exams, based on recent guidelines from the Society of Abdominal Radiology, Rectal and Anal Cancer Disease Focused Panel. Practical pearls and pitfalls are discussed, focusing on optimization of technique including, patient preparation and protocol recommendations, interpretation, and essentials of reporting.

Objective: This study aimed to evaluate the diagnostic accuracy of high-resolution T2-weighted magnetic resonance imaging (T2wi) in terms of detecting vestibular schwannoma compared with gadolinium-enhanced T1-weighted MRI (GdT1wi)., Data Sources: Five databases (PubMed, SCOPUS, Embase, the Web of Science, and the Cochrane database)., Data Selection: Two authors independently searched five databases up to January 2019 on diagnosis of vestibular schwannomas via T2wi., Data Extraction: In the included studies, tumor diameters reported using T2wi were compared with those revealed by GdT1wi and correlation coefficients were calculated. Data on true-positives, true-negatives, false-positives, and false-negatives were extracted from the relevant articles. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Inter-rater agreement among different observers and intra-rater agreement among different measurements made by a single observer was assessed., Data Synthesis: Outcomes subjected to analysis included diagnostic accuracy (the diagnostic odds ratio); summary receiver operating characteristic curve and area under the curve values. The summary intra-class correlation coefficient was used for various random-effects models. The quality of each study was analyzed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool., Conclusions: T2wi performed without the use of a contrast agent is a highly accurate diagnostic and monitor tool compared with GdT1wi and also demonstrated high reliability. However, further studies are required to confirm the results of this study.

Hourglass-like constriction neuropathy is a neurological condition caused by non-traumatic, non-compressive fascicular constrictions of one or more individual peripheral nerves. Based on clinical manifestations, it is very difficult to differentiate hourglass-like constriction neuropathy from idiopathic neuralgic amyotrophy. Focal hourglass-like constriction neuropathy may be erroneously diagnosed as idiopathic neuralgic amyotrophy. Previous studies demonstrated hourglass-like constrictions of peripheral nerves in the surgical exploration of patients diagnosed with idiopathic neuralgic amyotrophy. Recently, high-resolution ultrasound or magnetic resonance neurography (MRN) have been introduced to detect focal hourglass-like constrictions of peripheral nerves in spontaneous nerve palsy. We present a series of three cases in which the suprascapular nerve was affected by hourglass-like constrictions, which were visualized by high-solution MRN, including a nerve-selective morphological MR pulse sequence with strong fat- and water-signal suppression.

Background & Aims: Although gadoxetate disodium-enhanced magnetic resonance imaging (MRI) shows higher sensitivity for diagnosing hepatocellular carcinoma (HCC), its arterial-phase images may be unsatisfactory because of weak arterial enhancement. We investigated the clinical effectiveness of arterial subtraction images from gadoxetate disodium-enhanced MRI for diagnosing early-stage HCC using the Liver Imaging Reporting and Data System (LI-RADS) v2018., Methods: In 258 patients at risk of HCC who underwent gadoxetate disodium-enhanced MRI in 2016, a total of 372 hepatic nodules (273 HCCs, 18 other malignancies, and 81 benign nodules) of 3.0 cm or smaller were retrospectively analyzed. Final diagnosis was assessed histopathologically or clinically (marginal recurrence after treatment or change in lesion size on follow-up imaging). The detection rate for arterial hyperenhancement was compared between ordinary arterial-phase and arterial subtraction images, and the benefit of arterial subtraction images in diagnosing HCC using LI-RADS was assessed., Results: Arterial subtraction images had a significantly higher detection rate for arterial hyperenhancement than ordinary arterial-phase images, both for all hepatic nodules (72.3% vs. 62.4%, p <0.001) and HCCs (91.9% vs. 80.6%, p <0.001). Compared with ordinary arterial-phase images, arterial subtraction images significantly increased the sensitivity of LI-RADS category 5 for diagnosis of HCC (64.1% [173/270] vs. 55.9% [151/270], p <0.001), without significantly decreasing specificity (92.9% [91/98] vs. 94.9% [93/98], p = 0.155). For histopathologically confirmed lesions, arterial subtraction images significantly increased sensitivity to 68.8% (128/186) from the 61.3% (114/186) of ordinary arterial-phase images (p <0.001), with a minimal decrease in specificity to 84.8% (39/46) from 89.1% (41/46) (p = 0.151)., Conclusions: Arterial subtraction images of gadoxetate disodium-enhanced MRI can significantly improve the sensitivity of early-stage HCC diagnosis using LI-RADS, without a significant decrease in specificity., Lay Summary: Gadoxetate disodium-enhanced magnetic resonance imaging is an imaging technique with a high sensitivity for the diagnosis of hepatocellular carcinoma. However, arterial-phase images may be unsatisfactory because of weak arterial enhancement. We found that using arterial subtraction images led to clinically meaningful improvements in the diagnosis of early-stage hepatocellular carcinoma., (Copyright © 2019 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

Purpose: To label Clostridium novyi-NT spores (C. novyi-NT) with iron oxide nanoclusters and track distribution of bacteria during magnetic resonance (MR) imaging-monitored locoregional delivery to liver tumors using intratumoral injection or intra-arterial transcatheter infusion., Materials and Methods: Vegetative state C. novyi-NT were labeled with iron oxide particles followed by induction of sporulation. Labeling was confirmed with fluorescence microscopy and transmission electron microscopy (TEM). T2 and T2* relaxation times for magnetic clusters and magnetic microspheres were determined using 7T and 1.5T MR imaging scanners. In vitro assays compared labeled bacteria viability and oncolytic potential to unlabeled controls. Labeled spores were either directly injected into N1-S1 rodent liver tumors (n = 24) or selectively infused via the hepatic artery in rabbits with VX2 liver tumors (n = 3). Hematoxylin-eosin, Prussian blue, and gram staining were performed. Statistical comparison methods included paired t-test and ANOVA., Results: Both fluorescence microscopy and TEM studies confirmed presence of iron oxide labels within the bacterial spores. Phantom studies demonstrated that the synthesized nanoclusters produce R2 relaxivities comparable to clinical agents. Labeling had no significant impact on overall growth or oncolytic properties (P >.05). Tumor signal-to-noise ratio (SNR) decreased significantly following intratumoral injection and intra-arterial infusion of labeled spores (P <.05). Prussian blue and gram staining confirmed spore delivery., Conclusions: C. novyi-NT spores can be internally labeled with iron oxide nanoparticles to visualize distribution with MR imaging during locoregional bacteriolytic therapy involving direct injection or intra-arterial transcatheter infusion., (Copyright © 2019. Published by Elsevier Inc.)

Background Current Liver Imaging Reporting and Data System guidelines define the washout appearance of gadoxetic acid-enhanced MRI only during the portal venous phase. Defining washout only during the portal venous phase may lead to lower sensitivity for diagnosis of hepatocellular carcinoma (HCC). Purpose To compare the diagnostic performances of three gadoxetic acid-enhanced MRI criteria for HCC according to the phases during which washout appearance was determined. Materials and Methods In this retrospective study, patients with a hepatic nodule detected at US surveillance for HCC from January to December 2012 underwent gadoxetic acid-enhanced MRI. Three diagnostic MRI criteria for HCC were defined according to the phases during which washout appearance was observed, with the presence of arterial phase hyperenhancement and hypointensity noted (a) only during the portal venous phase, with washout confined to the portal venous phase; (b) during the portal venous phase or transitional phase, with washout extended to the transitional phase; or (c) during the portal venous, transitional, or hepatobiliary phase, with washout extended to the hepatobiliary phase. If a nodule showed marked T2 hyperintensity or a targetoid appearance, it was precluded from the diagnosis of HCC. The sensitivity and specificity were compared by using a generalized estimating equation. Results A total of 178 patients were included (mean age ± standard deviation, 55.3 years ± 9.1) with 203 surgically confirmed hepatic nodules (186 HCCs and 17 non-HCCs) measuring 3.0 cm or smaller. The sensitivity with washout extended to the hepatobiliary phase (95.2% [177 of 186]) was better than that with washout extended to the transitional phase (90.9% [169 of 186]; P = .01) and washout confined to the portal venous phase (75.3% [140 of 186]; P < .01). The specificity with extensions of washout to the transitional phase and hepatobiliary phase (82% [14 of 17] for both) was similar to that obtained with washout confined to the portal venous phase (94.1% [16 of 17]) ( P = .47). Conclusion After exclusion of typical hemangiomas and nodules with a targetoid appearance, extending washout appearance to the transitional or hepatobiliary phase (instead of restricting it to the portal venous phase) allowed higher sensitivity without a reduction in specificity. © RSNA, 2019 See also the editorial by Fowler and Sirlin in this issue.

Background: Quantitative susceptibility mapping (QSM) is emerging as a technique that quantifies the paramagnetic nonheme iron in brain tissue. Brain iron quantification during early development provides insights into the underlying mechanism of brain maturation., Purpose: To quantify the spatiotemporal variations of brain iron-related magnetic susceptibility in deep gray matter nuclei during early development by using QSM., Study Type: Retrospective., Subjects: Eighty-seven infants and children aged 1 month to 6 years., Field Strength/sequence: Enhanced T 2 *-weighted angiography using a 3D gradient-echo sequence at 3.0T., Assessment: QSM was calculated by modified sophisticated harmonic artifact reduction for phase data and sparse linear equations and sparse least squares-based algorithm. Means of susceptibility in deep gray matter nuclei (caudate nucleus, putamen, globus pallidus, thalamus) relative to that in splenium of corpus callosum were measured., Statistical Tests: Relationships of mean susceptibility with age and referenced iron concentration were tested by Pearson correlation. Differences of mean susceptibility between the selected nuclei in each age group were compared by one-way analysis of variance (ANOVA) and Fisher's Linear Significant Difference (LSD) test., Results: Positive correlations of susceptibility with both referenced iron concentration and age were found (P < 0.0001); particularly, globus pallidus showed the highest correlation with age (correlation coefficient, 0.882; slope, 1.203; P < 0.001) and greatest susceptibility (P < 0.05) among the selected nuclei., Data Conclusion: QSM allows the feasible quantification of iron deposition in deep gray matter nuclei in infants and young children, which exhibited gradual accumulation at different speeds. The fastest and highest iron accumulation was observed in the globus pallidus with increasing age during early development., Level of Evidence: 4 Technical Efficacy:Stage 2 J. Magn. Reson. Imaging 2018., (© 2018 International Society for Magnetic Resonance in Medicine.)

Background: Spontaneous isolated posterior inferior cerebellar artery (PICA) dissection has been reported more frequently since high-resolution vessel wall magnetic resonance imaging (HR vw-MRI) was introduced to the field. The intimal flap or double lumen, which is commonly reported to be a direct sign of the dissection, is not easily detectable on HR vw-MRI because the size of the PICA is very small and tortuous., Case Presentation: Two patients with posterior circulation ischemic stroke due to spontaneous isolated PICA dissection underwent HR vw-MRI. The curved multiplanar reconstruction image reconstructed using three-dimensional (3D) HR vw-MRI (3D curved MPR imaging) is helpful to observe tortuous blood vessels such as the PICA because it can visualize the entire vessel course in a single plane. In this report, routine HR vw-MRI revealed only an intramural hematoma in both patients. However, 3D curved MPR imaging discovered the intimal flap which was not observed on the routine HR vw-MRI. Therefore, these two patients were diagnosed with spontaneous isolated PICA dissection due to the intimal flap that was observed on the 3D curved MPR image., Conclusion: HR vw-MRI is useful for the early diagnosis of isolated PICA dissection. Furthermore, we believe that 3D curved MPR imaging could improve the possibility of diagnosing the dissection early because it can easily confirm direct signs such as an intimal flap or double lumen.

Purpose: This study aims to estimate PD, T 1 , T 2 , T 2 * , and Δ B 0 simultaneously using magnetic resonance fingerprinting (MRF) with compensation of the linearly varying background field., Methods: MRF based on fast imaging with steady-state precession (FISP) and multi-echo spoiled gradient (SPGR) schemes are alternatively used, which encode T 2 and T 2 * , respectively. Simulations are performed to determine the appropriate ratio of the FISP and SPGR sections with respect to the T 2 and T 2 * accuracy. Additionally, background field inhomogeneity (G z ) compensation using z-shim gradients are incorporated into the SPGR section and the dictionary. The background field compensation is tested in the phantom experiment under well-shimmed and poor-shimmed conditions. An in vivo experiment is performed and the estimated parameters are compared before and after G z compensation., Results: The T 1 , T 2 , and T 2 * values from the phantom results are in good agreement with the reference methods under well-shimmed condition. The underestimated T 2 and T 2 * values under poor-shimmed condition are recovered by G z compensation and the parameters are also in good agreement with the reference methods. In the human brain, T 2 and T 2 * values are restored by G z compensation in regions where the magnetic field is particularly inhomogeneous, such as near the sinus and ear canals., Conclusions: The proposed FISP and SPGR combined MRF provides a simultaneous estimation of PD, T 1 , T 2 , T 2 * , and Δ B 0 . By incorporating field inhomogeneity as a gradient term into both the sequence and dictionary, T 2 and T 2 * values can be restored where field inhomogeneity exists., (© 2018 International Society for Magnetic Resonance in Medicine.)