Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging.

Despite its superior soft tissue contrast and non-invasive nature, MRI requires long scan times due to its intrinsic signal acquisition principles, a main drawback which technological advancements in MRI have been focused on. In particular, scan time reduction is a natural requirement in neuroimaging due to detailed structures requiring high resolution imaging and often volumetric (3D) acquisitions, and numerous studies have recently attempted to harness deep learning (DL) technology in enabling scan time reduction and image quality improvement. Various DL-based image reconstruction products allow for additional scan time reduction on top of existing accelerated acquisition methods without compromising the image quality.

Dynamic Contrast-enhanced MRI Quantification of Altered Vascular Permeability in Autoimmune Encephalitis.

Background Blood-brain barrier (BBB) permeability change is a possible pathologic mechanism of autoimmune encephalitis. Purpose To evaluate the change in BBB permeability in patients with autoimmune encephalitis as compared with healthy controls by using dynamic contrast-enhanced (DCE) MRI and to explore its predictive value for treatment response in patients. Materials and Methods This single-center retrospective study included consecutive patients with probable or possible autoimmune encephalitis and healthy controls who underwent DCE MRI between April 2020 and May 2021. Automatic volumetric segmentation was performed on three-dimensional T1-weighted images, and volume transfer constant (Ktrans) values were calculated at encephalitis-associated brain regions. Ktrans values were compared between the patients and controls, with adjustment for age and sex with use of a nonparametric approach. The Wilcoxon rank sum test was performed to compare Ktrans values of the good (improvement in modified Rankin Scale [mRS] score of at least two points or achievement of an mRS score of ≤2) and poor (improvement in mRS score of less than two points and achievement of an mRS score >2) treatment response groups among the patients. Results Thirty-eight patients with autoimmune encephalitis (median age, 38 years [IQR, 29-59 years]; 20 [53%] female) and 17 controls (median age, 71 years [IQR, 63-77 years]; 12 [71%] female) were included. All brain regions showed higher Ktrans values in patients as compared with controls (P < .001). The median difference in Ktrans between the patients and controls was largest in the right parahippocampal gyrus (25.1 × 10-4 min-1 [95% CI: 17.6, 43.4]). Among patients, the poor treatment response group had higher baseline Ktrans values in both cerebellar cortices (P = .03), the left cerebellar cortex (P = .02), right cerebellar cortex (P = .045), left cerebral cortex (P = .045), and left postcentral gyrus (P = .03) than the good treatment response group. Conclusion DCE MRI demonstrated that BBB permeability was increased in all brain regions in patients with autoimmune encephalitis as compared with controls, and baseline Ktrans values were higher in patients with poor treatment response in the cerebellar cortex, left cerebral cortex, and left postcentral gyrus as compared with the good response group. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Filippi and Rocca in this issue.

Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients.

Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the non-enhancing T2 hyperintensity areas using conventional MRI alone. Quantitative DCE MRI parameters such as Ktrans and Ve convey permeability information of glioblastomas that cannot be provided by conventional MRI. We used the publicly available nnU-Net to train a deep learning model that incorporated both conventional and DCE MRI to detect the subtle difference in vessel leakiness due to neoangiogenesis between the non-recurrence area and the local recurrence area, which contains a higher proportion of high-grade glioma cells. We found that the addition of Ve doubled the sensitivity while nonsignificantly decreasing the specificity for prediction of local recurrence in glioblastomas, which implies that the combined model may result in fewer missed cases of local recurrence. The deep learning model predictive of local recurrence may enable risk-adapted radiotherapy planning in patients with grade 4 adult-type diffuse gliomas.

Blood-brain barrier disruption imaging in postoperative cerebral hyperperfusion syndrome using DCE-MRI.

Little has been reported about the association between cerebral hyperperfusion syndrome (CHS) and blood-brain barrier (BBB) disruption in human. We aimed to investigate the changes in permeability after bypass surgery in cerebrovascular steno-occlusive diseases using dynamic contrast-enhanced MRI (DCE-MRI) and to demonstrate the association between CHS and BBB disruption. This retrospective study included 36 patients (21 hemispheres in 18 CHS patients and 20 hemispheres in 18 controls) who underwent combined bypass surgery for moyamoya and atherosclerotic steno-occlusive diseases. DCE-MRI and arterial spin labeling perfusion-weighted imaging (ASL-PWI) were obtained at the baseline, postoperative state, and discharge. Perfusion and permeability parameters were calculated at the MCA territory (CBF(territorial), Ktrans(territorial), Vp(territorial)) and focal perianastomotic area (CBF(focal), Ktrans(focal), Vp(focal)) of operated hemispheres. As compared with the baseline, both CBF(territorial) and CBF(focal) increased in the postoperative period and decreased at discharge, corresponding well to symptoms in the CHS group. Vp(focal) was lower in the postoperative period and at discharge, as compared with the baseline. In the control group, no parameters significantly differed among the three points. In conclusion, Vp at the focal perianastomotic area significantly decreased in patients with CHS during the postoperative period. BBB disruption may be implicated in the development of CHS after bypass surgery.

Validation of Ultrasound and Computed Tomography-Based Risk Stratification System and Biopsy Criteria for Cervical Lymph Nodes in Preoperative Patients With Thyroid Cancer.

OBJECTIVE: This study aimed to validate the risk stratification system (RSS) and biopsy criteria for cervical lymph nodes (LNs) proposed by the Korean Society of Thyroid Radiology (KSThR). MATERIALS AND METHODS: This retrospective study included a consecutive series of preoperative patients with thyroid cancer who underwent LN biopsy, ultrasound (US), and computed tomography (CT) between December 2006 and June 2015. LNs were categorized as probably benign, indeterminate, or suspicious according to the current US- and CT-based RSS and the size thresholds for cervical LN biopsy as suggested by the KSThR. The diagnostic performance and unnecessary biopsy rates were calculated. RESULTS: A total of 277 LNs (53.1% metastatic) in 228 patients (mean age ± standard deviation, 47.4 years ± 14) were analyzed. In US, the malignancy risks were significantly different among the three categories (all P < 0.001); however, CT-detected probably benign and indeterminate LNs showed similarly low malignancy risks (P = 0.468). The combined US + CT criteria stratified the malignancy risks among the three categories (all P < 0.001) and reduced the proportion of indeterminate LNs (from 20.6% to 14.4%) and the malignancy risk in the indeterminate LNs (from 31.6% to 12.5%) compared with US alone. In all image-based classifications, nodal size did not affect the malignancy risks (short diameter [SD] ≤ 5 mm LNs vs. SD > 5 mm LNs, P ≥ 0.177). The criteria covering only suspicious LNs showed higher specificity and lower unnecessary biopsy rates than the current criteria, while maintaining sensitivity in all imaging modalities. CONCLUSION: Integrative evaluation of US and CT helps in reducing the proportion of indeterminate LNs and the malignancy risk among them. Nodal size did not affect the malignancy risk of LNs, and the addition of indeterminate LNs to biopsy candidates did not have an advantage in detecting LN metastases in all imaging modalities.

Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4).

Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.

OBJECTIVE: To compare the image quality and diagnostic performance between standard turbo spin-echo MRI and accelerated MRI with deep learning (DL)-based image reconstruction for degenerative lumbar spine diseases. MATERIALS AND METHODS: Fifty patients who underwent both the standard and accelerated lumbar MRIs at a 1.5-T scanner for degenerative lumbar spine diseases were prospectively enrolled. DL reconstruction algorithm generated coarse (DL_coarse) and fine (DL_fine) images from the accelerated protocol. Image quality was quantitatively assessed in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and qualitatively assessed using five-point visual scoring systems. The sensitivity and specificity of four radiologists for the diagnosis of degenerative diseases in both protocols were compared. RESULTS: The accelerated protocol reduced the average MRI acquisition time by 32.3% as compared to the standard protocol. As compared with standard images, DL_coarse and DL_fine showed significantly higher SNRs on T1-weighted images (T1WI; both p < .001) and T2-weighted images (T2WI; p = .002 and p < 0.001), higher CNRs on T1WI (both p < 0.001), and similar CNRs on T2WI (p = .49 and p = .27). The average radiologist assessment of overall image quality for DL_coarse and DL_fine was higher on sagittal T1WI (p = .04 and p < .001) and axial T2WI (p = .006 and p = .01) and similar on sagittal T2WI (p = .90 and p = .91). Both DL_coarse and DL_fine had better image quality of cauda equina and paraspinal muscles on axial T2WI (both p = .04 for cauda equina; p = .008 and p = .002 for paraspinal muscles). Differences in sensitivity and specificity for the detection of central canal stenosis and neural foraminal stenosis between standard and DL-reconstructed images were all statistically nonsignificant (p ≥ 0.05). CONCLUSION: DL-based protocol reduced MRI acquisition time without degrading image quality and diagnostic performance of readers for degenerative lumbar spine diseases. CLINICAL RELEVANCE STATEMENT: The deep learning (DL)-based reconstruction algorithm may be used to further accelerate spine MRI imaging to reduce patient discomfort and increase the cost efficiency of spine MRI imaging. KEY POINTS: • By using deep learning (DL)-based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol. • DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures. • The average radiologist's sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.

A single stage knowledge distillation network for brain tumor segmentation on limited MR image modalities.

BACKGROUND AND OBJECTIVE: Precisely segmenting brain tumors using multimodal Magnetic Resonance Imaging (MRI) is an essential task for early diagnosis, disease monitoring, and surgical planning. Unfortunately, the complete four image modalities utilized in the well-known BraTS benchmark dataset: T1, T2, Fluid-Attenuated Inversion Recovery (FLAIR), and T1 Contrast-Enhanced (T1CE) are not regularly acquired in clinical practice due to the high cost and long acquisition time. Rather, it is common to utilize limited image modalities for brain tumor segmentation. METHODS: In this paper, we propose a single stage learning of knowledge distillation algorithm that derives information from the missing modalities for better segmentation of brain tumors. Unlike the previous works that adopted a two-stage framework to distill the knowledge from a pre-trained network into a student network, where the latter network is trained on limited image modality, we train both models simultaneously using a single-stage knowledge distillation algorithm. We transfer the information by reducing the redundancy from a teacher network trained on full image modalities to the student network using Barlow Twins loss on a latent-space level. To distill the knowledge on the pixel level, we further employ a deep supervision idea that trains the backbone networks of both teacher and student paths using Cross-Entropy loss. RESULTS: We demonstrate that the proposed single-stage knowledge distillation approach enables improving the performance of the student network in each tumor category with overall dice scores of 91.11% for Tumor Core, 89.70% for Enhancing Tumor, and 92.20% for Whole Tumor in the case of only using the FLAIR and T1CE images, outperforming the state-of-the-art segmentation methods. CONCLUSIONS: The outcomes of this work prove the feasibility of exploiting the knowledge distillation in segmenting brain tumors using limited image modalities and hence make it closer to clinical practices.

Non-invasive flow mapping of parasagittal meningeal lymphatics using 2D interslice flow saturation MRI.

The clearance pathways of brain waste products in humans are still under debate in part due to the lack of noninvasive imaging techniques for meningeal lymphatic vessels (mLVs). In this study, we propose a new noninvasive mLVs imaging technique based on an inter-slice blood perfusion MRI called alternate ascending/descending directional navigation (ALADDIN). ALADDIN with inversion recovery (IR) at single inversion time of 2300 ms (single-TI IR-ALADDIN) clearly demonstrated parasagittal mLVs around the human superior sagittal sinus (SSS) with better detectability and specificity than the previously suggested noninvasive imaging techniques. While in many studies it has been difficult to detect mLVs and confirm their signal source noninvasively, the detection of mLVs in this study was confirmed by their posterior to anterior flow direction and their velocities and morphological features, which were consistent with those from the literature. In addition, IR-ALADDIN was compared with contrast-enhanced black blood imaging to confirm the detection of mLVs and its similarity. For the quantification of flow velocity of mLVs, IR-ALADDIN was performed at three inversion times of 2000, 2300, and 2600 ms (three-TI IR-ALADDIN) for both a flow phantom and humans. For this preliminary result, the flow velocity of the dorsal mLVs in humans ranged between 2.2 and 2.7 mm/s. Overall, (i) the single-TI IR-ALADDIN can be used as a novel non-invasive method to visualize mLVs in the whole brain with scan time of ~ 17 min and (ii) the multi-TI IR-ALADDIN can be used as a way to quantify the flow velocity of mLVs with a scan time of ~ 10 min (or shorter) in a limited coverage. Accordingly, the suggested approach can be applied to noninvasively studying meningeal lymphatic flows in general and also understanding the clearance pathways of waste production through mLVs in humans, which warrants further investigation.

Deep learning based automatic detection algorithm for acute intracranial haemorrhage: a pivotal randomized clinical trial.

Acute intracranial haemorrhage (AIH) is a potentially life-threatening emergency that requires prompt and accurate assessment and management. This study aims to develop and validate an artificial intelligence (AI) algorithm for diagnosing AIH using brain-computed tomography (CT) images. A retrospective, multi-reader, pivotal, crossover, randomised study was performed to validate the performance of an AI algorithm was trained using 104,666 slices from 3010 patients. Brain CT images (12,663 slices from 296 patients) were evaluated by nine reviewers belonging to one of the three subgroups (non-radiologist physicians, n = 3; board-certified radiologists, n = 3; and neuroradiologists, n = 3) with and without the aid of our AI algorithm. Sensitivity, specificity, and accuracy were compared between AI-unassisted and AI-assisted interpretations using the chi-square test. Brain CT interpretation with AI assistance results in significantly higher diagnostic accuracy than that without AI assistance (0.9703 vs. 0.9471, p < 0.0001, patient-wise). Among the three subgroups of reviewers, non-radiologist physicians demonstrate the greatest improvement in diagnostic accuracy for brain CT interpretation with AI assistance compared to that without AI assistance. For board-certified radiologists, the diagnostic accuracy for brain CT interpretation is significantly higher with AI assistance than without AI assistance. For neuroradiologists, although brain CT interpretation with AI assistance results in a trend for higher diagnostic accuracy compared to that without AI assistance, the difference does not reach statistical significance. For the detection of AIH, brain CT interpretation with AI assistance results in better diagnostic performance than that without AI assistance, with the most significant improvement observed for non-radiologist physicians.

Risk of thyroid cancer in a lung cancer screening population of the National Lung Screening Trial according to the presence of incidental thyroid nodules detected on low-dose chest CT.

PURPOSE: This study evaluated thyroid cancer risk in a lung cancer screening population according to the presence of an incidental thyroid nodule (ITN) detected on low-dose chest computed tomography (LDCT). METHODS: Of 47,837 subjects who underwent LDCT, a lung cancer screening population according to the National Lung Screening Trial results was retrospectively enrolled. The prevalence of ITN on LDCT was calculated, and the ultrasonography (US)/fine-needle aspiration (FNA)-based risk of thyroid cancer according to the presence of ITN on LDCT was compared using the Fisher exact or Student t-test as appropriate. RESULTS: Of the 2,329 subjects (female:male=44:2,285; mean age, 60.9±4.9 years), the prevalence of ITN on LDCT was 4.8% (111/2,329). The incidence of thyroid cancer was 0.8% (18/2,329, papillary thyroid microcarcinomas [PTMCs]) and was higher in the ITN-positive group than in the ITN-negative group (3.6% [4/111] vs. 0.6% [14/2,218], P=0.009). Among the 2,011 subjects who underwent both LDCT and thyroid US, all risks were higher (P<0.001) in the ITNpositive group than in the ITN-negative group: presence of thyroid nodule on US, 94.1% (95/101) vs. 48.6% (928/1,910); recommendation of FNA according to the American Thyroid Association guideline and Korean Thyroid Imaging Reporting and Data System guideline, 41.2% (42/101) vs. 2.4% (46/1,910) and 39.6% (40/101) vs. 1.9% (37/1,910), respectively. CONCLUSION: Despite a higher risk of thyroid cancer in the LDCT ITN-positive group than in the ITN-negative group in a lung cancer screening population, all cancers were PTMCs. A heavy smoking history may not necessitate thorough screening US for thyroid incidentalomas.

Quantifying infarct core volume in ischemic stroke: What is the optimal threshold and parameters of computed tomography perfusion?

OBJECTIVE: Although computed tomography perfusion (CTP) is used to select and guide decision-making processes in patients with acute ischemic stroke, there is no clear standardization of the optimal threshold to predict ischemic core volume accurately. The infarct core volume with a relative cerebral blood flow(rCBF) threshold of < 30% is commonly used. We aimed to assess the volumetric agreement of the infarct core volume with different CTP parameters and thresholds using CTP software (RAPID, VITREA) and the infarct volume on diffusion-weighted imaging (DWI), with a short interval time (within 60 min) between CTP and follow-up DWI. MATERIALS AND METHODS: This retrospective study included 42 acute ischemic stroke patients with occlusion of the large artery in the anterior circulation between April 2017-November 2020. RAPID identified infarct core as tissue rCBF < 20-38%. VITREA defined the infarct core as cerebral blood volume (CBV) < 26-56%. Olea Sphere was used to measure infarct core volume on DWI. The CTP-infarct core volume with different thresholds of perfusion parameters (CBF threshold vs CBV threshold) were compared with DWI-infarct core volumes. RESULTS: The median time between CTP and DWI was 37.5min. The commonly used threshold of CBV< 41% (4.3 mL) resulted in lower median infarct core volume difference compared to the commonly used thresholds of rCBF < 30% (8.2mL). On the other hand, the optimal thresholds of CBV < 26% (-1.0mL; 95% CI, -53.9 to 58.1 mL; 0.945) resulted in the lowest median infarct core volume difference, narrowest limits of agreement, and largest interclass correlation coefficient compared with the optimal thresholds of rCBF < 38% (4.9 mL; 95% CI, -36.4 to 62.9 mL; 0.939). CONCLUSION: Our study found that the both optimal and commonly used thresholds of CBV provided a more accurate prediction of the infarct core volume in patients with AIS than rCBF.

Prognostic value of ASPECTS on post-treatment diffusion-weighted imaging for acute ischemic stroke patients after endovascular thrombectomy: comparison with infarction.

KEY POINTS: • Post-treatment DWI ASPECTS may be an eligible surrogate of infarction volume for predicting the clinical outcome of stroke patients following endovascular thrombectomy.

An exploratory study on functional connectivity after mild traumatic brain injury: Preserved global but altered local organization.

INTRODUCTION: This study aimed to investigate alterations in whole-brain functional connectivity after a concussion using graph-theory analysis from global and local perspectives and explore the association between changes in the functional network properties and cognitive performance. METHODS: Individuals with mild traumatic brain injury (mTBI, n = 29) within a month after injury, and age- and sex-matched healthy controls (n = 29) were included. Graph-theory measures on functional connectivity assessed using resting state functional magnetic resonance imaging data were acquired from each participant. These included betweenness centrality, strength, clustering coefficient, local efficiency, and global efficiency. Multi-domain cognitive functions were correlated with the graph-theory measures. RESULTS: In comparison to the controls, the mTBI group showed preserved network characteristics at a global level. However, in the local network, we observed decreased betweenness centrality, clustering coefficient, and local efficiency in several brain areas, including the fronto-parietal attention network. Network strength at the local level showed mixed-results in different areas. The betweenness centrality of the right parahippocampus showed a significant positive correlation with the cognitive scores of the verbal learning test only in the mTBI group. CONCLUSION: The intrinsic functional connectivity after mTBI is preserved globally, but is suboptimally organized locally in several areas. This possibly reflects the neurophysiological sequelae of a concussion. The present results may imply that the network property could be used as a potential indicator for clinical outcomes after mTBI.

Validation of Ultrasound Risk Stratification Systems for Cervical Lymph Node Metastasis in Patients with Thyroid Cancer.

A malignancy risk stratification system (RSS) for cervical lymph nodes (LNs) has not been fully established. This study aimed to validate the current RSS for the diagnosis of cervical LN metastasis in thyroid cancer. In total, 346 LNs from 282 consecutive patients between December 2006 and June 2015 were included. We determined the malignancy risk of each ultrasound (US) feature and performed univariable and multivariable logistic regression analyses. Each risk category from the Korean Society of Thyroid Radiology (KSThR) and the European Thyroid Association (ETA) was applied to calculate malignancy risks. The effects of size, number of suspicious features, and primary tumor characteristics were analyzed to refine the current RSS. Suspicious features including echogenic foci, cystic change, hyperechogenicity, and abnormal vascularity were independently predictive of malignancy (p ≤ 0.045). The malignancy risks of probably benign, indeterminate, and suspicious categories were 2.2-2.5%, 26.8-29.0%, and 85.8-87.4%, respectively, according to the KSThR and ETA criteria. According to the ETA criteria, 15.1% of LNs were unclassifiable. In indeterminate LNs, multiplicity of the primary tumor was significantly associated with malignancy (odds ratio, 6.53; p = 0.004). We refined the KSThR system and proposed a US RSS for LNs in patients with thyroid cancer.

Myelin Content in Mild Traumatic Brain Injury Patients with Post-Concussion Syndrome: Quantitative Assessment with a Multidynamic Multiecho Sequence.

OBJECTIVE: This study aimed to explore the myelin volume change in patients with mild traumatic brain injury (mTBI) with post-concussion syndrome (PCS) using a multidynamic multiecho (MDME) sequence and automatic whole-brain segmentation. MATERIALS AND METHODS: Forty-one consecutive mTBI patients with PCS and 29 controls, who had undergone MRI including the MDME sequence between October 2016 and April 2018, were included. Myelin volume fraction (MVF) maps were derived from the MDME sequence. After three dimensional T1-based brain segmentation, the average MVF was analyzed at the bilateral cerebral white matter (WM), bilateral cerebral gray matter (GM), corpus callosum, and brainstem. The Mann-Whitney U-test was performed to compare MVF and myelin volume between patients with mTBI and controls. Myelin volume was correlated with neuropsychological test scores using the Spearman rank correlation test. RESULTS: The average MVF at the bilateral cerebral WM was lower in mTBI patients with PCS (median [interquartile range], 25.2% [22.6%-26.4%]) than that in controls (26.8% [25.6%-27.8%]) (p = 0.004). The region-of-interest myelin volume was lower in mTBI patients with PCS than that in controls at the corpus callosum (1.87 cm3 [1.70-2.05 cm3] vs. 2.21 cm3 [1.86-3.46 cm3]; p = 0.003) and brainstem (9.98 cm3 [9.45-11.00 cm3] vs. 11.05 cm3 [10.10-11.53 cm3]; p = 0.015). The total myelin volume was lower in mTBI patients with PCS than that in controls at the corpus callosum (0.45 cm3 [0.39-0.48 cm3] vs. 0.48 cm3 [0.45-0.54 cm3]; p = 0.004) and brainstem (1.45 cm3 [1.28-1.59 cm3] vs. 1.54 cm3 [1.42-1.67 cm3]; p = 0.042). No significant correlation was observed between myelin volume parameters and neuropsychological test scores, except for the total myelin volume at the bilateral cerebral WM and verbal learning test (delayed recall) (r = 0.425; p = 0.048). CONCLUSION: MVF quantified from the MDME sequence was decreased at the bilateral cerebral WM in mTBI patients with PCS. The total myelin volumes at the corpus callosum and brainstem were decreased in mTBI patients with PCS due to atrophic changes.

Prediction of hemorrhagic complications after ultrasound-guided biopsy of the thyroid and neck.

OBJECTIVES: Hemorrhage occasionally occurs after ultrasound (US)-guided biopsy of the thyroid and neck and sometimes leads to serious complications. We aimed to identify predictors of hemorrhagic complications after US-guided biopsy of the thyroid and neck. MATERIAL AND METHODS: In this retrospective study, we analyzed consecutive patients who underwent US-guided biopsy from April 2020 to November 2020. Procedure characteristics, US features, and peri- and post-procedural patient symptoms and signs were compared between patients with and without post-biopsy hemorrhage. Associations between clinical and imaging variables and post-biopsy hemorrhage were analyzed using univariate and multivariate regression analyses. RESULTS: A total of 305 patients who underwent US-guided biopsy of the thyroid and neck were included (219 women, 86 men; age range, 20-89 years). Seventeen (5.7%) cases of post-biopsy hemorrhage were detected 30 min after biopsy and manual compression. Among them, 10 developed hemorrhage at 30 min without immediate hemorrhage. In the multivariate analysis, a high tenderness score on the visual analog scale (VAS) at 30 min after biopsy (odds ratio [OR] 5.05, p < .001) was identified as an independent predictor of post-biopsy hemorrhage. In patients with hemorrhage at 30 min, tenderness scores significantly increased over 30 min of observation. CONCLUSIONS: High tenderness scores at 30 min after biopsy and manual compression were independent predictors of hemorrhage after US-guided biopsy of the thyroid and neck. The tenderness score could serve as a valuable marker to triage patients who require further observation and management after a US-guided biopsy of the thyroid and neck. KEY POINTS: • High tenderness scores at 30 min after compression were associated with the presence of delayed post-biopsy hemorrhage at 30 min. • Patients with hemorrhage at 30 min demonstrated a significant increase in tenderness scores over time. • High tenderness scores after biopsy site compression predicted the presence of delayed post-biopsy hemorrhage in the thyroid and neck.

Response prediction of vestibular schwannoma after gamma-knife radiosurgery using pretreatment dynamic contrast-enhanced MRI: a prospective study.

OBJECTIVES: There are few known predictive factors for response to gamma-knife radiosurgery (GKRS) in vestibular schwannoma (VS). We investigated the predictive role of pretreatment dynamic contrast-enhanced (DCE)-MRI parameters regarding the tumor response after GKRS in sporadic VS. METHODS: This single-center prospective study enrolled participants between April 2017 and February 2019. We performed a volumetric measurement of DCE-MRI-derived parameters before GKRS. The tumor volume was measured in a follow-up MRI. The pharmacokinetic parameters were compared between responders and nonresponders according to 20% or more tumor volume reduction. Stepwise multivariable logistic regression analyses were performed, and the diagnostic performance of DCE-MRI parameters for the prediction of tumor response was evaluated by receiver operating characteristic curve analysis. RESULTS: Ultimately, 35 participants (21 women, 52 ± 12 years) were included. There were 22 (62.9%) responders with a mean follow-up interval of 30.2 ± 5.7 months. Ktrans (0.036 min-1 vs. 0.057 min-1, p = .008) and initial area under the time-concentration curve within 90 s (IAUC90) (84.4 vs. 143.6, p = .003) showed significant differences between responders and nonresponders. Ktrans (OR = 0.96, p = .021) and IAUC90 (OR = 0.97, p = .004) were significant differentiating variables in each multivariable model with clinical variables for tumor response prediction. Ktrans showed a sensitivity of 81.8% and a specificity of 69.2%, and IAUC90 showed a sensitivity of 100% and a specificity of 53.8% for tumor response prediction. CONCLUSION: DCE-MRI (particularly Ktrans and IAUC90) has the potential to be a predictive factor for tumor response in VS after GKRS. KEY POINTS: •Pretreatment prediction of gamma-knife radiosurgery response in vestibular schwannoma is still challenging. •Dynamic contrast-enhanced MRI could have predictive value for the response of vestibular schwannoma after gamma-knife radiosurgery.

A systematic review and data synthesis of longitudinal changes in white matter integrity after mild traumatic brain injury assessed by diffusion tensor imaging in adults.

PURPOSE: This study aimed to review diffusion tensor imaging studies of mild traumatic brain injury (mTBI) in adults with longitudinal acquisition of data and investigate the variability of findings in association with related factors, such as the time post-injury. METHODS: Eligible studies from PubMed and EMBASE were searched to identify relevant studies for review. Of the 540 studies, 23 observational studies without intervention and with the following characteristics were included: original research in which adults with mTBI were examined, diffusion tensor imaging was acquired at least twice, white matter integrity was investigated by estimating diffusion metrics, and mode of injury was not restricted to sport- or blast-related mTBI. RESULTS: Baseline scans were acquired within 3 weeks post-injury, followed by longitudinal scans within 3 months and at 12 months post-injury. During the acute/subacute period, mixed results (increase, decrease, or no significant change) of fractional anisotropy (FA) were observed compared to those in controls. Some studies reported increased FA during the acute/subacute period compared to controls, followed by normalization of FA. Decreased FA was also reported during the acute/subacute period, which lasted long into the chronic phase. In the acute phase, the mean diffusivity (MD) was greater than that in the controls. Compared to the early phase of injury, MD was reduced in the follow-up phase in most studies in the mTBI group. Insignificant differences in FA and MD have been reported in several studies. Such variability limits the clinical usefulness of diffusion tensor metrics. CONCLUSIONS: There was a high variability in reported changes in white matter integrity. Decreased FA not only in acute/subacute but also in long-term period after injury may indicate long-term neurodegenerative processes after mTBI. Nevertheless, longitudinal changes in MD towards normalization suggest possible recovery. Long-term cohort studies with research initiatives should be considered to elucidate brain changes after mTBI.

Computed tomography complements ultrasound for the differential diagnosis of traumatic neuroma from recurrent tumor in patients with postoperative thyroid cancer.

OBJECTIVES: Traumatic neuromas (TNs) mimic recurrent tumors in US after total thyroidectomy (TT) and lateral neck dissection (LND) for thyroid cancer. We aimed to evaluate whether CT could complement US in the differential diagnosis of TNs from recurrent thyroid cancer in the dissected neck. MATERIAL AND METHODS: We retrospectively included a total of 97 consecutive US-detected lesions (28 TNs and 69 recurrent tumors) in patients with a previous history of TT and LND for thyroid cancer. The lesions were classified as benign, indeterminate, or suspicious according to the presence of benign or suspicious features on US and CT. Imaging features and categories on US and CT were compared between TNs and recurrent tumors. The diagnostic performances of US and CT for differentiating between TNs and recurrent tumors were calculated. RESULTS: On US, most TNs and recurrent tumors showed internal hyperechogenicity without hilar echogenicity or hilar vascularity and were categorized as suspicious lesions (23/28, 82.1% vs. 53/69, 76.8%). On CT, all TNs lacked strong enhancement without hilar fat or hilar vessel enhancement and were categorized as indeterminate lesions (28/28, 100%). In contrast, most recurrent tumors showed strong enhancement and were categorized as suspicious lesions (63/69, 91.3%). The addition of CT to US corrected 23 false-positive diagnoses in 28 TNs and 10 false-negative diagnoses in 69 recurrent tumors. CONCLUSIONS: CT complements US for the correct differentiation of TNs from recurrent tumors in postoperative thyroid cancer patients. The addition of CT to US may prevent unnecessary painful biopsy or surgery. KEY POINTS: • In the dissected neck, traumatic neuromas could mimic US suspicious LNs owing to its internal hyperechogenicity. • CT effectively differentiated traumatic neuromas from recurrent thyroid cancers by demonstrating significantly different enhancement patterns. • CT could complement US and may prevent unnecessary painful biopsy or surgery for US-detected lesions after thyroidectomy and neck dissection.