Knowledge-Augmented Deep Learning for Segmenting and Detecting Cerebral Aneurysms With CT Angiography: A Multicenter Study.

Background Deep learning (DL) could improve the labor-intensive, challenging processes of diagnosing cerebral aneurysms but requires large multicenter data sets. Purpose To construct a DL model using a multicenter data set for accurate cerebral aneurysm segmentation and detection on CT angiography (CTA) images and to compare its performance with radiology reports. Materials and Methods Consecutive head or head and neck CTA images of suspected unruptured cerebral aneurysms were gathered retrospectively from eight hospitals between February 2018 and October 2021 for model development. An external test set with reference standard digital subtraction angiography (DSA) scans was obtained retrospectively from one of the eight hospitals between February 2022 and February 2023. Radiologists (reference standard) assessed aneurysm segmentation, while model performance was evaluated using the Dice similarity coefficient (DSC). The model's aneurysm detection performance was assessed by sensitivity and comparing areas under the receiver operating characteristic curves (AUCs) between the model and radiology reports in the DSA data set with use of the DeLong test. Results Images from 6060 patients (mean age, 56 years ± 12 [SD]; 3375 [55.7%] female) were included for model development (training: 4342; validation: 1086; and internal test set: 632). Another 118 patients (mean age, 59 years ± 14; 79 [66.9%] female) were included in an external test set to evaluate performance based on DSA. The model achieved a DSC of 0.87 for aneurysm segmentation performance in the internal test set. Using DSA, the model achieved 85.7% (108 of 126 aneurysms [95% CI: 78.1, 90.1]) sensitivity in detecting aneurysms on per-vessel analysis, with no evidence of a difference versus radiology reports (AUC, 0.93 [95% CI: 0.90, 0.95] vs 0.91 [95% CI: 0.87, 0.94]; P = .67). Model processing time from reconstruction to detection was 1.76 minutes ± 0.32 per scan. Conclusion The proposed DL model could accurately segment and detect cerebral aneurysms at CTA with no evidence of a significant difference in diagnostic performance compared with radiology reports. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Payabvash in this issue.

Clot-based time attenuation curve as a novel imaging predictor of mechanical thrombectomy functional outcome in acute ischemia stroke.

OBJECTIVES: To investigate whether a novel assessment of thrombus permeability obtained from perfusion computed tomography (CTP) can act as a more accurate predictor of clinical response to mechanical thrombectomy (MT) in acute ischemic stroke (AIS). MATERIALS AND METHODS: We performed a study including two cohorts of AIS patients who underwent MT admitted to a single-center between April 2018 and February 2022: a retrospective development cohort (n = 71) and a prospective independent validation cohort (n = 96). Thrombus permeability was determined in terms of entire thrombus time-attenuation curve (TAC) on CTP. Association between thrombus TAC distributions and histopathological results was analyzed in the development cohort. Logistic regression was used to assess the performance of the TAC for predicting 90-day modified Rankin Scale (mRS) score, and good outcome was defined as a mRS score of ≤ 2. Basic clinical characteristics was used to build a routine clinical model. A combined model gathered TAC and basic clinical characteristics was also developed. The performance of the three models is compared on the independent validation set. RESULTS: Two TAC distributions were observed-unimodal (uTAC) and linear (lTAC). TAC distributions achieved strong correlations (|r|= 0.627, p < 0.001) with histopathological results, in which uTAC associated with fibrin- and platelet-rich clot while lTAC associated with red blood cell-rich clot. The uTAC was independently associated with poor outcome (odds ratio, 0.08 [95% confidence interval (CI), 0.02-0.31]; p < 0.001). TAC distributions yielded an AUC of 0.78 (95% CI, 0.70-0.87) for predicting clinical outcome. When combined clinical characteristics, the performance was significantly improved (AUC, 0.85 [95% CI, 0.76-0.93]; p < 0.001) and higher than routine clinical model (AUC, 0.69 [95% CI, 0.59-0.83]; p < 0.001). CONCLUSIONS: Thrombus TAC on CTP were found to be a promising new imaging biomarker to predict the outcomes of MT in AIS. CLINICAL RELEVANCE STATEMENT: This study revealed that clot-based time attenuation curve based on admission perfusion CT could reflect the permeability and composition of thrombus and, also, provide valuable information to predict the clinical outcomes of mechanical thrombectomy in patients with acute ischemia stroke. KEY POINTS: • Two time-attenuation curves distributions achieved strong correlations (|r|= 0.627, p < 0.001) with histopathological results. • The unimodal time-attenuation curve was independently associated with poor outcome (odds ratio, 0.08 [0.02-0.31]; p < 0.001). • The time-attenuation curve distributions yielded a higher performance for detecting clinical outcome than routine clinical model (AUC, 0.78 [0.70-0.87] vs 0.69 [0.59-0.83]; p < 0.001).

Deep learning-based automatic ASPECTS calculation can improve diagnosis efficiency in patients with acute ischemic stroke: a multicenter study.

OBJECTIVES: The Alberta Stroke Program Early CT Score (ASPECTS), a systematic method for assessing ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT), is often interpreted relying on expert experience and can vary between readers. This study aimed to develop a clinically applicable automatic ASPECTS system employing deep learning (DL). METHODS: This study enrolled 1987 NCCT scans that were retrospectively collected from four centers between January 2017 and October 2021. A DL-based system for automated ASPECTS assessment was trained on a development cohort (N = 1767) and validated on an independent test cohort (N = 220). The consensus of experienced physicians was regarded as a reference standard. The validity and reliability of the proposed system were assessed against physicians' readings. A real-world prospective application study with 13,399 patients was used for system validation in clinical contexts. RESULTS: The DL-based system achieved an area under the receiver operating characteristic curve (AUC) of 84.97% and an intraclass correlation coefficient (ICC) of 0.84 for overall-level analysis on the test cohort. The system's diagnostic sensitivity was 94.61% for patients with dichotomized ASPECTS at a threshold of ≥ 6, with substantial agreement (ICC = 0.65) with expert ratings. Combining the system with physicians improved AUC from 67.43 to 89.76%, reducing diagnosis time from 130.6 ± 66.3 s to 33.3 ± 8.3 s (p < 0.001). During the application in clinical contexts, 94.0% (12,591) of scans successfully processed by the system were utilized by clinicians, and 96% of physicians acknowledged significant improvement in work efficiency. CONCLUSION: The proposed DL-based system could accurately and rapidly determine ASPECTS, which might facilitate clinical workflow for early intervention. CLINICAL RELEVANCE STATEMENT: The deep learning-based automated ASPECTS evaluation system can accurately and rapidly determine ASPECTS for early intervention in clinical workflows, reducing processing time for physicians by 74.8%, but still requires validation by physicians when in clinical applications. KEY POINTS: The deep learning-based system for ASPECTS quantification has been shown to be non-inferior to expert-rated ASPECTS. This system improved the consistency of ASPECTS evaluation and reduced processing time to 33.3 seconds per scan. 94.0% of scans successfully processed by the system were utilized by clinicians during the prospective clinical application.

Clot-based radiomics model for cardioembolic stroke prediction with CT imaging before recanalization: a multicenter study.

OBJECTIVES: To develop a clot-based radiomics model using CT imaging radiomic features and machine learning to identify cardioembolic (CE) stroke before mechanical thrombectomy (MTB) in patients with acute ischemic stroke (AIS). MATERIALS AND METHODS: This retrospective four-center study consecutively included 403 patients with AIS who sequentially underwent CT and MTB between April 2016 and July 2021. These were grouped into training, testing, and external validation cohorts. Thrombus-extracted radiomic features and basic information were gathered to construct a machine learning model to predict CE stroke. The radiological characteristics and basic information were used to build a routine radiological model. A combined radiomics and radiological features model was also developed. The performances of all models were evaluated and compared in the validation cohort. A histological analysis helped further assess the proposed model in all patients. RESULTS: The radiomics model yielded an area under the curve (AUC) of 0.838 (95% confidence interval [CI], 0.771-0.891) for predicting CE stroke in the validation cohort, significantly higher than the radiological model (AUC, 0.713; 95% CI, 0.636-0.781; p = 0.007) but similar to the combined model (AUC, 0.855; 95% CI, 0.791-0.906; p = 0.14). The thrombus radiomic features achieved stronger correlations with red blood cells (|rmax|, 0.74 vs. 0.32) and fibrin and platelet (|rmax|, 0.68 vs. 0.18) than radiological characteristics. CONCLUSION: The proposed CT-based radiomics model could reliably predict CE stroke in AIS, performing better than the routine radiological method. KEY POINTS: • Admission CT imaging could offer valuable information to identify the acute ischemic stroke source by radiomics analysis. • The proposed CT imaging-based radiomics model yielded a higher area under the curve (0.838) than the routine radiological method (0.713; p = 0.007). • Several radiomic features showed significantly stronger correlations with two main thrombus constituents (red blood cells, |rmax|, 0.74; fibrin and platelet, |rmax|, 0.68) than routine radiological characteristics.

Cognitive impairment in mild traumatic brain injury: a diffusion kurtosis imaging and volumetric study.

BACKGROUND: A significant number of patients with mild traumatic brain injury (mTBI) would experience cognitive deficit. PURPOSE: To investigate the brain structural changes in sub-acute mTBI by diffusion kurtosis imaging (DKI) and volumetric analysis, and to assess the relationship between brain structural changes and cognitive functions. MATERIAL AND METHODS: A total of 23 patients with sub-acute mTBI and 24 control participants were recruited. All the participants underwent examinations of neuropsychological tests, DKI, and magnetic resonance imaging (MRI)-based morphological scans. Images were investigated using whole brain-based analysis and further regions of interest-based analysis for subcortical nuclei. The neuropsychological tests were compared between the mTBI and the control group. Correlation analysis was performed to examine the relationship between gray matter (GM) volume, DKI parameters, and cognitive functions. RESULTS: Compared with control participants, mTBI patients performed worse in the domains of verbal memory, attention and executive function (P < 0.05). No regional GM volume differences were observed between the mTBI and control groups (P > 0.05). Using DKI, patients with mTBI showed lower mean kurtosis (MK) in widespread white matter (WM) regions and several subcortical nuclei (P < 0.05), and higher mean diffusivity (MD) in the right pallidum (P < 0.05). Lower MK value of multiple WM regions and several subcortical nuclei correlated with cognitive impairment (P < 0.05). CONCLUSION: DKI was sensitive in detecting brain microstructural changes in patients with sub-acute mTBI showing lower MK value in widespread WM regions and several subcortical nuclei, which were statistically associated with cognitive deficits.

Changes in Temporal and Spatial Patterns of Intrinsic Brain Activity and Functional Connectivity in Upper-Limb Amputees: An fMRI Study.

Background: Amputation in adults is a serious procedure or traumatic outcome, one that leads to a possible "remapping" of limb representations (somatotopy) in the motor and sensory cortex. The temporal and spatial extent underlying reorganization of somatotopy is unclear. The aim of this study was to better understand how local and global structural plasticity in sensory-motor cortical networks changes temporally and spatially after upper-limb amputation. Methods: We studied 8 healthy nonamputee control subjects and 16 complete upper-limb amputees. Resting-state MRI (rs-fMRI) was used to measure local and large-scale relative differences (compared to controls) in both the amplitude of low-frequency fluctuations (ALFF) and degree of centrality (DC) at 2 months, 6 months, and 12 months after traumatic amputation. Results: In amputees, rs-fMRI scans revealed differences in spatial patterns of ALFF and DC among brain regions over time. Significant relative increases in ALFF and DC were detected not only in the sensory and motor cortex but also in related cortical regions believed to be involved in cognition and motor planning. We observed changes in the magnitude of ALFFs in the pre- and postcentral gyrus and primary sensory cortex, as well as in the anterior cingulate, parahippocampal gyrus, and hippocampus, 2 months after the amputation. The regional distribution of increases/decreases in ALFFs and DC documented at 2-month postamputation was very different from those at 6 and 12-month postamputation. Conclusion: Local and wide-spread changes in ALFFs in the sensorimotor cortex and cognitive-related brain regions after upper-limb amputation may imply dysfunction not only in sensory and motor function but also in areas responsible for sensorimotor integration and motor planning. These results suggest that cortical reorganization after upper extremity deafferentation is temporally and spatially more complicated than previously appreciated, affecting DC in widespread regions.

Inorganic Nanomaterial for Biomedical Imaging of Brain Diseases.

In the past few decades, brain diseases have taken a heavy toll on human health and social systems. Magnetic resonance imaging (MRI), photoacoustic imaging (PA), computed tomography (CT), and other imaging modes play important roles in disease prevention and treatment. However, the disadvantages of traditional imaging mode, such as long imaging time and large noise, limit the effective diagnosis of diseases, and reduce the precision treatment of diseases. The ever-growing applications of inorganic nanomaterials in biomedicine provide an exciting way to develop novel imaging systems. Moreover, these nanomaterials with special physicochemical characteristics can be modified by surface modification or combined with functional materials to improve targeting in different diseases of the brain to achieve accurate imaging of disease regions. This article reviews the potential applications of different types of inorganic nanomaterials in vivo imaging and in vitro detection of different brain disease models in recent years. In addition, the future trends, opportunities, and disadvantages of inorganic nanomaterials in the application of brain diseases are also discussed. Additionally, recommendations for improving the sensitivity and accuracy of inorganic nanomaterials in screening/diagnosis of brain diseases.

Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images.

Pulmonary cancer is considered as one of the major causes of death worldwide. For the detection of lung cancer, computer-assisted diagnosis (CADx) systems have been designed. Internet-of-Things (IoT) has enabled ubiquitous internet access to biomedical datasets and techniques; in result, the progress in CADx is significant. Unlike the conventional CADx, deep learning techniques have the basic advantage of an automatic exploitation feature as they have the ability to learn mid and high level image representations. We proposed a Computer-Assisted Decision Support System in Pulmonary Cancer by using the novel deep learning based model and metastasis information obtained from MBAN (Medical Body Area Network). The proposed model, DFCNet, is based on the deep fully convolutional neural network (FCNN) which is used for classification of each detected pulmonary nodule into four lung cancer stages. The performance of proposed work is evaluated on different datasets with varying scan conditions. Comparison of proposed classifier is done with the existing CNN techniques. Overall accuracy of CNN and DFCNet was 77.6% and 84.58%, respectively. Experimental results illustrate the effectiveness of proposed method for the detection and classification of lung cancer nodules. These results demonstrate the potential for the proposed technique in helping the radiologists in improving nodule detection accuracy with efficiency.

Subcortical nuclei in Alzheimer's disease: a volumetric and diffusion kurtosis imaging study.

Background Previous studies revealed that subcortical nuclei were harmed in the process of Alzheimer's disease (AD). Purpose To investigate the volumetric and diffusion kurtosis imaging (DKI) parameter changes of subcortical nuclei in AD and their relationship with cognitive function. Materials and Methods A total of 17 mild AD patients, 15 moderate to severe AD patients, and 16 controls underwent neuropsychological tests and magnetic resonance imaging (MRI) scans. Volume, mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA) were measured in hippocampus, thalamus, caudate, putamen, pallidum, and amygdala. MRI parameters were compared. Correlation analysis was performed between subcortical nuclei volume, DKI parameters, and MMSE score. Results Significant volume reduction was seen in the left hippocampus in mild AD, and the bilateral hippocampus, thalamus, putamen, left caudate, and right amygdala in moderate to severe AD ( P < 0.05). Increased MD values were observed in the left hippocampus, left amygdala, and right caudate in mild AD, and the bilateral hippocampus and right amygdala in moderate to severe AD ( P < 0.05). Decreased MK values were observed only in the bilateral hippocampus in moderate to severe AD ( P < 0.05). No group significances were found in FA value. MMSE score was positively correlated with the volume of the bilateral hippocampus, thalamus, and putamen, and MK value of the left hippocampus ( P < 0.05). A negative correlation was found with the MD value of the bilateral hippocampus and left amygdala ( P < 0.05). Conclusion Mild AD mainly has microscopic subcortical changes revealed by increased MD value, and moderate to severe AD mainly has macroscopic subcortical changes revealed by volume reduction. MK is more sensitive in severe AD than mild AD.

Assessment of condylar morphology and position using MSCT in an Asian population.

OBJECTIVES: The purpose of the present study was to investigate the volume, surface, morphometric index (MI), and position of the condyle in a normal population by applying Mimics 17.0 software. Then, the difference between left and right sides, sex, and age can be explored, which will contribute to establish the reference value of condylar morphology and position in normal individuals, and help us to study characteristics of condylar morphology and position in abnormal individuals. MATERIALS AND METHODS: Three-hundred subjects were enrolled in our study from the radiology department of Shanghai Jiao Tong University Affiliated Sixth People's Hospital. They were divided into three groups according to the age: group 1 (18-24 years old), group 2 (25-34 years old), and group 3 (35-44 years old). Each group included 100 subjects (with 50 males and 50 females). They were examined using multislice computed tomography (MSCT) after that. All images of condyle were reconstructed by Mimics 17.0 software, so as to measure the volume, surface, and MI of condyle, and to analyze the position of condyle in the articular fossa by means of joint spaces. RESULTS: The differences of condylar volume, surface, and MI between left and right sides were not obvious (P > 0.05). The condylar volume and surface were greater in males than females (P < 0.05), while their condylar MI existed no difference (P > 0.05). No statistical differences were found in volume and surface among three age groups. However, the MI of group 1 was statistically lower than that of group 3 (P < 0.05). On the other hand, no significant differences were found between left and right condylar position (P > 0.05). Nevertheless, there were significant differences of condylar position regarding the gender and age (P < 0.05). CONCLUSIONS: This study showed no significant differences in condylar morphology and position between left and right sides, but factors of gender and age were proven to have a certain influence on the morphology and position of the condyle. This information can be clinically useful in establishing the diagnostic criteria for condylar morphology and position in the normal Asian population. CLINICAL RELEVANCE: Examination of condylar morphology and position is important for evaluating the abnormalities and bony changes that affect the temporomandibular joint (TMJ). So, this will be conducive to the diagnosis and the evaluation of therapeutic effect of temporomandibular joint diseases. Also, it is important to evaluate these indexes prior to commencing orthodontic treatment, because TMJ abnormalities play a critical role in orthodontic treatment planning.

Incidental extracerebral findings on brain nonenhanced magnetic resonance imaging: frequency, nondetection rate, and clinical importance.

PURPOSE: This study aims to elucidate the frequency, nondetection rate, and clinical importance of incidental extracerebral findings (IECFs) on brain nonenhanced magnetic resonance imaging (MRI). METHODS: A total of 8284 brain MRIs performed between January 1, 2015 and December 31, 2015 were evaluated for the presence of IECFs and the distribution of IECFs was analyzed. IECFs were categorized as E1 (clinically unimportant, e.g., sinus mucosal thickening); E2 (likely unimportant, e.g., pharyngeal mucosal symmetrical thickening); and E3 (potentially important, e.g., pharyngeal mucosal asymmetrical thickening). The nondetection rate was determined by comparing the results of the structured approach with the initial MRI reports. The medical records were examined for patients with E3 IECFs to assess clinical importance and outcome of these lesions. RESULTS: A total of 5992 IECFs were found in 4469 of the 8284 patients (54.0%). E1 findings constituted 82.2% (4924/5992) of all IECFs; E2 constituted 16.6% (995/5992) and E3 constituted 1.2% (73/5992). Overall IECFs and E1 findings were significantly more common in male patients (P < 0.05). Statistically significant difference was also seen between the different age groups (P < 0.001). The nondetection rate was 56.9% (3409/5992) for overall IECFs and 32.9% (24/73) for E3 IECFs. Of the 73 patients with E3 IECFs, 34 (46.6%) received final diagnosis and appropriate treatment during the study period. CONCLUSIONS: IECFs are prevalent in clinical patients on brain MR images with a nondetection rate of 32.9% for potentially important (E3) findings. The reporting of IECFs according to clinical importance is helpful for patients' management.

miR-22 suppresses tumorigenesis and improves radiosensitivity of breast cancer cells by targeting Sirt1.

BACKGROUND: miR-22 has been shown to be frequently downregulated and act as a tumor suppressor in multiple cancers including breast cancers. However, the role of miR-22 in regulating the radioresistance of breast cancer cells, as well as its underlying mechanism is still not well understood. METHODS: The expressions of miR-22 and sirt1 at mRNA and protein levels were examined by qRT-PCR and Western Blot. The effects of miR-22 overexpression and sirt1 knockdown on cell viability, apoptosis, radiosensitivity, γ-H2AX foci formation were evaluated by CCK-8 assay, flow cytometry, colony formation assay, and γ-H2AX foci formation assay, respectively. Luciferase reporter assay and qRT-PCR analysis were performed to confirm the interaction between miR-22 and sirt1. RESULTS: miR-22 was downregulated and sirt1 was upregulated at both mRNA and protein levels in breast cancer cells. miR-22 overexpression or sirt1 knockdown significantly suppressed viability, induced apoptosis, reduced survival fraction, and increased the number of γ-H2AX foci in breast cancer cells. Sirt1 was identified as a target of miR-22 and miR-22 negatively regulated sirt1 expression. Ectopic expression of sirt1 dramatically reversed the inhibitory effect of miR-22 on cell viability and promotive effect on apoptotic rates and radiosensitivity in breast cancer cells. CONCLUSIONS: miR-22 suppresses tumorigenesis and improves radiosensitivity of breast cancer cells by targeting sirt1, providing a promising therapeutic target for breast cancer.

Value of contrast-enhanced MRI in the differentiation between nasopharyngeal lymphoid hyperplasia and T1 stage nasopharyngeal carcinoma.

OBJECTIVES: To investigate the value of contrast-enhanced MRI in differentiation between benign nasopharyngeal lymphoid hyperplasia (NPLH) and T1 stage nasopharyngeal carcinoma (NPC). METHODS: Eighty-six NPLH patients and 38 T1 stage NPC patients who underwent a contrast-enhanced MRI and an endoscopic biopsy were included. The patients' symptoms and MRI features of nasopharyngeal mucosal thickening like location, symmetry, nasopharyngeal bubble, superficial mucus, nasopharyngeal retention cysts, serrated protrusions, contrast-enhancement type were documented. Accompanying signs such as sinus mucosal thickening, middle ear effusion, and cervical lymph nodes enlargement were also recorded. These MRI features were compared by Chi-square tests. Logistic regression analysis was done to identify the most predictive MRI features of malignancy. RESULTS: NPLH patients had more symptoms of pharynx discomfort or pain, while T1 stage NPC patients had more symptom of neck mass (P < 0.05). The most common pattern of nasopharyngeal mucosal thickening in NPLH was diffuse wall thickening (38.37%), while unilateral posterolateral wall thickening (39.47%) was more in T1 stage NPC. Nasopharyngeal bubble, retention cysts, serrated protrusions, symmetry, homogeneous enhancement, slight enhancement and vertical stripes were more common in NPLH, while nasopharyngeal wall asymmetry, inhomogeneous enhancement, moderated or marked enhancement and cervical lymph nodes enlargement were more in T1 stage NPC (P < 0.05). Logistic regression analysis identified unilateral posterolateral wall thickening, nasopharyngeal wall asymmetry, inhomogeneous enhancement and cervical lymph nodes enlargement were the most predictive MRI features for malignancy. CONCLUSIONS: Careful analysis of contrast-enhanced MRI features of the nasopharynx and neck is helpful in differentiating benign NPLH from T1 stage NPC.

Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the extreme value distribution.

INTRODUCTION: This study aims to develop an automatic segmentation framework on the basis of extreme value distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images. METHODS: Two EVD-based segmentation methods, namely the Gumbel and Fréchet segmentation, were developed to detect WMHs on FLAIR (slice thickness = 5 mm; TR/TE/TI = 11,000/120/2,800 ms; flip angle = 90°) images. Another automatic segmentation method using a trimmed likelihood estimator (TLE) was implemented for comparison with our proposed segmentation framework. The performances of the three automatic segmentation methods were evaluated by comparing with the manual segmentation method. RESULTS: The Dice similarity coefficients (DSCs) of the two EVD-based segmentation methods were larger than those of the TLE-based segmentation method (Gumbel, 0.823 ± 0.063; Fréchet, 0.843 ± 0.057; TLE, 0.817 ± 0.068), demonstrating that the EVD-based segmentation outperformed the TLE-based segmentation. The Fréchet segmentation obtained larger DSCs on patients with moderate to severe lesion loads and a comparable performance on patients with mild lesion loads, indicating that the Fréchet segmentation was superior to the Gumbel segmentation. The Gumbel segmentation underestimated the lesion volumes of all patients, whereas the Fréchet and TLE-based segmentation methods obtained overestimated lesion volumes (Manual, 13.71 ± 14.02 cc; Gumbel, 12.73 ± 13.21 cc; Fréchet, 13.88 ± 13.96 cc; TLE, 13.54 ± 12.27 cc). Moreover, the EVD-based segmentation was demonstrated to be comparable to other state-of-the-art methods on a publicly available dataset. CONCLUSION: The proposed EVD-based segmentation framework is a promising, effective, and convenient tool for volumetric quantification and further study of WMHs in aging and dementia.

Timing of recanalization and outcome in ischemic-stroke patients treated with recombinant tissue plasminogen activator.

BACKGROUND: Intravenous administration of recombinant tissue plasminogen activator (rtPA) is approved treatment for acute ischemic stroke <3 h of symptom onset. PURPOSE: To determine the impact of the timing and degree of recanalization on clinical outcome after rtPA infusion in patients. MATERIAL AND METHODS: Seventy-five patients with ischemic stroke in the middle cerebral artery territory treated with intravenous rtPA within 3 h were studied consecutively. Magnetic resonance imaging (MRI), including magnetic resonance angiography (MRA), before, 6 h, and 24 h after thrombolytic therapy was undertaken. Depending on the MRA results acquired 6 h after rtPA infusion, recanalization was graded as: early recanalization (ER), delayed recanalization (DR), and no recanalization (NR). Clinical outcome was assessed using the National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS). RESULTS: Of patients in the ER, DR and NR groups, 71.4% (15/21), 13.3% (2/15), and 30.7% (12/39), respectively, showed dramatic improvement in NIHSS score 7 days after rtPA administration compared with those scores upon hospital admission. The 6-h and 24-h NIHSS scores and 3-month mRS scores of ER patients were significantly lower than those of the other two groups (P < 0.05). The 24-h, 7-d NHISS and mRS scores of DR patients were significantly higher than NR patients (P = 0.001, 0.002, 0.049, respectively). Three patients in the DR group died during follow-up. CONCLUSION: These data suggest that DR is associated with clinical deterioration. Patients treated with rtPA thrombolysis should be under close observation for 6-24 h. Corresponding treatment should be considered once DR appears.

Quantitative evaluation of hyperbaric oxygen efficacy in experimental traumatic brain injury: an MRI study.

To use DCE-magnetic resonance imaging (MRI) and diffusion-weighted imaging to evaluate the hyperbaric oxygen efficacy (HBO) in experimental traumatic brain injury (TBI). Forty-two rabbits were randomly divided into four groups: TBI, TBI + HBO, sham group, sham + HBO. The TBI + HBO and sham + HBO received a total of 10 HBO treatments within 7 days following TBI, and MRI was performed within a month after TBI. Functional assessments were performed pre-TBI, and at 1 and 30 days. In focal lesion area, K(trans) in TBI + HBO group was lower than TBI group at both acute and subacute phase (p < 0.05). ADC was higher in TBI + HBO group than TBI group at acute phase (p < 0.01), but lower at subacute phase (p < 0.05). In perifocal area, K(trans) were lower in TBI + HBO group than TBI group at acute phase (p < 0.01) after TBI. ADC was lower in the TBI + HBO group than in the TBI group at both acute and subacute phase (p < 0.01).The VCS was higher in TBI + HBO group than TBI group at 30 days (p < 0.05). HBO could improve the impaired BBB and cytotoxic edema after TBI and promote the recovery of neurofunction.

Microbleeds on susceptibility-weighted MRI in depressive and non-depressive patients after mild traumatic brain injury.

The aim of this study was to explore the relationship between abnormality on susceptibility-weighted imaging (SWI) and newly-developed depression after mild traumatic brain injury. The study registered 200 patients with closed TBI and normal finding at CT and conventional MRI. All patients underwent MRI including conventional MR sequences and SWI. The number and volume of microbleed lesions were semi-automatically outlined and manually counted. All patients were followed up with the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-IV) within 1 year after TBI. The difference in microbleed lesions on SWI was compared between the depressive and non-depressive groups. The depressive group had a higher rate of abnormality on SWI than did the non-depressive group (p < 0.001). Among patients that had exhibited microbleed lesions, the number and volume of lesions were greater in the depressive group than the non-depressive group (both p < 0.001). These differences in numbers and volume of lesions were found only at the frontal, parietal and temporal lobes (all p < 0.001). Among patients that had exhibited microbleed lesions, the number and volume of lesions in other areas were not significantly different between the depressive and non-depressive groups (all p > 0.05). In conclusion, SWI was useful to identify the microbleed lesions after mild TBI. The distribution range and location of microbleed lesions were correlated with depression after TBI.

Prevalence of unruptured cerebral aneurysms in Chinese adults aged 35 to 75 years: a cross-sectional study.

BACKGROUND: The reported prevalence of unruptured cerebral aneurysms (UCAs) varies widely. OBJECTIVE: To measure the prevalence of UCAs by using 3-dimensional time-of-flight magnetic resonance angiography in adults aged 35 to 75 years. DESIGN: Cross-sectional study done between June 2007 and June 2011. SETTING: Two communities chosen at random from 2 districts (1 urban and 1 suburban) in Shanghai, China. PARTICIPANTS: 4813 adults aged 35 to 75 years. MEASUREMENTS: Three-dimensional time-of-flight magnetic resonance angiography, interpreted by 3 observers blinded to the participants' information, was used to identify the location and size of UCAs and to estimate the overall, age-specific, and sex-specific prevalence. RESULTS: 369 UCAs were found in 336 participants (130 men and 206 women); 4477 participants had no evidence of UCAs. The prevalence was 7.0% overall (95% CI, 6.3% to 7.7%), with 5.5% for men (CI, 4.6% to 6.4%) and 8.4% for women (CI, 7.3% to 9.5%). The overall prevalence of UCAs was higher in women than in men (P < 0.001) and peaked at ages 55 to 64 years in men and women. The UCAs were mostly located in the internal carotid artery (81%), and 90.2% had a maximum diameter less than 5 mm. Mean diameter was larger in women than in men (3.7 mm vs. 3.2 mm; P < 0.009). LIMITATION: Participants were from 2 communities selected from 2 districts in Shanghai, and adults older than 75 years were not studied. CONCLUSION: The overall prevalence of UCAs was 7.0% in Chinese adults aged 35 to 75 years, and most lesions had a diameter less than 5 mm. PRIMARY FUNDING SOURCE: National Natural Science Foundation of China.

Emerging MRI and metabolic neuroimaging techniques in mild traumatic brain injury.

Traumatic brain injury (TBI) is one of the leading causes of death worldwide, and mild traumatic brain injury (mTBI) is the most common traumatic injury. It is difficult to detect mTBI using a routine neuroimaging. Advanced techniques with greater sensitivity and specificity for the diagnosis and treatment of mTBI are required. The aim of this review is to offer an overview of various emerging neuroimaging methodologies that can solve the clinical health problems associated with mTBI. Important findings and improvements in neuroimaging that hold value for better detection, characterization and monitoring of objective brain injuries in patients with mTBI are presented. Conventional computed tomography (CT) and magnetic resonance imaging (MRI) are not very efficient for visualizing mTBI. Moreover, techniques such as diffusion tensor imaging, magnetization transfer imaging, susceptibility-weighted imaging, functional MRI, single photon emission computed tomography, positron emission tomography and magnetic resonance spectroscopy imaging were found to be useful for mTBI imaging.