Utility of zero echo time (ZTE) sequence for assessing bony lesions of skull base and calvarium

BACKGROUND

The emergence of zero echo time (ZTE) imaging has transformed bone imaging, overcoming historical limitations in capturing detailed bone structures. By minimizing the time gap between radiofrequency excitation and data acquisition, ZTE generates CT-like images. While ZTE has shown promise in various applications, its potential in assessing skull base and calvarium lesions remains unexplored. Hence we aim to introduce a novel perspective by investigating the utility of inverted ZTE images (iZTE) and pseudoCT (pCT) images for studying lesions in the skull base and calvarium.

MATERIALS AND METHODS

A total of 35 eligible patients, with an average age of 42 years and a male/female ratio of 1:4, underwent ZTE MRI and images are processed to generate iZTE and pCT images were generated through a series of steps including intensity equalization, thresholding, and deep learning-based pCT generation. These images were then compared to CT scans using a rating scale; inter-rater kappa coefficient evaluated observer consensus while statistical metrics like sensitivity and specificity assessed their performance in capturing bone-related characteristics.

RESULTS

The study revealed excellent interobserver agreement for lesion assessment using both pCT and iZTE imaging modalities, with kappa coefficient of 0.91 (P < 0.0001) and 0.92 respectively (P < 0.0001). Also, pCT and iZTE accurately predicted various lesion characteristics with sensitivity ranging from 84.3% to 95.1% and 82.6%-94.2% (95% CI) with a diagnostic accuracy of 95.56% and 94.44% respectively. Although both of them encountered challenges with ground glassing, hyperostosis, and intralesional bony fragments, they showed good performance in other bony lesion assessments.

CONCLUSIONS

The pilot study suggests strong potential for integrating the ZTE imaging into standard care for skull base and calvarial bony lesions assessment. Additionally, larger-scale studies are needed for comprehensive assessment of its efficacy.

Accurate low and high grade glioma classification using free water eliminated diffusion tensor metrics and ensemble machine learning

Glioma, a predominant type of brain tumor, can be fatal. This necessitates an early diagnosis and effective treatment strategies. Current diagnosis is based on biopsy, prompting the need for non invasive neuroimaging alternatives. Diffusion tensor imaging (DTI) is a promising method for studying the pathophysiological impact of tumors on white matter (WM) tissue. Single-shell DTI studies in brain glioma patients have not accounted for free water (FW) contamination due to tumors. This study aimed to (a) assess the efficacy of a two-compartment DTI model that accounts for FW contamination and (b) identify DTI-based biomarkers to classify low-grade glioma (LGG) and high-grade glioma (HGG) patients. DTI data from 86 patients (LGG n = 39, HGG n = 47) were obtained using a routine clinical imaging protocol. DTI metrics of tumorous regions and normal-appearing white matter (NAWM) were evaluated. Advanced stacked-based ensemble learning was employed to classify LGG and HGG patients using both single- and two-compartment DTI model measures. The DTI metrics of the two-compartment model outperformed those of the standard single-compartment DTI model in terms of sensitivity, specificity, and area under the curve of receiver operating characteristic (AUC-ROC) score in classifying LGG and HGG patients. Four features (out of 16 features), namely fractional anisotropy (FA) of the edema and core region and FA and mean diffusivity of the NAWM region, showed superior performance (sensitivity = 92%, specificity = 90%, and AUC-ROC = 90%) in classifying LGG and HGG. This demonstrates that both tumorous and NAWM regions may be differentially affected in LGG and HGG patients. Our results demonstrate the significance of using a two-compartment DTI model that accounts for FW contamination by improving diagnostic accuracy. This improvement may eventually aid in planning treatment strategies for glioma patients.

Evaluating the diagnostic performance of non-contrast magnetic resonance angiography sequences in the pre-procedural comprehensive analysis of direct carotid cavernous fistula

PURPOSE

Endovascular treatment of direct carotid cavernous fistula (DCCF) requires invasive diagnostic cerebral angiography for diagnosis and planning; however, a less invasive modality like magnetic resonance angiography (MRA) can be useful, especially in high-risk cases. This single-centre study evaluated a newer MR angiography (MRA) sequence, silent MRA and the traditional time of flight (TOF) MRA for pre-procedural treatment planning of DCCF.

METHODS

All consecutive DCCF patients who underwent TOF, silent MRA and diagnostic cerebral angiography were included in the study. Angiographic features like rent size, location, draining veins and collateral communicating arteries were analysed and compared between the two MRA sequences, with digital subtraction angiography (DSA) as the gold standard.

RESULTS

Fifteen patients were included in the study. TOF MRA exhibited better sensitivity (76.9% vs 69.2%) in identifying the rent location, correctly pinpointing the location in 93.3% compared to 73.3% with silent MRA. Both MRA sequences showed good agreement with DSA for primary sac and rent size. TOF MRA correctly identified 86.2% of 210 total venous structures compared to 96% by silent MRA. Silent MRA demonstrated higher sensitivity (90% vs 76%) and accuracy (87.69 vs 94.36) in visualisation of involved veins compared to TOF MRA.

CONCLUSION

Arterial characteristics of DCCF like rent location and rent size were better assessed by TOF MRA. Although both MRA identified venous features, silent MRA correlated better with DSA irrespective of the size and proximity to the site of the fistula. Combining both sequences can evaluate various angioarchitectural features of DCCF useful for therapeutic planning.

Comparative study of non-contrast silent and time-of-flight magnetic resonance angiographic sequences in the evaluation of intracranial dural arteriovenous fistula

AIM

To compare the performance of two non-contrast magnetic resonance angiography (MRA) sequences, silent MRA and time of flight (TOF) MRA, in the evaluation of intracranial dural arteriovenous fistula (DAVF).

MATERIALS AND METHODS

Forty consecutive patients with DAVF were enrolled and evaluated prospectively using silent MRA, TOF MRA, and digital subtraction angiography (DSA). The location, Cognard classification, arterial feeders, and venous drainage were evaluated. The therapeutic strategy and possible route were predicted on both silent and TOF MRA and these were compared with DSA during subsequent endovascular treatment.

RESULTS

Sensitivity and accuracy of silent and TOF MRA for localisation (96.4% versus 96% and 96% versus 95%, respectively) and classification (96% versus 94% and 96% versus 93.5%, respectively) were high. Silent MRA showed higher sensitivity than TOF MRA for arterial feeders and draining veins (87% versus 79% and 81.6% versus 67%). This improved to a sensitivity of 96.4% and 89% when prominent feeders were considered. The sensitivity and accuracy were 92.6% and 85.8% for immediate draining veins. Both silent and TOF MRA were accurate for therapeutic planning (96% versus 85%), although silent MRA was more accurate.

CONCLUSION

Silent MRA can more reliably evaluate the various angioarchtectural components of DAVF compared to TOF MRA.

Experimenting with ASL-based arterialized cerebral blood volume as a novel imaging biomarker in grading glial neoplasms

BACKGROUND

Perfusion imaging is one of the methods used to grade glial neoplasms, and in this study we evaluated the role of ASL perfusion in grading brain glioma.

PURPOSE

The aim is to evaluate the role of arterialized cerebral blood volume (aCBV) of multi-delay ASL perfusion for grading glial neoplasm.

MATERIALS AND METHODS

This study is a prospective observational study of 56 patients with glial neoplasms of the brain who underwent surgery, and only cases with positive diagnosis of glioma are included to evaluate the novel diagnostic parameter.

RESULTS

In the study, ASL-derived normalized aCBV (naCBV) and T2*DSC-derived normalized CBV (nCBV) are showing very high correlation (Pearson's correlation coefficient value of 0.94) in grading glial neoplasms. naCBV and nCBF are also showing very high correlation (Pearson's correlation coefficient value of 0.876). The study also provides cutoff values for differentiating LGG from HGG for normalized aCBV(naCBV) of ASL, normalized CBV (nCBV), and normalized nCBF derived from T2* DCS as 1.12, 1.254, and 1.31, respectively. ASL-derived aCBV also shows better diagnostic accuracy than ASL-derived CBF.

CONCLUSION

This study is one of its kind to the best of our knowledge where multi-delay ASL perfusion-derived aCBV is used as a novel imaging biomarker for grading glial neoplasms, and it has shown high statistical correlation with T2* DSC-derived perfusion parameters.

Fractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning

Purpose: The main purpose of this study was to comprehensively investigate the potential of fractal dimension (FD) measures in discriminating brain gliomas into low-grade glioma (LGG) and high-grade glioma (HGG) by examining tumor constituents and non-tumorous gray matter (GM) and white matter (WM) regions. Methods: Retrospective magnetic resonance imaging (MRI) data of 42 glioma patients (LGG, n = 27 and HGG, n = 15) were used in this study. Using MRI, we calculated different FD measures based on the general structure, boundary, and skeleton aspects of the tumorous and non-tumorous brain GM and WM regions. Texture features, namely, angular second moment, contrast, inverse difference moment, correlation, and entropy, were also measured in the tumorous and non-tumorous regions. The efficacy of FD features was assessed by comparing them with texture features. Statistical inference and machine learning approaches were used on the aforementioned measures to distinguish LGG and HGG patients. Results: FD measures from tumorous and non-tumorous regions were able to distinguish LGG and HGG patients. Among the 15 different FD measures, the general structure FD values of enhanced tumor regions yielded high accuracy (93%), sensitivity (97%), specificity (98%), and area under the receiver operating characteristic curve (AUC) score (98%). Non-tumorous GM skeleton FD values also yielded good accuracy (83.3%), sensitivity (100%), specificity (60%), and AUC score (80%) in classifying the tumor grades. These measures were also found to be significantly (p < 0.05) different between LGG and HGG patients. On the other hand, among the 25 texture features, enhanced tumor region features, namely, contrast, correlation, and entropy, revealed significant differences between LGG and HGG. In machine learning, the enhanced tumor region texture features yielded high accuracy, sensitivity, specificity, and AUC score. Conclusion: A comparison between texture and FD features revealed that FD analysis on different aspects of the tumorous and non-tumorous components not only distinguished LGG and HGG patients with high statistical significance and classification accuracy but also provided better insights into glioma grade classification. Therefore, FD features can serve as potential neuroimaging biomarkers for glioma.

Baseline Predictors of Longitudinal Cognitive Outcomes in Persons with Mild Cognitive Impairment

INTRODUCTION

The study aimed to explore longitudinal cognitive outcomes and to ascertain predictors of conversion to dementia in a hospital-based mild cognitive impairment (MCI) cohort classified according to the neuropsychological phenotype at baseline.

MATERIALS AND METHODS

Subjects aged &gt;55 years who had a clinical diagnosis of MCI at initial visit between 2010 and 2018, with at least one formal neuropsychological assessment at baseline and follow-up of a minimum of 2 years were included. The prospective study was completed based on evaluation at last follow-up to gauge conversion to dementia, quantification of performance on activities of daily living and when available, longitudinal neuropsychological test scores.

RESULTS

Ninety-five patients with MCI met the inclusion criteria with a mean age of 68.4 ± 6.4 years at baseline and a mean duration of follow-up for 6.4 ± 3.2 years. The cumulative conversion rate to dementia was 22.2% (21/95) and the annualized conversion rate was 3.3% per year of follow-up. The majority of subjects who had converted had multidomain MCI (66%). Only white matter changes on MRI brain revealed correlation with baseline neuropsychology tests. The multivariate logistic regression analysis revealed the utility of lower baseline list recognition (adjusted odds ratio: 0.735 [95% confidence interval: 0.589-0.916]; p 0.006), lower immediate logical memory (0.885 [0.790-0.990]; p 0.03), and high perseverative error scores on set shifting (3.116 [1.425-6.817]; p 0.004) as predictors of conversion. A model score of +2.615 could predict conversion with sensitivity of 72% and specificity of 98% over 6.4 years follow-up.

CONCLUSION

There was a higher risk of conversion associated with multidomain MCI. Logistic regression-based estimations of dementia risk utilizing domain-based neuropsychology test scores in MCI have high specificity for diagnosis at baseline.

Non-invasive angiographic analysis of dural carotid cavernous fistula using time-of-flight MR angiography and silent MR angiography: a comparative study

BACKGROUND

A non-invasive, reliable imaging modality that characterizes cavernous sinus dural arteriovenous fistula (CSDAVF) is beneficial for diagnosis and to assess resolution on follow-up.

PURPOSE

To assess the utility of 3D time-of-flight (TOF) and silent magnetic resonance angiography (MRA) for evaluation of CSDAVF from an endovascular perspective.

MATERIAL AND METHODS

This prospective study included 37 patients with CSDAVF, who were subjected to digital subtraction angiography (DSA) and 3-T MR imaging with 3D TOF and silent MRA. The main arterial feeders, fistula site, and venous drainage pattern were evaluated, and the results were compared with DSA findings. The diagnostic confidence scores were also recorded using a 4-point Likert scale.

RESULTS

Silent MRA correlated better for shunt site localization and angiographic classification (86% vs. 75% and 83% vs. 75%, respectively) compared to TOF MRA. The proportion of arterial feeders detected was marginally significant for silent MRA over TOF MRA sequences (92.8% vs. 89.5%; P=0.048), though for veins both were comparable. Sensitivity of silent MRA was higher for identification of cortical venous reflux (CVR) (90.9% vs. 81.8%) and deep venous drainage (82.4% vs. 64.7%), while specificity was >90% for both modalities. The overall diagnostic confidence score fared better for silent MRA for venous assessment (P < 0.001) as well as fistula point identification (P < 0.001), while no significant difference was evident with TOF MRA for arterial feeders (P=0.06).

CONCLUSION

Various angiographic components of CSDAVF could be identified and delineated by 3D TOF and silent MRA, though silent MRA was superior for overall diagnostic assessment.

Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology

Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.

An optimal brain tumor segmentation algorithm for clinical MRI dataset with low resolution and non-contiguous slices

Background

Segmenting brain tumor and its constituent regions from magnetic resonance images (MRI) is important for planning diagnosis and treatment. In clinical routine often an experienced radiologist delineates the tumor regions using multimodal MRI. But this manual segmentation is prone to poor reproducibility and is time consuming. Also, routine clinical scans are usually of low resolution. To overcome these limitations an automated and precise segmentation algorithm based on computer vision is needed.

Methods

We investigated the performance of three widely used segmentation methods namely region growing, fuzzy C means and deep neural networks (deepmedic). We evaluated these algorithms on the BRATS 2018 dataset by choosing randomly 48 patients data (high grade, n = 24 and low grade, n = 24) and on our routine clinical MRI brain tumor dataset (high grade, n = 15 and low grade, n = 28). We measured their performance using dice similarity coefficient, Hausdorff distance and volume measures.

Results

Region growing method performed very poorly when compared to fuzzy C means (fcm) and deepmedic network. Dice similarity coefficient scores for FCM and deepmedic algorithms were close to each other for BRATS and clinical dataset. The accuracy was below 70% for both these methods in general.

Conclusion

Even though the deepmedic network showed very high accuracy in BRATS challenge for brain tumor segmentation, it has to be custom trained for the low resolution routine clinical scans. It also requires large training data to be used as a stand-alone algorithm for clinical applications. Nevertheless deepmedic may be a better algorithm for brain tumor segmentation when compared to region growing or FCM.

Utility of silent magnetic resonance angiography in the evaluation and characterisation of intracranial dural arteriovenous fistula

AIM

To evaluate the utility of silent magnetic resonance angiography (MRA) in the diagnosis, characterisation, and therapeutic planning of intracranial dural arteriovenous fistula (DAVF).

MATERIALS AND METHODS

Twenty consecutive patients with DAVF were enrolled prospectively and were evaluated using silent MRA and digital subtraction angiography (DSA) as a part of routine work-up. The diagnosis and location of fistula, Borden and Cognard classification, entire arterial feeders, and venous drainage were analysed. A therapeutic strategy was formulated, and the accessible route and vessel were predicted, which was confirmed on endovascular treatment.

RESULTS

Silent MRA was 100% sensitive and accurate for location and classification of fistulas. Silent MRA showed a sensitivity of 82% and 76.5% for entire arterial feeders and draining veins, which improved to a sensitivity of 90% and 94% when prominent feeders and immediate venous drainage was considered. Among the missed veins, thrombosed sinus, slow sinus flow, small calibre, reduced image quality were the causes. The therapeutic decision matched with DSA in all cases and silent MRA accurately identified the potential accessible feeder in 94% cases.

CONCLUSION

Silent MRA is a promising MR technique that can provide both diagnostic and therapeutic information similar to that obtained from DSA.

A Study of the Developing Paediatric Skullbase Anatomy and its Application to Endoscopic Endonasal Approaches in Children

Objective

The sinonasal anatomy in growing children undergoes change in size along with progressive pneumatization, this is of particular significance in endoscopic endonasal surgery. We aim to measure and quantify the sinonasal dimensions in the Indian paediatric population, which are relevant to skull base surgeons.

Material and Methods

This is a retrospective radio anatomical study of sinonasal measurements and volumetric analysis of the sphenoid sinus performed on archived CT images of children less than 18 years of age.

Results

CT scan images of 110 patients (male, 68; female, 42) were included for the study. The number of patients in each age groups was as follows [0-6 years, 17; 7-9 years, 20; 10-12 years, 27; 13-15 years, 29; 16-18 years, 17]. The distance from the nares to the sphenoid and from the sphenoid to the sella was significantly greater in children of 13-15 years (69.4 ± 5.2 mm) as compared to children less than six years (62.6 ± 6.7 mm) (P < 0.003). The volume of the sphenoid in children between 0 and 6 years was 4641.4 ± 1924.7 mm³. The pneumatized sphenoid volume in the same age group was 1655 ± 1631.1 mm³. In older children between 13 and 15 years, the total volume of sphenoid sinus was 11732.8 ± 2614.4 mm³. The volume of pneumatization in the sphenoid sinus in this group was 6287.5 ± 2157.9 mm³. The total volume of the sphenoid sinus (Pearson coefficient (r) = 0.704, P < 0.001) and the volume of pneumatization of the sphenoid sinus was also seen to have a positive correlation to the age of the child (r = 0.62, P < 0.0001). The narrowest distance between both the internal carotid arteries was seen at the level of the proximal dural ring. In children less than six years of age it was 13.4 ± 2.0 mm, a significant change was seen by the age of 10-12 years where this distance was 15.6 ± 2.2 mm (P = 0.036).

Conclusion

The sinonasal anatomy shows progressive development between the age of 6 to 15 years after which it plateaus. The pneumatization in young children may be incomplete, which necessitates drilling in the sphenoid sinus. The intercarotid distance was not seen to be a hindrance for endoscopic endonasal surgery. None of the measurements of the skull-base, made in this study appear to restrict endoscopic endonasal surgery in children. However, a meticulous preoperative assessment of the CT scan may be needed for optimal surgical outcome.

Persistent Falcine Sinus-The Variant Venous Structure With Anterior Interhemispheric Type And The Classic Posterior Location: Report of Two Cases

A 14-year-old girl presented with suspected dermoid cyst of scalp underwent Neuroimaging to look for intracranial communication. Her neurological examination was essentially normal. On Magnetic Resonance Imaging(MRI), an anterior persistent falcine sinus(PFS) was noted draining into the middle portion of the superior sagittal sinus. There was developmental venous anomaly (DVA) involving the right frontal lobe. To our knowledge, it is a rare instance of the combination of an anterior persistent falcine sinus and associated DVA.

Differential Distribution of Cerebral Microbleeds in Subtypes of Acute Ischemic Minor Stroke and TIA as well as its Association with Vascular Risk Factors

BACKGROUND

Cerebral microbleed (CMB) is a novel neuroimaging marker of cerebral small vessel disease.

OBJECTIVE

To determine the prevalence of CMB in the subtypes of acute ischemic minor stroke (AIS) and transient ischemic attack (TIA) and to identify the risk factors associated with location and number of CMB.

MATERIALS AND METHODS

Patients with AIS (National Institute of Health Stroke Scale of 5 or less) or TIA were included. CMB was characterized using the Microbleed Anatomical Rating Scale (MARS).

RESULTS

Of the 488 subjects [mean age (standard deviation): 57.5 years (14.4 years), males (77.7%)] recruited, CMB was noted in 140 (28.7%). About 35% with CMB had a lacunar stroke etiology, whereas LAA and CE subtype constituted 33.6 and 10.7%, respectively (P = 0.000). Lacunar subtype was more likely to harbor multiple CMB (four or more) and CMB in all locations (lobar, deep or infratentorial). On multivariate analysis, systemic hypertension [P = 0.025; odds ratio (OR) 0.33 (95% confidence interval (CI) 0.129-0.874)], serum triglyceride (TG) levels below 150 mg/dL [P = 0.001; OR 3.70 (95% CI 1.698-8.072)], and presence of white matter hyperintensities on magnetic resonance imaging brain [P = 0.026; OR 2.18 (95% CI 1.096-4.337)] were associated with the presence of CMB. Those with serum TG levels of less than 150 mg/dL were more likely to harbor lobar (P = 0.002) or infratentorial CMB (P = 0.022), whereas those with serum creatinine levels of more than1.5 mg/dL have lobar CMB (P = 0.033).

CONCLUSION

Our study showed a differential distribution of CMB in ischemic stroke subtypes and association of risk factors with the presence, number and location of CMB.

Vessel Wall Thickening and Enhancement in High-Resolution Intracranial Vessel Wall Imaging: A Predictor of Future Ischemic Events in Moyamoya Disease

BACKGROUND AND PURPOSE

Very few data are available with regard to high-resolution intracranial vessel wall imaging characteristics of Moyamoya disease and their relation to ischemic stroke risk. We investigated the high resolution imaging characteristics of MMD and its correlation with recent ischemic events.

MATERIALS AND METHODS

Patients with Moyamoya disease confirmed by DSA, including patients after revascularization, were enrolled. All the patients underwent high-resolution intracranial vessel wall imaging. Vessel wall thickening, enhancement, and the remodeling index of the bilateral distal ICA and proximal MCA were noted. The patients were followed up at 3 months and 6 months after high-resolution intracranial vessel wall imaging and the association of ischemic events with imaging characteristics was assessed.

RESULTS

Twenty-nine patients with Moyamoya disease were enrolled. The median age at symptom onset was 12 years (range, 1-51 years). A total of 166 steno-occlusive lesions were detected by high-resolution intracranial vessel wall imaging. Eleven lesions with concentric wall thickening (6.6%) were noted in 9 patients. Ten concentric contrast-enhancing lesions were observed in 8 patients, of which 3 patients (4 lesions) showed grade II enhancement. The presence of contrast enhancement (P = .01) and wall thickening (P ≤ .001) showed a statistically significant association with ischemic events within 3 months before and after the vessel wall imaging. Grade II enhancement showed a statistically significant (P = .02) association with ischemic events within 4 weeks of high-resolution intracranial vessel wall imaging. The mean ± standard deviation outer diameter of the distal ICA (right, -3.3 ± 0.68 mm; left, 3.4 ± 0.60 mm) and the remodeling index (right, 0.71 ± 0.13; left, 0.69 ± 0.13) were lower in Moyamoya disease.

CONCLUSIONS

High-resolution intracranial vessel wall imaging characteristics of concentric wall thickening and enhancement are relatively rare in our cohort of patients with Moyamoya disease. The presence of wall thickening and enhancement may predict future ischemic events in patients with Moyamoya disease.

Structural correlates of mild cognitive impairment: A clinicovolumetric study

Context:

Annually 10-12% of patients with mild cognitive impairment (MCI) are likely to progress to Alzheimer's Disease (AD). The morphometric profile in stable non-converters has not been adequately characterized.

Aims

To determine the structural differences between amnestic MCI and early AD using volumetric magnetic resonance imaging (MRI) and its correlation with neuropsychological test performances.

Settings and Design:

This was a hospital-based case-control study.

Materials and Methods:

Twenty-four patients classified as having "non-progressor" MCI, 13 as having an early AD, and 25 controls, and assessed using neuropsychological evaluation, and three-dimensional T1-weighted 1.5T magnetic resonance maging (MRI) were included in the study. We used both voxel-based morphometry and automated regional volumetry to assess the topographical patterns of volume loss.

Statistical Analysis Used

Post-hoc analysis of variance was done for comparison between means, and partial correlation analysis was done for correlating volumetric and cognitive measures.

Results:

Consistently, significant atrophy of the superior temporal gyrus, left hippocampus, and mesial frontoparietal regions were identified in patients with MCI in comparison to controls. Increased atrophy in the limbic regions, temporal neocortex, and precuneus was identified in patients with early AD in comparison to patients with MCI. While differences in retention and recall scores between the groups were independent of age and volumetric variables, significant correlations were observed between the learning and recall scores and the volume of hippocampus in patients with MCI as well as temporal neocortex in patients with AD. Atrophy of the superior temporal gyrus and mesial neocortical regions represents the structural correlate of amnestic MCI parallel to the development of hippocampal atrophy.

Conclusions:

Identification of the pattern of volumetric abnormalities in patients with amnestic MCI in addition to atrophy of the medial temporal lobes necessitates a close follow up to continuously assess these patients for their progression to early AD.

Comparative Analysis of Volumetric High-Resolution Heavily T2-Weighted MRI and Time-Resolved Contrast-Enhanced MRA in the Evaluation of Spinal Vascular Malformations

BACKGROUND AND PURPOSE

Volumetric high-resolution heavily T2-weighted imaging or time-resolved contrast-enhanced MRA is used in the detection and characterization of spinal vascular malformations, though inherent trade-offs can affect their overall sensitivity and accuracy. We compared the efficacy of volumetric high-resolution heavily T2-weighted and time-resolved contrast-enhanced images in spinal vascular malformation diagnosis and feeder characterization and assessed whether a combined evaluation improved the overall accuracy of diagnosis.

MATERIALS AND METHODS

Twenty-eight patients with spinal vascular malformations (spinal dural arteriovenous fistula, spinal cord arteriovenous malformation, and perimedullary arteriovenous fistula) were prospectively enrolled. MR images were separately analyzed by 2 neuroradiologists blinded to the final diagnosis.

RESULTS

Both sequences demonstrated 100% sensitivity and 93.5% accuracy for the detection of spinal vascular malformations. Volumetric high-resolution heavily T2-weighted imaging was superior to time-resolved contrast-enhanced MR imaging for identification of spinal cord arteriovenous malformations (100% versus 90% sensitivity and 96.7% versus 93.5% accuracy), however, for the diagnosis of perimedullary arteriovenous fistula, time resolved contrast enhanced MRI was found to perform better than the volumetric T2 sequence (80% versus 60% sensitivity and 96.7% versus 93.5% accuracy). Both sequences showed equal sensitivity (100%) and accuracy (87%) for spinal dural arteriovenous fistulas. Combined evaluation improved the overall accuracy across all types of spinal vascular malformation. Volumetric high-resolution heavily T2-weighted imaging was superior or equal to time-resolved contrast-enhanced MR imaging for feeder identification of spinal dural arteriovenous fistulas for both observers (90.9% and 72.7% versus 72.7%), which improved to 90.9% when the sequences were combined. Time-resolved contrast-enhanced MR imaging performed better for major and total feeder identification of spinal cord arteriovenous malformation (80% versus 60%) and perimedullary arteriovenous fistula (80% versus 60%-80%).

CONCLUSIONS

Combined volumetric high-resolution heavily T2-weighted imaging and time-resolved contrast-enhanced MR imaging can improve the sensitivity and accuracy of spinal vascular malformation diagnosis, classification, and feeder characterization.

Do quantified sleep architecture abnormalities underlie cognitive disturbances in amnestic mild cognitive impairment?

The study was designed to gauge association between occult sleep-related breathing disturbances and sleep architecture changes on cognitive trajectories in subjects with amnestic mild cognitive impairment (aMCI) relative to cognitively normal healthy controls, phenotyped by neuroimaging. Subjects with aMCI and normal cognition were prospectively recruited. Following standardized neuropsychological and sleep questionnaire assessment they underwent a single overnight polysomnography (PSG); multimodality MRI was used to ascertain age-corrected radiological differences between the 2 groups. The aMCI cohort was followed up longitudinally with serial cognitive assessments for the next 3 years. Thirty seven subjects with aMCI and 24 control subjects consented for evaluation. Although occult moderate to severe obstructive sleep apnea (OSA) was more prevalent in aMCI (43.6%) as opposed to controls (22.7%); higher median apnea-hypopnea index (AHI = 11.5) and total apnea-hypopnea time (26.6 min) were also noted in aMCI relative to controls (6.6 and 11.4 min respectively), the differences were not statistically significant. In the aMCI group, better sleep efficiency, longer duration of REM sleep correlated with higher associative learning, free-recall/recognition memory performance. Higher AHI had negative correlation with visual memory scores. However longitudinal cognitive trends in the aMCI group over 3 years reflected relative stability (only 5% progressed to AD) notwithstanding imaging differences from controls and appeared to be independent of sleep parameters. The study concluded that despite associations between sleep efficiency, REM sleep and sleep-related breathing variables with neuropsychological test-scores in aMCI, these appear to be comorbidities rather than causative factors for the degree of cognitive impairment or its longitudinal trajectory.

Resting fMRI as an alternative for task-based fMRI for language lateralization in temporal lobe epilepsy patients: a study using independent component analysis

PURPOSE

Our aim is to investigate whether rs-fMRI can be used as an effective technique to study language lateralization. We aim to find out the most appropriate language network among different networks identified using ICA.

METHODS

Fifteen healthy right-handed subjects, sixteen left, and sixteen right temporal lobe epilepsy patients prospectively underwent MR scanning in 3T MRI (GE Discovery™ MR750w), using optimized imaging protocol. We obtained task-fMRI data using a visual-verb generation paradigm. Rs-fMRI and language-fMRI analysis were conducted using FSL software. Independent component analysis (ICA) was used to estimate rs-fMRI networks. Dice coefficient was calculated to examine the similarity in activated voxels of a common language template and the rs-fMRI language networks. Laterality index (LI) was calculated from the task-based language activation and rs-fMRI language network, for a range of LI thresholds at different z scores.

RESULTS

Measurement of hemispheric language dominance with rs-fMRI was highly concordant with task-fMRI results. Among the evaluated z scores for a range of LI thresholds, rs-fMRI yielded a maximum accuracy of 95%, a sensitivity of 83%, and specificity of 92.8% for z = 2 at 0.05 LI threshold.

CONCLUSION

The present study suggests that rs-fMRI networks obtained using ICA technique can be used as an alternative for task-fMRI language laterality. The novel aspect of the work is suggestive of optimal thresholds while applying rs-fMRI, is an important endeavor given that many patients with epilepsy have co-morbid cognitive deficits. Thus, an accurate method to determine language laterality without requiring a patient to complete the language task would be advantageous.