Explore the vast range of titles available.

In Multiple Sclerosis (MS), detection of T2-hyperintense white matter (WM) lesions on magnetic resonance imaging (MRI) has become a crucial criterion for diagnosis and predicting prognosis in early disease. Automated lesion detection is not only desirable with regard to time and cost effectiveness but also constitutes a prerequisite to minimize user bias. Here, we developed and evaluated an algorithm for automated lesion detection requiring a three-dimensional (3D) gradient echo (GRE) T1-weighted and a FLAIR image at 3 Tesla (T). Our tool determines the three tissue classes of gray matter (GM) and WM as well as cerebrospinal fluid (CSF) from the T1-weighted image, and, then, the FLAIR intensity distribution of each tissue class in order to detect outliers, which are interpreted as lesion beliefs. Next, a conservative lesion belief is expanded toward a liberal lesion belief. To this end, neighboring voxels are analyzed and assigned to lesions under certain conditions. This is done iteratively until no further voxels are assigned to lesions. Herein, the likelihood of belonging to WM or GM is weighed against the likelihood of belonging to lesions. We evaluated our algorithm in 53 MS patients with different lesion volumes, in 10 patients with posterior fossa lesions, and 18 control subjects that were all scanned at the same 3T scanner (Achieva, Philips, Netherlands). We found good agreement with lesions determined by manual tracing (R2 values of over 0.93 independent of FLAIR slice thickness up to 6mm). These results require validation with data from other protocols based on a conventional FLAIR sequence and a 3D GRE T1-weighted sequence. Yet, we believe that our tool allows fast and reliable segmentation of FLAIR-hyperintense lesions, which might simplify the quantification of lesions in basic research and even clinical trials.

Introduction Central tegmental tract hyperintensity (CTTH) on T2-weighted imaging is an uncommon neuroimaging finding in pediatric patients with unclear clinical significance. CTTH may represent either a physiological or pathological process. This study evaluates the relationship between CTTH and MRI sequences (FLAIR, DWI) to explore its diagnostic value. Methods We retrospectively analyzed 3462 pediatric brain MRI scans conducted between July 2011 and January 2022, identifying 104 patients with bilateral CTTH. DWI, FLAIR sequences, and follow-up scans were visually assessed for T2/FLAIR mismatch and diffusion restriction. Clinical data were obtained from electronic patient records. Statistical analysis was performed using SPSS, with significance set at p  < .05. Results A total of 104 pediatric patients with CTTH were included, ranging from 1 month to 16 years old (mean age: 31.34 months). Epilepsy, metabolic diseases, and cerebral palsy were the most common clinical diagnoses. Diffusion restriction was observed in 40.8% of patients, while 39.6% had FLAIR hyperintensity. T2/FLAIR mismatch, defined for the first time in CTTH, was found in 60.4% of patients. A significant correlation was found between T2/FLAIR mismatch and clinical diagnoses ( p  = .020), as well as between diffusion restriction and T2/FLAIR mismatch ( p  = .017). Conclusion CTTH in pediatric patients may arise from two distinct processes: a transient, developmental phenomenon or a pathological process marked by irreversible myelin degeneration. T2/FLAIR mismatch and diffusion restriction provide valuable diagnostic markers, offering insights into the severity and chronicity of CTTH. Further studies are needed to validate these findings and their clinical implications.

One of the most common challenges in brain MRI scans is to perform different MRI sequences depending on the type and properties of tissues. In this paper, we propose a generative method to translate T2-Weighted (T2W) Magnetic Resonance Imaging (MRI) volume from T2-weight-Fluid-attenuated-Inversion-Recovery (FLAIR) and vice versa using Generative Adversarial Networks (GAN). To evaluate the proposed method, we propose a novel evaluation schema for generative and synthetic approaches based on radiomic features. For the evaluation purpose, we consider 510 pair-slices from 102 patients to train two different GAN-based architectures Cycle GAN and Dual Cycle-Consistent Adversarial network (DC2Anet). The results indicate that generative methods can produce similar results to the original sequence without significant change in the radiometric feature. Therefore, such a method can assist clinics to make decisions based on the generated image when different sequences are not available or there is not enough time to re-perform the MRI scans.

PurposeDue to the infiltrative nature of glioblastoma (GBM) outside of the contrast-enhancing region on MRI, there is interest in exploring supratotal resections (SpTR) that extend beyond the contrast-enhancing portion of the tumor. However, there is currently no consensus on the potential survival benefit of SpTR in GBM compared to gross total resection (GTR). In this study, we compare the impact of SpTR versus GTR on overall survival (OS) of GBM patients.MethodsWe performed a systematic review and meta-analysis of literature published on PubMed, Embase, The Cochrane Library, Web of Science, Scopus, and ClinicalTrials.gov, from inception to August 16, 2018, to identify articles comparing OS after SpTR versus GTR.ResultsWe identified 8902 unique citations, of which 11 articles met study inclusion criteria. 810 patients underwent SpTR out of a total of 2056 patients. 9 of 11 studies demonstrated improved outcomes with SpTR compared to GTR (median improvement in OS of 10.5 months), with no significant difference in postoperative complication rate. Overall study quality was variable, with ten studies presenting level IV evidence and one study presenting level IIIb evidence. Subgroup meta-analysis based on SpTR definition demonstrated a statistically significant 35% lower risk of mortality in patients who underwent anatomical SpTR compared to patients who underwent GTR (Hazard ratio = 0.65, 95% CI 0.47- 0.91, p = 0.003).ConclusionOur systematic review indicates SpTR may be associated with improved OS compared to GTR for GBM, especially with anatomical SpTR. However, this is limited by variable study design and significant clinical and methodological heterogeneity among studies. There is need for prospective clinical data to further guide parameters regarding the use of SpTR in GBM.

The T2-FLAIR mismatch sign, in which a low-grade glioma is hyperintense on T2-weighted MR and centrally hypointense on T2-weighted FLAIR MR, has been reported as 100% specific for IDH -mutant astrocytomas in several series. We report several cases of “false positive” T2-FLAIR mismatch sign occurring outside the context of IDH -mutant astrocytomas, predominantly in children or young adults with pediatric-type gliomas. These results suggest caution in the interpretation of the T2-FLAIR mismatch sign in the pediatric glioma population.

Understanding structure–function relationships in the brain after stroke is reliant not only on the accurate anatomical delineation of the focal ischemic lesion, but also on previous infarcts, remote changes and the presence of white matter hyperintensities. The robust definition of primary stroke boundaries and secondary brain lesions will have significant impact on investigation of brain–behavior relationships and lesion volume correlations with clinical measures after stroke. Here we present an automated approach to identify chronic ischemic infarcts in addition to other white matter pathologies, that may be used to aid the development of post-stroke management strategies. Our approach uses Bayesian–Markov Random Field (MRF) classification to segment probable lesion volumes present on fluid attenuated inversion recovery (FLAIR) MRI. Thereafter, a random forest classification of the information from multimodal (T1-weighted, T2-weighted, FLAIR, and apparent diffusion coefficient (ADC)) MRI images and other context-aware features (within the probable lesion areas) was used to extract areas with high likelihood of being classified as lesions. The final segmentation of the lesion was obtained by thresholding the random forest probabilistic maps. The accuracy of the automated lesion delineation method was assessed in a total of 36 patients (24 male, 12 female, mean age: 64.57±14.23yrs) at 3months after stroke onset and compared with manually segmented lesion volumes by an expert. Accuracy assessment of the automated lesion identification method was performed using the commonly used evaluation metrics. The mean sensitivity of segmentation was measured to be 0.53±0.13 with a mean positive predictive value of 0.75±0.18. The mean lesion volume difference was observed to be 32.32%±21.643% with a high Pearson's correlation of r=0.76 (p<0.0001). The lesion overlap accuracy was measured in terms of Dice similarity coefficient with a mean of 0.60±0.12, while the contour accuracy was observed with a mean surface distance of 3.06mm±3.17mm. The results signify that our method was successful in identifying most of the lesion areas in FLAIR with a low false positive rate. [Display omitted] •Segmentation of chronic ischemic and other lesions impacting poststroke depression.•Hierarchical segmentation of the probable lesion from FLAIR using Bayesian-MRF.•Random-forest (RF) probabilistic classification on probable lesion class.•Context-rich features used in RF include multimodal MRI and lesion likelihood.

The extent of surgical resection (EOR) has been recorded as conditioning outcome in glioblastoma multiforme (GBM) patients but no significant improvements were recorded in survival. The study aimed to evaluate the impact of EOR on survival, investigating the role of fluid-attenuated inversion recovery (FLAIR) abnormalities removal. 282 newly diagnosed GBM patients were treated with surgery followed by concurrent and adjuvant chemo-radiotherapy. The EOR was defined as: SUPr, in case of resection amounting to 100% of enhanced and FLAIR areas; gross total (GTR) in case of resection between 90 and 100% of enhanced areas with variable amount of FLAIR abnormalities; sub-total (STR), between 10 and 89%; biopsy (B) <10%. FLAIR-RTV was dichotomized in percentage values to identify the best separation threshold for progression free survival (PFS) and overall survival (OS). SUPr was obtained in 21 patients (7.4%), GTR in 60 (21.3%), STR in 143 (50.7%) and biopsy only in 58 (20.6%). The median, 1, 2-year PFS were 10.4 ± 0.4 months, 39.0 ± 3.0, and 17.0 ± 2.0%; the median, 1, 2-year OS were 14.5 ± 0.5 months, 63.3 ± 3.0, and 23.1 ± 3.1%. EOR was significantly influencing survival (p < 0.001). The median, 1, 2-year OS were 28.6 ± 5.2 months, 90.0 ± 6.0, 71.0 ± 10.0% for patients underwent SUPr vs. 16.2 ± 1.2 months, 81.0 ± 5.0, 24.0 ± 6.0% for GTR. The FLAIR removal threshold conditioning survival was 45%. Minor complications were recorded in 14 (5%) patients and major in 8 (2.8%). surgical resection beyond contrast-enhancing boundaries could represent a promising strategy to improve outcome in GBM patients. The identification of a FLAIR-RTV threshold can be useful in clinical practice and it was recorded as factor influencing survival.

The standard of care for isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM) is maximal resection followed by chemotherapy and radiation. Studies investigating the resection of GBM have primarily focused on the contrast enhancing portion of the tumor on magnetic resonance imaging. Histopathological studies, however, have demonstrated tumor infiltration within peri-tumoral fluid-attenuated inversion recovery (FLAIR) abnormalities, which is often not resected. The histopathology of FLAIR and local recurrence patterns of GBM have prompted interest in the resection of peri-tumoral FLAIR, or FLAIRectomy. To this point, recent studies have suggested a significant survival benefit associated with safe peri-tumoral FLAIR resection. In this review, we discuss the evidence surrounding the composition of peri-tumoral FLAIR, outcomes associated with FLAIRectomy, future directions of the field, and potential implications for patients.

Prior methods in characterizing age-related white matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have mainly been limited to understanding the sizes of, and occasionally the locations of WMH lesions. Systematic morphological characterization has been missing. In this work, we proposed innovative methods to fill this knowledge gap. We developed an innovative and proof-of-concept method to characterize and quantify the shape (based on Zernike transformation) and texture (based on fuzzy logic) of WMH lesions. We have also developed a multi-dimension feature vector approach to cluster WMH lesions into distinctive groups based on their shape and then texture features. We then developed an approach to calculate the potential growth index (PGI) of WMH lesions based on the image intensity distributions at the edge of the WMH lesions using a region-growing algorithm. High-quality T2 FLAIR images containing clearly identifiable WMH lesions with various sizes from six cognitively normal older adults were used in our method development Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH group clusters in terms of either the shape (P = 1.06×10-2) or the texture (P < 1×10-20) features. In conclusion, we propose a systematic framework on which the shape and texture features of WMH lesions can be quantified and may be used to predict lesion growth in older adults.