New Subependymal Enhancement After Radiation Therapy in High-Grade Glioma: Utilizing Morphological Features and DSC Perfusion MRI in Differentiate Progression and Post-Radiation Changes.

BACKGROUND: The specific patterns of subependymal enhancement (SE) that frequently occur as radiation-induced changes in high-grade gliomas following radiotherapy are often overlooked. Perfusion MRI may offer a diagnostic clue. PURPOSE: To distinguish between radiation-induced SE and progression in adult high-grade diffuse gliomas after standard treatment. STUDY TYPE: Retrospective. POPULATION: Ninety-four consecutive high-grade diffuse glioma patients (mean age, 55 ± 14 years; 54 [57.4%] males) with new SE identified in follow-up MRI after completion of surgery plus chemoradiation: progression (N = 74) vs. regression (N = 20). FIELD STRENGTH/SEQUENCE: 3 T, gradient-echo dynamic susceptibility contrast-enhanced MRI, 3D gradient-echo contrast-enhanced T1-weighted imaging. ASSESSMENT: To differentiate between radiation changes and progression in SE evaluation, multivariable logistic regression was performed using significant variables among SE appearance interval, IDH mutation, morphological features, and rCBV. Cox regression was performed to predict the tumor progression. For the added value of the rCBV, a log-rank test was conducted between the multivariable logistic regression models with and without the rCBV. STATISTICAL TESTS: Logistic regression, Cox regression, receiver operating characteristic analysis, log-rank test. RESULTS: 38.3% (36/94) patients had first specific SE (9.2 ± 9.5 months after surgery), which disappeared in 21.3% (20/94) after 5.8 ± 5.8 months after initial appearance on post-radiation MRI. IDH mutation, elongated, small lesions with lower rCBV tended to regress: IDH mutation, elongation, diameter, and rCBV_p95; odds ratio, 0.32, 1.92, 1.70, and 2.47, respectively. Qualitative evaluation of shape revealed that thin and curvilinear-shaped SE tended to regress, indicating a significant correlation with quantitative shape features (r = 0.31). In Cox regression, rCBV and lesion shape were significant (hazard ratio = 1.09 and 0.54, respectively). For sub-centimeter lesions, the rCBV showed added value in predicting outcomes (area under the curve, 0.873 vs. 0.836; log-rank test). DATA CONCLUSION: Smaller, elongated lesions with lower rCBV and IDH mutation are associated with regression when differentiating radiation changes from progression in high-grade glioma with post-radiotherapy SE. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Neuroimaging in Nonsyndromic Craniosynostosis: Key Concepts to Unlock Innovation.

Craniosynostoses (CRS) are caused by the premature fusion of one or more cranial sutures, with isolated nonsyndromic CRS accounting for most of the clinical manifestations. Such premature suture fusion impacts both skull and brain morphology and involves regions far beyond the immediate area of fusion. The combined use of different neuroimaging tools allows for an accurate depiction of the most prominent clinical-radiological features in nonsyndromic CRS but can also contribute to a deeper investigation of more subtle alterations in the underlying nervous tissue organization that may impact normal brain development. This review paper aims to provide a comprehensive framework for a better understanding of the present and future potential applications of neuroimaging techniques for evaluating nonsyndromic CRS, highlighting strategies for optimizing their use in clinical practice and offering an overview of the most relevant technological advancements in terms of diagnostic performance, radiation exposure, and cost-effectiveness.

Intraventricular neurocytoma: A diagnostic challenge with prognostic value.

Intraventricular neurocytoma is a low incidence central nervous system tumor. It predominantly affects young adults with no apparent gender predilection. The main symptoms include headache, nausea and vomiting. These result from hydrocephalus due to the obstruction of cerebrospinal fluid flow. On diagnostic imaging, neurocytoma can be suspected by some features, such as peripheral cysts, lobulated contours and septa that bridge the ventricular wall, giving a "scalloped" appearance. There are other characteristics, but they are less specific for the diagnosis. The atypical variant of neurocytoma is even rarer and leads to a worst prognosis. Atypical neurocytomas develop higher proliferative potential identified by the Ki-67 biomarker and higher recurrence rate. There are few studies about the imaging characteristics of atypical neurocytomas. At this point, there are no reliable distinctive features to differentiate atypical neurocytomas, especially due to their low incidence. We present the case of a 20-year-old female patient with symptoms of intracraneal hypertension. CT and MRI of the brain revealed a mass occupying the body of the left lateral ventricle, adjacent to the foramen of Monro. The mass was primarily solid with discrete peripheral cyst and a few scalloped areas. It also showed signs of supratentorial obstructive hydrocephalus. The tumor was partially removed because of bleeding and compromise of vascular structures. Immunohistochemistry revealed positive synaptophysin, elevated Ki-67 (7%), increased number of blood vessels and moderate nuclear atypia. After surgery, the patient persisted with signs of intracranial hypertension, not improving with clinical management and requiring aggressive surgical procedures. While rare, atypical neurocytoma requires a better characterization, especially through imaging, to optimize immediate management and explore new therapeutic options.

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

BACKGROUND: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. METHODS: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). RESULTS: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). CONCLUSIONS: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

Background: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. Methods: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). Results: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). Conclusions: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Imaging in pleural Mesothelioma: A review of the 16th International Conference of the International Mesothelioma Interest Group.

Imaging continues to gain a greater role in the assessment and clinical management of patients with mesothelioma. This communication summarizes the oral presentations from the imaging session at the 2023 International Conference of the International Mesothelioma Interest Group (iMig), which was held in Lille, France from June 26 to 28, 2023. Topics at this session included an overview of best practices for clinical imaging of mesothelioma as reported by an iMig consensus panel, emerging imaging techniques for surgical planning, radiologic assessment of malignant pleural effusion, a radiomics-based transfer learning model to predict patient response to treatment, automated assessment of early contrast enhancement, and tumor thickness for response assessment in peritoneal mesothelioma.

Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling.

This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.

Longitudinal Evaluation of DCE-MRI as an Early Indicator of Progression after Standard Therapy in Glioblastoma.

Background and Purpose: Distinguishing treatment-induced imaging changes from progressive disease has important implications for avoiding inappropriate discontinuation of a treatment. Our goal in this study is to evaluate the utility of dynamic contrast-enhanced (DCE) perfusion MRI as a biomarker for the early detection of progression. We hypothesize that DCE-MRI may have the potential as an early predictor for the progression of disease in GBM patients when compared to the current standard of conventional MRI. Methods: We identified 26 patients from 2011 to 2023 with newly diagnosed primary glioblastoma by histopathology and gross or subtotal resection of the tumor. Then, we classified them into two groups: patients with progression of disease (POD) confirmed by pathology or change in chemotherapy and patients with stable disease without evidence of progression or need for therapy change. Finally, at least three DCE-MRI scans were performed prior to POD for the progression cohort, and three consecutive DCE-MRI scans were performed for those with stable disease. The volume of interest (VOI) was delineated by a neuroradiologist to measure the maximum values for Ktrans and plasma volume (Vp). A Friedman test was conducted to evaluate the statistical significance of the parameter changes between scans. Results: The mean interval between subsequent scans was 57.94 days, with POD-1 representing the first scan prior to POD and POD-3 representing the third scan. The normalized maximum Vp values for POD-3, POD-2, and POD-1 are 1.40, 1.86, and 3.24, respectively (FS = 18.00, p = 0.0001). It demonstrates that Vp max values are progressively increasing in the three scans prior to POD when measured by routine MRI scans. The normalized maximum Ktrans values for POD-1, POD-2, and POD-3 are 0.51, 0.09, and 0.51, respectively (FS = 1.13, p < 0.57). Conclusions: Our analysis of the longitudinal scans leading up to POD significantly correlated with increasing plasma volume (Vp). A longitudinal study for tumor perfusion change demonstrated that DCE perfusion could be utilized as an early predictor of tumor progression.

Test-retest reliability and time-of-day variations of perfusion imaging at rest and during a vigilance task.

Arterial spin-labeled perfusion and blood oxygenation level-dependent functional MRI are indispensable tools for noninvasive human brain imaging in clinical and cognitive neuroscience, yet concerns persist regarding the reliability and reproducibility of functional MRI findings. The circadian rhythm is known to play a significant role in physiological and psychological responses, leading to variability in brain function at different times of the day. Despite this, test-retest reliability of brain function across different times of the day remains poorly understood. This study examined the test-retest reliability of six repeated cerebral blood flow measurements using arterial spin-labeled perfusion imaging both at resting-state and during the psychomotor vigilance test, as well as task-induced cerebral blood flow changes in a cohort of 38 healthy participants over a full day. The results demonstrated excellent test-retest reliability for absolute cerebral blood flow measurements at rest and during the psychomotor vigilance test throughout the day. However, task-induced cerebral blood flow changes exhibited poor reliability across various brain regions and networks. Furthermore, reliability declined over longer time intervals within the day, particularly during nighttime scans compared to daytime scans. These findings highlight the superior reliability of absolute cerebral blood flow compared to task-induced cerebral blood flow changes and emphasize the importance of controlling time-of-day effects to enhance the reliability and reproducibility of future brain imaging studies.

Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network.

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time-concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue curves were created from true AIFs combined with compartment-model parameters from a random distribution. Using Bloch simulations, a dictionary was constructed for a saturation-recovery 3D radial stack-of-stars sequence, accounting for deviations such as flip angle, T2* effects, and residual longitudinal magnetization after the saturation. A preliminary simulation study established the optimal tissue curve number using a bidirectional long short-term memory (Bi-LSTM) network with just AIF loss. Further optimization of the loss function involves comparing just AIF loss, AIF with compartment-model-based parameter loss, and AIF with compartment-model tissue loss. The optimized network was examined with both simulation and hybrid data, which included in vivo 3D stack-of-star datasets for testing. The AIF peak value accuracy and ktrans results were assessed. Results: Increasing the number of tissue curves can be beneficial when added tissue curves can provide extra information. Using just the AIF loss outperforms the other two proposed losses, including adding either a compartment-model-based tissue loss or a compartment-model parameter loss to the AIF loss. With the simulated data, the Bi-LSTM network reduced the AIF peak error from -23.6 ± 24.4% of the AIF using the dictionary method to 0.2 ± 7.2% (AIF input only) and 0.3 ± 2.5% (AIF + ten tissue curve inputs) of the network AIF. The corresponding ktrans error was reduced from -13.5 ± 8.8% to -0.6 ± 6.6% and 0.3 ± 2.1%. With the hybrid data (simulated data for training; in vivo data for testing), the AIF peak error was 15.0 ± 5.3% and the corresponding ktrans error was 20.7 ± 11.6% for the AIF using the dictionary method. The hybrid data revealed that using the AIF + tissue inputs reduced errors, with peak error (1.3 ± 11.1%) and ktrans error (-2.4 ± 6.7%). Conclusions: Integrating tissue curves with AIF curves into network inputs improves the precision of AI-driven AIF corrections. This result was seen both with simulated data and with applying the network trained only on simulated data to a limited in vivo test dataset.

Valor pronóstico de la perfusión cerebral por RM en el estudio inicial de los gliomas de alto grado / Prognostic value of brain perfusion by MRI in the initial study of high grade gliomas

Objetivos: Valorar si la perfusión tumoral en el estudio diagnóstico inicial de RM es un marcador pronóstico para la supervivencia en pacientes diagnosticados de gliomas de alto grado. Analizar los factores de riesgo que influyen en la mortalidad por gliomas de alto grado para poder cuantificar la supervivencia global esperada del paciente. Pacientes y métodos: Se seleccionaron las RM de todos los pacientes diagnosticados de glioma de alto grado en un hospital de tercer nivel entre los años 2017 y 2019. Se recogieron variables clínicas y tumorales. Se usó el análisis de supervivencia para determinar la asociación entre la perfusión tumoral y el tiempo de supervivencia. Se estudió la relación entre las variables recogidas y la supervivencia mediante el estadístico de Wald, cuantificando esta relación mediante la regresión de Cox. Por último, se analizó el tipo de relación existente entre la perfusión tumoral y la supervivencia a través del estudio de regresión lineal. Estos análisis estadísticos se realizaron con el software SPSS v.17. Resultados: Se incluyeron 38 pacientes (media de edad 61,1años). La supervivencia media global fue de 20,6meses. Se observó asociación entre la perfusión tumoral en la RM diagnóstica y la supervivencia global, mostrando el grupo con valores intratumorales de volumen sanguíneo cerebral relativo (rVSC) >3,0 una disminución significativa en el tiempo medio de supervivencia respecto al grupo con valores <3,0 (14,6meses vs 22,8meses, p=0,046). También han demostrado influir significativamente en la supervivencia media variables como la escala de Karfnosky y el tiempo de recidiva desde la intervención. Conclusiones: Se ha evidenciado que la perfusión tumoral por RM tiene valor pronóstico en el estudio inicial de los gliomas de alto grado.(AU)

Objectives: To evaluate if the tumour perfusion at the initial MRI scan is a marker of prognosis for survival in patients diagnosed with high grade gliomas (HGG). To analyse the risk factors which influence on the mortality from HGG to quantify the overall survival to be expected in patients. Patients and methods: The patients diagnosed with HGG through a MRI scan in a third-level hospital between 2017 and 2019 were selected. Clinical and tumour variables were collected. The survival analysis was used to determine the association between the tumour perfusion and the survival time. The relation between the collected variables and the survival period was assessed through Wald's statistical method, measuring the relationship via Cox's regression model. Finally, the type of relationship that exists between the tumour perfusion and the survival was analysed through the lineal regression method.Those statistical analysis were carried out using the software SPSS v.17. Results: Thirty-eight patients were included (average age: 61.1years old). The general average survival period was 20.6months. A relationship between the tumour perfusion at the MRI scan and the overall survival has been identified, in detail, a group with intratumor values of relative cerebral blood volume (rCBV) >3.0 has shown a significant decline in the average survival period with regard to the average survival period of the group with values <3.0 (14.6months vs. 22.8months, P=.046). It has also been proved that variables like Karnofsky's scale and the response time since the intervention significantly influence on the survival period. Conclusions: It has become evident that the tumour perfusion via MRI scan has a prognostic value in the initial analysis of HGG. The average survival period of patients with rCBV less than or equal to 3.0 is significantly higher than those patients whose values are higher, which allows to be more precise with the prognosis of each patient.(AU)

Influence of arterial transit time delays on the differentiation between tumor progression and pseudoprogression in glioblastoma by arterial spin labeling magnetic resonance imaging.

Arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) have shown potential for differentiating tumor progression from pseudoprogression. For pseudocontinuous ASL with a single postlabeling delay, the presence of delayed arterial transit times (ATTs) could affect the evaluation of ASL-MRI perfusion data. In this study, the influence of ATT artifacts on the perfusion assessment and differentiation between tumor progression and pseudoprogression were studied. This study comprised 66 adult patients (mean age 60 ± 13 years; 40 males) with a histologically confirmed glioblastoma who received postoperative radio (chemo)therapy. ASL-MRI and DSC-MRI scans were acquired at 3 months postradiotherapy as part of the standard clinical routine. These scans were visually scored regarding (i) the severity of ATT artifacts (%) on the ASL-MRI scans only, scored by two neuroradiologists; (ii) perfusion of the enhancing tumor lesion; and (iii) radiological evaluation of tumor progression versus pseudoprogression by one neuroradiologist. The final outcome was based on combined clinical and radiological follow-up until 9 months postradiotherapy. ATT artifacts were identified in all patients based on the mean scores of two raters. A significant difference between the radiological evaluation of ASL-MRI and DSC-MRI was observed only for ASL images with moderate ATT severity (30%-65%). The perfusion assessment showed ASL-MRI tending more towards hyperperfusion than DSC-MRI in the case of moderate ATT artifacts. In addition, there was a significant difference between the prediction of tumor progression with ASL-MRI and the final outcome in the case of severe ATT artifacts (McNemar test, p = 0.041). Despite using ASL imaging parameters close to the recommended settings, ATT artifacts frequently occur in patients with treated brain tumors. Those artifacts could hinder the radiological evaluation of ASL-MRI data and the detection of true disease progression, potentially affecting treatment decisions for patients with glioblastoma.

Prognostic value of brain perfusion by MRI in the initial study of high grade gliomas.

OBJECTIVES: To evaluate if the tumour perfusion at the initial MRI scan is a marker of prognosis for survival in patients diagnosed with High Grade Gliomas (HGG). To analyse the risk factors which influence on the mortality from HGG to quantify the overall survival to be expected in patients. PATIENTS AND METHODS: The patients diagnosed with HGG through a MRI scan in a third-level hospital between 2017 and 2019 were selected. Clinical and tumour variables were collected. The survival analysis was used to determine the association between the tumour perfusion and the survival time. The relation between the collected variables and the survival period was assessed through Wald's statistical method, measuring the relationship via Cox's regression model. Finally, the type of relationship that exists between the tumour perfusion and the survival was analysed through the Lineal Regression method.Those statistical analysis were carried out using the software SPSS v.17. RESULTS: 38 patients were included (average age: 61.1 years old). The general average survival period was 20.6 months. A relationship between the tumour perfusion at the MRI scan and the overall survival has been identified, in detail, a group with intratumor values of relative cerebral blood volume (rCBV)>3.0 has shown a significant decline in the average survival period with regard to the average survival period of the group with values <3.0 (14.6 months vs. 22.8 months, p = 0.046). It has also been proved that variables like Karnofsky's scale and the response time since the intervention significantly influence on the survival period. CONCLUSIONS: It has become evident that the tumour perfusion via MRI scan has a prognostic value in the initial analysis of HGG. The average survival period of patients with rCBV less than or equal to 3.0 is significantly higher than those patients whose values are higher, which allows to be more precise with the prognosis of each patient.

Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI).

Background: Magnetic resonance imaging (MRI) is a primary non-invasive imaging modality for tumor segmentation, leveraging its exceptional soft tissue contrast and high resolution. Current segmentation methods typically focus on structural MRI, such as T1-weighted post-contrast-enhanced or fluid-attenuated inversion recovery (FLAIR) sequences. However, these methods overlook the blood perfusion and hemodynamic properties of tumors, readily derived from dynamic susceptibility contrast (DSC) enhanced MRI. This study introduces a novel hybrid method combining density-based analysis of hemodynamic properties in time-dependent perfusion imaging with deep learning spatial segmentation techniques to enhance tumor segmentation. Methods: First, a U-Net convolutional neural network (CNN) is employed on structural images to delineate a region of interest (ROI). Subsequently, Hierarchical Density-Based Scans (HDBScan) are employed within the ROI to augment segmentation by exploring intratumoral hemodynamic heterogeneity through the investigation of tumor time course profiles unveiled in DSC MRI. Results: The approach was tested and evaluated using a cohort of 513 patients from the open-source University of Pennsylvania glioblastoma database (UPENN-GBM) dataset, achieving a 74.83% Intersection over Union (IoU) score when compared to structural-only segmentation. The algorithm also exhibited increased precision and localized predictions of heightened segmentation boundary complexity, resulting in a 146.92% increase in contour complexity (ICC) compared to the reference standard provided by the UPENN-GBM dataset. Importantly, segmenting tumors with the developed new approach uncovered a negative correlation of the tumor volume with the scores in the Karnofsky Performance Scale (KPS) clinically used for assessing the functional status of patients (-0.309), which is not observed with the prevailing segmentation standard. Conclusions: This work demonstrated that including hemodynamic properties of tissues from DSC MRI can improve existing structural or morphological feature-based tumor segmentation techniques with additional information on tumor biology and physiology. This approach can also be applied to other clinical indications that use perfusion MRI for diagnosis or treatment monitoring.

A 3D dual-echo spiral sequence for simultaneous dynamic susceptibility contrast and dynamic contrast-enhanced MRI with single bolus injection.

PURPOSE: Perfusion MRI reveals important tumor physiological and pathophysiologic information, making it a critical component in managing brain tumor patients. This study aimed to develop a dual-echo 3D spiral technique with a single-bolus scheme to simultaneously acquire both dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) data and overcome the limitations of current EPI-based techniques. METHODS: A 3D spiral-based technique with dual-echo acquisition was implemented and optimized on a 3T MRI scanner with a spiral staircase trajectory and through-plane SENSE acceleration for improved speed and image quality, in-plane variable-density undersampling combined with a sliding-window acquisition and reconstruction approach for increased speed, and an advanced iterative deblurring algorithm. Four volunteers were scanned and compared with the standard of care (SOC) single-echo EPI and a dual-echo EPI technique. Two patients were scanned with the spiral technique during a preload bolus and compared with the SOC single-echo EPI collected during the second bolus injection. RESULTS: Volunteer data demonstrated that the spiral technique achieved high image quality, reduced geometric artifacts, and high temporal SNR compared with both single-echo and dual-echo EPI. Patient perfusion data showed that the spiral acquisition achieved accurate DSC quantification comparable to SOC single-echo dual-dose EPI, with the additional DCE information. CONCLUSION: A 3D dual-echo spiral technique was developed to simultaneously acquire both DSC and DCE data in a single-bolus injection with reduced contrast use. Preliminary volunteer and patient data demonstrated increased temporal SNR, reduced geometric artifacts, and accurate perfusion quantification, suggesting a competitive alternative to SOC-EPI techniques for brain perfusion MRI.

MRI hypoperfusion as a determinant of cognitive impairment in adults with Moyamoya angiopathy.

INTRODUCTION: In Moyamoya angiopathy (MMA), mechanisms underlying cognitive impairment remain debated. We aimed to assess the association of cognitive impairment with the degree and the topography of cerebral hypoperfusion in MMA. METHODS: A retrospective analysis of neuropsychological and perfusion MRI data from adults with MMA was performed. Ischemic and haemorrhagic lesion masks were created to account for cerebral lesions in the analysis of cerebral perfusion. Whole brain volume of hypoperfused parenchyma was outlined on perfusion maps using different Tmax thresholds from 4 to 12 s. Regional analysis produced mean Tmax values at different regions of interest. Analyses compared perfusion ratios in patients with and without cognitive impairment, with multivariable logistic regression analysis to identify predictive factors. RESULTS: Cognitive impairment was found in 20/48 (41.7%) patients. Attention/processing speed and memory were equally impaired (24%) followed by executive domain (23%). After adjustment, especially for lesion volume, hypoperfused parenchyma volume outlined by Tmax > 4 s or Tmax > 5 s thresholds was an independent factor of cognitive impairment (OR for Tmax > 4 s = 1.06 [CI 95% 1.008-1.123]) as well as attention/processing speed (OR for Tmax > 4 s = 1.07 [CI 95% 1.003-1.133]) and executive domains (OR for Tmax > 5 s = 1.08 [CI 95% 1.004-1.158]). Regarding cognitive functions, patients with processing speed and flexibility impairment had higher frontal Tmax compared to other ROIs and to patients with normal test scores. DISCUSSION: Cerebral hypoperfusion emerged as an independent factor of cognitive impairment in MMA particularly in attention/processing speed and executive domains, with a strong contribution of frontal areas. CONCLUSION: Considering this association, revascularization surgery could improve cognitive impairment.

Comparison of ASL and DSC perfusion methods in the evaluation of response to treatment in patients with a history of treatment for malignant brain tumor.

OBJECTIVE: Perfusion MRI is of great benefit in the post-treatment evaluation of brain tumors. Interestingly, dynamic susceptibility contrast-enhanced (DSC) perfusion has taken its place in routine examination for this purpose. The use of arterial spin labeling (ASL), a perfusion technique that does not require exogenous contrast material injection, has gained popularity in recent years. The aim of the study was to compare two different perfusion techniques, ASL and DSC, using qualitative and quantitative measurements and to investigate the diagnostic effectiveness of both. The fact that the number of patients is higher than in studies conducted with 3D pseudo-continious ASL (pCASL), the study group is heterogeneous as it consists of patients with both metastases and glial tumors, the use of 3D Turbo Gradient Spin Echo (TGSE), and the inclusion of visual (qualitative) assessment make our study unique. METHODS: Ninety patients, who were treated for malignant brain tumor, were enrolled in the retrospective study. DSC Cerebral Blood Volume (CBV), Cerebral Blood Flow (CBF) and ASL CBF maps of each case were obtained. In qualitative analysis, the lesions of the cases were visually classified as treatment-related changes (TRC) and relapse/residual mass (RRT). In the quantitative analysis, three regions of interest (ROI) measurements were taken from each case. The average of these measurements was compared with the ROI taken from the contralateral white matter and normalized values (n) were obtained. These normalized values were compared across events. RESULTS: Uncorrected DSC normalized CBV (nCBV), DSC normalized CBF (nCBF) and ASL nCBF values of RRT cases were higher than those of TRC cases (p < 0.001). DSC nCBV values were correlated with DSC nCBF (r: 0.94, p < 0.001) and correlated with ASL nCBF (r: 0.75, p < 0.001). Similarly, ASL nCBF was positively correlated with DSC nCBF (r: 0.79 p < 0.01). When the ROC curve parameters were evaluated, the cut-off values were determined as 1.211 for DSC nCBV (AUC: 0.95, 93% sensitivity, 82% specificity), 0.896 for DSC nCBF (AUC; 0.95, 93% sensitivity, 82% specificity), and 0.829 for ASL nCBF (AUC: 0.84, 78% sensitivity, 75% specificity). For qualitative evaluation (visual evaluation), inter-observer agreement was found to be good for ASL CBF (0.714), good for DSC CBF (0.790), and excellent for DSC CBV (0.822). Intra-observer agreement was also evaluated. For the first observer, good agreement was found in ASL CBF (0.626, 70% sensitive, 93% specific), in DSC CBF (0.713, 76% sensitive, 95% specific), and in DSC CBV (0.755, 87% sensitive - 88% specific). In the second observer, moderate agreement was found in ASL CBF (0.584, 61% sensitive, 97% specific) and DSC CBF (0.649, 65% sensitive, 100% specific), and excellent agreement in DSC CBV (0.800, 89% sensitive, 90% specific). CONCLUSION: It was observed that uncorrected DSC nCBV, DSC nCBF and ASL nCBF values were well correlated with each other. In qualitative evaluation, inter-observer and intra-observer agreement was higher in DSC CBV than DSC CBF and ASL CBF. In addition, DSC CBV is found more sensitive, ASL CBF and DSC CBF are found more specific for both observers. From a diagnostic perspective, all three parameters DSC CBV, DSC CBF and ASL CBF can be used, but it was observed that the highest rate belonged to DSC CBV.

Referral of a Patient With Ocular Symptoms to the Stroke Clinic: Not Always the Usual Suspect!

A 77-year-old male attended the stroke clinic as a delayed presentation of a stroke and was initially managed as an occipital stroke. He presented with a gradual decline in visual acuity with an initial suspicion of field deficit over a period of three to four months. He underwent extensive tests including imaging for a confirmatory diagnosis. He had a rapid deterioration of his vision, function, and cognition over a few weeks resulting eventually in death. The case highlights a rare variant of sporadic Creutzfeld-Jakob disease (sCJD), the Heidenhain Variant (HV-CJD). CJD is the commonest of human prion diseases. In HV-CJD, pathologic prions display demyelinating neurotropism for the occipital lobes resulting in visual changes and hallucinations.

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma. The convolutional neural network trained on ∼50,000 voxels from 40 patients provides intratumor probability maps that yield clinical-grade diagnosis. Performance is tested in 400 additional cases and an external validation cohort of 128 patients. The tool reaches a three-way accuracy of 0.78, superior to the conventional MRI metrics cerebral blood volume (0.55) and percentage of signal recovery (0.59), showing high value as a support diagnostic tool. Our open-access software, Diagnosis In Susceptibility Contrast Enhancing Regions for Neuro-oncology (DISCERN), demonstrates its potential in aiding medical decisions for brain tumor diagnosis using standard-of-care MRI.

Low rCBV values in glioblastoma tumor progression under chemoradiotherapy.

PURPOSE: After standard treatment for glioblastoma, perfusion MRI remains challenging for differentiating tumor progression from post-treatment changes. Our objectives were (1) to correlate rCBV values at diagnosis and at first tumor progression and (2) to analyze the relationship of rCBV values at tumor recurrence with enhancing volume, localization of tumor progression, and time elapsed since the end of radiotherapy in tumor recurrence. METHODS: Inclusion criteria were (1) age > 18 years, (2) histologically confirmed glioblastoma treated with STUPP regimen, and (3) tumor progression according to RANO criteria > 12 weeks after radiotherapy. Co-registration of segmented enhancing tumor VOIs with dynamic susceptibility contrast perfusion MRI was performed using Olea Sphere software. For tumor recurrence, we correlated rCBV values with enhancing tumor volume, with recurrence localization, and with time elapsed from the end of radiotherapy to progression. Analyses were performed with SPSS software. RESULTS: Sixty-four patients with glioblastoma were included in the study. Changes in rCBV values between diagnosis and first tumor progression were significant (p < 0.001), with a mean and median decreases of 32% and 46%, respectively. Mean rCBV values were also different (p < 0.01) when tumors progressed distally (radiation field rCBV values of 1.679 versus 3.409 distally). However, changes and, therefore, low rCBV values after radiotherapy in tumor recurrence were independent of time. CONCLUSION: Chemoradiation alters tumor perfusion and rCBV values may be decreased in the setting of tumor progression. Changes in rCBV values with respect to diagnosis, with low rCBV in tumor progression, are independent of time but related to the site of recurrence.