Prediction of pseudoprogression in post-treatment glioblastoma using dynamic susceptibility contrast-derived oxygenation and microvascular transit time heterogeneity measures.

OBJECTIVES: To evaluate the added value of MR dynamic susceptibility contrast (DSC)-perfusion-weighted imaging (PWI)-derived tumour microvascular and oxygenation information with cerebral blood volume (CBV) to distinguish pseudoprogression from true progression (TP) in post-treatment glioblastoma. METHODS: This retrospective single-institution study included patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma and a newly developed or enlarging measurable contrast-enhancing mass within 12 weeks after concurrent chemoradiotherapy. CBV, capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), and cerebral metabolic rate of oxygen (CMRO2) were obtained from DSC-PWI. Predictors were selected using univariable logistic regression, and performance was measured with adjusted diagnostic odds with tumour volume and area under the curve (AUC) of receiver operating characteristics analysis. RESULTS: A total of 103 patients were included (mean age, 59.6 years; 59 women), with 67 cases of TP and 36 cases of pseudoprogression. Pseudoprogression exhibited higher CTH (4.0 vs. 3.4, p = .019) and higher OEF (12.7 vs. 10.7, p = .014) than TP, but a similar CBV (1.48 vs. 1.53, p = .13) and CMRO2 (7.7 vs. 7.3s, p = .598). Independent of tumour volume, both high CTH (adjusted odds ratio [OR] 1.52; 95% confidence interval [CI]: 1.11-2.09, p = .009) and high OEF (adjusted OR 1.17; 95% CI:1.03-1.33, p = .016) were predictors of pseudoprogression. The combination of CTH, OEF, and CBV yielded higher diagnostic performance (AUC 0.71) than CBV alone (AUC 0.65). CONCLUSION: High intratumoural capillary transit heterogeneity and high oxygen extraction fraction derived from DSC-PWI have enhanced the diagnostic value of CBV in pseudoprogression of post-treatment IDH-wild type glioblastoma. CLINICAL RELEVANCE STATEMENT: In the early post-treatment stage of glioblastoma, pseudoprogression exhibited both high oxygen extraction fraction and high capillary transit heterogeneity and these dynamic susceptibility contrast-perfusion weighted imaging derived parameters have added value in cerebral blood volume-based noninvasive differentiation of pseudoprogression from true progression. KEY POINTS: • Capillary transit time heterogeneity and oxygen extraction fraction can be measured noninvasively through processing of dynamic susceptibility contrast imaging. • Pseudoprogression exhibited higher capillary transit time heterogeneity and higher oxygen extraction fraction than true progression. • A combination of cerebral blood volume, capillary transit time heterogeneity, and oxygen extraction fraction yielded the highest diagnostic performance (area under the curve 0.71).

Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model.

OBJECTIVES: Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning. METHODS: The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. RESULTS: The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2-F4), advanced fibrosis (F3-F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). CONCLUSIONS: Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning-based liver fibrosis staging algorithms. KEY POINTS: • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage.

Theophylline as an Add-On to Thrombolytic Therapy in Acute Ischemic Stroke: A Randomized Placebo-Controlled Trial.

BACKGROUND AND PURPOSE: Delayed recanalization increases the risk of infarct growth and poor clinical outcome in acute ischemic stroke. The vasoactive agent theophylline has shown neuroprotective effects in animal stroke models but inconclusive results in case series and randomized clinical trials. The primary objective of this study was to evaluate whether theophylline, as an add-on to thrombolytic therapy, is safe and effective in acute ischemic stroke patients. METHODS: The TEA-Stroke trial (The Theophylline in Acute Ischemic Stroke) was an investigator-initiated 2-center, proof-of-concept, phase II clinical study with a randomized, double-blinded, placebo-controlled design. The main inclusion criteria were magnetic resonance imaging-verified acute ischemic stroke, moderate to severe neurological deficit (National Institutes of Health Stroke Scale score of ≥4), and treatment with thrombolysis within 4.5 hours of onset. Participants were randomly assigned in the ratio 1:1 to either 220 mg of intravenous theophylline or placebo. The co-primary outcomes were early clinical improvement on the National Institutes of Health Stroke Scale score and infarct growth on magnetic resonance imaging at 24-hour follow-up. RESULTS: Theophylline as an add-on to thrombolytic therapy improved the National Institutes of Health Stroke Scale score at 24 hours by mean 4.7 points (SD, 5.6) compared with an improvement of 1.3 points (SD, 7.5) in the control group (P=0.044). Mean infarct growth was 141.6% (SD, 126.5) and 104.1% (SD, 62.5) in the theophylline and control groups, respectively (P=0.146). Functional independence at 90 days was 61% in the theophylline group and 58% in the control group (P=0.802). CONCLUSIONS: This proof-of-concept trial investigated theophylline administration as an add-on to thrombolytic therapy in acute ischemic stroke. The co-primary end points early clinical improvement and infarct growth at 24-hour follow-up were not significantly different after post hoc correction for multiplicity (Bonferroni technique). The small study size precludes a conclusion as to whether theophylline has a neuroprotective effect but provides a promising clinical signal that may support a future clinical trial. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: EudraCT number 2013-001989-42.

Capillary flow disturbances after experimental subarachnoid hemorrhage: A contributor to delayed cerebral ischemia?

BACKGROUND: The high mortality and morbidity after SAH is partly due to DCI, which is traditionally ascribed to development of angiographic vasospasms. This relation has been challenged, and capillary flow disturbances are proposed as another mechanism contributing to brain damage after SAH. OBJECTIVE: To investigate capillary flow changes 4 days following experimental SAH. METHODS: SAH was induced by endovascular perforation of circle of Willis. We used TPM to evaluate blood flow characteristics. Cortical capillary diameters were investigated by both TPM and histology. RESULTS: We found elevated CTH and MTT of blood in SAH mice compared to sham animals. We observed capillaries with stagnant RBCs, and capillaries with increased RBC LD in the SAH group, suggesting severe blood maldistribution among cortical capillaries. Favoring that these capillary flow changes were primary to upstream vasoconstrictions, TPM showed no significant differences in arteriolar diameter between groups, while histological examination showed reduced capillary diameter in SAH group. CONCLUSION: Our study shows profound subacute hypoperfusion and capillary flow disturbances in a mouse SAH model and suggests that these changes are the result of changes in capillary function, rather than upstream vasospasm.

Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning.

BACKGROUND AND PURPOSE: Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. We wish to develop and validate a predictive model capable of automatically identifying and combining acute imaging features to accurately predict final lesion volume. METHODS: Using acute magnetic resonance imaging, we developed and trained a deep convolutional neural network (CNNdeep) to predict final imaging outcome. A total of 222 patients were included, of which 187 were treated with rtPA (recombinant tissue-type plasminogen activator). The performance of CNNdeep was compared with a shallow CNN based on the perfusion-weighted imaging biomarker Tmax (CNNTmax), a shallow CNN based on a combination of 9 different biomarkers (CNNshallow), a generalized linear model, and thresholding of the diffusion-weighted imaging biomarker apparent diffusion coefficient (ADC) at 600×10-6 mm2/s (ADCthres). To assess whether CNNdeep is capable of differentiating outcomes of ±intravenous rtPA, patients not receiving intravenous rtPA were included to train CNNdeep,-rtpa to access a treatment effect. The networks' performances were evaluated using visual inspection, area under the receiver operating characteristic curve (AUC), and contrast. RESULTS: CNNdeep yields significantly better performance in predicting final outcome (AUC=0.88±0.12) than generalized linear model (AUC=0.78±0.12; P=0.005), CNNTmax (AUC=0.72±0.14; P<0.003), and ADCthres (AUC=0.66±0.13; P<0.0001) and a substantially better performance than CNNshallow (AUC=0.85±0.11; P=0.063). Measured by contrast, CNNdeep improves the predictions significantly, showing superiority to all other methods (P≤0.003). CNNdeep also seems to be able to differentiate outcomes based on treatment strategy with the volume of final infarct being significantly different (P=0.048). CONCLUSIONS: The considerable prediction improvement accuracy over current state of the art increases the potential for automated decision support in providing recommendations for personalized treatment plans.

Boosted Tree Model Reforms Multimodal Magnetic Resonance Imaging Infarct Prediction in Acute Stroke.

BACKGROUND AND PURPOSE: Stroke imaging is pivotal for diagnosis and stratification of patients with acute ischemic stroke to treatment. The potential of combining multimodal information into reliable estimates of outcome learning calls for robust machine learning techniques with high flexibility and accuracy. We applied the novel extreme gradient boosting algorithm for multimodal magnetic resonance imaging-based infarct prediction. METHODS: In a retrospective analysis of 195 patients with acute ischemic stroke, fluid-attenuated inversion recovery, diffusion-weighted imaging, and 10 perfusion parameters were derived from acute magnetic resonance imaging scans. They were integrated to predict final infarct as seen on follow-up T2-fluid-attenuated inversion recovery using the extreme gradient boosting and compared with a standard generalized linear model approach using cross-validation. Submodels for recanalization and persistent occlusion were calculated and were used to identify the important imaging markers. Performance in infarct prediction was analyzed with receiver operating characteristics. Resulting areas under the curve and accuracy rates were compared using Wilcoxon signed-rank test. RESULTS: The extreme gradient boosting model demonstrated significantly higher performance in infarct prediction compared with generalized linear model in both cross-validation approaches: 5-folds (P<10e-16) and leave-one-out (P<0.015). The imaging parameters time-to-peak, mean transit time, time-to-maximum, and diffusion-weighted imaging were indicated as most valuable for infarct prediction by the systematic algorithm rating. Notably, the performance improvement was higher with 5-folds cross-validation approach than leave-one-out. CONCLUSIONS: We demonstrate extreme gradient boosting as a state-of-the-art method for clinically applicable multimodal magnetic resonance imaging infarct prediction in acute ischemic stroke. Our findings emphasize the role of perfusion parameters as important biomarkers for infarct prediction. The effect of cross-validation techniques on performance indicates that the intrapatient variability is expressed in nonlinear dynamics of the imaging modalities.

ABCD1 dysfunction alters white matter microvascular perfusion.

Cerebral X-linked adrenoleukodystrophy is a devastating neurodegenerative disorder caused by mutations in the ABCD1 gene, which lead to a rapidly progressive cerebral inflammatory demyelination in up to 60% of affected males. Selective brain endothelial dysfunction and increased permeability of the blood-brain barrier suggest that white matter microvascular dysfunction contributes to the conversion to cerebral disease. Applying a vascular model to conventional dynamic susceptibility contrast magnetic resonance perfusion imaging, we demonstrate that lack of ABCD1 function causes increased capillary flow heterogeneity in asymptomatic hemizygotes predominantly in the white matter regions and developmental stages with the highest probability for conversion to cerebral disease. In subjects with ongoing inflammatory demyelination we observed a sequence of increased capillary flow heterogeneity followed by blood-brain barrier permeability changes in the perilesional white matter, which predicts lesion progression. These white matter microvascular alterations normalize within 1 year after treatment with haematopoietic stem cell transplantation. For the first time in vivo, our studies unveil a model to assess how ABCD1 alters white matter microvascular function and explores its potential as an earlier biomarker for monitoring disease progression and response to treatment.

Multiparametric Model for Penumbral Flow Prediction in Acute Stroke.

BACKGROUND AND PURPOSE: Identification of salvageable penumbra tissue by dynamic susceptibility contrast magnetic resonance imaging is a valuable tool for acute stroke patient stratification for treatment. However, prior studies have not attempted to combine the different perfusion maps into a predictive model. In this study, we established a multiparametric perfusion imaging model and cross-validated it using positron emission tomography perfusion for detection of penumbral flow. METHODS: In a retrospective analysis of 17 subacute stroke patients with consecutive magnetic resonance imaging and H2O15 positron emission tomography scans, perfusion maps of cerebral blood flow, cerebral blood volume, mean transit time, time-to-maximum, and time-to-peak were constructed and combined using a generalized linear model (GLM). Both the GLM maps and the single perfusion maps alone were cross-validated with positron emission tomography-cerebral blood flow scans to predict penumbral flow on a voxel-wise level. Performance was tested by receiver-operating characteristics curve analysis, that is, the area under the curve, and the models' fits were compared using the likelihood ratio test. RESULTS: The GLM demonstrated significantly improved model fit compared with each of the single perfusion maps (P<1×e-5) and demonstrated higher performance, with an area under the curve of 0.91. However, the absolute difference between the performance of GLM and the best-performing single perfusion parameter (time-to-maximum) was relatively low (area under the curve difference =0.04). CONCLUSIONS: Our results support a dynamic susceptibility contrast magnetic resonance imaging-based GLM as an improved model for penumbral flow prediction in stroke patients. With given perfusion maps, this model is a straightforward and observer-independent alternative for therapy stratification.

Reliable estimation of microvascular flow patterns in patients with disrupted blood-brain barrier using dynamic susceptibility contrast MRI.

PURPOSE: To present and quantify the performance of a method to compute tissue hemodynamic parameters from dynamic susceptibility contrast (DSC) MRI data in brain tissue with possible nonintact blood-brain barrier. THEORY AND MATERIALS AND METHODS: We propose a Bayesian scheme to obtain perfusion metrics, including capillary transit-time heterogeneity (CTH), from DSC-MRI data in the presence of contrast agent extravasation. Initial performance assessment is performed through simulations. Next, we assessed possible over- or under correction for tracer extravasation in two patients receiving contrast agent preloading and two patients not receiving preloading. Perfusion metrics for N = 60 patients diagnosed with either grade III (N = 14) or grade IV gliomas (N = 46) were analyzed across tissue types to evaluate the ability to distinguish regions with different hemodynamic patterns. Finally, N = 4 patient cases undergoing anti-angiogenic treatment are evaluated qualitatively for treatment effects. All patient data were acquired at 3.0 Tesla. RESULTS: The simulation studies showed good robustness against low signal-to-noise ratios, exemplified with Pearson correlations of R = 0.833 (mean transit time) and R = 0.738 (CTH) at signal-to-noise ratio = 20. Region-of-interest analysis of the N = 60 glioma patients showed that cerebral blood volume (CBV) significantly separated enhancing core from edema (grade IV: P < 10-8 , grade III: P < 0.05) and enhancing core from normal appearing ipsilateral white matter (NAWM) (grade IV: P < 10-8 , grade III: P < 0.05). The microvascular parameters were particularly good in separating edematous tissue from NAWM tissue in grade IV gliomas (P < 0.001). Finally, CTH separated grade III and grade IV core tissue (P < 0.05). CONCLUSION: We have demonstrated robustness of the proposed Bayesian algorithm against experimental noise and demonstrated complementary value in microvascular parameters to the CBV parameter in separating tissue types in gliomas. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:537-549.

High Prestroke Physical Activity Is Associated with Reduced Infarct Growth in Acute Ischemic Stroke Patients Treated with Intravenous tPA and Randomized to Remote Ischemic Perconditioning.

BACKGROUND: A high prestroke physical activity (PA) level is associated with reduced stroke rate, stroke mortality, better functional outcome, and possible neuroprotective abilities. The aim of the present study was to examine the possible neuroprotective effect of prestroke PA on 24-h cerebral infarct growth in a cohort of acute ischemic stroke patients treated with intravenous tPA and randomized to remote ischemic perconditioning. METHODS: In this predefined subanalysis, data from a randomized clinical trial investigating the effect of remote ischemic perconditioning (RIPerC) on AIS was used. Prestroke (7 days before admission) PA was quantified using the PA Scale for the Elderly (PASE) questionnaire at baseline. Infarct growth was evaluated using MRI (acute, 24-h, and 1-month). RESULTS: PASE scores were obtained from 102 of 153 (67%) patients with a median (interquartile range) age of 66 (58-73) years. A high prestroke PA level correlated significantly with reduced acute infarct growth (24 h) in the linear regression model (4th quartile prestroke PA level compared with the 1st quartile), ß4th quartile = -0.82 (95% CI -1.54 to -0.10). However, the effect of prestroke PA was present mainly in patients randomized to RIPerC, ß4th quartile = -1.14 (95% CI -2.04 to -0.25). In patients randomized to RIPerC, prestroke PA was a predictor of final infarct size (1-month infarct volume), ß4th quartile = -1.78 (95% CI -3.15 to -0.41). CONCLUSION: In AIS patients treated with RIPerC, as add-on to intravenous thrombolysis, the level of PA the week before the stroke was associated with decreased 24-h infarct growth and final infarct size. These results are highly encouraging and stress the need for further exploration of the potentially protective effects of both PA and remote ischemic conditioning.

Capillary dysfunction is associated with symptom severity and neurodegeneration in Alzheimer's disease.

INTRODUCTION: We examined whether cortical microvascular blood volume and hemodynamics in Alzheimer's disease (AD) are consistent with tissue hypoxia and whether they correlate with cognitive performance and the degree of cortical thinning. METHODS: Thirty-two AD patients underwent cognitive testing, structural magnetic resonance imaging (MRI), and perfusion MRI at baseline and after 6 months. We measured cortical thickness, microvascular cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and capillary transit time heterogeneity (CTH) and estimated tissue oxygen tension (PtO2). RESULTS: At baseline, poor cognitive performance and regional cortical thinning correlated with lower CBF and CBV, with higher MTT and CTH and with low PtO2 across the cortex. Cognitive decline over time was associated with increasing whole brain relative transit time heterogeneity (RTH = CTH/MTT). DISCUSSION: Our results confirm the importance of microvascular pathology in AD. Deteriorating microvascular hemodynamics may cause hypoxia, which is known to precipitate amyloid retention.

Automated estimation of salvageable tissue: Comparison with expert readers.

PURPOSE: To assess the performance of an automatic perfusion-diffusion mismatch outlining algorithm, in a cohort of acute ischemic stroke patients imaged as part of a multicenter study. MATERIALS AND METHODS: Magnetic resonance imaging (MRI) from 167 patients with anterior circulation strokes scanned at either 3T or 1.5T systems were analyzed retrospectively through an automatic perfusion-diffusion mismatch detection algorithm. In addition, four expert raters manually outlined perfusion lesions on time-to-peak (TTP) maps and diffusion lesions on diffusion-weighted images (DWI), and reference perfusion-diffusion mismatch masks were obtained as the areas where at least three experts were in agreement that tissue was part of the perfusion-weighted imaging (PWI) lesion, but not the diffusion lesion. Per-subject analyses of mismatch volumes and mismatch overlap were subsequently performed. RESULTS: The use of the automatic perfusion-diffusion mismatch detection algorithm resulted in a 4.0 ml mean (standard deviation 28.7 ml) difference in mismatch volume compared to the reference expert consensus (Pearson correlation, r = 0.91, P < 0.0001). The median spatial agreement was 0.71, with an interquartile range of 0.28. CONCLUSION: We demonstrated excellent agreement between the perfusion-diffusion mismatch masks estimated by our proposed automatic algorithm and those achieved by expert consensus.

Making sense: Dopamine activates conscious self-monitoring through medial prefrontal cortex.

When experiences become meaningful to the self, they are linked to synchronous activity in a paralimbic network of self-awareness and dopaminergic activity. This network includes medial prefrontal and medial parietal/posterior cingulate cortices, where transcranial magnetic stimulation may transiently impair self-awareness. Conversely, we hypothesize that dopaminergic stimulation may improve self-awareness and metacognition (i.e., the ability of the brain to consciously monitor its own cognitive processes). Here, we demonstrate improved noetic (conscious) metacognition by oral administration of 100 mg dopamine in minimal self-awareness. In a separate experiment with extended self-awareness dopamine improved the retrieval accuracy of memories of self-judgment (autonoetic, i.e., explicitly self-conscious) metacognition. Concomitantly, magnetoencephalography (MEG) showed increased amplitudes of oscillations (power) preferentially in the medial prefrontal cortex. Given that electromagnetic activity in this region is instrumental in self-awareness, this explains the specific effect of dopamine on explicit self-awareness and autonoetic metacognition.

Biased visualization of hypoperfused tissue by computed tomography due to short imaging duration: improved classification by image down-sampling and vascular models.

OBJECTIVES: Lesion detection in acute stroke by computed-tomography perfusion (CTP) can be affected by incomplete bolus coverage in veins and hypoperfused tissue, so-called bolus truncation (BT), and low contrast-to-noise ratio (CNR). We examined the BT-frequency and hypothesized that image down-sampling and a vascular model (VM) for perfusion calculation would improve normo- and hypoperfused tissue classification. METHODS: CTP datasets from 40 acute stroke patients were retrospectively analysed for BT. In 16 patients with hypoperfused tissue but no BT, repeated 2-by-2 image down-sampling and uniform filtering was performed, comparing CNR to perfusion-MRI levels and tissue classification to that of unprocessed data. By simulating reduced scan duration, the minimum scan-duration at which estimated lesion volumes came within 10% of their true volume was compared for VM and state-of-the-art algorithms. RESULTS: BT in veins and hypoperfused tissue was observed in 9/40 (22.5%) and 17/40 patients (42.5%), respectively. Down-sampling to 128 × 128 resolution yielded CNR comparable to MR data and improved tissue classification (p = 0.0069). VM reduced minimum scan duration, providing reliable maps of cerebral blood flow and mean transit time: 5 s (p = 0.03) and 7 s (p < 0.0001), respectively). CONCLUSIONS: BT is not uncommon in stroke CTP with 40-s scan duration. Applying image down-sampling and VM improve tissue classification. KEY POINTS: • Too-short imaging duration is common in clinical acute stroke CTP imaging. • The consequence is impaired identification of hypoperfused tissue in acute stroke patients. • The vascular model is less sensitive than current algorithms to imaging duration. • Noise reduction by image down-sampling improves identification of hypoperfused tissue by CTP.

Capillary dysfunction: its detection and causative role in dementias and stroke.

In acute ischemic stroke, critical hypoperfusion is a frequent cause of hypoxic tissue injury: As cerebral blood flow (CBF) falls below the ischemic threshold of 20 mL/100 mL/min, neurological symptoms develop and hypoxic tissue injury evolves within minutes or hours unless the oxygen supply is restored. But is ischemia the only hemodynamic source of hypoxic tissue injury? Reanalyses of the equations we traditionally use to describe the relation between CBF and tissue oxygenation suggest that capillary flow patterns are crucial for the efficient extraction of oxygen: without close capillary flow control, "functional shunts" tend to form and some of the blood's oxygen content in effect becomes inaccessible to tissue. This phenomenon raises several questions: Are there in fact two hemodynamic causes of tissue hypoxia: Limited blood supply (ischemia) and limited oxygen extraction due to capillary dysfunction? If so, how do we distinguish the two, experimentally and in patients? Do flow-metabolism coupling mechanisms adjust CBF to optimize tissue oxygenation when capillary dysfunction impairs oxygen extraction downstream? Cardiovascular risk factors such as age, hypertension, diabetes, hypercholesterolemia, and smoking increase the risk of both stroke and dementia. The capillary dysfunction phenomenon therefore forces us to consider whether changes in capillary morphology or blood rheology may play a role in the etiology of some stroke subtypes and in Alzheimer's disease. Here, we discuss whether certain disease characteristics suggest capillary dysfunction rather than primary flow-limiting vascular pathology and how capillary dysfunction may be imaged and managed.

The impact of reliable prebolus T 1 measurements or a fixed T 1 value in the assessment of glioma patients with dynamic contrast enhancing MRI.

INTRODUCTION: Accurate quantification of hemodynamic parameters using dynamic contrast enhanced (DCE) MRI requires a measurement of tissue T 1 prior to contrast injection (T 1). We evaluate (i) T 1 estimation using the variable flip angle (VFA) and the saturation recovery (SR) techniques and (ii) investigate if accurate estimation of DCE parameters outperform a time-saving approach with a predefined T 1 value when differentiating high- from low-grade gliomas. METHODS: The accuracy and precision of T 1 measurements, acquired by VFA and SR, were investigated by computer simulations and in glioma patients using an equivalence test (p > 0.05 showing significant difference). The permeability measure, K trans, cerebral blood flow (CBF), and - volume, V p, were calculated in 42 glioma patients, using fixed T 1 of 1500 ms or an individual T 1 measurement, using SR. The areas under the receiver operating characteristic curves (AUCs) were used as measures for accuracy to differentiate tumor grade. RESULTS: The T 1 values obtained by VFA showed larger variation compared to those obtained using SR both in the digital phantom and the human data (p > 0.05). Although a fixed T 1 introduced a bias into the DCE calculation, this had only minor impact on the accuracy differentiating high-grade from low-grade gliomas, (AUCfix = 0.906 and AUCind = 0.884 for K trans; AUCfix = 0.863 and AUCind = 0.856 for V p; p for AUC comparison > 0.05). CONCLUSION: T 1 measurements by VFA were less precise, and the SR method is preferable, when accurate parameter estimation is required. Semiquantitative DCE values, based on predefined T 1 values, were sufficient to perform tumor grading in our study.

Spatial distribution of malignant tissue in gliomas: correlations of 11C-L-methionine positron emission tomography and perfusion- and diffusion-weighted magnetic resonance imaging.

BACKGROUND: The prognosis of glioma patients is contingent on precise target selection for stereotactic biopsies and the extent of tumor resection. (11)C-L-methionine (MET) positron emission tomography (PET) demonstrates tumor heterogeneity and invasion with high diagnostic accuracy. PURPOSE: To compare the spatial tumor distribution delineated by MET PET with that by perfusion- and diffusion-weighted magnetic resonance imaging (MRI), in order to understand the diagnostic value of these MRI methods, when PET is not available. MATERIAL AND METHODS: Presurgical MET PET and MRI, including perfusion- and diffusion-weighted MRI, were acquired in 13 patients (7 high-grade gliomas, 6 low-grade gliomas). A quantitative volume of interest analysis was performed to compare the modalities objectively, supplemented by a qualitative evaluation that assessed the clinical applicability. RESULTS: The inaccuracy of conventional MRI was confirmed (area under the curve for predicting voxels with high MET uptake = 0.657), whereas cerebral blood volume (CBV) maps calculated from perfusion data improved accuracy (area under the curve = 0.760). We considered CBV maps diagnostically comparable to MET PET in 5/7 cases of high-grade gliomas, but insufficient in all cases of low-grade gliomas when evaluated subjectively. Cerebral blood flow and apparent diffusion coefficient maps did not contribute to further accuracy. CONCLUSION: Adding perfusion-weighted MRI to the presurgical protocol can increase the diagnostic accuracy of conventional MRI and is a simple and well-established method compared to MET PET. However, the definition of low-grade gliomas with subtle or no alterations on cerebral blood volume maps remains a diagnostic challenge for stand-alone MRI.

Remote ischemic perconditioning as an adjunct therapy to thrombolysis in patients with acute ischemic stroke: a randomized trial.

BACKGROUND AND PURPOSE: Remote ischemic preconditioning is neuroprotective in models of acute cerebral ischemia. We tested the effect of prehospital rPerC as an adjunct to treatment with intravenous alteplase in patients with acute ischemic stroke. METHODS: Open-label blinded outcome proof-of-concept study of prehospital, paramedic-administered rPerC at a 1:1 ratio in consecutive patients with suspected acute stroke. After neurological examination and MRI, patients with verified stroke receiving alteplase treatment were included and received MRI at 24 hours and 1 month and clinical re-examination after 3 months. The primary end point was penumbral salvage, defined as the volume of the perfusion-diffusion mismatch not progressing to infarction after 1 month. RESULTS: Four hundred forty-three patients were randomized after provisional consent, 247 received rPerC and 196 received standard treatment. Patients with a nonstroke diagnosis (n=105) were excluded from further examinations. The remaining patients had transient ischemic attack (n=58), acute ischemic stroke (n=240), or hemorrhagic stroke (n=37). Transient ischemic attack was more frequent (P=0.006), and National Institutes of Health Stroke Scale score on admission was lower (P=0.016) in the intervention group compared with controls. Penumbral salvage, final infarct size at 1 month, infarct growth between baseline and 1 month, and clinical outcome after 3 months did not differ among groups. After adjustment for baseline perfusion and diffusion lesion severity, voxelwise analysis showed that rPerC reduced tissue risk of infarction (P=0.0003). CONCLUSIONS: Although the overall results were neutral, a tissue survival analysis suggests that prehospital rPerC may have immediate neuroprotective effects. Future clinical trials should take such immediate effects, and their duration, into account. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00975962.

Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT-SEQUOIA.

BACKGROUND: Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time-consuming and labor-intensive. PURPOSE: To investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low-dose computed tomography (LDCT) as an anatomical reference for PET-assessed vascular disease. METHODS: A software pipeline was developed including automated segmentation using a 3D U-Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U-Net, 352 non-contrast LDCTs from (2-[18F]FDG and Na[18F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold-out internal test set. The remaining dataset was randomly split into training (n = 264; 80%) and validation (n = 66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values. RESULTS: Fully automated segmentation of the aorta using a 3D U-Net was feasible in LDCT obtained from PET/CT scans. The external test set yielded a DSC of 0.867 ± 0.030 and HD of 1.0 [0.6-1.4] mm, similar to an open-source model with a DSC of 0.864 ± 0.023 and HD of 1.4 [1.0-1.8] mm. Quantification of calcium and uptake values were in excellent agreement with clinical software (ICC: 1.00 [1.00-1.00] and 0.99 [0.93-1.00] for calcium and uptake values, respectively). CONCLUSIONS: We present an automated pipeline to segment the ascending aorta, aortic arch, descending aorta, and abdominal aorta on LDCT from PET/CT and to accurately provide uptake values, calcium scores, background measurement, radiomics features, and a 2D visualization. We call this algorithm SEQUOIA (SEgmentation, QUantification, and visualizatiOn of the dIseased Aorta) and is available at https://github.com/UMCG-CVI/SEQUOIA. This model could augment the utility of aortic evaluation at PET/CT studies tremendously, irrespective of the tracer, and potentially provide fast and reliable quantification of cardiovascular diseases in clinical practice, both for primary diagnosis and disease monitoring.