Evaluating the effect of noise reduction strategies in CT perfusion imaging for predicting infarct core with deep learning.

This study evaluates the efficacy of deep learning models in identifying infarct tissue on computed tomography perfusion (CTP) scans from patients with acute ischemic stroke due to large vessel occlusion, specifically addressing the potential influence of varying noise reduction techniques implemented by different vendors. We analyzed CTP scans from 60 patients who underwent mechanical thrombectomy achieving a modified thrombolysis in cerebral infarction (mTICI) score of 2c or 3, ensuring minimal changes in the infarct core between the initial CTP and follow-up MR imaging. Noise reduction techniques, including principal component analysis (PCA), wavelet, non-local means (NLM), and a no denoising approach, were employed to create hemodynamic parameter maps.

Myocardial T1 mapping using an instantaneous signal loss simulation modeling and a Bayesian estimation method: A robust T1 extraction method free of tuning parameters.

The Instantaneous Signal Loss Simulation (InSiL) model is a promising alternative to the classical mono-exponential fitting of the Modified Look-Locker Inversion-recovery (MOLLI) sequence in cardiac T mapping applications, which achieves better accuracy and is less sensitive to heart rate (HR) variations. Classical non-linear least squares (NLLS) estimation methods require some parameters of the model to be fixed a priori in order to give reliable T estimations and avoid outliers. This introduces further bias in the estimation, reducing the advantages provided by the InSiL model.

Cerebral collaterals are associated with pre-treatment brain-blood barrier permeability in acute ischemic stroke patients.

Introduction: To investigate the relationship between collaterals and blood-brain barrier (BBB) permeability on pre-treatment MRI in a cohort of acute ischemic stroke (AIS) patients treated with thrombectomy.

Methods: We conducted a retrospective analysis of the HIBISCUS-STROKE cohort, a single-center observational study that enrolled patients treated with thrombectomy from 2016 to 2022. Dynamic-susceptibility MRIs were post-processed to generate K2 maps with arrival-time correction, which were co-registered with apparent diffusion coefficient (ADC) maps.

The PREMISE database of 20 Macaca fascicularis PET/MRI brain images available for research.

Non-human primate studies are unique in translational research, especially in neurosciences where neuroimaging approaches are the preferred methods used for cross-species comparative neurosciences. In this regard, neuroimaging database development and sharing are encouraged to increase the number of subjects available to the community, while limiting the number of animals used in research. Here we present a simultaneous positron emission tomography (PET)/magnetic resonance (MR) dataset of 20 Macaca fascicularis images structured according to the Brain Imaging Data Structure standards.

Fast WASABI post-processing: Access to rapid B and B correction in clinical routine for CEST MRI.

CEST MRI methods, such as APT and NOE imaging reveal biomarkers with significant diagnostic potential due to their ability to access molecular tissue information. Regardless of the technique used, CEST MRI data are affected by static magnetic B and radiofrequency B field inhomogeneities that degrade their contrast. For this reason, the correction of B field-induced artefacts is essential, whereas accounting for B field inhomogeneities have shown significant improvements in image readability.

Blood-Brain Barrier Permeability and Kinetics of Inflammatory Markers in Acute Stroke Patients Treated With Thrombectomy.

Background And Objectives: The aim of this study was to investigate the relationship between baseline blood-brain barrier (BBB) permeability and the kinetics of circulating inflammatory markers in a cohort of acute ischemic stroke (AIS) patients treated with mechanical thrombectomy.

Methods: The CoHort of Patients to Identify Biological and Imaging markerS of CardiovascUlar Outcomes in Stroke includes AIS patients treated with mechanical thrombectomy after admission MRI and undergoing a sequential assessment of circulating inflammatory markers. Baseline dynamic susceptibility perfusion MRI was postprocessed with arrival time correction to provide K2 maps reflecting BBB permeability.

Identification of infarct core and ischemic penumbra using computed tomography perfusion and deep learning.

Purpose: The size and location of infarct and penumbra are key to decision-making for acute ischemic stroke (AIS) management. CT perfusion (CTP) software estimate infarct and penumbra volume using contralateral hemisphere relative thresholding. This approach is not robust and widely contested by the scientific community.

Clinical Imaging of the Penumbra in Ischemic Stroke: From the Concept to the Era of Mechanical Thrombectomy.

The ischemic penumbra is defined as the severely hypoperfused, functionally impaired, at-risk but not yet infarcted tissue that will be progressively recruited into the infarct core. Early reperfusion aims to save the ischemic penumbra by preventing infarct core expansion and is the mainstay of acute ischemic stroke therapy. Intravenous thrombolysis and mechanical thrombectomy for selected patients with large vessel occlusion has been shown to improve functional outcome.

Direct Comparison of Bayesian and Fermi Deconvolution Approaches for Myocardial Blood Flow Quantification: and Clinical Validations.

Cardiac magnetic resonance myocardial perfusion imaging can detect coronary artery disease and is an alternative to single-photon emission computed tomography or positron emission tomography. However, the complex, non-linear MR signal and the lack of robust quantification of myocardial blood flow have hindered its widespread clinical application thus far. Recently, a new Bayesian approach was developed for brain imaging and evaluation of perfusion indexes (Kudo et al.

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status.

Background: Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status.

Methods: Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified.

Computed Tomography Perfusion Measurements in Renal Lesions Obtained by Bayesian Estimation, Advanced Singular-Value Decomposition Deconvolution, Maximum Slope, and Patlak Models: Intermodel Agreement and Diagnostic Accuracy of Tumor Classification.

Objectives: The aims of this study were to evaluate the agreement of computed tomography (CT)-perfusion parameter values of the normal renal cortex and various renal tumors, which were obtained by different mathematical models, and to evaluate their diagnostic accuracy.

Materials And Methods: Perfusion imaging was performed prospectively in 35 patients to analyze 144 regions of interest of the normal renal cortex and 144 regions of interest of renal tumors, including 21 clear-cell renal cell carcinomas (RCC), 6 papillary RCCs, 5 oncocytomas, 1 chromophobe RCC, 1 angiomyolipoma with minimal fat, and 1 tubulocystic RCC. Identical source data were postprocessed and analyzed on 2 commercial software applications with the following implemented mathematical models: maximum slope, Patlak plot, standard singular-value decomposition (SVD), block-circulant SVD, oscillation-limited block-circulant SVD, and Bayesian estimation technique.

Perfusion deconvolution in DSC-MRI with dispersion-compliant bases.

Perfusion imaging of the brain via Dynamic Susceptibility Contrast MRI (DSC-MRI) allows tissue perfusion characterization by recovering the tissue impulse response function and scalar parameters such as the cerebral blood flow (CBF), blood volume (CBV), and mean transit time (MTT). However, the presence of bolus dispersion causes the data to reflect macrovascular properties, in addition to tissue perfusion. In this case, when performing deconvolution of the measured arterial and tissue concentration time-curves it is only possible to recover the effective, i.

Bayesian estimation of cerebral perfusion using reduced-contrast-dose dynamic susceptibility contrast perfusion at 3T.

Background And Purpose: DSC perfusion has been increasingly used in conjunction with other contrast-enhanced MR applications and therefore there is need for contrast-dose reduction when feasible. The purpose of this study was to establish the feasibility of reduced-contrast-dose brain DSC perfusion by using a probabilistic Bayesian method and to compare the results with the commonly used singular value decomposition technique.

Materials And Methods: Half-dose (0.

Bayesian analysis of perfusion-weighted imaging to predict infarct volume: comparison with singular value decomposition.

Purpose: We compared the performances of a Bayesian estimation method and oscillation index singular value decomposition (oSVD) deconvolution for predicting final infarction using data previously obtained from 10 cynomolgus monkeys with permanent unilateral middle cerebral artery (MCA) occlusion.

Methods: We conducted baseline perfusion-weighted imaging 3 hours after MCA occlusion and generated time to peak, first moment of transit, cerebral blood flow, cerebral blood volume, and mean transit time maps using Bayesian and oSVD methods. Final infarct volume was determined by follow-up diffusion-weighted imaging (DWI) scanned 47 hours after MCA occlusion and from histological specimens.

Assessment of the accuracy of a Bayesian estimation algorithm for perfusion CT by using a digital phantom.

Introduction: A new deconvolution algorithm, the Bayesian estimation algorithm, was reported to improve the precision of parametric maps created using perfusion computed tomography. However, it remains unclear whether quantitative values generated by this method are more accurate than those generated using optimized deconvolution algorithms of other software packages. Hence, we compared the accuracy of the Bayesian and deconvolution algorithms by using a digital phantom.

Bayesian hemodynamic parameter estimation by bolus tracking perfusion weighted imaging.

A delay-insensitive probabilistic method for estimating hemodynamic parameters, delays, theoretical residue functions, and concentration time curves by computed tomography (CT) and magnetic resonance (MR) perfusion weighted imaging is presented. Only a mild stationarity hypothesis is made beyond the standard perfusion model. New microvascular parameters with simple hemodynamic interpretation are naturally introduced.

Detection of gamma rays from a starburst galaxy.

Starburst galaxies exhibit in their central regions a highly increased rate of supernovae, the remnants of which are thought to accelerate energetic cosmic rays up to energies of approximately 10(15) electron volts. We report the detection of gamma rays--tracers of such cosmic rays--from the starburst galaxy NGC 253 using the High Energy Stereoscopic System (H.E.

Radio imaging of the very-high-energy gamma-ray emission region in the central engine of a radio galaxy.

The accretion of matter onto a massive black hole is believed to feed the relativistic plasma jets found in many active galactic nuclei (AGN). Although some AGN accelerate particles to energies exceeding 10(12) electron volts and are bright sources of very-high-energy (VHE) gamma-ray emission, it is not yet known where the VHE emission originates. Here we report on radio and VHE observations of the radio galaxy Messier 87, revealing a period of extremely strong VHE gamma-ray flares accompanied by a strong increase of the radio flux from its nucleus.

Energy spectrum of cosmic-ray electrons at TeV energies.

The very large collection area of ground-based gamma-ray telescopes gives them a substantial advantage over balloon or satellite based instruments in the detection of very-high-energy (>600 GeV) cosmic-ray electrons. Here we present the electron spectrum derived from data taken with the High Energy Stereoscopic System (H.E.

Limits on an energy dependence of the speed of light from a flare of the active galaxy PKS 2155-304.

In the past few decades, several models have predicted an energy dependence of the speed of light in the context of quantum gravity. For cosmological sources such as active galaxies, this minuscule effect can add up to measurable photon-energy dependent time lags. In this Letter a search for such time lags during the High Energy Stereoscopic System observations of the exceptional very high energy flare of the active galaxy PKS 2155-304 on 28 July 2006 is presented.