RESUMO
Dynamic contrast-enhanced MRI (DCE-MRI) is increasingly used to quantify and map the spatial distribution of blood-brain barrier (BBB) leakage in neurodegenerative disease, including cerebral small vessel disease and dementia. However, the subtle nature of leakage and resulting small signal changes make quantification challenging. While simplified one-dimensional simulations have probed the impact of noise, scanner drift, and model assumptions, the impact of spatio-temporal effects such as gross motion, k-space sampling and motion artefacts on parametric leakage maps has been overlooked. Moreover, evidence on which to base the design of imaging protocols is lacking due to practical difficulties and the lack of a reference method. To address these problems, we present an open-source computational model of the DCE-MRI acquisition process for generating four dimensional Digital Reference Objects (DROs), using a high-resolution brain atlas and incorporating realistic patient motion, extra-cerebral signals, noise and k-space sampling. Simulations using the DROs demonstrated a dominant influence of spatio-temporal effects on both the visual appearance of parameter maps and on measured tissue leakage rates. The computational model permits greater understanding of the sensitivity and limitations of subtle BBB leakage measurement and provides a non-invasive means of testing and optimising imaging protocols for future studies.
Assuntos
Barreira Hematoencefálica/diagnóstico por imagem , Simulação por Computador , Meios de Contraste , Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Doenças Neurodegenerativas/diagnóstico por imagem , Artefatos , Barreira Hematoencefálica/metabolismo , Permeabilidade Capilar/fisiologia , Doenças de Pequenos Vasos Cerebrais/diagnóstico por imagem , Doenças de Pequenos Vasos Cerebrais/metabolismo , Meios de Contraste/metabolismo , Humanos , Modelos Neurológicos , Movimento (Física) , Doenças Neurodegenerativas/metabolismoRESUMO
Interpretation of high resolution images provided by localization-based microscopy techniques is a challenge due to imaging artefacts that can be categorized by their origin. They can be introduced by the optical system, by the studied sample or by the applied algorithms. Some artefacts can be eliminated via precise calibration procedures, others can be reduced only below a certain value. Images studied both theoretically and experimentally are qualified either by pattern specific metrics or by a more general metric based on fluorescence correlation spectroscopy.
Assuntos
Artefatos , Microscopia de Fluorescência/métodos , Algoritmos , CalibragemRESUMO
Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO's performance does not deteriorate as PVS volume increases. Second, the performance of all "vesselness" filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.
Assuntos
Sistema Glinfático , Acidente Vascular Cerebral , Encéfalo/diagnóstico por imagem , Encéfalo/patologia , Humanos , Imageamento por Ressonância Magnética/métodos , Acidente Vascular Cerebral/patologiaRESUMO
In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19-54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.
RESUMO
In this work PeakForce tapping (PFT) imaging was demonstrated with carbon nanotube atomic force microscopy (CNT-AFM) probes; this imaging mode shows great promise for providing simple, stable imaging with CNT-AFM probes, which can be difficult to apply. The PFT mode is used with CNT-AFM probes to demonstrate high resolution imaging on samples with features in the nanometre range, including a Nioprobe calibration sample and gold nanoparticles on silicon, in order to demonstrate the modes imaging effectiveness, and to also aid in determining the diameter of very thin CNT-AFM probes. In addition to stable operation, the PFT mode is shown to eliminate "ringing" artefacts that often affect CNT-AFM probes in tapping mode near steep vertical step edges. This will allow for the characterization of high aspect ratio structures using CNT-AFM probes, an exercise which has previously been challenging with the standard tapping mode.