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Advanced design and experimental validation of MRI contrast agents for fluid pressure mapping using microbubbles
No full textThis work is related to monitoring fluid pressure using Magnetic Resonance Imaging or MRI and includes numerical simulations and experimental MRI. The nature of this study is such that techniques other than MRI have been extensively used to assess the contrast agent for its physical behaviour. These techniques include rheometry, light scattering, optical and scanning electron microscopy. Six MRI experiments in total were performed: The first two experiments use standard spin echo imaging techniques to test various lipid preparations which are then used as a contrast agent to pressure in a porous medium. The remaining experiments are performed using a fast imaging technique and investigate various improvements to the contrast agent which resulted in the development of an agent exhibiting an unprecedented level of sensitivity. A variety of lipid preparations are utilised throughout the experiments. Initial testing reveals that the DSPC lipid offers the greatest stability, although a fluorinated lipid is used in a later study for an improved synergy between the shell and gas microbubble components. Having assessed the microbubble stability, preparations are prepared as in the work previously published in the area. This preparation is tested in two porous media to investigate the sensitivity of the contrast agent to changes in pressure. A sensitivity of 20% signal change per bar is found in porous media although a drift of 11%h-1 is also observed. An improved preparation was then developed by using an alternative polysaccharide gel, gellan gum
Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration.
Get PDFOBJECTIVE: To quantify the regional and global cerebral atrophy rates and assess acceleration rates in healthy controls, subjects with mild cognitive impairment (MCI), and subjects with mild Alzheimer disease (AD). METHODS: Using 0-, 6-, 12-, 18-, 24-, and 36-month MRI scans of controls and subjects with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we calculated volume change of whole brain, hippocampus, and ventricles between all pairs of scans using the boundary shift integral. RESULTS: We found no evidence of acceleration in whole-brain atrophy rates in any group. There was evidence that hippocampal atrophy rates in MCI subjects accelerate by 0.22%/year2 on average (p = 0.037). There was evidence of acceleration in rates of ventricular enlargement in subjects with MCI (p = 0.001) and AD (p < 0.001), with rates estimated to increase by 0.27 mL/year2 (95% confidence interval 0.12, 0.43) and 0.88 mL/year2 (95% confidence interval 0.47, 1.29), respectively. A post hoc analysis suggested that the acceleration of hippocampal loss in MCI subjects was mainly driven by the MCI subjects that were observed to progress to clinical AD within 3 years of baseline, with this group showing hippocampal atrophy rate acceleration of 0.50%/year2 (p = 0.003). CONCLUSIONS: The small acceleration rates suggest a long period of transition to the pathologic losses seen in clinical AD. The acceleration in hippocampal atrophy rates in MCI subjects in the ADNI seems to be driven by those MCI subjects who concurrently progressed to a clinical diagnosis of AD
Signal-Intensity Informed Multi-Coil MRI Encoding Operator for Improved Physics-Guided Deep Learning Reconstruction of Dynamic Contrast-Enhanced MRI
No full textDynamic contrast enhanced (DCE) MRI acquires a series of images following the administration of a contrast agent, and plays an important clinical role in diagnosing various diseases. DCE MRI typically necessitates rapid imaging to provide sufficient spatio-temporal resolution and coverage. Conventional MRI acceleration techniques exhibit limited image quality at such high acceleration rates. Recently, deep learning (DL) methods have gained interest for improving highly-accelerated MRI. However, DCE MRI series show substantial variations in SNR and contrast across images. This hinders the quality and generalizability of DL methods, when applied across time frames. In this study, we propose signal intensity informed multi-coil MRI encoding operator for improved DL reconstruction of DCE MRI. The output of the corresponding inverse problem for this forward operator leads to more uniform contrast across time frames, since the proposed operator captures signal intensity variations across time frames while not altering the coil sensitivities. Our results in perfusion cardiac MRI show that high-quality images are reconstructed at very high acceleration rates, with substantial improvement over existing methods.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ImPhys/Computational ImagingImPhys/Medical Imagin
Deep Learning for 4D Longitudinal Segmentation of MRI Brain Tissues and Glioma
No full textGlioma is a kind of slow-growing brain tumor which may result in severe seizures. Currently a major tool used to detect and diagnose the glioma is MRI scan. To better analyze the medical image, segmentation is usually conducted as a basic step for further processing, which partitions an integrate image into multiple physically meaningful regions by annotating objects and boundaries. Deep learning based segmentation methods have attracted significant interest due to their high efficiency and strong generalization ability. With the increasing demands of high-quality segmentation of bio-tissues in medical region, plenty of innovative approaches were proposed to expand the boundary of segmentation capability of deep learning models by taking the spatial or temporal constraints of bio-structure into consideration. Although longitudinal segmentation in 2D natural image sequences has made a lot of success, the potential of deep learning network in segmenting a series of chronological 3DMRI images in terms of improving consistency remains unclear. This thesis aims to investigate whether deep learning models are able to increase segmentation accuracy as well as consistency in longitudinal 3D images, specifically focusing on introducing Recurrent Neural Network(RNN) to 3D Convolutional Neural Network(CNN) for 4D segmentation. In addition to the implementation of several U-Net variants as CNN backbone, three types of longitudinal connection strategies are proposed. A hierarchical workflow is followed to create the optimal version of longitudinal network based on combining multiple CNN variants and connection strategies. The evaluation of the 4D network shows that segmentation accuracy of the longitudinal model is limited by its CNN backbone and temporal information can partially improve the segmentation consistency with regard to maintaining the highest proportion of normal tissue unchangeable over time.Mechanical Engineering | Biomechanical Design - BioRobotic
Academic authorship: who, why and in what order?
Get PDFWe are frequently asked by our colleagues and students for advice on authorship for scientific articles. This short paper outlines some of the issues that we have experienced and the advice we usually provide. This editorial follows on from our work on submitting a paper1 and also on writing an academic paper for publication.2 We should like to start by noting that, in our view, there exist two separate, but related issues: (a) authorship and (b) order of authors. The issue of authorship centres on the notion of who can be an author, who should be an author and who definitely should not be an author, and this is partly discipline specific. The second issue, the order of authors, is usually dictated by the academic tradition from which the work comes. One can immediately envisage disagreements within a multi-disciplinary team of researchers where members of the team may have different approaches to authorship order
Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data
No full textDiffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample
An evaluation of prospective motion correction (PMC) for high resolution quantitative MRI.
Get PDFQuantitative imaging aims to provide in vivo neuroimaging biomarkers with high research and diagnostic value that are sensitive to underlying tissue microstructure. In order to use these data to examine intra-cortical differences or to define boundaries between different myelo-architectural areas, high resolution data are required. The quality of such measurements is degraded in the presence of motion hindering insight into brain microstructure. Correction schemes are therefore vital for high resolution, whole brain coverage approaches that have long acquisition times and greater sensitivity to motion. Here we evaluate the use of prospective motion correction (PMC) via an optical tracking system to counter intra-scan motion in a high resolution (800 μm isotropic) multi-parameter mapping (MPM) protocol. Data were acquired on six volunteers using a 2 × 2 factorial design permuting the following conditions: PMC on/off and motion/no motion. In the presence of head motion, PMC-based motion correction considerably improved the quality of the maps as reflected by fewer visible artifacts and improved consistency. The precision of the maps, parameterized through the coefficient of variation in cortical sub-regions, showed improvements of 11-25% in the presence of deliberate head motion. Importantly, in the absence of motion the PMC system did not introduce extraneous artifacts into the quantitative maps. The PMC system based on optical tracking offers a robust approach to minimizing motion artifacts in quantitative anatomical imaging without extending scan times. Such a robust motion correction scheme is crucial in order to achieve the ultra-high resolution required of quantitative imaging for cutting edge in vivo histology applications
Scoping clinicians’ perspectives on pre-treatment multidisciplinary care for young women with breast cancer
No full textArden L Corter,1 May Lynn Quan,2 Frances L Wright,3 Erin D Kennedy,4 Marko RI Simunovic,5 Juliet Shao,1 Nancy N Baxter1,6 1Department of Surgery, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada; 2Department of Surgery and Oncology, University of Calgary, Calgary, Canada; 3Department of Surgery, University of Toronto, Toronto, Canada; 4Division of General Surgery, University Health Network, Mount Sinai Hospital, Toronto, Canada; 5Department of Surgery, McMaster University, Hamilton, Canada; 6Dalla Lana School of Public Health, University of Toronto, Toronto, Canada Background: Young women with breast cancer (YWBC) experience worse medical and psychosocial outcomes than their older counterparts. Early input from a multidisciplinary team via pre-treatment multidisciplinary cancer conferences (pMCCs) may be important for addressing the complex needs of YWBC. However, pMCCs are not common. This study has two parts: a survey and workshop aimed at assessing clinicians’ perspectives on pMCCs, including the importance of pMCCs in the care of YWBC, as well as barriers to, and strategies for supporting their implementation.Methods: Survey results highlight variability across sites in the delivery of multidisciplinary care in general. However, both survey and workshop results emphasize clinicians’ agreement on the importance of pMCCs and suggest that numerous practical and systems levels barriers be addressed before pMCCs can be implemented.Conclusions: pMCCs have the potential to improve surgical treatment and psychosocial outcomes for YWBC. A combined practical and policy approach to their implementation, which sees extension of existing standards to include pMCCs, may support their adoption and subsequent audit practices to assess the effect of pMCCs on outcomes for YWBC. Keywords: multidisciplinary care, pre-treatment, cancer conference, breast cancer, young wome
K-Space Trajectory Design for Reduced MRI Scan Time
No full textThe development of compressed sensing (CS) techniques for magnetic resonance imaging (MRI) is enabling a speedup of MRI scanning. To increase the incoherence in the sampling, a random selection of points on the k-space is deployed and a continuous trajectory is obtained by solving a traveling salesman problem (TSP) through these points. A feasible trajectory satisfying the gradient constraints is then obtained by parameterizing it using state-of-the-art methods. In this paper, a constrained convex optimization based method to obtain feasible trajectories is proposed. The method is motivated by the fact that the readout time is proportional to the number of sample points and includes the lengths of the segments of the trajectory in the cost function to obtain variable length trajectories. The proposed method provides a reduction in readout time by more than 50% for random-like trajectories with an improvement of about 1.5 dB in peak signal-to-noise ratio (PSNR) and 0.0762 in structural similarity (SSIM) index on average for a realistic brain phantom MRI image adopting single-shot trajectories.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
Learning-based method for k-space trajectory design in MRI
No full textVariable density sampling of the k-space in MRI is an integral part of trajectory design. It has been observed that data-driven trajectory design methods provide a better image reconstruction as compared to trajectories obtained from a fixed or a parametric density function. In this paper, a data-driven strategy has been proposed to obtain non-Cartesian continuous k-space sampling trajectories for MRI under the compressed sensing framework (greedy non-Cartesian (GNC)). A stochas-tic version of the algorithm (stochastic greedy non-Cartesian (SGNC)) is also proposed that reduces the computation time. We compare the proposed trajectory with a traveling salesman problem (TSP)-based trajectory and an echo planar imaging-like trajectory obtained by a greedy method called stochastic greedy-Cartesian (SGC) algorithm. The training images are taken from knee images of the fastMRI dataset. It is observed that the proposed algorithms outperform the TSP-based and the SGC trajectories for similar read-out times.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
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