المؤلفون: Siow, Bernard, Drobnjak, Ivana, Ianus, Andrada, Christie, Isabel N., Lythgoe, Mark F., Alexander, Daniel C.

مرشدي الرسالة: University College London, Universität Leipzig

المصدر: Diffusion fundamentals. 18(1):1-6

مصطلحات موضوعية: info:eu-repo/classification/ddc/530, ddc:530, Diffusion, Transport, diffusion, transport

الوصف: The estimation of axon radius provides insights into brain function [1] and could provide progression and classification biomarkers for a number of white matter diseases [2-4]. A recent in silico study [5] has shown that optimised gradient waveforms (GEN) and oscillating gradient waveform spin echo (OGSE) have increased sensitivity to small axon radius compared to pulsed gradient spin echo (PGSE) diffusion MR sequences. In a follow-up study [6], experiments with glass capillaries show the practical feasibility of GEN sequences and verify improved pore-size estimates. Here, we compare PGSE with sine, sine with arbitrary phase, and square wave OGSE (SNOGSE, SPOGSE, SWOGSE, respectively) for axon radius mapping in the corpus callosum of a rat, ex-vivo. Our results suggest improvements in pore size estimates from OGSE over PGSE, with greatest improvement from SWOGSE, supporting theoretical results from [5] and other studies [7-9].

العلاقة: urn:nbn:de:bsz:15-qucosa-178897; qucosa:13496

الإتاحة: https://ul.qucosa.de/id/qucosa%3A13707Test
https://ul.qucosa.de/api/qucosa%3A13707/attachment/ATT-0Test/

المؤلفون: Schilling, Kurt G, Grussu, Francesco, Ianus, Andrada, Hansen, Brian, Barrett, Rachel L C, Aggarwal, Manisha, Michielse, Stijn, Nasrallah, Fatima, Syeda, Warda, Wang, Nian, Veraart, Jelle, Roebroeck, Alard, Bagdasarian, Andrew F, Eichner, Cornelius, Sepehrband, Farshid, Zimmermann, Jan, Soustelle, Lucas, Bowman, Christien, Tendler, Benjamin C, Hertanu, Andreea, Jeurissen, Ben, Frydman, Lucio, van de Looij, Yohan, Hike, David, Dunn, Jeff F, Miller, Karla, Landman, Bennett A, Shemesh, Noam, Anderson, Adam, McKinnon, Emilie, Farquharson, Shawna, Acqua, Flavio Dell', Pierpaoli, Carlo, Drobnjak, Ivana, Leemans, Alexander, Harkins, Kevin D, Descoteaux, Maxime, Xu, Duan, Huang, Hao, Santin, Mathieu D, Grant, Samuel C., Obenaus, Andre, Kim, Gene S, Wu, Dan, Bihan, Denis Le, Blackband, Stephen J, Ciobanu, Luisa, Fieremans, Els, Bai, Ruiliang, Leergaard, Trygve, Zhang, Jiangyang, Dyrby, Tim B, Johnson, G Allan, Cohen-Adad, Julien, Budde, Matthew D, Jelescu, Ileana O

مصطلحات موضوعية: Physics - Medical Physics, Quantitative Biology - Tissues and Organs

الوصف: The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio and spatial resolution compared to in vivo studies, and more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage is direct comparison with histological data as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging, but ultimately affect what questions can be answered using the data. This work represents 'Part 2' of a series of recommendations and considerations for preclinical dMRI, where we focus on best practices for dMRI of ex vivo tissue. We first describe the value that ex vivo imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We then give guidelines for ex vivo protocols, including tissue preparation, imaging sequences and data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available ex vivo dMRI datasets and dedicated software packages. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist, and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
Comment: 59 pages, 12 figures, part of ongoing efforts on ISMRM Diffusion Study Group initiative 'Best Practices (Consensus) for diffusion MRI'. arXiv admin note: text overlap with arXiv:2209.12994

الوصول الحر: http://arxiv.org/abs/2209.13371Test

المؤلفون: Jelescu, Ileana O, Grussu, Francesco, Ianus, Andrada, Hansen, Brian, Barrett, Rachel L C, Aggarwal, Manisha, Michielse, Stijn, Nasrallah, Fatima, Syeda, Warda, Wang, Nian, Veraart, Jelle, Roebroeck, Alard, Bagdasarian, Andrew F, Eichner, Cornelius, Sepehrband, Farshid, Zimmermann, Jan, Soustelle, Lucas, Bowman, Christien, Tendler, Benjamin C, Hertanu, Andreea, Jeurissen, Ben, Verhoye, Marleen, Frydman, Lucio, van de Looij, Yohan, Hike, David, Dunn, Jeff F, Miller, Karla, Landman, Bennett A, Shemesh, Noam, Anderson, Adam, McKinnon, Emilie, Farquharson, Shawna, Acqua, Flavio Dell', Pierpaoli, Carlo, Drobnjak, Ivana, Leemans, Alexander, Harkins, Kevin D, Descoteaux, Maxime, Xu, Duan, Huang, Hao, Santin, Mathieu D, Grant, Samuel C., Obenaus, Andre, Kim, Gene S, Wu, Dan, Bihan, Denis Le, Blackband, Stephen J, Ciobanu, Luisa, Fieremans, Els, Bai, Ruiliang, Leergaard, Trygve, Zhang, Jiangyang, Dyrby, Tim B, Johnson, G Allan, Cohen-Adad, Julien, Budde, Matthew D, Schilling, Kurt G

مصطلحات موضوعية: Physics - Medical Physics, Quantitative Biology - Tissues and Organs

الوصف: The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
Comment: 69 pages, 6 figures, 1 table

الوصول الحر: http://arxiv.org/abs/2209.12994Test

المؤلفون: Borges, Pedro, Shaw, Richard, Varsavsky, Thomas, Klaser, Kerstin, Thomas, David, Drobnjak, Ivana, Ourselin, Sebastien, Cardoso, M Jorge

مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Medical Physics

الوصف: Combining multi-site data can strengthen and uncover trends, but is a task that is marred by the influence of site-specific covariates that can bias the data and therefore any downstream analyses. Post-hoc multi-site correction methods exist but have strong assumptions that often do not hold in real-world scenarios. Algorithms should be designed in a way that can account for site-specific effects, such as those that arise from sequence parameter choices, and in instances where generalisation fails, should be able to identify such a failure by means of explicit uncertainty modelling. This body of work showcases such an algorithm, that can become robust to the physics of acquisition in the context of segmentation tasks, while simultaneously modelling uncertainty. We demonstrate that our method not only generalises to complete holdout datasets, preserving segmentation quality, but does so while also accounting for site-specific sequence choices, which also allows it to perform as a harmonisation tool.
Comment: 25 pages, 8 figures

الوصول الحر: http://arxiv.org/abs/2111.04094Test

المؤلفون: Borges, Pedro, Shaw, Richard, Varsavsky, Thomas, Klaser, Kerstin, Thomas, David, Drobnjak, Ivana, Ourselin, Sebastien, Cardoso, M Jorge

مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Medical Physics

الوصف: Being able to adequately process and combine data arising from different sites is crucial in neuroimaging, but is difficult, owing to site, sequence and acquisition-parameter dependent biases. It is important therefore to design algorithms that are not only robust to images of differing contrasts, but also be able to generalise well to unseen ones, with a quantifiable measure of uncertainty. In this paper we demonstrate the efficacy of a physics-informed, uncertainty-aware, segmentation network that employs augmentation-time MR simulations and homogeneous batch feature stratification to achieve acquisition invariance. We show that the proposed approach also accurately extrapolates to out-of-distribution sequence samples, providing well calibrated volumetric bounds on these. We demonstrate a significant improvement in terms of coefficients of variation, backed by uncertainty based volumetric validation.
Comment: 10 pages, 3 figures, published in: Simulation and Synthesis in Medical Imaging 6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

الوصول الحر: http://arxiv.org/abs/2111.02771Test

المؤلفون: Borges, Pedro, Sudre, Carole, Varsavsky, Thomas, Thomas, David, Drobnjak, Ivana, Ourselin, Sebastien, Cardoso, M. Jorge

مصطلحات موضوعية: Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing

الوصف: Magnetic Resonance Imaging (MRI) is one of the most flexible and powerful medical imaging modalities. This flexibility does however come at a cost; MRI images acquired at different sites and with different parameters exhibit significant differences in contrast and tissue appearance, resulting in downstream issues when quantifying brain anatomy or the presence of pathology. In this work, we propose to combine multiparametric MRI-based static-equation sequence simulations with segmentation convolutional neural networks (CNN), to make these networks robust to variations in acquisition parameters. Results demonstrate that, when given both the image and their associated physics acquisition parameters, CNNs can produce segmentations that exhibit robustness to acquisition variations. We also show that the proposed physics-informed methods can be used to bridge multi-centre and longitudinal imaging studies where imaging acquisition varies across a site or in time.
Comment: Accepted at SASHIMI2019

الوصول الحر: http://arxiv.org/abs/2001.10767Test

المؤلفون: Van Niekerk, Daniel, Peréz-Ortiz, María, Shawe-Taylor, John, Orlic, Davor, Drobnjak, ivana, Kay, Jackie, Siegel, Noah, Evans, Katherine, Moorosi, Nyalleng, Eliassi-Rad, Tina, Tanczer, Leonie Maria, Holmes, Wayne, Deisenroth, Marc Peter, Straw, Isabel, Fasli, Maria, Adams, Rachel, Oliver, Nuria, Mladenić, Dunja, Aneja, Urvashi

المصدر: UNESCO, IRCAI: Paris, France.

مصطلحات موضوعية: Artificial Intelligence, Gender, Bias

الوصف: The International Research Centre on Artificial Intelligence (IRCAI), under the auspices of UNESCO, in collaboration with UNESCO HQ, has released a comprehensive report titled “Challenging Systematic Prejudices: An Investigation into Gender Bias in Large Language Models”. This groundbreaking study sheds light on the persistent issue of gender bias within artificial intelligence, emphasizing the importance of implementing normative frameworks to mitigate these risks and ensure fairness in AI systems globally. We are excited to announce this new in-depth report in a partnership with a number of authors, set for release on International Women’s Day on March 8, 2024.

وصف الملف: text

العلاقة: https://discovery.ucl.ac.uk/id/eprint/10188772/1/388971eng.pdfTest; https://discovery.ucl.ac.uk/id/eprint/10188772Test/

الإتاحة: https://discovery.ucl.ac.uk/id/eprint/10188772/1/388971eng.pdfTest
https://discovery.ucl.ac.uk/id/eprint/10188772Test/

المؤلفون: Hill, Ioana, Palombo, Marco, Santin, Mathieu, Branzoli, Francesca, Philippe, Anne-Charlotte, Wassermann, Demian, Aigrot, Marie-Stephane, Stankoff, Bruno, Evercooren, Anne Baron-Van, Felfi, Mehdi, Langui, Dominique, Zhang, Hui, Lehericy, Stephane, Petiet, Alexandra, Alexander, Daniel C., Ciccarelli, Olga, Drobnjak, Ivana

مصطلحات موضوعية: Physics - Medical Physics, Quantitative Biology - Neurons and Cognition

الوصف: The intra-axonal water exchange time {\tau}i, a parameter associated with axonal permeability, could be an important biomarker for understanding demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI is sensitive to changes in permeability, however, the parameter has remained elusive due to the intractability of the mathematical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters, and recently, a theoretical framework using a random forest (RF) suggests this is a promising approach. In this study, we adopt such an RF approach and experimentally investigate its suitability as a biomarker for demyelinating pathologies through direct comparison with histology. For this, we use an in-vivo cuprizone (CPZ) mouse model of demyelination with available ex-vivo electron microscopy (EM) data. We test our model on noise-free simulations and find very strong correlations between the predicted and ground truth parameters. For realistic noise levels as in our in-vivo data, the performance is affected, however, the parameters are still well estimated. We apply our RF model on in-vivo data from 8 CPZ and 8 wild-type (WT) mice and validate the RF estimates using histology. We find a strong correlation between the in-vivo RF estimates of {\tau}i and the EM measurements of myelin thickness ({\rho_\tau}i = 0.82), and between RF estimates and EM measurements of intra-axonal volume fraction ({\rho_f} = 0.98). When comparing {\tau}i in CPZ and WT mice we find a statistically significant decrease in the corpus callosum of the CPZ compared to the WT mice, in line with our expectations that {\tau}i is lower in regions where the myelin sheath is damaged. Overall, these results demonstrate the suitability of machine learning compartment models with permeability as a potential biomarker for demyelinating pathologies.

الوصول الحر: http://arxiv.org/abs/1907.02324Test

المؤلفون: Ianuş, Andrada, Jespersen, Sune N., Duarte, Teresa Serradas, Alexander, Daniel C., Drobnjak, Ivana, Shemesh, Noam

مصطلحات موضوعية: Physics - Medical Physics

الوصف: Microscopic diffusion anisotropy ({\mu}A) has been recently gaining increasing attention for its ability to decouple the average compartment anisotropy from orientation dispersion. Advanced diffusion MRI sequences, such as double diffusion encoding (DDE) and double oscillating diffusion encoding (DODE) have been used for mapping {\mu}A. However, the time-dependence of {\mu}A has not been investigated insofar, and furthermore, the accuracy of {\mu}A estimation vis-\`a-vis different b-values was not assessed. Here, we investigate both these concepts using theory, simulation, and experiments in the mouse brain. In the first part, simulations and experimental results show that the conventional estimation of microscopic anisotropy from the difference of D(O)DE sequences with parallel and orthogonal gradient directions yields values that highly depend on the choice of b-value. To mitigate this undesirable bias, we propose a multi-shell approach that harnesses a polynomial fit of the signal difference up to third order terms in b-value. In simulations, this approach yields more accurate {\mu}A metrics, which are similar to the ground truth values. The second part of this work uses the proposed multi-shell method to estimate the time/frequency dependence of {\mu}A. The data shows either an increase or no change in {\mu}A with frequency depending on the region of interest, both in white and gray matter. When comparing the experimental results with simulations, it emerges that simple geometric models such as infinite cylinders with either negligible or finite radii cannot replicate the measured trend, and more complex models, which, for example, incorporate structure along the fibre direction are required. Thus, measuring the time dependence of microscopic anisotropy can provide valuable information for characterizing tissue microstructure.

الوصول الحر: http://arxiv.org/abs/1712.01954Test

المؤلفون: Warner, William, Palombo, Marco, Cruz, Renata, Callaghan, Ross, Shemesh, Noam, Jones, Derek K., Dell'Acqua, Flavio, Ianus, Andrada, Drobnjak, Ivana

الوصف: Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes ...

وصف الملف: application/pdf

العلاقة: https://orca.cardiff.ac.uk/id/eprint/156569/3/Palombo.%20Temporal%20Diffusion%20Ratio.pdfTest; Warner, William, Palombo, Marco https://orca.cardiff.ac.uk/view/cardiffauthors/A2668511F.htmlTest orcid:0000-0003-4892-7967 orcid:0000-0003-4892-7967, Cruz, Renata, Callaghan, Ross, Shemesh, Noam, Jones, Derek K. https://orca.cardiff.ac.uk/view/cardiffauthors/A023147D.htmlTest orcid:0000-0003-4409-8049 orcid:0000-0003-4409-8049, Dell'Acqua, Flavio, Ianus, Andrada and Drobnjak, Ivana 2023. Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: optimisation and pre-clinical demonstration. NeuroImage 269 , 119930. 10.1016/j.neuroimage.2023.119930 https://doi.org/10.1016/j.neuroimage.2023.119930Test file https://orca.cardiff.ac.uk/id/eprint/156569/3/Palombo.%20Temporal%20Diffusion%20Ratio.pdfTest

الإتاحة: https://doi.org/10.1016/j.neuroimage.2023.119930Test
https://orca.cardiff.ac.uk/id/eprint/156569Test/
https://orca.cardiff.ac.uk/id/eprint/156569/3/Palombo.%20Temporal%20Diffusion%20Ratio.pdfTest