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1. Pre-processing and quality control of large clinical CT head datasets for intracranial arterial calcification segmentation

Author

Jin, Benjamin, Hernández, Maria del C. Valdés, Fontanella, Alessandro, Li, Wenwen, Platt, Eleanor, Armitage, Paul, Storkey, Amos, Wardlaw, Joanna M., and Mair, Grant

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

As a potential non-invasive biomarker for ischaemic stroke, intracranial arterial calcification (IAC) could be used for stroke risk assessment on CT head scans routinely acquired for other reasons (e.g. trauma, confusion). Artificial intelligence methods can support IAC scoring, but they have not yet been developed for clinical imaging. Large heterogeneous clinical CT datasets are necessary for the training of such methods, but they exhibit expected and unexpected data anomalies. Using CTs from a large clinical trial, the third International Stroke Trial (IST-3), we propose a pipeline that uses as input non-enhanced CT scans to output regions of interest capturing selected large intracranial arteries for IAC scoring. Our method uses co-registration with templates. We focus on quality control, using information presence along the z-axis of the imaging to group and apply similarity measures (structural similarity index measure) to triage assessment of individual image series. Additionally, we propose superimposing thresholded binary masks of the series to inspect large quantities of data in parallel. We identify and exclude unrecoverable samples and registration failures. In total, our pipeline processes 10,659 CT series, rejecting 4,322 (41%) in the entire process, 1,450 (14% of the total) during quality control, and outputting 6,337 series. Our pipeline enables effective and efficient region of interest localisation for targeted IAC segmentation., Comment: Accepted at the 2nd Data Engineering in Medical Imaging workshop @ MICCAI 2024

Published
2024

2. Development of a Deep Learning Method to Identify Acute Ischemic Stroke Lesions on Brain CT

Author

Fontanella, Alessandro, Li, Wenwen, Mair, Grant, Antoniou, Antreas, Platt, Eleanor, Armitage, Paul, Trucco, Emanuele, Wardlaw, Joanna, and Storkey, Amos

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Computed Tomography (CT) is commonly used to image acute ischemic stroke (AIS) patients, but its interpretation by radiologists is time-consuming and subject to inter-observer variability. Deep learning (DL) techniques can provide automated CT brain scan assessment, but usually require annotated images. Aiming to develop a DL method for AIS using labelled but not annotated CT brain scans from patients with AIS, we designed a convolutional neural network-based DL algorithm using routinely-collected CT brain scans from the Third International Stroke Trial (IST-3), which were not acquired using strict research protocols. The DL model aimed to detect AIS lesions and classify the side of the brain affected. We explored the impact of AIS lesion features, background brain appearances, and timing on DL performance. From 5772 unique CT scans of 2347 AIS patients (median age 82), 54% had visible AIS lesions according to expert labelling. Our best-performing DL method achieved 72% accuracy for lesion presence and side. Lesions that were larger (80% accuracy) or multiple (87% accuracy for two lesions, 100% for three or more), were better detected. Follow-up scans had 76% accuracy, while baseline scans 67% accuracy. Chronic brain conditions reduced accuracy, particularly non-stroke lesions and old stroke lesions (32% and 31% error rates respectively). DL methods can be designed for AIS lesion detection on CT using the vast quantities of routinely-collected CT brain scan data. Ultimately, this should lead to more robust and widely-applicable methods., Comment: 12 pages, 5 figures

Published
2023

3. Challenges of building medical image datasets for development of deep learning software in stroke

Author

Fontanella, Alessandro, Li, Wenwen, Mair, Grant, Antoniou, Antreas, Platt, Eleanor, Martin, Chloe, Armitage, Paul, Trucco, Emanuele, Wardlaw, Joanna, and Storkey, Amos

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Despite the large amount of brain CT data generated in clinical practice, the availability of CT datasets for deep learning (DL) research is currently limited. Furthermore, the data can be insufficiently or improperly prepared for machine learning and thus lead to spurious and irreproducible analyses. This lack of access to comprehensive and diverse datasets poses a significant challenge for the development of DL algorithms. In this work, we propose a complete semi-automatic pipeline to address the challenges of preparing a clinical brain CT dataset for DL analysis and describe the process of standardising this heterogeneous dataset. Challenges include handling image sets with different orientations (axial, sagittal, coronal), different image types (to view soft tissues or bones) and dimensions, and removing redundant background. The final pipeline was able to process 5,868/10,659 (45%) CT image datasets. Reasons for rejection include non-axial data (n=1,920), bone reformats (n=687), separated skull base/vault images (n=1,226), and registration failures (n=465). Further format adjustments, including image cropping, resizing and scaling are also needed for DL processing. Of the axial scans that were not localisers, bone reformats or split brains, 5,868/6,333 (93%) were accepted, while the remaining 465 failed the registration process. Appropriate preparation of medical imaging datasets for DL is a costly and time-intensive process., Comment: 9 pages, 5 figures

Published
2023

4. Spatio-Angular Convolutions for Super-resolution in Diffusion MRI

Author

Lyon, Matthew, Armitage, Paul, and Álvarez, Mauricio A

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Diffusion MRI (dMRI) is a widely used imaging modality, but requires long scanning times to acquire high resolution datasets. By leveraging the unique geometry present within this domain, we present a novel approach to dMRI angular super-resolution that extends upon the parametric continuous convolution (PCConv) framework. We introduce several additions to the operation including a Fourier feature mapping, global coordinates, and domain specific context. Using this framework, we build a fully parametric continuous convolution network (PCCNN) and compare against existing models. We demonstrate the PCCNN performs competitively while using significantly less parameters. Moreover, we show that this formulation generalises well to clinically relevant downstream analyses such as fixel-based analysis, and neurite orientation dispersion and density imaging.

Published
2023

5. Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder

Author

Lyon, Matthew, Armitage, Paul, and Álvarez, Mauricio A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

High resolution diffusion MRI (dMRI) data is often constrained by limited scanning time in clinical settings, thus restricting the use of downstream analysis techniques that would otherwise be available. In this work we develop a 3D recurrent convolutional neural network (RCNN) capable of super-resolving dMRI volumes in the angular (q-space) domain. Our approach formulates the task of angular super-resolution as a patch-wise regression using a 3D autoencoder conditioned on target b-vectors. Within the network we use a convolutional long short term memory (ConvLSTM) cell to model the relationship between q-space samples. We compare model performance against a baseline spherical harmonic interpolation and a 1D variant of the model architecture. We show that the 3D model has the lowest error rates across different subsampling schemes and b-values. The relative performance of the 3D RCNN is greatest in the very low angular resolution domain. Code for this project is available at https://github.com/m-lyon/dMRI-RCNN., Comment: Accepted to published in MIDL'22. Openreview link: https://openreview.net/forum?id=U6HJMtAgW-N

Published
2022

7. An empirical investigation into project management assets as a source of competitive advantage in local government

Author

Armitage, Paul Andrew and Bamford, David

Subjects
658.4, HJ Public Finance, JN101 Great Britain, JS Local government Municipal government
Abstract

The aim of this research is to present and discuss the challenges of achieving sustainable competitive advantage through effective project management assets, processes and practices,within a Local Authority Third-Sector collaborating scheme. Post 2008 global financial crisis the UK public sector has undergone unprecedentedreform and severe fiscal retrenchment (Evans, Hills & Orme, 2012). Increased competition and a financial landscape radically changed the way public sector services are delivered (Westwood, 2011). A significant challenge of austerity was the retrenchment of local authority grant dependant third-sector funding. In the UK, local authorities would enter into a collaborative contract with third-sector organisations to deliver services across a wide range of social needs. However, these arrangements were becoming increasingly financially unviable. Thus, local authorities need to find ways to make their third-sector collaborating arrangements sustainable, within a context of increasing competition, whilst honouring their range of public duties and services. Project management practice is recognised as a management discipline to manage change and execute strategy (Shenhar, 2001). A growing body of knowledge link the deliberate investment in project management assets and associated processes and practices as a strategic source of competitive advantage (Mathur, Jugdev & Fung, 2013, 2014). The opportunity presented in this research was how Local Authority collaborating third sector arrangements can achieve sustainability through effective project management practices;in particular the acknowledgement, development, deployment and exploitation of project management assets, processes and practices as a source of competitive advantage. However,the unique context of this investigation poses two challenges: i) the notion that competition is not relevant; and, ii) the non-professional project management nature of both the local authority and their collaborating third sector organisations. Thus, in partnership with a UK local authority and 26 third-sector partner organisations(LASIS), the RBV VRIO framework (Barney, 1991; Barney & Wright, 1998) was the lens empirically operationalised within a mixed methodology approach. Designed to identify which,project management assets and associated processes and practices LASIS strategic managers should deliberately acknowledge, develop, deploy and exploit when conceiving competitive advantage strategies, to deliver project impact and sustainable competitive advantage. Hence,the research seeks to identify the specific project management assets, processes and practice endowments leveraging degrees of competitive advantage, how advantage is provided and identify endowment mix that are more likely to indicate performance. Academic literatures on resource-based view, project management and the public-sector post 2008 financial crisis was reviewed, in order to establish knowledge gaps and a foundation to advance theoretical positions and practitioner solutions. A significant set of quantitative and qualitative empirical analysis identified several models of theoretical, conceptual and practitioner significance. Data was collected via survey questionnaire (n=70), semi-structured interviews (n=13) and informal conversations (n=9) during a 30-month period in which the researcher had full access to the LASIS. The core contributions of this research include: i) a mix of tangible and intangible project management assets leverage sustainable competitive advantage (SCA); ii) a mix of Acquire Assets and Facilitating Process Assets are necessary for SCA; iii) how advantage is provided; and, iv) the conceptual formula >vrio + >pk = >cap linking - the degree of deliberate project management investment and the level of project management performance knowledge, as moderators of competitive advantage and performance. Finally, this research makes a contribution to: v) practice knowledge through the empirical development of competitive advantage models exploiting project management assets;vi) advancing theoretical strategic management knowledge of a new application for the VRIO framework; and,vii) to the general project management knowledge, that maybe of practitioner value to strategic managers across the wider public and third sectors.

Published
2019

8. Direct Estimation of Pharmacokinetic Parameters from DCE-MRI using Deep CNN with Forward Physical Model Loss

Author

Ulas, Cagdas, Tetteh, Giles, Thrippleton, Michael J., Armitage, Paul A., Makin, Stephen D., Wardlaw, Joanna M., Davies, Mike E., and Menze, Bjoern H.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Dynamic contrast-enhanced (DCE) MRI is an evolving imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters in body tissues, in which series of T1-weighted images are collected following the administration of a paramagnetic contrast agent. Unfortunately, in many applications, conventional clinical DCE-MRI suffers from low spatiotemporal resolution and insufficient volume coverage. In this paper, we propose a novel deep learning based approach to directly estimate the PK parameters from undersampled DCE-MRI data. Specifically, we design a custom loss function where we incorporate a forward physical model that relates the PK parameters to corrupted image-time series obtained due to subsampling in k-space. This allows the network to directly exploit the knowledge of true contrast agent kinetics in the training phase, and hence provide more accurate restoration of PK parameters. Experiments on clinical brain DCE datasets demonstrate the efficacy of our approach in terms of fidelity of PK parameter reconstruction and significantly faster parameter inference compared to a model-based iterative reconstruction method., Comment: Accepted at MICCAI 2018. 9 pages, 6 figures

Published
2018

9. Validating Segmentation of the Zebrafish Vasculature

Author

Kugler, Elisabeth, Chico, Timothy, Armitage, Paul A., Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zheng, Yalin, editor, Williams, Bryan M., editor, and Chen, Ke, editor

Published
2020
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10. Analysis of Spatial Spectral Features of Dynamic Contrast-Enhanced Brain Magnetic Resonance Images for Studying Small Vessel Disease

Author

Bernal, Jose, Valdés-Hernández, Maria del C., Escudero, Javier, Armitage, Paul A., Makin, Stephen, Touyz, Rhian M., Wardlaw, Joanna M., Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zheng, Yalin, editor, Williams, Bryan M., editor, and Chen, Ke, editor

Published
2020
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11. Fetal Brain Segmentation Using Convolutional Neural Networks with Fusion Strategies

Author

Rampun, Andrik, Jarvis, Deborah, Griffiths, Paul, Armitage, Paul, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bebis, George, editor, Yin, Zhaozheng, editor, Kim, Edward, editor, Bender, Jan, editor, Subr, Kartic, editor, Kwon, Bum Chul, editor, Zhao, Jian, editor, Kalkofen, Denis, editor, and Baciu, George, editor

Published
2020
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12. Automated 2D Fetal Brain Segmentation of MR Images Using a Deep U-Net

Author

Rampun, Andrik, Jarvis, Deborah, Griffiths, Paul, Armitage, Paul, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Palaiahnakote, Shivakumara, editor, Sanniti di Baja, Gabriella, editor, Wang, Liang, editor, and Yan, Wei Qi, editor

Published
2020
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15. Development of imaging-based risk scores for prediction of intracranial haemorrhage and ischaemic stroke in patients taking antithrombotic therapy after ischaemic stroke or transient ischaemic attack: a pooled analysis of individual patient data from cohort studies

Author

Harkness, Kirsty, Shaw, Louise, Sword, Jane, Mohd Nor, Azlisham, Sharma, Pankaj, Kelly, Deborah, Harrington, Frances, Randall, Marc, Smith, Matthew, Mahawish, Karim, Elmarim, Abduelbaset, Esisi, Bernard, Cullen, Claire, Nallasivam, Arumug, Price, Christopher, Barry, Adrian, Roffe, Christine, Coyle, John, Hassan, Ahamad, Birns, Jonathan, Cohen, David, Sekaran, Lakshmanan, Parry-Jones, Adrian, Parry, Anthea, Hargroves, David, Proschel, Harald, Datta, Prabel, Darawil, Khaled, Manoj, Aravindakshan, Burn, Mathew, Patterson, Chris, Giallombardo, Elio, Smyth, Nigel, Mansoor, Syed, Anwar, Ijaz, Marsh, Rachel, Ispoglou, Sissi, Chadha, Dinesh, Prabhakaran, Mathuri, Meenakishundaram, Sanjeevikumar, O'Connell, Janice, Scott, Jon, Krishnamurthy, Vinodh, Aghoram, Prasanna, McCormick, Michael, Sprigg, Nikola, O'Mahony, Paul, Cooper, Martin, Choy, Lillian, Wilkinson, Peter, Leach, Simon, Caine, Sarah, Burger, Ilse, Gunathilagan, Gunaratam, Guyler, Paul, Emsley, Hedley, Davis, Michelle, Manawadu, Dulka, Pasco, Kath, Mamun, Maam, Luder, Robert, Sajid, Mahmud, Okwera, James, Warburton, Elizabeth, Saastamoinen, Kari, England, Timothy, Putterill, Janet, Flossman, Enrico, Power, Michael, Dani, Krishna, Mangion, David, Suman, Appu, Corrigan, John, Lawrence, Enas, Vahidassr, Djamil, Shakeshaft, Clare, Brown, Martin, Charidimou, Andreas, Cohen, Hannah, Banerjee, Gargi, Houlden, Henry, White, Mark, Yousry, Tarek, Flossmann, Enrico, Muir, Keith, Gratz, Pascal, Mattle, Heinrich, Panos, Leonidas, Korczyn, Amos, Kliper, Efrat, Maeder, Philippe, Gass, Achim, Pachai, Chahin, Bracoub, Luc, Douste-Blazy, Marie-Yvonne, Fratacci, Marie Dominique, Vicaut, Eric, Sato, Shoichiro, Miwa, Kaori, Fujita, Kyohei, Ide, Toshihiro, Ma, Henry, Ly, John, Singhal, Shaloo, Chandra, Ronil, Slater, Lee-Anne, Soufan, Cathy, Moran, Christopher, Traenka, Christopher, Thilemann, Sebastian, Fladt, Joachim, Gensicke, Henrik, Bonati, Leo, Kim, Beom Joon, Han, Moon-Ku, Kang, Jihoon, Ko, Eunbin, Yang, Mi Hwa, Jang, Myung Suk, Murphy, Sean, Carty, Fiona, Akijian, Layan, Thornton, John, Schembri, Mark, Douven, Elles, Delgado-Mederos, Raquel, Marín, Rebeca, Camps-Renom, Pol, Guisado-Alonso, Daniel, Nuñez, Fidel, Medrano-Martorell, Santiago, Merino, Elisa, Iida, Kotaro, Ikeda, Syuhei, Irie, Hiroyuki, Demirelli, Derya Selcuk, Medanta, Jayesh Modi, Zerna, Charlotte, Hernández, Maria Valdés, Armitage, Paul, Heye, Anna, Muñoz-Maniega, Susana, Sakka, Eleni, Thrippleton, Michael, Dennis, Martin, Beigneux, Ysoline, Silva, Mauro, Venketasubramanian, Narayanaswamy, Ho, Shu Leung, Cheung, Raymond Tak Fai, Chan, Koon Ho, Teo, Kay Cheong, Hui, Edward, Kwan, Joseph Shiu Kwong, Chang, Richard, Tse, Man Yu, Hoi, Chu Peng, Chan, Chung Yan, Chan, Oi Ling, Cheung, Ryan Hoi Kit, Wong, Edmund Ka Ming, Leung, Kam Tat, Tsang, Suk Fung, Ip, Hing Lung, Ma, Sze Ho, Ma, Karen, Fong, Wing Chi, Li, Siu Hung, Li, Richard, Ng, Ping Wing, Wong, Kwok Kui, Liu, Wenyan, Wong, Lawrence, Ramos, Lino, De Schryver, Els, Jöbsis, Joost, van der Sande, Jaap, Brouwers, Paul, Roos, Yvo, Stam, Jan, Bakker, Stef, Verbiest, Henk, Schoonewille, Wouter, Linn, Cisca, Hertzberger, Leopold, van Gemert, Maarten, Berntsen, Paul, Van Dam-Nolen, Dianne, Kooi, M Eline, Van der Lugt, Aad, Koudstaal, Peter, Leff, Alexander, Ward, Nicholas, Nachev, Parashkev, Perry, Richard, Ozkan, Hatice, Mitchell, John, Best, Jonathan G, Ambler, Gareth, Wilson, Duncan, Lee, Keon-Joo, Lim, Jae-Sung, Shiozawa, Masayuki, Koga, Masatoshi, Li, Linxin, Lovelock, Caroline, Chabriat, Hugues, Hennerici, Michael, Wong, Yuen Kwun, Mak, Henry Ka Fung, Prats-Sanchez, Luis, Martínez-Domeño, Alejandro, Inamura, Shigeru, Yoshifuji, Kazuhisa, Arsava, Ethem Murat, Horstmann, Solveig, Purrucker, Jan, Lam, Bonnie Yin Ka, Wong, Adrian, Kim, Young Dae, Song, Tae-Jin, Lemmens, Robin, Eppinger, Sebastian, Gattringer, Thomas, Uysal, Ender, Tanriverdi, Zeynep, Bornstein, Natan M, Ben Assayag, Einor, Hallevi, Hen, Molad, Jeremy, Nishihara, Masashi, Tanaka, Jun, Coutts, Shelagh B, Polymeris, Alexandros, Wagner, Benjamin, Seiffge, David J, Lyrer, Philippe, Algra, Ale, Kappelle, L Jaap, Al-Shahi Salman, Rustam, Jäger, Hans R, Lip, Gregory Y H, Fischer, Urs, El-Koussy, Marwan, Mas, Jean-Louis, Legrand, Laurence, Karayiannis, Christopher, Phan, Thanh, Gunkel, Sarah, Christ, Nicolas, Abrigo, Jill, Leung, Thomas, Chu, Winnie, Chappell, Francesca, Makin, Stephen, Hayden, Derek, Williams, David J, Mess, Werner H, Nederkoorn, Paul J, Barbato, Carmen, Browning, Simone, Wiegertjes, Kim, Tuladhar, Anil M, Maaijwee, Noortje, Guevarra, Anne Cristine, Yatawara, Chathuri, Mendyk, Anne-Marie, Delmaire, Christine, Köhler, Sebastian, van Oostenbrugge, Robert, Zhou, Ying, Xu, Chao, Hilal, Saima, Gyanwali, Bibek, Chen, Christopher, Lou, Min, Staals, Julie, Bordet, Régis, Kandiah, Nagaendran, de Leeuw, Frank-Erik, Simister, Robert, Hendrikse, Jeroen, Kelly, Peter J, Wardlaw, Joanna, Soo, Yannie, Fluri, Felix, Srikanth, Velandai, Calvet, David, Jung, Simon, Kwa, Vincent I H, Engelter, Stefan T, Peters, Nils, Smith, Eric E, Hara, Hideo, Yakushiji, Yusuke, Orken, Dilek Necioglu, Fazekas, Franz, Thijs, Vincent, Heo, Ji Hoe, Mok, Vincent, Veltkamp, Roland, Ay, Hakan, Imaizumi, Toshio, Gomez-Anson, Beatriz, Lau, Kui Kai, Jouvent, Eric, Rothwell, Peter M, Toyoda, Kazunori, Bae, Hee-Joon, Marti-Fabregas, Joan, and Werring, David J

Published
2021
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19. Image Analysis in Light Sheet Fluorescence Microscopy Images of Transgenic Zebrafish Vascular Development

Author

Kugler, Elisabeth, Chico, Timothy, Armitage, Paul, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Nixon, Mark, editor, Mahmoodi, Sasan, editor, and Zwiggelaar, Reyer, editor

Published
2018
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20. Cerebral microbleeds and stroke risk after ischaemic stroke or transient ischaemic attack: a pooled analysis of individual patient data from cohort studies

Author

Harkness, Kirsty, Shaw, Louise, Sword, Jane, Mohd Nor, Azlisham, Sharma, Pankaj, Kelly, Deborah, Harrington, Frances, Randall, Marc, Smith, Matthew, Mahawish, Karim, Elmarim, Abduelbaset, Esisi, Bernard, Cullen, Claire, Nallasivam, Arumug, Price, Christopher, Barry, Adrian, Roffe, Christine, Coyle, John, Hassan, Ahamad, Birns, Jonathan, Cohen, David, Sekaran, Lakshmanan, Parry-Jones, Adrian, Parry, Anthea, Hargroves, David, Proschel, Harald, Datta, Prabel, Darawil, Khaled, Manoj, Aravindakshan, Burn, Mathew, Patterson, Chris, Giallombardo, Elio, Smyth, Nigel, Mansoor, Syed, Anwar, Ijaz, Marsh, Rachel, Ispoglou, Sissi, Chadha, Dinesh, Prabhakaran, Mathuri, Meenakishundaram, Sanjeevikumar, O'Connell, Janice, Scott, Jon, Krishnamurthy, Vinodh, Aghoram, Prasanna, McCormick, Michael, Sprigg, Nikola, O'Mahony, Paul, Cooper, Martin, Choy, Lillian, Wilkinson, Peter, Leach, Simon, Caine, Sarah, Burger, Ilse, Gunathilagan, Gunaratam, Guyler, Paul, Emsley, Hedley, Davis, Michelle, Manawadu, Dulka, Pasco, Kath, Mamun, Maam, Luder, Robert, Sajid, Mahmud, Okwera, James, Warburton, Elizabeth, Saastamoinen, Kari, England, Timothy, Putterill, Janet, Flossman, Enrico, Power, Michael, Dani, Krishna, Mangion, David, Suman, Appu, Corrigan, John, Lawrence, Enas, Vahidassr, Djamil, Shakeshaft, Clare, Brown, Martin, Charidimou, Andreas, Cohen, Hannah, Banerjee, Gargi, Houlden, Henry, White, Mark, Yousry, Tarek, Flossmann, Enrico, Muir, Keith, El-Koussy, Marwan, Gratz, Pascal, Molad, Jeremy, Korczyn, Amos, Kliper, Efrat, Maeder, Philippe, Gass, Achim, Pachai, Chahin, Bracoub, Luc, Douste-Blazy, Marie-Yvonne, Fratacci, Marie Dominique, Vicaut, Eric, Sato, Shoichiro, Miwa, Kaori, Fujita, Kyohei, Ide, Toshihiro, Ma, Henry, Ly, John, Singhal, Shahoo, Chandra, Ronil, Slater, Lee-Anne, Soufan, Cathy, Moran, Christopher, Traenka, Christopher, Thilemann, Sebastian, Fladt, Joachim, Gensicke, Henrik, Bonati, Leo, Kim, Beom Joon, Han, Moon-Ku, Kang, Jihoon, Ko, Eunbin, Yang, Mi Hwa, Jang, Myung Suk, Murphy, Sean, Carty, Fiona, Akijian, Layan, Thornton, John, Schembri, Mark, Douven, Elles, Delgado-Mederos;, Raquel, Marín, Rebeca, Camps-Renom, Pol, Guisado-Alonso, Daniel, Nuñez, Fidel, Medrano-Martorell, Santiago, Merino, Elisa, Iida, Kotaro, Ikeda, Syuhei, Nishihara, Masashi, Irie, Hiroyuki, Demirelli, Derya Selcuk, Medanta, Jayesh Modi, Zerna, Charlotte, Hernández, Maria Valdés, Armitage, Paul, Heye, Anna, Muñoz-Maniega, Susana, Sakka, Eleni, Thrippleton, Michael, Dennis, Martin, Beigneux, Ysoline, Silva, Mauro, Venketasubramanian, Narayanaswamy, Ho, Shu Leung, Cheung, Raymond Tak Fai, Chan, Koon Ho, Teo, Kay Cheong, Hui, Edward, Kwan, Joseph Shiu Kwong, Chang, Richard, Tse, Man Yu, Hoi, Chu Peng, Chan, Chung Yan, Chan, Oi Ling, Cheung, Ryan Hoi Kit, Wong, Edmund Ka Ming, Leung, Kam Tat, Tsang, Suk Fung, Ip, Hing Lung, Ma, Sze Ho, Ma, Karen, Fong, Wing Chi, Li, Siu Hung, Li, Richard, Ng, Ping Wing, Wong, Kwok Kui, Liu, Wenyan, Wong, Lawrence, Ramos, Lino, De Schryver, Els, Jöbsis, Joost, van der Sande, Jaap, Brouwers, Paul, Roos, Yvo, Stam, Jan, Bakker, Stef, Verbiest, Henk, Schoonewille, Wouter, Linn, Cisca, Hertzberger, Leopold, van Gemert, Maarten, Berntsen, Paul, Hendrikse, Jeroen, Nederkoorn, Paul, Mess, Werner, Koudstaal, Peter, Leff, Alexander, Ward, Nicholas, Nachev, Parashkev, Perry, Richard, Ozkan, Hatice, Mitchell, John, Wilson, Duncan, Ambler, Gareth, Lee, Keon-Joo, Lim, Jae-Sung, Shiozawa, Masayuki, Koga, Masatoshi, Li, Linxin, Lovelock, Caroline, Chabriat, Hugues, Hennerici, Michael, Wong, Yuen Kwun, Mak, Henry Ka Fung, Prats-Sánchez, Luis, Martínez-Domeño, Alejandro, Inamura, Shigeru, Yoshifuji, Kazuhisa, Arsava, Ethem Murat, Horstmann, Solveig, Purrucker, Jan, Lam, Bonnie Yin Ka, Wong, Adrian, Kim, Young Dae, Song, Tae-Jin, Schrooten, Maarten, Lemmens, Robin, Eppinger, Sebastian, Gattringer, Thomas, Uysal, Ender, Tanriverdi, Zeynep, Bornstein, Natan M, Assayag, Einor Ben, Hallevi, Hen, Tanaka, Jun, Hara, Hideo, Coutts, Shelagh B, Hert, Lisa, Polymeris, Alexandros, Seiffge, David J, Lyrer, Philippe, Algra, Ale, Kappelle, Jaap, Al-Shahi Salman, Rustam, Jäger, Hans R, Lip, Gregory Y H, Mattle, Heinrich P, Panos, Leonidas D, Mas, Jean-Louis, Legrand, Laurence, Karayiannis, Christopher, Phan, Thanh, Gunkel, Sarah, Christ, Nicolas, Abrigo, Jill, Leung, Thomas, Chu, Winnie, Chappell, Francesca, Makin, Stephen, Hayden, Derek, Williams, David J, Kooi, M Eline, van Dam-Nolen, Dianne H K, Barbato, Carmen, Browning, Simone, Wiegertjes, Kim, Tuladhar, Anil M, Maaijwee, Noortje, Guevarra, Christine, Yatawara, Chathuri, Mendyk, Anne-Marie, Delmaire, Christine, Köhler, Sebastian, van Oostenbrugge, Robert, Zhou, Ying, Xu, Chao, Hilal, Saima, Gyanwali, Bibek, Chen, Christopher, Lou, Min, Staals, Julie, Bordet, Régis, Kandiah, Nagaendran, de Leeuw, Frank-Erik, Simister, Robert, van der Lugt, Aad, Kelly, Peter J, Wardlaw, Joanna M, Soo, Yannie, Fluri, Felix, Srikanth, Velandai, Calvet, David, Jung, Simon, Kwa, Vincent I H, Engelter, Stefan T, Peters, Nils, Smith, Eric E, Yakushiji, Yusuke, Orken, Dilek Necioglu, Fazekas, Franz, Thijs, Vincent, Heo, Ji Hoe, Mok, Vincent, Veltkamp, Roland, Ay, Hakan, Imaizumi, Toshio, Gomez-Anson, Beatriz, Lau, Kui Kai, Jouvent, Eric, Rothwell, Peter M, Toyoda, Kazunori, Bae, Hee-Joon, Marti-Fabregas, Joan, and Werring, David J

Published
2019
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23. Development of the Platreef in the northern limb of the Bushveld Complex at Sandsloot, Mokopane District, South Africa

Author

Armitage, Paul Edward Blake, Leharne, Stephen, and McDonald, Iain

Subjects
500, QE Geology
Abstract

The Platreef is a Ni-Cu-PGE mineralised tabular body at the base of the Rustenburg Layered Suite in the northern limb of the Bushveld Complex. The reef lies unconformably on a footwall (floor) sequence of Transvaal Supergroup sedimentary rocks and Archaean granite/gneiss basement, and is overlain by a hangingwall (roof) of Main Zone gabbronorites. Structural relationships suggest that the Platreef was emplaced as a broadly horizontal sill-like sheet into the Transvaal Supergroup, but local variations in its thickness and path of intrusion were caused by pre-existing structures in the country rocks. As the Platreef cooled and was nearly crystallised, ductile deformation occurred, possibly as an episode in a longer event. Main Zone magma was emplaced above the deformed, nearly consolidated Platreef and eroded the uppermost portion, locally assimilating mineralised reef. The Main Zone magma also intruded into shear zones as thin dykes down through the Platreef and metasedimentary floor. Structural patterns around a prominent dome in the floor rocks suggest that regional deformation may still have been active when the earliest Main Zone layers were developing, but ceased by Upper Main Zone time. Other studies of the Platreef beyond Sandsloot have shown that its earliest Ni-Cu-PGE mineralisation was orthomagmatic, largely preserved where the floor rocks are unreactive basement granite/gneiss. However, interaction between he Platreef magma and surrounding sedimentary rocks has produced different mineralogical associations and assemblages that were influenced by the local floor and roof rocks along the strike of the reef. At Sandsloot, the floor rocks are represented by reactive siliceous dolomites of the Malmani Subgroup. The Platreef magma caused contact metamorphism and metasomatism of the dolomites, releasing volatiles that entered the reef. These hydrothermal fluids stripped PGE from primary sulphides and redistributed the PGE within the reef and into the metasedimentary country rocks. In places, primary platinum group minerals were overprinted by lower-temperature species.

Published
2011

24. Automatic Rating of Perivascular Spaces in Brain MRI Using Bag of Visual Words

Author

González-Castro, Víctor, Valdés Hernández, María del C., Armitage, Paul A., Wardlaw, Joanna M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Campilho, Aurélio, editor, and Karray, Fakhri, editor

Published
2016
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27. Quantitative magnetic resonance diffusion imaging of the human brain

Author

Armitage, Paul Anthony

Subjects
615.84
Abstract

In conventional experiments, diffusion was treated as a scalar quantity, based on models assuming spherical or isotropic diffusion. In most living tissues, this is an oversimplification as diffusion is not isotropic and must be treated as a tensor quantity. As such, the likely errors arising from performing diffusion measurements assuming spherical or cylindrical symmetry were investigated using computer simulations. Large and unpredictable errors were found to result if diffusion was not treated as a tensor quantity and models assuming spherical or cylindrical symmetry were used. Diffusion measurements are also highly susceptible to the effects of experimental noise. Previously suggested measurements as to the extent of noise corruption in diffusion experiments are generally found to be extremely dependent on the experimental parameters used and so are difficult to apply in a general situation. To overcome this, a measurement of noise was found that gives an analysis that is largely independent of the experimental conditions. Achieving an accurate quantitative analysis has been shown to require a careful balance between obtaining a high enough degree of diffusion weighting, while achieving sufficient signal-to-noise ratio. A theoretical method was developed for producing reliable diffusion measurements by optimising the diffusion weighting b-value and the number of acquisitions obtained. Both in vitro and in vivo data were found to give reliable quantitative data from an acquisition scheme based on the theoretical method. Many different ways for displaying diffusion data have been proposed. An analysis of the different levels of contrast and sensitivity arising from various diffusion anisotropy indices was also undertaken, resulting in the development of a method for displaying diffusion data with improved contrast. During the course of this work quantitative diffusion imaging has been performed in a clinical setting on over 100 acute stroke patients, 16 head injury patients and 5 CJD patients.

Published
2000

42. Scaling And Commercialising Strategies: Key Forms Of Agreement

Author

Armitage, Paul

Subjects
Intellectual property law -- Practice, Nondisclosure agreements -- Laws, regulations and rules, Government regulation, Business, international
Abstract

Non-disclosure agreements Vivian Desmonts (VD): Let us start with standard Non-disclosure agreements (NDAs). These are something that companies will easily think of and sign when they begin sharing confidential information, [...]

Published
2022

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