Browse by Aston Author

Griffiths-King, Daniel, Wood, Amanda and Novak, Jan (2023). Predicting 'Brainage' in late childhood to adolescence (6-17yrs) using structural MRI, morphometric similarity, and machine learning. Scientific Reports, 13 (1),

Powell, Stephen J, Withey, Stephanie B, Sun, Yu, Grist, James T, Novak, Jan, MacPherson, Lesley, Abernethy, Laurence, Pizer, Barry, Grundy, Richard, Morgan, Paul S, Jaspan, Tim, Bailey, Simon, Mitra, Dipayan, Auer, Dorothee P, Avula, Shivaram, Arvanitis, Theodoros N and Peet, Andrew (2023). Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data. British Journal of Radiology, 96 (1145),

Griffiths-King, Daniel, Wood, Amanda and Novak, Jan (2023). Predicting ‘Brainage’ in the Developmental Period using Structural MRI, Morphometric Similarity, and Machine Learning. Other. Research Square.

Griffiths-King, Daniel, Shephard, Adam, Novak, Jan, Catroppa, Cathy, Anderson, Vicki A. and Wood, Amanda (2023). Proposed Methodology for Reducing Bias in Structural MRI Analysis in the Presence of Lesions: Data from a Pediatric Traumatic Brain Injury Cohort. Other. bioRxiv.

Withey, Stephanie B, MacPherson, Lesley, Oates, Adam, Powell, Stephen, Novak, Jan, Abernethy, Laurence, Pizer, Barry, Grundy, Richard, Morgan, Paul S, Bailey, Simon, Mitra, Dipayan, Arvanitis, Theodoros N, Auer, Dorothee P, Avula, Shivaram and Peet, Andrew C (2022). Dynamic susceptibility-contrast magnetic resonance imaging with contrast agent leakage correction aids in predicting grade in pediatric brain tumours: a multicenter study. Pediatric Radiology, 52 (6), pp. 1134-1149.

Grist, James T., Withey, Stephanie, Bennett, Christopher, Rose, Heather E. L., MacPherson, Lesley, Oates, Adam, Powell, Stephen, Novak, Jan, Abernethy, Laurence, Pizer, Barry, Bailey, Simon, Clifford, Steven C., Mitra, Dipayan, Arvanitis, Theodoros N., Auer, Dorothee P., Avula, Shivaram, Grundy, Richard and Peet, Andrew C. (2021). Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors. Scientific Reports, 11 ,

Novak, Jan, Zarinabad, Niloufar, Rose, Heather, Arvanitis, Theodoros, MacPherson, Lesley, Pinkey, Benjamin, Oates, Adam, Hales, Patrick, Grundy, Richard, Auer, Dorothee, Gutierrez, Daniel Rodriguez, Jaspan, Tim, Avula, Shivaram, Abernethy, Laurence, Kaur, Ramneek, Hargrave, Darren, Mitra, Dipayan, Bailey, Simon, Davies, Nigel, Clark, Christopher and Peet, Andrew (2021). Classification of paediatric brain tumours by diffusion weighted imaging and machine learning. Scientific Reports, 11 (1),

Grist, James T, Withey, Stephanie, Bennett, Christopher, Rose, Heather, MacPherson, Lesley, Oates, Adam, Powell, Stephen, Novak, Jan, Abernethy, Laurence, Pizer, Barry, Bailey, Simon, Mitra, Dipayan, Arvanitis, Theodoros N, Auer, Dorothee P, Avula, Shivaram, Grundy, Richard and Peet, Andrew C (2020). IMG-06. PREDICTING SURVIVAL FROM PERFUSION AND DIFFUSION MRI BY MACHINE LEARNING. Neuro-Oncology, 22 (Supple), iii356-iii356.

King, Daniel J., Novak, Jan, Shephard, Adam J., Beare, Richard, Anderson, Vicki A. and Wood, Amanda G. (2020). Lesion Induced Error on Automated Measures of Brain Volume: Data From a Pediatric Traumatic Brain Injury Cohort. Frontiers in Neuroscience, 14 ,

Grist, James T., Withey, Stephanie, Macpherson, Lesley, Oates, Adam, Powell, Stephen, Novak, Jan, Abernethy, Laurence, Pizer, Barry, Grundy, Richard, Bailey, Simon, Mitra, Dipayan, Arvanitis, Theodoros N., Auer, Dorothee P., Avula, Shivaram and Peet, Andrew C (2020). Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: a multi-site study. NeuroImage: Clinical, 25 ,

Metcalfe-Smith, E., Meeus, E.M., Novak, J., Dehghani, H., Peet, A.C. and Zarinabad, N. (2019). Auto-Regressive Discrete Acquisition Points Transformation for Diffusion Weighted MRI Data. IEEE Transactions on Biomedical Engineering, 66 (9), 2617 - 2628.

Novak, J., Withey, S.B., Lateef, S., Macpherson, L., Pinkey, B. and Peet, A.C. (2019). A comparison of pseudo-continuous arterial spin labelling and dynamic susceptibility contrast MrI with and without contrast agent leakage correction in paediatric brain tumours. British Journal of Radiology, 92 ,

Meeus, E.M., Zarinabad, N., Manias, K.A., Novak, J., Rose, H.E.L., Dehghani, H., Foster, K., Morland, B. and Peet, A.C. (2018). Diffusion-weighted MRI and intravoxel incoherent motion model for diagnosis of pediatric solid abdominal tumors. Journal of Magnetic Resonance Imaging, 47 (6), pp. 1475-1486.

Meeus, E.M., Novak, J., Dehghani, H. and Peet, A.C. (2018). Rapid measurement of intravoxel incoherent motion (IVIM) derived perfusion fraction for clinical magnetic resonance imaging. Magnetic Resonance Materials in Physics, Biology and Medicine, 31 , 269–283.

Meeus, E.M., Novak, J., Withey, S.B., Zarinabad, N., Dehghani, H. and Peet, A.C. (2017). Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue. Journal of Magnetic Resonance Imaging, 45 (5), pp. 1325-1334.

Withey, S.B., Novak, J., MacPherson, L. and Peet, A.C. (2016). Arterial input function and gray matter cerebral blood volume measurements in children. Journal of Magnetic Resonance Imaging, 43 (4), pp. 981-989.

Novak, J. and Britton, M.M. (2013). Magnetic resonance imaging of the rheology of ionic liquid colloidal suspensions. Soft matter, 9 , pp. 2730-2737.

Thompson, B.W., Novak, J., Wilson, M.C.T., Britton, M.M. and Taylor, A.F. (2010). Inward propagating chemical waves in Taylor vortices. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 81 ,

Milsom, E.V., Novak, J., Green, S.J., Zhang, Xiaohang, Stott, S.J., Mortimer, R.J., Edler, K. and Marken, F. (2007). Layer-by-layer deposition of open-pore mesoporous TiO 2- Nafion® film electrodes. Journal of Solid State Electrochemistry, 11 ,