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Using MRI of Brain Iron to Assist in Differential Diagnosis of Neurodegeneration Zobair Arya #, Jean-Pierre Hagen and Joanna F. Collingwood School of Engineering;

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Presentation on theme: "Using MRI of Brain Iron to Assist in Differential Diagnosis of Neurodegeneration Zobair Arya #, Jean-Pierre Hagen and Joanna F. Collingwood School of Engineering;"— Presentation transcript:

1 Using MRI of Brain Iron to Assist in Differential Diagnosis of Neurodegeneration Zobair Arya #, Jean-Pierre Hagen and Joanna F. Collingwood School of Engineering; # Department of Physics It is known that neurodegenerative diseases (e.g. Alzheimer's and Parkinson's) are associated with changes in regional brain iron levels. Using MRI data, there is scope for improving diagnostic tools for these diseases and this can potentially be utilised to better understand pathogenesis, disease progression and monitor possible treatments. The aim of this study was to help develop robust quantitative MRI sequences for measurements of brain iron. The sequences developed in this project were then implemented to measure and analyse in vivo brain iron levels in healthy volunteers, which will form a baseline for future studies of neurodegenerative diseases. The following regions of the brain, which have previously been shown strongly links to neurodegeneration, were measured: Substantia Nigra (SN) Globus Pallidus (GP) Putamen (PUT) Caudate Nucleus (CN) The sequences that were optimised and used to measure the regions were: T2 multi-echo T2 single-echo T2 EPI T2* multi-echo The above sequences were optimised at Philips clinical scanners at the University of Nottingham and BUIC, with scanning taking place at BUIC. T2 single-echo and multi-echo sequences were also optimised at UHCW at both their 1.5T and 3T GE scanners and then applied to healthy volunteers in different age groups. The ability to have access to two scanners of different strengths meant FDRI values for different regions of the brain could be calculated. A program was also developed which allowed for efficient FDRI measurements for the whole volume of a region as opposed to just single slices. The following regions of the brain, which have previously been shown strongly links to neurodegeneration, were measured: Substantia Nigra (SN) Globus Pallidus (GP) Putamen (PUT) Caudate Nucleus (CN) The sequences that were optimised and used to measure the regions were: T2 multi-echo T2 single-echo T2 EPI T2* multi-echo The above sequences were optimised at Philips clinical scanners at the University of Nottingham and BUIC, with scanning taking place at BUIC. T2 single-echo and multi-echo sequences were also optimised at UHCW at both their 1.5T and 3T GE scanners and then applied to healthy volunteers in different age groups. The ability to have access to two scanners of different strengths meant FDRI values for different regions of the brain could be calculated. A program was also developed which allowed for efficient FDRI measurements for the whole volume of a region as opposed to just single slices. Introduction Methodology The graphs and tables on the right show the FDRI values measured at UHCW. The expected increase with age can be seen, since nanoparticulate iron deposition is expected to increase with age. This can be seen especially in the SN where this correlation is expected to be strongest. For other regions this increase can be seen between the volunteers aged 46 and 68, but less clearly when the volunteer aged 21 is included. This is due to the fact that the sequences had not been optimised to include all regions for this volunteer and the SN was the only region where a sufficient amount of volume was captured. Therefore it is safe to assume the trend shown between the other two volunteers are more valid. The graph below shows an example of typical R2 values obtained from the BUIC and UHCW 3T scanners for one volunteer. Different sequences clearly result in different R2 values even though they are all measuring the same physical characteristic, there are even differences between the same sequences implemented at different scanners. The graphs and tables on the right show the FDRI values measured at UHCW. The expected increase with age can be seen, since nanoparticulate iron deposition is expected to increase with age. This can be seen especially in the SN where this correlation is expected to be strongest. For other regions this increase can be seen between the volunteers aged 46 and 68, but less clearly when the volunteer aged 21 is included. This is due to the fact that the sequences had not been optimised to include all regions for this volunteer and the SN was the only region where a sufficient amount of volume was captured. Therefore it is safe to assume the trend shown between the other two volunteers are more valid. The graph below shows an example of typical R2 values obtained from the BUIC and UHCW 3T scanners for one volunteer. Different sequences clearly result in different R2 values even though they are all measuring the same physical characteristic, there are even differences between the same sequences implemented at different scanners. Results This study is the first, to our knowledge, to have carried out FDRI measurements using single echo sequences and the results are promising (more volunteers have been scanned at UWHC in addition to the three shown). A large scale study comparing control with disease data using this method could potentially make big progress towards creating a clinical diagnostic tool. Tools and protocols which will assist in the large scale study have been developed in this project. Comparing different sequences has also shown there is a need to standardise MRI data acquisition techniques in the MRI community, otherwise the problem of disagreeing results in literature is likely to continue to be a big obstacle to progressing the field. This study is the first, to our knowledge, to have carried out FDRI measurements using single echo sequences and the results are promising (more volunteers have been scanned at UWHC in addition to the three shown). A large scale study comparing control with disease data using this method could potentially make big progress towards creating a clinical diagnostic tool. Tools and protocols which will assist in the large scale study have been developed in this project. Comparing different sequences has also shown there is a need to standardise MRI data acquisition techniques in the MRI community, otherwise the problem of disagreeing results in literature is likely to continue to be a big obstacle to progressing the field. Conclusion Left Hemisphere Right Hemisphere What are T2, R2 and FDRI? The time taken for the transverse component of the spin of nuclei to decay to 37% of its initial value after a pulse is T2. R2 is simply the reciprocal of this. Different tissues have different T2, hence a T2 map of the brain can be created. In the brain, the amount of iron is thought to be an important factor affecting the magnitude of R2. Much of the iron in the brain is stored in ferritin and FDRI provides a specific measure of tissue ferritin content. It involves obtaining R2 maps of the brain at two different field strengths and subtracting them from each other to obtain an FDRI map as shown on the right. Acknowledgements: With thanks to URSS and Materials GRP for making this study possible, thanks to Prof C. Hutchinson for advising and being the ethics lead, Dr S. Wayte and E. Ngandwe for their help with scanning at UHCW, Dr A. Chowdhury for help with scanning at BUIC, Prof P. Gowland and A. Blazejewska for advise and help with sequence optimisation and thanks to all the volunteers who took part in the study. References: G. Bartzokis, J. Mintz, et al., In Vivo MR Evaluation of Age-Related Increases in Brain Iron, Am. J. Neuroradiol. 15, (1994) V. Antharam, J.F. Collingwood, J.P. Bullivant, et al., High Field Magnetic Resonance Microscopy of the Human Hippocampus in Alzheimer's Disease: Quantitative Imaging and Correlation with Iron, Neuroimage, 59(2): , (2012) FDRI (s -1 ) Volunteer 1 (Aged 21) Volunteer 2 (Aged 46) Volunteer 3 (Aged 68) SN GP PUT CN5.0 FDRI (s -1 ) Volunteer 1 (Aged 21) Volunteer 2 (Aged 46) Volunteer 3 (Aged 68) SN GP PUT CN Estimated uncertainty of 5% PUT GP SN


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