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Journal articleKing MD, Grech-Sollars M, 2016,
A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain Monte Carlo (MCMC) simulation
, F1000 Research, Vol: 5, ISSN: 2046-1402The focus of this study is the development of a statistical modelling procedure for characterisingintra-tumour heterogeneity, motivated by recent clinical literature indicating that a varietyof tumours exhibit a considerable degree of genetic spatial variability. A formal spatial statisticalmodel has been developed and used to characterise the structural heterogeneity of anumber of supratentorial primitive neuroecto-dermal tumours (PNETs), based on diffusionweightedmagnetic resonance imaging. Particular attention is paid to the spatial dependenceof diffusion close to the tumour boundary, in order to determine whether the data providestatistical evidence to support the proposition that water diffusivity in the boundary region ofsome tumours exhibits a deterministic dependence on distance from the boundary, in excessof an underlying random 2D spatial heterogeneity in diffusion. Tumour spatial heterogeneitymeasures were derived from the diffusion parameter estimates obtained using a Bayesianspatial random effects model. The analyses were implemented using Markov chain MonteCarlo (MCMC) simulation. Posterior predictive simulation was used to assess the adequacyof the statistical model. The main observations are that the previously reported relationshipbetween diffusion and boundary proximity remains observable and achieves statistical significanceafter adjusting for an underlying random 2D spatial heterogeneity in the diffusionmodel parameters. A comparison of the magnitude of the boundary-distance effect with theunderlying random 2D boundary heterogeneity suggests that both are important sources ofvariation in the vicinity of the boundary. No consistent pattern emerges from a comparison ofthe boundary and core spatial heterogeneity, with no indication of a consistently greater levelof heterogeneity in one region compared with the other. The results raise the possibility thatDWI might provide a surrogate marker of intra-tumour genetic regional heterogeneity, whichwould
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Conference paperJaijee S, Statton B, Quinlan M, et al., 2016,
Right ventricular function in acute and chronic pulmonary hypertension using exercise cardiac magnetic resonance imaging
, Congress of the European-Society-of-Cardiology (ESC), Publisher: OXFORD UNIV PRESS, Pages: 1186-1186, ISSN: 0195-668X -
Journal articleCawley P, Few K, Greenwood R, et al., 2016,
Does magnetic resonance brain scanning at 3.0 Tesla pose a hyperthermic challenge to term neonates?
, The Journal of Pediatrics, Vol: 175, Pages: 228-230.e1, ISSN: 0022-3476Next-generation 3-Tesla magnetic resonance (MR) scanners offer improved neonatal neuroimaging, but the greater associated radiofrequency radiation may increase the risk of hyperthermia. Safety data for neonatal 3-T MR scanning are lacking. We measured rectal temperatures continuously in 25 neonates undergoing 3-T brain MR imaging and observed no significant hyperthermic threat.
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Conference paperMcGill L-A, Ferreira P, Scott A, et al., 2016,
Non-invasive Interrogation of Myocardial Disarray in Hypertrophic Cardiomyopathy
, Annual Conference of the British Cardiovascular Society (BCS) on Prediction and Prevention, Publisher: BMJ Publishing Group, Pages: A96-A96, ISSN: 1355-6037 -
Conference paperJaijee S, Quinlan M, Tokarczuk P, et al., 2016,
DETERIORATION OF RIGHT VENTRICULAR FUNCTION ON EXERCISE DETECTED BY EXERCISE CARDIAC MAGNETIC RESONANCE IMAGING IN PATIENTS WITH PULMONARY ARTERIAL HYPERTENSION
, Annual Conference of the British-Cardiovascular-Society (BCS) on Prediction and Prevention, Publisher: BMJ PUBLISHING GROUP, Pages: A88-A89, ISSN: 1355-6037 -
Journal articleMontaldo P, Oliveira V, Lally PJ, et al., 2016,
Therapeutic hypothermia in neonatal cervical spine injury
, Archives of Disease in Childhood: Fetal & Neonatal Edition, Vol: 101, Pages: F468-F468, ISSN: 1468-2052 -
Journal articleLogan K, Emsley RJ, Jeffries S, et al., 2016,
Development of Early Adiposity in Infants of Mothers With Gestational Diabetes Mellitus
, Diabetes Care, Vol: 39, Pages: 1045-1051, ISSN: 0149-5992OBJECTIVEInfants born to mothers with gestational diabetes mellitus (GDM) are at greaterrisk of later adverse metabolic health. We examined plausible candidate mediators;adipose tissue (AT) quantity and distribution, and intrahepatocellular lipid(IHCL) content, comparing infants of mothers with GDM and without GDM (controlgroup) over the first 3 postnatal months.RESEARCH DESIGN AND METHODSWe conducted a prospective longitudinal study using MRI and spectroscopy toquantify whole-body and regional AT volumes, and IHCL content, within 2 weeksand 8–12 weeks after birth. We adjusted for infant size and sex, and maternalprepregnancy BMI. Values are reported as the mean difference (95% CI).RESULTSWe recruited 86 infants (GDM group 42 infants; control group 44 infants). Motherswith GDM had good pregnancy glycemic control. Infants were predominantlybreast fed up to the time of the second assessment (GDM group 71%; controlgroup 74%). Total AT volumes were similar in the GDM group compared with thecontrol group at a median age of 11 days (228 cm3 [95% CI 2121, 65], P = 0.55), butwere greater in the GDM group at a median age of 10 weeks (247 cm3 [56, 439], P =0.01). After adjustment for size, the GDM group had significantly greater total ATvolume at 10 weeks than control group infants (16.0% [6.0, 27.1], P = 0.002). ATdistribution and IHCL content were not significantly different at either time point.CONCLUSIONSAdiposity in GDM infants is amplified in early infancy, despite good maternalglycemic control and predominant breast-feeding, suggesting a potential causalpathway to later adverse metabolic health. Reduction in postnatal adiposity maybe a therapeutic target to reduce later health risks.
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Journal articleMontaldo P, Addison S, Oliveira V, et al., 2016,
Quantification of Maceration Changes using Post Mortem MRI in Fetuses
, BMC MEDICAL IMAGING, Vol: 16, ISSN: 1471-2342BackgroundPost mortem imaging is playing an increasingly important role in perinatal autopsy, andcorrect interpretation of imaging changes is paramount. This is particularly importantfollowing intra-uterine fetal death, where there may be fetal maceration. The aim of thisstudy was to investigate whether any changes seen on a whole body fetal post mortemmagnetic resonance imaging (PMMR) correspond to maceration at conventionalautopsy.Methods: We performed pre-autopsy PMMR in 75 fetuses using a 1.5 Tesla SiemensAvanto MR scanner (Erlangen, Germany). PMMR images were reported blinded to theclinical history and autopsy data using a numerical severity scale (0 = no macerationchanges to 2 = severe maceration changes) for 6 different visceral organs (total 12).The degree of maceration at autopsy was categorized according to severity on anumerical scale (1 = no maceration to 4 = severe maceration). We also generatedquantitative maps to measure the liver and lung T2.Results: The mean PMMR maceration score correlated well with the autopsymaceration score (R2=0.93). A PMMR score of ≥ 4.5 had a sensitivity of 91%,specificity of 64%, for detecting moderate or severe maceration at autopsy. Liver andlung T2 were increased in fetuses with maceration scores of 3-4 in comparison tothose with 1-2 (liver p=0.03, lung p=0.02).Conclusions: There was a good correlation between PMMR maceration score and theextent of maceration seen at conventional autopsy. This score may be useful ininterpretation of fetal PMMR.
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Conference paperAsad M, Yang G, Slabaugh G, 2016,
Supervised partial volume effect unmixing for brain tumor characterization using multi-voxel MR spectroscopic imaging
, 13th International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 436-439, ISSN: 1945-7928A major challenge faced by multi-voxel Magnetic Resonance Spectroscopy (MV-MRS) imaging is partial volume effect (PVE), where signals from two or more tissue types may be mixed within a voxel. This problem arises due to the low resolution data acquisition, where the size of a voxel is kept relatively large to improve the signal to noise ratio. We propose a novel supervised Signal Mixture Model (SMM), which characterizes the MV-MRS signal into normal, low grade (infiltrative) and high grade (necrotic) brain tissue types, while accounting for in-type variation. An optimization problem is solved based on differential equations, to unmix the tissue by estimating mixture coefficients corresponding to each tissue type at each voxel. This enables visualization of probability heatmaps, useful for characterizing heterogeneous tumors. Experimental results show an overall accuracy of 91.67% and 88.89% for classifying tumors into either low or high grade against histopathology, and demonstrate the method's potential for non-invasive computer-aided diagnosis.
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Conference paperYang G, Ye X, Slabaugh G, et al., 2016,
Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images
, Medical Imaging 2016: Image Processing, Publisher: Society of Photo Optical Instrumentation EngineersIn this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise.
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Contact
For enquiries about the MRI Physics Collective, please contact:
Mary Finnegan
Senior MR Physicist at the Imperial College Healthcare NHS Trust
Pete Lally
Assistant Professor in Magnetic Resonance (MR) Physics at Imperial College
Jan Sedlacik
MR Physicist at the Robert Steiner MR Unit, Hammersmith Hospital Campus