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• Journal article
  Montaldo P, Addison S, Oliveira V, Lally PJ, Taylor AM, Sebire NJ, Thayyil S, Arthurs OJet al., 2016,

  Quantification of Maceration Changes using Post Mortem MRI in Fetuses

  , BMC MEDICAL IMAGING, Vol: 16, ISSN: 1471-2342

  BackgroundPost 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 paper
  Asad 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: 1945-7928

  A 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 paper
  Yang G, Ye X, Slabaugh G, Keegan J, Mohiaddin R, Firmin Det 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 Engineers

  In 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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• Journal article
  Nazaran A, Wisco JJ, Hageman N, Schettler SP, Wong A, Vinters HV, Teng C-C, Bangerter NKet al., 2016,

  Methodology for computing white matter nerve fiber orientation in human histological slices

  , Journal of Neuroscience Methods, Vol: 261, Pages: 75-84, ISSN: 0165-0270
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• Conference paper
  Park DJ, Bangerter N, Palmer AJR, Wang H, Sveinsson B, Hargreaves B, Glyn-Jones Set al., 2016,

  Toward a 7T MRI protocol for the evaluation of early osteoarthritis in knee cartilage

  , ISMRM 24th Annual Meeting
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• Conference paper
  Taylor M, Wang H, Palmer AJR, Carr A, Glyn-Jones Set al., 2016,

  Rapid High Resolution Morphological Imaging of Cartilage at 7T: Contrast Optimization and Comparison of DESS, Phase-Cycled bSSFP, and 3D SPACE

  , ISMRM 24th Annual Meeting
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• Conference paper
  Miller KL, Bangerter N, Almagro FA, Thomas DL, Yacoub E, Xu J, Bartsch AJ, Jbabdi S, Sotiropoulos SN, Jenkinson M, Andersson J, Griffanti L, Weale P, Dragonu I, Garratt S, Hudson S, Collins R, Matthews PM, Smith SMet al., 2016,

  UK Biobank: Brain imaging protocols and first data release

  , ISMRM 24th Annual Meeting
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• Conference paper
  Morrell G, Kaggie J, Stein M, Parker S, Bangerter Net al., 2016,

  Rapid high-resolution sodium relaxometry in human breast

  , ISMRM 24th Annual Meeting
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• Conference paper
  Howell F, Wang H, Park DJ, Taylor M, Sveinsson B, Bangerter Net al., 2016,

  Simultaneous Extraction of ADC and T2

  , ISMRM 24th Annual Meeting
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• Conference paper
  Whitaker ST, Taylor M, Wang H, Bangerter Net al., 2016,

  SNR and Banding Artifact Reduction Analysis of Phase-Cycled Elliptical Signal Model bSSFP

  , ISMRM 24th Annual Meeting of the ISMRM
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