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4 results on '"Dikaios N"'

1. Direct parametric reconstruction from undersampled (k, t)-space data in dynamic contrast enhanced MRI.

Author

Dikaios N, Arridge S, Hamy V, Punwani S, and Atkinson D

Subjects
Algorithms, Bayes Theorem, Computer Simulation, Humans, Male, Phantoms, Imaging, Reproducibility of Results, Respiratory Mechanics, Sensitivity and Specificity, Contrast Media pharmacokinetics, Image Enhancement methods, Image Interpretation, Computer-Assisted methods, Magnetic Resonance Imaging methods, Prostatic Neoplasms pathology
Abstract

The Magnetic Resonance Imaging (MRI) signal can be made sensitive to functional parameters that provide information about tissues. In dynamic contrast enhanced (DCE) MRI these functional parameters are related to the microvasculature environment and the concentration changes that occur rapidly after the injection of a contrast agent. Typically DCE images are reconstructed individually and kinetic parameters are estimated by fitting a pharmacokinetic model to the time-enhancement response; these methods can be denoted as "indirect". If undersampling is present to accelerate the acquisition, techniques such as kt-FOCUSS can be employed in the reconstruction step to avoid image degradation. This paper suggests a Bayesian inference framework to estimate functional parameters directly from the measurements at high temporal resolution. The current implementation estimates pharmacokinetic parameters (related to the extended Tofts model) from undersampled (k, t)-space DCE MRI. The proposed scheme is evaluated on a simulated abdominal DCE phantom and prostate DCE data, for fully sampled, 4 and 8-fold undersampled (k, t)-space data. Direct kinetic parameters demonstrate better correspondence (up to 70% higher mutual information) to the ground truth kinetic parameters (of the simulated abdominal DCE phantom) than the ones derived from the indirect methods. For the prostate DCE data, direct kinetic parameters depict the morphology of the tumour better. To examine the impact on cancer diagnosis, a peripheral zone prostate cancer diagnostic model was employed to calculate a probability map for each method., (Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.)

Published
2014
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2. Respiratory motion correction in dynamic MRI using robust data decomposition registration - application to DCE-MRI.

Author

Hamy V, Dikaios N, Punwani S, Melbourne A, Latifoltojar A, Makanyanga J, Chouhan M, Helbren E, Menys A, Taylor S, and Atkinson D

Subjects
Algorithms, Computer Simulation, Contrast Media, Female, Humans, Intestinal Diseases diagnosis, Intestine, Small, Liver Diseases diagnosis, Male, Principal Component Analysis, Prostatic Diseases diagnosis, Reproducibility of Results, Sensitivity and Specificity, Image Enhancement methods, Image Interpretation, Computer-Assisted methods, Magnetic Resonance Imaging methods, Movement, Respiration
Abstract

Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC60) at early enhancement., (Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.)

Published
2014
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3. Evolution of multi-parametric MRI quantitative parameters following transrectal ultrasound-guided biopsy of the prostate

Author

Latifoltojar, A, Dikaios, N, Ridout, A, Moore, C, Illing, R, Kirkham, A, Taylor, S, Halligan, S, Atkinson, D, Allen, C, Emberton, M, and Punwani, S

Subjects
Adult, Male, Contrast Media, Prostatic Neoplasms, Middle Aged, Image Enhancement, Magnetic Resonance Imaging, Diffusion Magnetic Resonance Imaging, Image Processing, Computer-Assisted, Humans, Original Article, Neoplasm Grading, Endoscopic Ultrasound-Guided Fine Needle Aspiration, Aged
Abstract

Background: To determine the evolution of prostatic multi-parametric magnetic resonance imaging (mp-MRI) signal following transrectal ultrasound (TRUS)-guided biopsy. Methods: Local ethical permission and informed written consent was obtained from all the participants (n=14, aged 43–69, mean 64 years). Patients with a clinical suspicion of prostate cancer (PSA range 2.2–11.7, mean 6.2) and a negative (PIRAD 1–2/5) pre-biopsy mp-MRI (pre-contrast T1, T2, diffusion-weighted and dynamic-contrast-enhanced MRI) who underwent 10-core TRUS-guided biopsy were recruited for additional mp-MRI examinations performed at 1, 2 and 6 months post biopsy. We quantified mp-MRI peripheral zone (PZ) and transition zone (TZ) normalized T2 signal intensity (nT2-SI); T1 relaxation time (T10); diffusion-weighted MRI, apparent diffusion coefficient (ADC); dynamic contrast-enhanced MRI, maximum enhancement (ME); slope of enhancement (SoE) and area-under-the-contrast-enhancement-curve at 120 s (AUC120). Significant changes in mp-MRI parameters were identified by analysis of variance with Dunnett's post testing. Results: Diffuse signal changes were observed post-biopsy throughout the PZ. No significant signal change occurred following biopsy within the TZ. Left and right PZ mean nT2-SI (left PZ: 5.73, 5.16, 4.90 and 5.12; right PZ: 5.80, 5.10, 4.84 and 5.05 at pre-biopsy, 1, 2 and 6 months post biopsy, respectively) and mean T10 (left PZ: 1.02, 0.67, 0.78, 0.85; right PZ: 1.29, 0.64, 0.78, 0.87 at pre-biopsy, 1, 2 and 6 months post biopsy, respectively) were reduced significantly (P

Published
2015

4. Noise estimation from averaged diffusion weighted images: Can unbiased quantitative decay parameters assist cancer evaluation?

Author

Nikolaos, Dikaios, Shonit, Punwani, Valentin, Hamy, Pierpaolo, Purpura, Scott, Rice, Martin, Forster, Ruheena, Mendes, Stuart, Taylor, David, Atkinson, Dikaios, N, Punwani, S, Hamy, V, Purpura, P, Rice, S, Forster, M, Mendes, R, Taylor, S, and Atkinson, D

Subjects
Adult, Keywords: diffusion weighted magnetic resonance imaging, diffusion weighted magnetic resonance imaging, Middle Aged, Models, Theoretical, Signal-To-Noise Ratio, Image Enhancement, IVIM, Diffusion Magnetic Resonance Imaging, Head and Neck Neoplasms, Lymphatic Metastasis, Image Interpretation, Computer-Assisted, Carcinoma, Squamous Cell, Humans, noise estimation, Imaging Methodology—Full Papers, Algorithms, Aged
Abstract

Purpose Multiexponential decay parameters are estimated from diffusion-weighted-imaging that generally have inherently low signal-to-noise ratio and non-normal noise distributions, especially at high b-values. Conventional nonlinear regression algorithms assume normally distributed noise, introducing bias into the calculated decay parameters and potentially affecting their ability to classify tumors. This study aims to accurately estimate noise of averaged diffusion-weighted-imaging, to correct the noise induced bias, and to assess the effect upon cancer classification. Methods A new adaptation of the median-absolute-deviation technique in the wavelet-domain, using a closed form approximation of convolved probability-distribution-functions, is proposed to estimate noise. Nonlinear regression algorithms that account for the underlying noise (maximum probability) fit the biexponential/stretched exponential decay models to the diffusion-weighted signal. A logistic-regression model was built from the decay parameters to discriminate benign from metastatic neck lymph nodes in 40 patients. Results The adapted median-absolute-deviation method accurately predicted the noise of simulated (R2 = 0.96) and neck diffusion-weighted-imaging (averaged once or four times). Maximum probability recovers the true apparent-diffusion-coefficient of the simulated data better than nonlinear regression (up to 40%), whereas no apparent differences were found for the other decay parameters. Conclusions Perfusion-related parameters were best at cancer classification. Noise-corrected decay parameters did not significantly improve classification for the clinical data set though simulations show benefit for lower signal-to-noise ratio acquisitions. Magn Reson Med, 2013. © 2013 Wiley Periodicals, Inc.

Published
2013

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