Quantitative whole-body diffusion-weighted MRI (WB-DWI) is now possible using semi-automatic segmentation techniques. The method enables whole-body estimates of global Apparent Diffusion Coefficient (gADC) and total Diffusion Volume (tDV), both of which have demonstrated considerable utility for assessing treatment response in patients with bone metastases from primary prostate and breast cancers. Here we investigate the agreement (inter-observer repeatability) between two radiologists in their definition of Volumes Of Interest (VOIs) and subsequent assessment of tDV and gADC on an exploratory patient cohort of nine. Furthermore, each radiologist was asked to repeat his or her measurements on the same patient data sets one month later to identify the intra-observer repeatability of the technique. Using a Markov Chain Monte Carlo (MCMC) estimation method provided full posterior probabilities of repeatability measures along with maximum a-posteriori values and 95% confidence intervals. Our estimates of the inter-observer Intraclass Correlation Coefficient (ICCinter) for log-tDV and median gADC were 1.00 (0.97–1.00) and 0.99 (0.89–0.99) respectively, indicating excellent observer agreement for these metrics. Mean gADC values were found to have ICCinter = 0.97 (0.81–0.99) indicating a slight sensitivity to outliers in the derived distributions of gADC. Of the higher order gADC statistics, skewness was demonstrated to have good inter-user agreement with ICCinter = 0.99 (0.86–1.00), whereas gADC variance and kurtosis performed relatively poorly: 0.89 (0.39–0.97) and 0.96 (0.69–0.99) respectively. Estimates of intra-observer repeatability (ICCintra) demonstrated similar results: 0.99 (0.95–1.00) for log-tDV, 0.98 (0.89–0.99) and 0.97 (0.83–0.99) for median and mean gADC respectively, 0.64 (0.25–0.88) for gADC variance, 0.85 (0.57–0.95) for gADC skewness and 0.85 (0.57–0.95) for gADC kurtosis. Further investigation of two anomalous patient cases revealed that a very small proportion of voxels with outlying gADC values lead to instability in higher order gADC statistics. We therefore conclude that estimates of median/mean gADC and tumour volume demonstrate excellent inter- and intra-observer repeatability whilst higher order statistics of gADC should be used with caution when ascribing significance to clinical changes.
Purpose To characterise the voxel-wise uncertainties of Apparent Diffusion Coefficient (ADC) estimation from whole-body diffusion-weighted imaging (WBDWI). This enables the calculation of a new parametric map based on estimates of ADC and ADC uncertainty to improve WBDWI imaging standardization and interpretation: NoIse-Corrected Exponentially-weighted diffusion-weighted MRI (niceDWI) Methods Three approaches to the joint modelling of voxel-wise ADC and ADC uncertainty (uADC) are evaluated: (i) direct weighted least squares (DWLS), (ii) iterative linear-weighted least-squares (IWLS), and (iii) smoothed IWLS (SIWLS). The statistical properties of these approaches in terms of ADC/uADC accuracy and precision is compared using Monte Carlo simulations. Our proposed post-processing methodology (niceDWI) is evaluated using an ice-water phantom, by comparing the contrast-to-noise ratio (CNR) with conventional exponentially-weighted DWI. We apply niceDWI to a pilot cohort of 16 patients with metastatic prostate cancer undergoing WBDWI to determine its clinical utility. Results The statistical properties of ADC and uADC conformed closely to the theoretical predictions for DWLS, IWLS, and SIWLS fitting routines (a minor bias in parameter estimation is observed with DWLS). Ice-water phantom experiments demonstrated that a range of CNR could be generated using the niceDWI approach, and could improve CNR compared to conventional methods. We successfully implemented the niceDWI technique in our patient cohort, which visually improved the in-plane bias field compared with conventional WBDWI. Conclusions Measurement of the statistical uncertainty in ADC estimation provides a practical way to standardise WBDWI across different scanners, by providing quantitative image signals which can improve its reliability. Our proposed method can overcome inter-scanner and intra-scanner WBDWI signal variations that can confound image interpretation.
Dynamic contrast enhanced (DCE) MRI is a minimally invasive technique that is able to quantitatively investigate the tumor vasculature microenvironment. Such information shows great potential for treatment stratification and response monitoring. However, DCE typically suffers from low spatial resolution, Rician noise bias, and errors due to complex perfusion modeling. Model-based reconstruction, in which DCE parameters are estimated directly from k-space, may overcome these shortcomings. In this study, we implemented model-based reconstruction for DCE-MRI data, validated it in simulations, and showed its performance in-vivo. With model-based reconstruction the estimated parameter maps exhibited less noise and preserved more anatomical details.
Recent interest in the development of wireless sensor networks for surveillance introduces new problems that will need to be addressed when developing target tracking algorithms for use in such networks. Specifically the power and stealth requirements when combined with the wireless communications architecture will lead to potentially significant delays in the measurement collection process. The recent development of out-of-sequence tracking algorithms and posterior Cramer-Rao lower bounds for tracking with measurement origin uncertainty makes it possible to investigate how robust these new tracking algorithms are to a wide range of communications delays and a range of false alarm densities. This paper brings together these various components and presents the performance analysis for a simulated wireless network. Results show that position estimate accuracy close to the lower bound should be possible for communications intervals up to 4 s for challenging false alarm densities.
Motivation: T1 and T2 accuracy in the brain is difficult to assess, since there is no ground truth available. Goal(s): To investigate how well relaxometry methods agree. Approach: We compare Magnetic Resonance Fingerprinting (MRF) T1 and T2 mapping with Variable Flip Angle (VFA) T1 mapping and Multi-Echo Spin Echo (T2) mapping in 11 anatomical brain regions for 10 healthy volunteers, and in the relevant spheres of the NIST phantom. Results: MRF underestimates T1 and T2 in comparison with T1 VFA and T2 MESE in the human brain, especially in myelin-dense areas. Less T1 and no T2 bias is present in the NIST phantom. Impact: Quantitative T1 and T2 relaxometry techniques are more consistent in the NIST phantom than the human brain. Deviations could be caused by magnetisation transfer, whose impact on T1 and T2 relaxation mechanisms needs further investigation.
Purpose: To qualitatively and quantitatively investigate the effect of common vendor-related sequence variations in fat suppression techniques on the diagnostic performance of free-breathing DW protocols for lung imaging.Methods: 8 patients with malignant lung lesions were scanned in free breathing using two diffusion-weighted (DW) protocols with different fat suppression techniques: DWA used short-tau inversion recovery (STIR), and DWB used Spectral Adiabatic Inversion Recovery (SPAIR). Both techniques were obtained at two time points, between 1 hour and 1 week apart. Image quality was assessed using a 5-point scoring system. The number of lesions visible within lung, mediastinum and at thoracic inlet on the DW (b=800 s/mm2) images was compared. Signal-to-noise ratios (SNR) were calculated for lesions and para-spinal muscle. Repeatability of ADC values of the lesions was estimated for both protocols together and separately.Results: There was a signal void at the thoracic inlet in all patients with DWB but not with DWA. DWA images were rated significantly better than DWB images overall quality domains. (Cohen’s κ = 1). Although 8 more upper mediastinal/thoracic inlet lymph nodes were detected with DWA than DWB, this did not reach statistical significance (p = 0.23). Tumour ADC values were not significantly different between protocols (p=0.93), their ADC reproducibility was satisfactory (CoV=7.7%) and repeatability of each protocol separately was comparable (CoVDWA=3.7% (95% CI 2.5 – 7.1%) and CoVDWB=4.6% (95% CI 3.1 – 8.8%)).Conclusion: In a free-breathing DW-MRI protocol for lung, STIR fat suppression produced images of better diagnostic quality than SPAIR, while maintaining comparable SNR and providing repeatable quantitative ADC acceptable for use in a multicentre trial setting.
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