Emma Lewis

Correction of differential intensity inhomogeneity in longitudinal MR images.

Longitudinal MR imaging is increasingly being used to measure cerebral atrophy progression in dementia and other neurological disorders. Differences in intensity inhomogeneity between serial scans can confound these measurements. This differential bias also distorts nonlinear registration and makes both manual and automated segmentation of tissue type less reliable. A technique is described for the correction of this differential bias that makes no assumptions about signal distribution, bias field or signal homogeneity. Instead, the bias field calculation is performed on the basis that the remaining structure in the difference image of registered serial scans has small-scale structure. The differential bias field is of much larger scale and can thus be obtained by applying an appropriate filter to the difference image. The serial scan pair is then corrected for the differential bias field and atrophy measurement can be performed on the corrected scan pair. Application of a known, simulated bias field to real serial MR images was shown to alter atrophy measurements significantly. The differential correction method recovered the applied differential bias field and thereby improved atrophy measurements. This method was then applied to serial imaging in patients with dementia using a set of serial scan pairs with visually identified, significant differential bias and a set of scan pairs with negligible differential bias. Differential bias correction specifically reduced the variance of the atrophy measure significantly for the scans with significant differential bias.

Differentiating AD from aging using semiautomated measurement of hippocampal atrophy rates

Manual segmentation of the hippocampus is the gold standard in volumetric hippocampal magnetic resonance imaging (MRI) analysis; however, this is difficult to achieve reproducibly. This study explores whether application of local registration and calculation of the hippocampal boundary shift integral (HBSI) can reduce random variation compared with manual measures. Hippocampi were outlined on the baseline and registered-repeat MRIs of 32 clinically diagnosed Alzheimers disease (AD) patients and 47 matched controls (37-86 years) with a wide range of scanning intervals (175-1173 days). The scans were globally registered using 9 degrees of freedom and subsequently locally registered using 6 degrees of freedom and HBSI was then calculated automatically. HBSI significantly reduced the mean rate (P < 0.01) and variation in controls (P < 0.001) and increased group separation between AD cases and controls. When comparing HBSI atrophy rates with manually derived atrophy rates at 90% sensitivity, specificities were 98% and 81%, respectively. From logistic regression models, a 1% increase in HBSI atrophy rates was associated with an 11-fold (CI 3, 36) increase in the odds of a diagnosis of AD. For manually derived atrophy rates, the equivalent odds ratio was 3 (CI 2,4). We conclude that HBSI-derived atrophy rates reduce operator time and error, and are at least as effective as the manual equivalent as a diagnostic marker and are a potential marker of progression in longitudinal studies and trials.

Automatic calculation of hippocampal atrophy rates using a hippocampal template and the boundary shift integral.

We describe a method of automatically calculating hippocampal atrophy rates on T1-weighted MR images without manual delineation of hippocampi. This method was applied to a group of Alzheimers disease (AD) (n=36) and control (n=19) subjects and compared with manual methods (manual segmentation of baseline and repeat-image hippocampi) and semi-automated methods (manual segmentation of baseline hippocampi only). In controls, mean (S.D.) atrophy rates for manual, semi-automated, and automated methods were 18.1 (53.5), 15.3 (50.2) and 11.3 (50.4) mm3 loss per year, respectively. In AD patients these rates were 174.6 (106.5) 159.4 (101.2) and 172.1 (123.1) mm3 loss per year, respectively. The automated method was a significant predictor of disease (p=0.001) and gave similar group discrimination compared with both semi-automated and manual methods. The automated hippocampal analysis in this small study took approximately 20 min per hippocampal pair on a 3.4 GHz Intel Xeon server, whereas manual delineation of each hippocampal pair took approximately 90 min of operator-intensive labour. This method may be useful diagnostically or in studies where analysis of many scans may be required.

Improved reliability of hippocampal atrophy rate measurement in mild cognitive impairment using fluid registration.

MRI-derived rates of hippocampal atrophy may serve as surrogate markers of disease progression in mild cognitive impairment (MCI). Manual delineation is the gold standard in hippocampal volumetry; however, this technique is time-consuming and subject to errors. We aimed to compare regional non-linear (fluid) registration measurement of hippocampal atrophy rates against manual delineation in MCI. Hippocampi of 18 subjects were manually outlined twice on MRI scan-pairs (interval+/-SD: 2.01+/-0.11 years), and volumes were subtracted to calculate change over time. Following global affine and local rigid registration, regional fluid registration was performed from which atrophy rates were derived from the Jacobian determinants over the hippocampal region. Atrophy rates as derived by fluid registration were computed using both forward (repeat onto baseline) and backward (baseline onto repeat) registration. Reliability for both methods and agreement between methods was assessed. Mean+/-SD hippocampal atrophy rates (%/year) derived by manual delineation were: left: 2.13+/-1.62; right: 2.36+/-1.78 and for regional fluid registration: forward: left: 2.39+/-1.68; right: 2.49+/-1.52 and backward: left: 2.21+/-1.51; right: 2.42+/-1.49. Mean hippocampal atrophy rates did not differ between both methods. Reliability for manual hippocampal volume measurements (cross-sectional) was high (intraclass correlation coefficient (ICC): baseline and follow-up, left and right, >0.99). However, the resulting ICC for manual measurements of hippocampal volume change (longitudinal) was considerably lower (left: 0.798; right: 0.850) compared with regional fluid registration (forward: left: 0.985; right: 0.988 and backward: left: 0.975; right: 0.989). We conclude that regional fluid registration is more reliable than manual delineation in assessing hippocampal atrophy rates, without sacrificing sensitivity to change. This method may be useful to quantify hippocampal volume change, given the reduction in operator time and improved precision.

Automated measurement of hippocampal atrophy using fluid-registered serial MRI in AD and controls.

Objective: To assess hippocampal atrophy rates calculated from fluid registration methods. Methods: Hippocampi were segmented on baseline and registered-repeat scans of 32 probable Alzheimer disease (AD) subjects and 55 controls. Fluid-based atrophy rates were calculated. Results: In AD patients, the mean (SD) atrophy rates for manual, fluidly propagated, and Jacobian methods were 5.09 (3.59), 5.34 (3.43), and 3.55 (2.70) (percentage per year). In controls, atrophy rates were 1.31 (2.00), 0.89 (0.75), and 0.56 (1.12) (percentage per year). In AD, fluid propagation and manual rates were similar in means (P = 0.55) and variances (P = 0.71). Jacobian rates were smaller in mean (P = 0.002) and variance (P = 0.026) than in manual rates. In controls, fluid-propagated rates were similar in mean to manual rates (P = 0.12), but less variable (P < 0.0001). Jacobian rates were smaller in mean (P = 0.014) and less variable (P < 0.0001) than in manual rates. Both fluid methods were superior to manual measures in separating AD from controls (P < 0.0001). Conclusions: Fluid-based methods may be useful in large serial hippocampal studies.

A Particle Filter Approach to Respiratory Motion Estimation in Nuclear Medicine Imaging

With the continual improvement in spatial resolution of Nuclear Medicine (NM) scanners, it has become increasingly important to accurately compensate for patient motion during image acquisition. Respiratory motion produced by normal lung ventilation is a major source of artefacts in NM emission imaging that can affect large parts of the abdominal thoracic cavity. As such, a particle filter (PF) is proposed as a powerful method for motion correction in emission imaging which can successfully account for previously unseen motion. This paper explores a basic PF approach and demonstrates that it is possible to estimate temporally non-stationary motion using training data consisting of only a single respiratory cycle. Evaluation using the XCAT phantom suggests that the PF is a highly promising approach, and can appropriately handle the complex data that arises in clinical situations.

Validation of a Digital Mammography Image Simulation Chain with Automated Scoring of CDMAM Images

A wide variety of digital mammography systems are available for breast cancer imaging, each varying in physical performance. However, the relationship between physical performance assessment and clinical outcome is not clear. Thus, a means of simulating technically and clinically realistic images from different systems would represent a first step towards elucidating the impact of physical performance on clinical outcome. To this end, a framework for simulating technically realistic images has been developed. A range of simulated test objects, including CDMAM have been used to determine whether the simulation chain correctly reproduces these objects thus validating the simulation framework. Results evaluated for two digital mammography systems have been promising, with simulated images proving similar to experimental images for Modulation Transfer Function and Normalised Noise Power Spectrum measurements differing by approximately 3%.

Characterisation of respiratory motion extracted from 4D MRI

Nuclear Medicine (NM) imaging is currently the most sensitive approach for functional imaging of the human body. However, in order to achieve high-resolution imaging, one of the factors degrading the detail or apparent resolution in the reconstructed image, namely respiratory motion, has to be overcome. All respiratory motion correction approaches depend on some assumption or estimate of respiratory motion. In this paper, the respiratory motion found from 4D MRI is analysed and characterised. The characteristics found are compared with previous studies and will be incorporated into the process of estimating respiratory motion.

Image resampling effects in mammographic image simulation.

This work describes the theory of resampling effects within the context of image simulation for mammographic images. The process of digitization associated with using digital imaging technology needs to be correctly addressed in any image simulation process. Failure to do so can lead to overblurring in the final synthetic image. A method for weighted neighbourhood averaging is described for non-integer scaling factors in resampling images. The use of the method is demonstrated by comparing simulated and real images of an edge test object acquired on two clinical mammography systems. Images were simulated using two setups: from idealized images and from images obtained with clinical systems. A Gaussian interpolation method is proposed as a single-step solution to modelling blurring filters for the simulation process.

Marker-less tracking for respiratory motion correction in nuclear medicine

This paper present preliminary work in developing a method of using a marker-less tracking system to analyze the natural temporal variations in chest wall configuration during breathing, thus avoiding reliance on a limited number of fiducial markers. This involves using a marker-less video capture of the motion of the abdominal-chest surface and the development of a B-spline model to parameterize this motion. The advantage of the marker-less system that is non-invasive and non-ionizing, thus facilitating high throughput without the need for marker-based patient set-up time