A reformulation of pLSA for uncertainty estimation and hypothesis testing in bio-imaging.

MOTIVATION: Probabilistic latent semantic analysis (pLSA) is commonly applied to describe mass spectra (MS) images. However, the method does not provide certain outputs necessary for the quantitative scientific interpretation of data. In particular, it lacks assessment of statistical uncertainty and the ability to perform hypothesis testing. We show how linear Poisson modelling advances pLSA, giving covariances on model parameters and supporting χ2 testing for the presence/absence of MS signal components. As an example, this is useful for the identification of pathology in MALDI biological samples. We also show potential wider applicability, beyond MS, using magnetic resonance imaging (MRI) data from colorectal xenograft models. RESULTS: Simulations and MALDI spectra of a stroke-damaged rat brain show MS signals from pathological tissue can be quantified. MRI diffusion data of control and radiotherapy-treated tumours further show high sensitivity hypothesis testing for treatment effects. Successful χ2 and degrees-of-freedom are computed, allowing null-hypothesis thresholding at high levels of confidence. AVAILABILITY AND IMPLEMENTATION: Open-source image analysis software available from TINA Vision, www.tina-vision.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A new method for the high-precision assessment of tumor changes in response to treatment.

Motivation: Imaging demonstrates that preclinical and human tumors are heterogeneous, i.e. a single tumor can exhibit multiple regions that behave differently during both development and also in response to treatment. The large variations observed in control group, tumors can obscure detection of significant therapeutic effects due to the ambiguity in attributing causes of change. This can hinder development of effective therapies due to limitations in experimental design rather than due to therapeutic failure. An improved method to model biological variation and heterogeneity in imaging signals is described. Specifically, linear Poisson modeling (LPM) evaluates changes in apparent diffusion co-efficient between baseline and 72 h after radiotherapy, in two xenograft models of colorectal cancer. The statistical significance of measured changes is compared to those attainable using a conventional t-test analysis on basic apparent diffusion co-efficient distribution parameters. Results: When LPMs were applied to treated tumors, the LPMs detected highly significant changes. The analyses were significant for all tumors, equating to a gain in power of 4-fold (i.e. equivalent to having a sample size 16 times larger), compared with the conventional approach. In contrast, highly significant changes are only detected at a cohort level using t-tests, restricting their potential use within personalized medicine and increasing the number of animals required during testing. Furthermore, LPM enabled the relative volumes of responding and non-responding tissue to be estimated for each xenograft model. Leave-one-out analysis of the treated xenografts provided quality control and identified potential outliers, raising confidence in LPM data at clinically relevant sample sizes. Availability and implementation: TINA Vision open source software is available from www.tina-vision.net. Supplementary information: Supplementary data are available at Bioinformatics online.

Quantifying biological samples using Linear Poisson Independent Component Analysis for MALDI-ToF mass spectra.

Motivation: Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian. Results: We provide evidence that the Gaussian assumption is incorrect, motivating the need for our Poisson approach. The method is demonstrated by making proportion measurements from lipid-rich binary mixtures of lamb brain and liver, and also goat and cow milk. These allow our measurements and error predictions to be compared to ground truth. Availability and implementation: Software is available via the open source image analysis system TINA Vision, www.tina-vision.net. Contact: paul.tar@manchester.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Estimating False Positive Contamination in Crater Annotations from Citizen Science Data.

Web-based citizen science often involves the classification of image features by large numbers of minimally trained volunteers, such as the identification of lunar impact craters under the Moon Zoo project. Whilst such approaches facilitate the analysis of large image data sets, the inexperience of users and ambiguity in image content can lead to contamination from false positive identifications. We give an approach, using Linear Poisson Models and image template matching, that can quantify levels of false positive contamination in citizen science Moon Zoo crater annotations. Linear Poisson Models are a form of machine learning which supports predictive error modelling and goodness-of-fits, unlike most alternative machine learning methods. The proposed supervised learning system can reduce the variability in crater counts whilst providing predictive error assessments of estimated quantities of remaining true verses false annotations. In an area of research influenced by human subjectivity, the proposed method provides a level of objectivity through the utilisation of image evidence, guided by candidate crater identifications.

A fast, model-independent method for cerebral cortical thickness estimation using MRI.

Several algorithms for measuring the cortical thickness in the human brain from MR image volumes have been described in the literature, the majority of which rely on fitting deformable models to the inner and outer cortical surfaces. However, the constraints applied during the model fitting process in order to enforce spherical topology and to fit the outer cortical surface in narrow sulci, where the cerebrospinal fluid (CSF) channel may be obscured by partial voluming, may introduce bias in some circumstances, and greatly increase the processor time required. In this paper we describe an alternative, voxel based technique that measures the cortical thickness using inversion recovery anatomical MR images. Grey matter, white matter and CSF are identified through segmentation, and edge detection is used to identify the boundaries between these tissues. The cortical thickness is then measured along the local 3D surface normal at every voxel on the inner cortical surface. The method was applied to 119 normal volunteers, and validated through extensive comparisons with published measurements of both cortical thickness and rate of thickness change with age. We conclude that the proposed technique is generally faster than deformable model-based alternatives, and free from the possibility of model bias, but suffers no reduction in accuracy. In particular, it will be applicable in data sets showing severe cortical atrophy, where thinning of the gyri leads to points of high curvature, and so the fitting of deformable models is problematic.

Prediction of the jugular venous waveform using a model of CSF dynamics.

BACKGROUND AND PURPOSE: We have previously reported a model of cerebral hydrodynamics in the form of an equivalent electrical circuit. The aim of this work was to demonstrate that the model could predict venous flow patterns seen in the superior sagittal sinus (SSS), straight sinus (STS), and jugular vein (JV) in normal volunteers. MATERIALS AND METHODS: An electrical equivalence model of CSF and cerebral blood flow was fitted to measured arterial and CSF data from 16 healthy volunteers. Predictions of the venous outflow waveform derived from the model were compared with measured venous flows in the SSS, STS, and JV. RESULTS: The model accurately predicted the measured jugular waveform. The measured waveforms from SSS and STS showed a less pronounced and delayed systolic peak compared with the predicted outflow. The fitted bulk model parameters provided relative values that correspond approximately to the impedance of arterial capillaries (1.0), cerebral aqueduct ( approximately 0), venous capillaries ( approximately 0), and arteries (0.01) and for the elastic capacitance of the ventricles (4.11), capillaries ( approximately 0), and veins (271). The elastic capacitance of the major cerebral arteries was large and could not be accurately determined. CONCLUSIONS: We have confirmed the ability of the model to predict the venous waveforms in healthy persons. The absence of any statistically significant component of the venous waveform not described by the model implies that measurements of venous flow could be used to constrain further the model-fitting process.

Comparison of cerebral blood volume maps generated from T2* and T1 weighted MRI data in intra-axial cerebral tumours.

We compared parametric maps, measured values and value distributions of cerebral blood volume (CBV) derived from (1) first pass T1 weighted dynamic contrast-enhanced (DCE) data (T1-CBV) using the recently described leakage profile model and (2) conventional T2* weighted DCE data (T2*-CBV) using a conventional curve fitting technique, in nine patients with intraaxial tumours. Regions of interest were defined around enhancing tumour tissue on matched slices. Median tumour values and conspicuity indexes of CBV from the two techniques were compared, demonstrating good correlation (r = 0.667,p<0.05) in enhancing tumour and no significant difference in conspicuity. Pixel-by-pixel scattergrams of values in normal brain in a representative matched slice were produced for each case, which showed excellent correlation (r = 0.96,p<0.001). Distortion of blood vessels around susceptibility interfaces was evident on T2* CBV but not on T1 CBV maps. Leakage-free T1 CBV maps do not suffer from the susceptibility artifacts seen in T2* CBV maps, although they present comparable biological information.

Voxel based analysis of tissue volume from MRI data.

There are many techniques available for the analysis of MRI data. Often these methods are presented as completed algorithms, which specify what processing must be performed, but they are rarely presented in a way which makes clear the assumptions that must hold in order that these algorithms will provide valid results. The aim of this review article is to relate the common forms of algorithms and to explain the assumptions behind them. This is done in the context of the use of quantitative statistical methods, which we understand to be the only self-consistent method for any data analysis. We hope that this will go some way towards helping with the choice of which algorithm to use for particular analysis tasks.

Can dynamic susceptibility contrast magnetic resonance imaging perfusion data be analyzed using a model based on directional flow?

PURPOSE: To examine the implications of a physiological model of cerebral blood that uses the contradictory assumption that blood flow in all voxels of DSCE-MRI data sets is directional in nature. Analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSCE-MRI) uses techniques based on indicator dilution theory. Underlying this approach is an assumption that blood flow through pixels of gray and white matter is entirely random in direction. MATERIALS AND METHODS: We have used a directional flow model to estimate theoretical blood flow velocities that would be observed through normal cerebral tissues. Estimates of flow velocities from individual pixels were made by measuring the mean transit time for net flow (nMTT). Measurements of nMTT were made for each voxel by estimating the mean difference in contrast arrival time between each of the adjacent six voxels. RESULTS: Examination of the spatial distribution of contrast arrival time from DSCE-MRI data sets in normal volunteers demonstrated clear evidence of directional flow both in large vessels and in gray and white matter. The mean velocities of blood flow in gray and white matter in 12 normal volunteers were 0.25 +/- 0.013 and 0.21 +/- 0.014 cm/second, respectively, compared to predicted values of 0.25 and 0.18 cm/second. These values give measured nMTT for a 1-mm isotropic voxel of gray and white matter of 0.45 +/- 0.12 and 0.52 +/- 0.11 seconds, respectively, compared to predicted values of 0.47 and 0.55 seconds. CONCLUSION: A directional model of blood flow provides an alternative approach to the calculation of cerebral blood flow from (CBF) DSCE-MRI data.

Breath-hold perfusion and permeability mapping of hepatic malignancies using magnetic resonance imaging and a first-pass leakage profile model.

We have applied a novel pharmacokinetic model of the distribution of contrast media to dynamic contrast-enhanced MRI data from patients with hepatic neoplasms. The model uses data collected during the passage of a bolus of contrast medium and allows breath-hold image acquisition. The aims of the study were to investigate the feasibility of permeability mapping using the first pass technique and breath-hold acquisitions, and to examine the reproducibility of the technique and the effect of the liver's dual vascular supply on the assumptions of the model. Imaging was performed in 14 patients with hepatic neoplasms. Dynamic data clearly demonstrated differences in the timing and shape of the contrast medium concentration-time course curve in the systemic arterial and portal venous systems. Mapping of the arrival time (T(0)) of contrast medium allowed identification of tissue supplied by the hepatic arteries and portal vein. Hepatic tumours all showed typical hepatic arterial enhancement. Repeated measurements of endothelial permeability surface area product (k(fp)) and relative blood volume (rBV), performed in five patients, showed excellent reproducibility with variance ratios (V(r)) of 0.134 and 0.113, respectively. Measurement of enhancing tumour volume was also highly reproducible (V(r) = 0.096) and this was further improved by the use of T(0) maps to identify pixels supplied by the hepatic artery (V(r) = 0.026). Estimates of k(fp) and rBV in normal hepatic tissue supplied by the portal vein were highly inaccurate and these pixels were identified by use of the T(0) parameter and excluded from the analysis. In conclusion, dynamic MRI contrast enhancement combined with a pharmacokinetic model of the distribution of contrast media in the first pass allows us to produce highly reproducible parametric maps of k(fp) and rBV from hepatic tumours that are supplied by the hepatic arterial system using breath-hold acquisitions.

Improved high resolution MR imaging for surface coils using automated intensity non-uniformity correction: feasibility study in the orbit.

This study examined the effects of a recently developed automated intensity non-uniformity correction on surface coil images using the orbit as an exemplar. Images were obtained using a standard head coil and a range of surface coils. Slices through the optic nerve head and cavernous sinus were subjected to the correction algorithm. Blind forced-choice rankings of the subjective image quality were performed. Quantitative measurements were taken of the similarity between vitreous humor at two depths from the coil, and of the conspicuity between orbital fat and temporalis muscle intensities. The combined qualitative ranks for corrected surface coil images were higher than for the equivalent uncorrected images in all cases. Intensity non-uniformity correction produced statistically significant improvements in orbital surface coil images, bringing their intensity uniformity in homogeneous tissue to the level of head coil images. The subjective quality of the corrected surface coil images was superior to head coil images, due to increased spatial resolution combined with improved signal to noise ratio across the image.

Mathematical segmentation of grey matter, white matter and cerebral spinal fluid from MR image pairs.

The aims of this study were (1) to design a mathematical segmentation technique to allow extraction of grey matter, white matter and cerebral spinal fluid volumes from paired high resolution MR images and (2) to document the statistical accuracy of the method with different image combinations. A series of linear equations were derived that describe proportional tissue volumes in individual image voxels. The equations use estimates of pure tissue values to derive the proportion of each tissue within a single voxel. Repeatability of manual estimations of pure tissue values was assessed both using regions of interest and thresholding techniques. Statistical accuracy of tissue estimations for a variety of image pairs was assessed from measurements of root-mean-square noise and mean grey level intensity. The technique was used to produce parametric images of grey and white matter distribution. The segmentation technique showed greatest statistical accuracy when the first image has high grey/white matter contrast and the second image has little contrast or the rank order of the signal intensities from pure tissue is reversed. A combination of inversion recovery fast spin echo and fast FLAIR images produced a statistical error of 11% for grey matter and 10% for white matter for any given voxel. The effect of increasing sample size improves both of these figures to give a 1% statistical error on a 100 pixel sample.

A fast model independent method for automatic correction of intensity nonuniformity in MRI data.

A novel nonparametric approach for correcting intensity nonuniformity in magnetic resonance (MR) images is described. This approach is based solely on the assumption that the various sources of nonuniformity in MR imaging give rise to smooth variations in image intensity, and that these variations can be extracted and corrected for. The advantage of this computationally fast method is that it can be applied early in quantitative analysis while being independent of pulse sequence and is insensitive to pathological processes. This algorithm has been tested on both simulated and real data. Application to tissue segmentation and functional MR imaging has shown a marked improvement in quantitative analysis. J. Magn. Reson. Imaging 1999;10:550-562.

Improved quality of re-sliced MR images using re-normalized sinc interpolation.

This article shows that re-normalizing the interpolation kernel for a constant integral can make a significant improvement in performance of sinc interpolation methods. A comparison was performed between standard and re-normalized sinc kernels of various sizes using data from four commonly used magnetic resonance (MR) imaging sequences. Standard rotations were performed and compared with a "gold standard" data set generated by use of a large (13 x 13 x 13) sinc kernel. Measurements of systematic pixel intensity offset error and variance of generated residuals were used to estimate resultant interpolation error. Theoretical estimates of the consequent savings in computation time were compared with the measured time required for each algorithm and with the automated image registration (AIR) program. The use of a small (5 x 5 x 5) re-normalized kernel produced relative errors comparable to those in the gold standard data set, allowing saving in computation time of up to 30 times in comparison with standard sinc interpolation. This approach brings the implementation of MR volume re-slicing much closer to the demands of a clinical environment. J. Magn. Reson. Imaging 1999;10:582-588.

The effects of motion on parametric fMRI analysis techniques.

Subject motion during the time course of functional activation studies has been shown to cause spurious signals which can mimic 'true' activation. Therefore, the importance of motion correction has been widely recognized. Correction with post-processing using image registration software is common practice in functional imaging and analysis. Many image registration algorithms, developed for analysis requirements other than fMRI, assume rigid body motion. Although these techniques are now routinely used by a number of groups, rigid body coregistration has not yet been shown to reduce the effects of motion to an acceptable level in fMRI analysis, i.e. the effects on resulting correlation analysis directly. In this paper we have used volume data to assess rigid body co-registration in terms of motion artefacts for the different correlation approaches used in fMRI. We have developed a new way of visualizing motion effects in correlation analysis based on generating a scatter plot of correlation score against local image gradient. This technique has been tested on fMRI data sets from a functional paradigm suffering from motion correlated artefacts, with and without rigid body motion correction. Although we do not attempt to estimate the actual residual motion, this technique can be used to verify the results of analysis and select regions of relatively unambiguous activation. This paper assesses directly the rigid body assumption and proves the need for, and effectiveness of, co-registration for all correlation based analysis techniques. The specific differences between the popular correlation forms used are investigated and explained. We show that for certain forms of correlation analysis the effects of motion, while not removed altogether, are effectively statistically eliminated.