Habitat Imaging of Tumors Enables High Confidence Sub-Regional Assessment of Response to Therapy.

Imaging biomarkers are used in therapy development to identify and quantify therapeutic response. In oncology, use of MRI, PET and other imaging methods can be complicated by spatially complex and heterogeneous tumor micro-environments, non-Gaussian data and small sample sizes. Linear Poisson Modelling (LPM) enables analysis of complex data that is quantitative and can operate in small data domains.

Considering tumour volume for motion corrected DWI of colorectal liver metastases increases sensitivity of ADC to detect treatment-induced changes.

ADC is a potential post treatment imaging biomarker in colorectal liver metastasis however measurements are affected by respiratory motion. This is compounded by increased statistical uncertainty in ADC measurement with decreasing tumour volume. In this prospective study we applied a retrospective motion correction method to improve the image quality of 15 tumour data sets from 11 patients.

A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases.

Apparent Diffusion Coefficient (ADC) is a potential quantitative imaging biomarker for tumour cell density and is widely used to detect early treatment changes in cancer therapy. We propose a strategy to improve confidence in the interpretation of measured changes in ADC using a data-driven model that describes sources of measurement error. Observed ADC is then standardised against this estimation of uncertainty for any given measurement.

The Accuracy of ADC Measurements in Liver Is Improved by a Tailored and Computationally Efficient Local-Rigid Registration Algorithm.

This study describes post-processing methodologies to reduce the effects of physiological motion in measurements of apparent diffusion coefficient (ADC) in the liver. The aims of the study are to improve the accuracy of ADC measurements in liver disease to support quantitative clinical characterisation and reduce the number of patients required for sequential studies of disease progression and therapeutic effects. Two motion correction methods are compared, one based on non-rigid registration (NRA) using freely available open source algorithms and the other a local-rigid registration (LRA) specifically designed for use with diffusion weighted magnetic resonance (DW-MR) data.

Quantitative shape analysis with weighted covariance estimates for increased statistical efficiency.

Background: The introduction and statistical formalisation of landmark-based methods for analysing biological shape has made a major impact on comparative morphometric analyses. However, a satisfactory solution for including information from 2D/3D shapes represented by 'semi-landmarks' alongside well-defined landmarks into the analyses is still missing. Also, there has not been an integration of a statistical treatment of measurement error in the current approaches.

TINA manual landmarking tool: software for the precise digitization of 3D landmarks.

Background: Interest in the placing of landmarks and subsequent morphometric analyses of shape for 3D data has increased with the increasing accessibility of computed tomography (CT) scanners. However, current computer programs for this task suffer from various practical drawbacks. We present here a free software tool that overcomes many of these problems.

Quantifying heterogeneity in human tumours using MRI and PET.

Most tumours, even those of the same histological type and grade, demonstrate considerable biological heterogeneity. Variations in genomic subtype, growth factor expression and local microenvironmental factors can result in regional variations within individual tumours. For example, localised variations in tumour cell proliferation, cell death, metabolic activity and vascular structure will be accompanied by variations in oxygenation status, pH and drug delivery that may directly affect therapeutic response.

Multicomponent MR Image Denoising.

Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multicomponent nature of MR images.

Robust tissue boundary detection for cerebral cortical thickness estimation.

This paper presents an algorithm for determining regional cerebral grey matter cortical thickness from magnetic resonance scans. In particular, the modification of a gradient-based edge detector into an iso-grey-level boundary detector for reliably determining the low-contrast grey-white matter interface is described and discussed. The reproducibility of the algorithm over 31 gyral regions is assessed using repeat scans of four subjects, and a technique for correcting the misplacement of the grey-white matter boundary is shown to significantly reduce the systematic error on the reproducibility.

Robust quantification of short echo time 1H magnetic resonance spectra using the Padé approximant.

Accurate quantification of in vivo short echo time spectra is hampered by the presence of overlapping peaks and a significant baseline. In this work the Padé approximant in conjunction with Monte Carlo simulation is used to extract peak areas from short echo time 1H spectra. We exploit the fact that the Padé approximant is known to model broad non-Lorentzian signals as arbitrary sums of Lorentzian components to separate baseline components from sharper metabolite signals by combining the Padé approximant with Monte Carlo simulation.

Effects of intramolecular dipolar coupling on the isotropic-nematic phase transition of a hard spherocylinder fluid.

The thermodynamics of a simple model, containing the minimum set of features required to provide liquid crystal-like phase behavior and the dipolar coupling observable in the NMR spectrum of orientationally ordered fluids, are presented within the framework of Onsager theory. The model comprises a fluid of hard spherocylinders with a pair of embedded freely rotating magnetic dipoles. The behavior of the isotropic-nematic phase transition is explored as a function magnetic field strength and of the relative orientation between the nematic director and the external magnetic field.

Dementing disorders: volumetric measurement of cerebrospinal fluid to distinguish normal from pathologic findings -- feasibility study.

The authors describe a magnetic resonance (MR) imaging technique to quantify the severity and distribution of cerebral atrophy by using automated volumetric analysis of the distribution of cerebrospinal fluid. The MR imaging technique demonstrated high diagnostic sensitivity and specificity in a group of healthy subjects and patients with dementing diseases. The authors conclude that this approach provides valuable clinical information that is complementary to information acquired with standard diagnostic practices.

Using Bayesian tissue classification to improve the accuracy of vestibular schwannoma volume and growth measurement.

Background And Purpose: True 3D measurements of tumor volume are time-consuming and subject to errors that are particularly pronounced in cases of small tumors. These problems complicate the routine clinical assessment of tumor growth rates. We examined the accuracy of currently available methods of size and growth measurement of vestibular schwannomas compared with that of a novel fast partial volume tissue classification algorithm.

Supervised Learning Extensions to the CLAM Network.

The contextual layered associative memory (CLAM) has been developed as a self-generating structure which implements a probabilistic encoding scheme. The training algorithms are geared towards the unsupervised generation of a layerable associative mapping ([Thacker and Mayhew, 1989]). We show here that the resulting structure will support layers which can be trained to produce outputs that approximate conditional probabilities of classification.