C J Taylor

Magnetic resonance image synthesis using a flexible model

Image synthesis methods are based on the hypothesis that a magnetic resonance (MR) image with optimized contrast can be reproduced by synthesis from three calculated basic images of T1, T2 and spin density. This method, however, is limited by noise due to uncertainties in the initial measurements. The principal component analysis (PCA) method is based on an information theory approach that decomposes MR images into a small set of characteristic feature images. PCA images, or eigenimages, show morphology by condensing the structural information from the source images. Eigenimages have also been shown to improve contrast-to-noise ratio (CNR) compared with source images. In this study we have developed a method of synthesizing MR images using a flexible model, comprising a set of eigenimages derived from PCA. A matching process has been carried out to find the best fit between the model and a synthetic image calculated from the Bloch equations. The method has been applied to MR images obtained from a group of patients with intracranial lesions. The images derived from the flexible model show increased lesion conspicuity, reduced artefact and comparable CNR to the directly acquired images while maintaining the MR characteristic information for diagnosis.

Image search using trained flexible shape models.

This paper describes a technique for building compact models of the shape and appearance of flexible objects seen in two-dimensional images. The models are derived from the statistics of sets of images of example objects with ‘landmark’ points labelled on each object. Each model consists of a flexible shape template, describing how the landmark points can vary, and a statistical model of the expected grey levels in regions around each point. Such models have proved useful in a wide variety of applications. We describe how the models can be used in local image search and give examples of their application.

Off-resonance binomial composite pulses in 2D and 3D fat/water imaging at 0.5 T

A new method of acquiring two-dimensional (2D) and three-dimensional (3D) fat/water decomposed images on a 0.5 T magnetic resonance (MR) imaging system is described. The method uses a gradient-recalled echo (GRE) sequence incorporating a composite 121 pulse, originally used for magnetization transfer contrast (MTC) experiments, for frequency-selective pre-saturation. By applying the 121 pulse off resonance, a sharp discrimination between the chemically shifted water and fat signals may be obtained using a composite pulse as short as 4 ms. Phantom studies demonstrate that suppression ratios as high as 50:1 are achievable. The new method has been applied to volunteers and patients, and good fat/water images of limb, abdomen, head and neck obtained. The new method can be incorporated into other pulse sequences related to GRE, such as phase contrast (PC) and time-of-flight (TOF), and represents the first practical (i.e. rapid) method for 3D fat/water imaging at an operating field as low as 0.5 T.

System architectures for interactive knowledge-based image-interpretation

We discuss hardware and software architecture for automated image-interpretation. The importance of considering the complete system is emphasized leading in particular to the conclusion that high-level and low-level processing are intimately linked. We present arguments to support the idea that automated image-interpretation systems should be knowledge-based and interactive. We attempt to identify the main architectural problems which such systems must address and outline a systematic strategy for acquiring, structuring and using knowledge.