This paper proffers two non-linear empirical parametric models—linear slope and Ricker—for use in characterising contrast enhancement in dynamic contrast enhanced (DCE) MRI. The advantage of these models over existing empirical parametric and pharmacokinetic models is that they can be fitted using linear least squares (LS). This means that fitting is quick, there is no need to specify initial parameter estimates, and there are no convergence issues. Furthermore the LS fit can itself be used to provide initial parameter estimates for a subsequent NLS fit (self-starting models). The results of an empirical evaluation of the goodness of fit (GoF) of these two models, measured in terms of both MSE and R^2, relative to a two-compartment pharmacokinetic model and the Hayton model are also presented. The GoF was evaluated using both routine clinical breast MRI data and a single high temporal resolution breast MRI data set. The results demonstrate that the linear slope model fits the routine clinical data better than any of the other models and that the two parameter self-starting Ricker model fits the data nearly as well as the three parameter Hayton model. This is also demonstrated by the results for the high temporal data and for several temporally sub-sampled versions of this data.

We present a new approach to visualising parametric vol-umes obtained in voxel-wise model fitting of dynamic contrast-enhanced(DCE) MRI data of the breast. The visualisation makes use of hardware-accelerated rendering to obtain an interactive, 3D colour-correct maxi-mum intensity projection (MIP). The method has been realised in soft-ware that permits the user to not only interactively visualise the paramet-ric volume but also to delineate 3D regions of interest using a 2D slice-wise interface. Experimental results, based on 14 DCE-MRI data setsfrom routine clinical practice, show that using the visualisation/tracingtool a medically qualified operator can achieve the same sensitivity forthe detection of malignancy as a radiologist using conventional manualinterpretation, but with better specificity. The results demonstrate thatthe visualisation methodology/software has potential as a tool for assist-ing the radiologist with the task of interpreting 4D DCE-MRI data inthe routine clinical setting.

Virtual microscopy (VM) is a comparatively recent innovation that is revolutionizing both the teaching of microscopic structure in human medicine and the concept of online diagnosis and telemedicine. The interactivity of the various commercially available browsers attempts to simulate the experience of looking down a microscope while offering advantages over traditional microscopy that include clarity of image, reduced infrastructure, and high flexibility, as the images are accessible online. We developed our own VM system, including customized software and a browser that was simple and intuitive to use, with the added advantage of further modifications possible to assist student learning. In this article, we report on a preliminary study wherein VM was introduced to veterinary science students in one course and directly compared to traditional microscopy to determine whether students would readily accept this new technology and which aspects of VM were advantageous. Responses from a survey form showed that students rated VM significantly higher than traditional microscopy as a tool to learn histology because it offers clearer images, the ability to learn collaboratively, more effective use of time, and the flexibility of online learning. Students also indicated a strong preference for the use of VM in future courses. These results suggest that VM is a flexible and enjoyable resource that could be useful to enhance the learning of microscopic structure in veterinary science courses.

In this paper, we describe a virtual microscope system, based on JPEG 2000, which utilizes extended depth of field (EDF) imaging. Through a series of observer trials we show that EDF imaging improves both the local image quality of individual fields of view (FOV) and the accuracy with which the FOVs can be mosaiced (stitched) together. In addition, we estimate the required bit rate to adequately render a set of histology and cytology specimens at a quality suitable for on-line learning and collaboration. We show that, using JPEG 2000, we can efficiently represent high-quality, high-resolution colour images of microscopic specimens with less than 1 bit per pixel.

In this paper we present an image stitching method based on dynamic programming and describe its application to automated slide acquisition for Virtual Microscopy (VM). Given a large number of fields of view (FOVs) acquired from a single microscope slide, we composite these images into a single large ‘virtual slide’ image. The location of each FOV is determined using a new algorithm based on dynamic programming. We compare the performance of the proposed algorithm to an existing greedy algorithm. In a visual trial it is shown that the new algorithm provides a significant improvement in perceived image quality at image boundaries compared to the existing algorithm.

The interpretation of dynamic contrast enhanced (DCE) MR images of the breast is predicated on the assessment of tissue enhancement kinetics. Both pharmacokinetic (PK) and empirical parametric (EP) models have been developed to quantify this change in enhancement. The former aim to measure physiologically meaningful parameters while the latter seek to measure the shape of the enhancement curve. Given that different PK models can yield markedly different estimates of the same physiological parameter (because of model assumptions) and that such models need to be fitted using non-linear least squares (NLS) which is in itself problematic (need to specify starting values, convergence issues), EP models remain of interest. Herein we propose two such models—linear-slope [1] and Ricker [2]—which have the advantage that they can be fitted using linear least squares (LS) meaning that fitting is quick and that there is no need to specify initial parameter estimates. Furthermore, if desired, the LS fit can be used to provide parameter estimates for a subsequent NLS fit. The results of an empirical evaluation of the goodness-of-fit (GoF) of these two models relative to the pharmacokinetically-inspired Hayton model [2], and the simplified gamma-variate model [4] are also presented.