Peyton H. Bland

Policies and regulations in the current health care environment have impacted the manner in which patient data - especially protected health information (PHI) - are handled in the clinical and research settings. Specifically, it is now more challenging to obtain de-identified PHI from the clinic for use in research while still adhering to the requirements dictated by the new policies and regulations. To meet this challenge, we have designed and implemented a novel web-based interface that uses a workflow model to manage the communication of data (for example, biopsy results) between the clinic and research environments without revealing PHI to the research team or associated research identifiers to the clinical collaborators. At the heart of the scheme is a web application that coordinates message passing between researchers and clinical collaborators by use of a protocol that protects confidentiality. We describe the design requirements of the messaging/communication protocol, as well as implementation details of the web application and its associated database. We conclude that this scheme provides a useful communication mechanism that facilitates clinical research while maintaining confidentiality of patient data.

Standard clinical radiological techniques for determining lesion volume changes in interval exams are, as far as we know, quantitatively non-descriptive or approximate at best. We investigate two new registration based methods that help sketch an improved quantitative picture of lesion volume changes in hepatic interval CT exams. The first method, Jacobian Integration, employs a constrained Thin Plate Spline warp to compute the deformation of the lesion of interest over the intervals. The resulting jacobian map of the deformation is integrated to yield the net lesion volume change. The technique is fast, accurate and requires no segmentation, but is sensitive to misregistration. The second scheme uses a Weighted Gray Value Difference image of two registered interval exams to estimate the change in lesion volume. A linear weighting and trimming curve is used to accurately account for the contribution of partial voxels. This technique is insensitive to slight misregistration and useful in analyzing simple lesions with uniform contrast or lesions with insufficient mutual information to allow the computation of an accurate warp. The methods are tested on both synthetic and in vivo liver lesions and results are evaluated against estimates obtained through careful manual segmentation of the lesions. Our findings so far have given us reason to believe that the estimators are reliable. Further experiments on numerous in vivo lesions will probably establish the improved efficacy of these methods in supporting earlier detection of new disease or conversion from stable to progressive disease in comparison to existing clinical estimation techniques.

Registration of medical images (intra- or multi-modality) is the first step before any analysis is performed. The analysis includes treatment monitoring, diagnosis, volumetric measurements or classification to mention a few. While pairwise registration, i.e., aligning a floating image to a fixed reference, is straightforward, it is not immediately clear what cost measures could be exploited for the groupwise alignment of several images (possibly multimodal) simultaneously. Recently however there has been increasing interest in this problem applied to atlas construction, statistical shape modeling, or simply joint alignment of images to get a consistent correspondence of voxels across all images based on a single cost measure. The aim of this paper is twofold, a) propose a cost function - alpha mutual information computed using entropic graphs that is a natural extension to Shannon mutual information for pairwise registration and b) compare its performance with the pairwise registration of the image set. We show that this measure can be reliably used to jointly align several images to a common reference. We also test its robustness by comparing registration errors for the registration process repeated at varying noise levels. In our experiments we used simulated data, applying different B-spline based geometric transformations to the same image and adding independent filtered Gaussian noise to each image. Non-rigid registration was employed with Thin Plate Splines(TPS) as the geometric interpolant.

The interest in registering a set of images has quickly risen in the field of medical image analysis. Mutual information (MI) based methods are well-established for pairwise registration but their extension to higher dimensions (multiple images) has encountered practical implementation difficulties. We extend the use of alpha mutual information (αMI) as the similarity measure to simultaneously register multiple images. αMI of a set of images can be directly estimated using entropic graphs spanning feature vectors extracted from the images, which is demonstrated to be practically feasible for joint registration. In this paper we are specifically interested in monitoring malignant tumor changes using simultaneous registration of multiple interval MR or CT scans. Tumor scans are typically a decorrelating sequence due to the cycles of heterogeneous cell death and growth. The accuracy of joint and pairwise registration using entropic graph methods is evaluated by registering several sets of interval exams. We show that for the parameters we investigated simultaneous joint registration method yields lower average registration errors compared to pairwise. Different degrees of decorrelation in the serial scans are studied and registration performance suggests that an appropriate scanning interval can be determined for efficiently monitoring lesion changes. Different levels of observation noise are added to the image sequences and the experimental results show that entropic graph based methods are robust and can be used reliably for multiple image registration.

Assessment of the effectiveness of cancer therapy traditionally relies on comparison of tumor images acquired before and after therapeutic intervention by inspection of gross anatomical images to evaluate changes in tumor size. The potential for imaging to provide additional insights related to the therapeutic impact would be enhanced if a specific parameter or combination of parameters could be identified that reflect tissue changes at the cellular or physiological level. This information could also provide a more sensitive and earlier indicator of treatment response in an individual animal or patient. Diffusion magnetic resonance imaging can detect relatively small changes in tissue structure at the cellular level and thus provides an opportunity to quantitatively and serially follow therapeutic-induced changes in solid tumors. This article provides an overview of the use of diffusion magnetic resonance imaging as a surrogate marker for quantitating treatment responsiveness in both preclinical and clinical studies.