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.
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.
M. McNitt-Gray, S. Armato, C. Meyer, A. Reeves, G. McLennan, R. Pais, J. Freymann, M. Brown, R. Engelmann, P. Bland, G. Laderach, C. Piker, J. Guo, D. Qing, D. Yankelevitz, D. Aberle, E. van Beek, H. MacMahon, E. Kazerooni, B. Croft, L. Clarke
KEYWORDS: Data processing, Image processing, Databases, Lung, Medical imaging, Computed tomography, Telecommunications, Data communications, Computer aided diagnosis and therapy, Medical research
The LIDC is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource. A unique multi-center data collection process and communication system were developed to share image data and to capture the location and spatial extent of lung nodules as marked by expert radiologists. A two-phase data collection process was designed to allow multiple radiologists at different centers to asynchronously review and annotate each CT image series. Four radiologists reviewed each case using this process. In the first or "blinded" phase, each radiologist reviewed the CT series independently. In the second or "unblinded" review phase, the results from all four blinded reviews are compiled and presented to each radiologist for a second review. This allows each radiologist to review their own annotations along with those of the other radiologists. The results from each radiologist's unblinded review were compiled to form the final unblinded review. There is no forced consensus in this process. An XML-based message system was developed to communicate the results of each reading. This two-phase data collection process was designed, tested and implemented across the LIDC. It has been used for more than 130 CT cases that have been read and annotated by four expert readers and are publicly available at (http://ncia.nci.nih.gov). A data collection process was developed, tested and implemented that allowed multiple readers to review each case multiple times and that allowed each reader to observe the annotations of other readers.
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.
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