Validation for 2D/3D registration. I: A new gold standard data set.

PURPOSE: In this article, the authors propose a new gold standard data set for the validation of two-dimensional/three-dimensional (2D/3D) and 3D/3D image registration algorithms. METHODS: A gold standard data set was produced using a fresh cadaver pig head with attached fiducial markers. The authors used several imaging modalities common in diagnostic imaging or radiotherapy, which include 64-slice computed tomography (CT), magnetic resonance imaging using T1, T2, and proton density sequences, and cone beam CT imaging data. Radiographic data were acquired using kilovoltage and megavoltage imaging techniques. The image information reflects both anatomy and reliable fiducial marker information and improves over existing data sets by the level of anatomical detail, image data quality, and soft-tissue content. The markers on the 3D and 2D image data were segmented using ANALYZE 10.0 (AnalyzeDirect, Inc., Kansas City, KN) and an in-house software. RESULTS: The projection distance errors and the expected target registration errors over all the image data sets were found to be less than 2.71 and 1.88 mm, respectively. CONCLUSIONS: The gold standard data set, obtained with state-of-the-art imaging technology, has the potential to improve the validation of 2D/3D and 3D/3D registration algorithms for image guided therapy.

Efficient implementation of the rank correlation merit function for 2D/3D registration.

A growing number of clinical applications using 2D/3D registration have been presented recently. Usually, a digitally reconstructed radiograph is compared iteratively to an x-ray image of the known projection geometry until a match is achieved, thus providing six degrees of freedom of rigid motion which can be used for patient setup in image-guided radiation therapy or computer-assisted interventions. Recently, stochastic rank correlation, a merit function based on Spearman's rank correlation coefficient, was presented as a merit function especially suitable for 2D/3D registration. The advantage of this measure is its robustness against variations in image histogram content and its wide convergence range. The considerable computational expense of computing an ordered rank list is avoided here by comparing randomly chosen subsets of the DRR and reference x-ray. In this work, we show that it is possible to omit the sorting step and to compute the rank correlation coefficient of the full image content as fast as conventional merit functions. Our evaluation of a well-calibrated cadaver phantom also confirms that rank correlation-type merit functions give the most accurate results if large differences in the histogram content for the DRR and the x-ray image are present.

Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review.

The emergence of chemical imaging (CI) has gifted spectroscopy an additional dimension. Chemical imaging systems complement chemical identification by acquiring spatially located spectra that enable visualization of chemical compound distributions. Such techniques are highly relevant to pharmaceutics in that the distribution of excipients and active pharmaceutical ingredient informs not only a product's behavior during manufacture but also its physical attributes (dissolution properties, stability, etc.). The rapid image acquisition made possible by the emergence of focal plane array detectors, combined with publication of the Food and Drug Administration guidelines for process analytical technology in 2001, has heightened interest in the pharmaceutical applications of CI, notably as a tool for enhancing drug quality and understanding process. Papers on the pharmaceutical applications of CI have been appearing in steadily increasing numbers since 2000. The aim of the present paper is to give an overview of infrared, near-infrared and Raman imaging in pharmaceutics. Sections 2 and 3 deal with the theory, device set-ups, mode of acquisition and processing techniques used to extract information of interest. Section 4 addresses the pharmaceutical applications.

Monitoring galenical process development by near infrared chemical imaging: one case study.

The objective of this study is to evaluate by NIR imaging the homogeneity of process intermediates obtained with different process parameters during the development of a new pharmaceutical solid form. The process under investigation is a solid dosage form based on extrusion. The parameters are two kinds of crystallizations, two sizes of particle of API, two screw speeds during the extrusion and two milling screens used to reduce the extrudates into a granulate form. Two kinds of intermediates are evaluated: the extrudates and the cores. Two approaches are used to analyze the data: the univariate NIR analysis which consists in wavelength selection and multivariate analysis, i.e., Classical Least Squares (CLS), which takes into account the whole spectra. The univariate method reveals good chemical homogeneity of the extrudates but differences in their physical aspect. CLS shows well-distributed excipients for all the cores; differences in the sizes of the granules have also been revealed. The univariate method can be applied on simple chemical systems such as binary mixtures. When complex samples are analyzed, multivariate analysis is the method of choice. This study demonstrates that NIR imaging can be a useful tool for the optimization of the process and for the selection of the final parameters of the process.