Jennifer L. Boes

Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations

This paper applies and evaluates an automatic mutual information-based registration algorithm across a broad spectrum of multimodal volume data sets. The algorithm requires little or no pre-processing, minimal user input and easily implements either affine, i.e. linear or thin-plate spline (TPS) warped registrations. We have evaluated the algorithm in phantom studies as well as in selected cases where few other algorithms could perform as well, if at all, to demonstrate the value of this new method. Pairs of multimodal gray-scale volume data sets were registered by iteratively changing registration parameters to maximize mutual information. Quantitative registration errors were assessed in registrations of a thorax phantom using PET/CT and in the National Library of Medicines Visible Male using MRI T2-/T1-weighted acquisitions. Registrations of diverse clinical data sets were demonstrated including rotate-translate mapping of PET/MRI brain scans with significant missing data, full affine mapping of thoracic PET/CT and rotate-translate mapping of abdominal SPECT/CT. A five-point thin-plate spline (TPS) warped registration of thoracic PET/CT is also demonstrated. The registration algorithm converged in times ranging between 3.5 and 31 min for affine clinical registrations and 57 min for TPS warping. Mean error vector lengths for rotate-translate registrations were measured to be subvoxel in phantoms. More importantly the rotate-translate algorithm performs well even with missing data. The demonstrated clinical fusions are qualitatively excellent at all levels. We conclude that such automatic, rapid, robust algorithms significantly increase the likelihood that multimodality registrations will be routinely used to aid clinical diagnoses and post-therapeutic assessment in the near future.

Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression

Chronic obstructive pulmonary disease (COPD) is increasingly being recognized as a highly heterogeneous disorder, composed of varying pathobiology. Accurate detection of COPD subtypes by image biomarkers is urgently needed to enable individualized treatment, thus improving patient outcome. We adapted the parametric response map (PRM), a voxel-wise image analysis technique, for assessing COPD phenotype. We analyzed whole-lung computed tomography (CT) scans acquired at inspiration and expiration of 194 individuals with COPD from the COPDGene study. PRM identified the extent of functional small airways disease (fSAD) and emphysema as well as provided CT-based evidence that supports the concept that fSAD precedes emphysema with increasing COPD severity. PRM is a versatile imaging biomarker capable of diagnosing disease extent and phenotype while providing detailed spatial information of disease distribution and location. PRMs ability to differentiate between specific COPD phenotypes will allow for more accurate diagnosis of individual patients, complementing standard clinical techniques.

Mutual Information for Automated Unwarping of Rat Brain Autoradiographs

An automated multimodal warping based on mutual information metric (MI) as a mapping cost function is demonstrated. Mutual information (I) is calculated from a two-dimensional (2D) gray scale histogram of an image pair, and MI (= -I) provides a matching cost function which can be effective in registration of two- or three-dimensional data sets independent of modality. Most histological image data, though information rich and high resolution, present nonlinear deformations due to the specimen sectioning and need reconstitution into deformation-corrected volumes prior to geometric mapping to an anatomical volume for spatial analyses. Section alignment via automatic 2D registrations employing MI as a global cost function and thin-plate-spline (TPS) warping is applied to deoxy-D-[14C]glucose autoradiographic image slices of a rat brain with video reference images of the uncut block face to reconstitute a cerebral glucose metabolic volume data. Unlike the traditional feature-based TPS warping algorithms, initial control points are defined independently from feature landmarks. Registration quality using automated multimodal image warping is validated by comparing MIs of the image pair registered by automated affine registration and manual warping method. The MI proves to be a robust objective matching cost function effective for automatic multimodality warping for 2D data sets and can be readily applied to volume registrations.

Motion Correction in fMRI via Registration of Individual Slices Into an Anatomical Volume

An automated retrospective image registration based on mutual information is adapted to a multislice functional magnetic resonance imaging (fMRI) acquisition protocol to provide accurate motion correction. Motion correction is performed by mapping each slice to an anatomic volume data set acquired in the same fMRI session to accommodate inter‐slice head motion. Accuracy of the registration parameters was assessed by registration of simulated MR data of the known truth. The widely used rigid body volume registration approach based on stacked slices from the time series data may hinder statistical accuracy by introducing inaccurate assumptions of no motion between slices for multislice fMRI data. Improved sensitivity and specificity of the fMRI signal from mapping‐each‐slice‐to‐volume method is demonstrated in comparison with a stacked‐slice correction method by examining functional data from two normal volunteers. The data presented in a standard anatomical coordinate system suggest the reliability of the mapping‐each‐slice‐to‐volume method to detect the activation signals consistent between the two subjects. Magn Reson Med 41:964–972, 1999.

Semiautomatic registration of volumetric ultrasound scans

We demonstrate the ability to register easily and accurately volumetric ultrasound scans without significant data preprocessing or user intervention. Two volumetric ultrasound breast scan data sets were acquired from two different patients with breast cancer. Volumetric scan data were acquired by manually sweeping a linear array transducer mounted on a linear slider with a position encoder. The volumetric data set pairs consisted of color flow and/or power mode Doppler data sets acquired serially on the same patients. A previously described semiautomatic registration method based on maximizing mutual information was used to determine the transform between data sets. The results suggest that, even for the deformable breast, three-dimensional full affine transforms can be sufficient to obtain clinically useful registrations; warping may be necessary for increased registration accuracy. In conclusion, mutual information-based automatic registration as implemented on modern workstations is capable of yielding clinically useful registrations in times <35 min.

Multi-variate Mutual Information for Registration

An extension of the mutual information metric to a three-variate cost function for driving the registration of a volume to pair of co-registered volumes is presented. While mutual information has typically been applied to pairs of variables, it is possible to compute multi-variate mutual information. The implementation of multi-variate mutual information is described. This metric is demonstrated using the problem of registering a deformed t2 slice of the visible male magnetic resonance data set to either a single t1 slice or a pair of co-registered t1 and proton density slices. Two-variable and three-variable metric registration results are compared. Adding the extra proton density information to the registration cost metric leads to faster optimization convergence and better final accuracy. Multi-variate mutual information has potential application in problems where the addition of more information can lead to solution convergence or improve accuracy.

Ultrasonic characterization of in vitro osteoarthritic articular cartilage with validation by confocal microscopy.

The majority of adults over the age of 65 y develop osteoarthritis (OA), a joint disease characterized by degeneration of articular cartilage and subchondral sclerosis. Early in the disease, the articular cartilage surface begins to change histologically from a smooth to a rough or fibrillated appearance. A prerequisite for any chondroprotective pharmacological intervention is detection of OA in its preclinical phase. Current diagnostic imaging modalities, such as radiographs or (nuclear) magnetic resonance imaging, either cannot directly image the cartilage surface or lack sufficient resolution to detect surface fibrillations. We have developed an ultrasonic technique that can be used to characterize these surface fibrillations directly. We present our in vitro results with validation by laser-based confocal microscopic imaging.

Generating a normalized geometric liver model using warping

RATIONALE AND OBJECTIVES.Automated liver surface determination in abdominal computed tomography scans, currently difficult to achieve, is of interest to determine liver location and size for various medical applications, including radiation therapy treatment planning, surgical planning, and oncologic monitoring. The authors propose to facilitate automation by the addition of a priori shape information in the form of a liver model. METHODS.The normalized geometric liver model is generated by averaging outlines from a set of normal liver studies previously registered using thin-plate spline warping. The model consists of an averaged liver surface, a set of anatomic landmarks, and a deformation function. RESULTS.A liver model is presented and its ability to represent normal liver shapes is demonstrated. CONCLUSIONS.Liver surface warping provides a means of data normalization for model construction and a means of model deformation for representation of liver organ shapes.

Mutual Information for Automated Multimodal Image Warping

A quantitative assessment of mapping accuracy based on mutual information index (MI), calculated from gray scale 2D histogram, is applied to an automated multimodality (un)warping algorithm. Information rich histological image data, which present non-linear deformations due to the specimen sectioning, is reconstituted into deformation-corrected, 3D volumes for geometric mapping to anatomical data for spatial analyses. Thin-plate-spline (TPS) algorithm has been implemented for automatic unwarping of the distortions using a multivariate optimizer and MI as a global cost function. The MI proves to be a robust objective matching criterion effective for automatic multimodality warping for 2D data sets and can be readily applied to volumetric 3D registrations. The improved performance of TPS warping compared to full affine transformation is quantified by comparison of MIs of both methods.

Evaluation of Control Point Selection in Automatic, Mutual Information Driven, 3D Warping

We have implemented automatic 3D thin-plate spline warping as a geometric interpolant to map one dataset volume onto another. Homologous control points in one space are iteratively moved by an optimizer to maximize the global mutual information between the two data volumes. Given two different poses between highly deformed objects we desire to compute the relative geometric deformation using a minimal set of control points as determined by number and placement. The general solution to this problem is not known. In this paper we assess retrospective control point selection for the case of significant patient motion during MRI breast imaging.