Charles R. Meyer

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.

Functional diffusion map as an early imaging biomarker for high-grade glioma: correlation with conventional radiologic response and overall survival.

PURPOSE Assessment of radiologic response (RR) for brain tumors utilizes the Macdonald criteria 8 to 10 weeks from the start of treatment. Diffusion magnetic resonance imaging (MRI) using a functional diffusion map (fDM) may provide an earlier measure to predict patient survival. PATIENTS AND METHODS Sixty patients with high-grade glioma were enrolled onto a study of intratreatment MRI at 1, 3, and 10 weeks. Receiver operating characteristic curve analysis was used to evaluate imaging parameters as a function of patient survival at 1 year. Both log-rank and Cox proportional hazards models were utilized to assess overall survival. RESULTS Greater increases in diffusion in response to therapy over time were observed in those patients alive at 1 year compared with those who died as a result of disease. The volume of tumor with increased diffusion by fDM at 3 weeks was the strongest predictor of patient survival at 1 year, with larger fDM predicting longer median survival (52.6 v 10.9 months; log-rank, P < .003; hazard ratio [HR] = 2.7; 95% CI, 1.5 to 5.9). Radiologic response at 10 weeks had similar prognostic value (median survival, 31.6 v 10.9 months; log-rank P < .0007; HR = 2.9; 95% CI, 1.7 to 7.2). Radiologic response and fDM differed in 25% of cases. A composite index of response including fDM and RR provided a robust predictor of patient survival and may identify patients in whom RR does not correlate with clinical outcome. CONCLUSION Compared with conventional neuroimaging, fDM provided an earlier assessment of equal predictive value, and the combination of fDM and RR provided a more accurate prediction of patient survival than either metric alone.

Retrospective correction of intensity inhomogeneities in MRI

Medical imaging data sets are often corrupted by multiplicative inhomogeneities, often referred to as nonuniformities or intensity variations, that hamper the use of quantitative analyses. The authors describe an automatic technique that not only improves the worst situations, such as those encountered with magnetic resonance imaging (MRI) surface coils, but also corrects typical inhomogeneities encountered in routine volume data sets, such as MRI head scans, without generating additional artifact. Because the technique uses only the patient data set, the technique can be applied retrospectively to all data sets, and corrects both patient independent effects, such as rf coil design, and patient dependent effects, such as attenuation of overlying tissue experienced both in high field MRI and X-ray computed tomography (CT). The authors show results for several MRI imaging situations including thorax, head, and breast. Following such corrections, region of interest analyses, volume histograms, and thresholding techniques are more meaningful. The value of such correction algorithms may increase dramatically with increased use of high field strength magnets and associated patient-dependent rf attenuation in overlying tissues.

The parametric response map is an imaging biomarker for early cancer treatment outcome

Here we describe the parametric response map (PRM), a voxel-wise approach for image analysis and quantification of hemodynamic alterations during treatment for 44 patients with high-grade glioma. Relative cerebral blood volume (rCBV) and flow (rCBF) maps were acquired before treatment and after 1 and 3 weeks of therapy. We compared the standard approach using region-of-interest analysis for change in rCBV or rCBF to the change in perfusion parameters on the basis of PRM (PRMrCBV and PRMrCBF) for their accuracy in predicting overall survival. Neither the percentage change of rCBV or rCBF predicted survival, whereas the regional response evaluations made on the basis of PRM were highly predictive of survival. Even when accounting for baseline rCBV, which is prognostic, PRMrCBV proved more predictive of overall survival.

Parametric Response Map As an Imaging Biomarker to Distinguish Progression From Pseudoprogression in High-Grade Glioma

PURPOSE To assess whether a new method of quantifying therapy-associated hemodynamic alterations may help to distinguish pseudoprogression from true progression in patients with high-grade glioma. PATIENTS AND METHODS Patients with high-grade glioma received concurrent chemoradiotherapy. Relative cerebral blood volume (rCBV) and blood flow (rCBF) maps were acquired before chemoradiotherapy and at week 3 during treatment on a prospective institutional review board-approved study. Pseudoprogression was defined as imaging changes 1 to 3 months after chemoradiotherapy that mimic tumor progression but stabilized or improved without change in treatment or for which resection revealed radiation effects only. Clinical and conventional magnetic resonance (MR) parameters, including average percent change of rCBV and CBF, were evaluated as potential predictors of pseudoprogression. Parametric response map (PRM), an innovative, voxel-by-voxel method of image analysis, was also performed. RESULTS Median radiation dose was 72 Gy (range, 60 to 78 Gy). Of 27 patients, stable disease/partial response was noted in 13 patients and apparent progression was noted in 14 patients. Adjuvant temozolomide was continued in all patients. Pseudoprogression occurred in six patients. Based on PRM analysis, a significantly reduced blood volume (PRM(rCBV)) at week 3 was noted in patients with progressive disease as compared with those with pseudoprogression (P < .01). In contrast, change in average percent rCBV or rCBF, MR tumor volume changes, age, extent of resection, and Radiation Therapy Oncology Group recursive partitioning analysis classification did not distinguish progression from pseudoprogression. CONCLUSION PRM(rCBV) at week 3 during chemoradiotherapy is a potential early imaging biomarker of response that may be helpful in distinguishing pseudoprogression from true progression in patients with high-grade glioma.

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.

Polynomial modeling and reduction of RF body coil spatial inhomogeneity in MRI

The usefulness of statistical clustering algorithms developed for automatic segmentation of lesions and organs in magnetic resonance imaging (MRI) intensity data sets suffers from spatial nonstationarities introduced into the data sets by the acquisition instrumentation. The major intensity inhomogeneity in MRI is caused by variations in the B1-field of the radio frequency (RF) coil. A three-step method was developed to model and then reduce the effect. Using a least squares formulation, the inhomogeneity is modeled as a maximum variation order two polynomial. In the log domain the polynomial model is subtracted from the actual patient data set resulting in a compensated data set. The compensated data set is exponentiated and rescaled. Statistical comparisons indicate volumes of significant corruption undergo a large reduction in the inhomogeneity, whereas volumes of minimal corruption are not significantly changed. Acting as a preprocessor, the proposed technique can enhance the role of statistical segmentation algorithms in body MRI data sets.

Diffusion MRI: A new strategy for assessment of cancer therapeutic efficacy

The use of anatomical imaging in clinical oncology practice traditionally relies on comparison of patient scans acquired before and following completion of therapeutic intervention. Therapeutic success is typically determined from inspection of gross anatomical images to assess changes in tumor size. Imaging could provide significant additional insight into therapeutic impact if a specific parameter or combination of parameters could be identified which reflect tissue changes at the cellular or physiologic level. This would provide an early indicator or treatment response/outcome in an individual patient before completion of therapy. Moreover, response of a tumor to therapeutic intervention may be heterogeneous. The use of imaging could assist in delineating therapeutic-induced spatial heterogeneity within a tumor mass by providing information related to specific regions that are resistant or responsive to treatment. Largely untapped potential resides in exploratory methods such as diffusion MRI, which is a nonvolumetric intravoxel measure of tumor response based upon water molecular mobility. Alterations in water mobility reflect changes in tissue structure at the cellular level. While the clinical utility of diffusion MRI for oncologic practice is still under active investigation, this overview on the use of diffusion MRI for the evaluation of brain tumors will serve to introduce how this approach may be applied in the future for the management of patients with solid tumors.