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Intensity-based similarity measures assume that the original signal intensity of different modality images can provide statistically consistent information regarding the two modalities to be co-registered. In multi-modal registration problems, however, intensity-based similarity measures are often inadequate to identify an optimal transformation. Texture features can improve the performance of the multi-modal co-registration by providing more similar appearance representations of the two images to be co-registered, compared to the signal intensity representations. Furthermore, texture features extracted at different length scales (neighborhood sizes) can reveal similar underlying structural attributes between the images to be co-registered similarities that may not be discernible on the signal intensity representation alone. However one limitation of using texture features is that a number of them may be redundant and dependent and hence there is a need to identify non-redundant representations. Additionally it is not clear which features at which specific scales reveal similar attributes across the images to be co-registered. To address this problem, we introduced a novel approach for multimodal co-registration that employs new multi-scale image representations. Our approach comprises 4 distinct steps: (1) texure feature extraction at each length scale within both the target and template images, (2) independent component analysis (ICA) at each texture feature length scale, and (3) spectrally embedding (SE) the ICA components (ICs) obtained for the texture features at each length scale, and finally (4) identifying and combining the optimal length scales at which to perform the co-registration. To combine and co-register across different length scales, -mutual information (-MI) was applied in the high dimensional space of spectral embedding vectors to facilitate co-registration. To validate our multi-scale co-registration approach, we aligned 45 pairs of prostate MRI and histology images corresponding to the prostate with the objective of mapping extent of prostate cancer annotated by a pathologist on the pathology onto the pre-operative MRI. The registration results showed higher correlation between template and target images with average correlation ratio of 0.927 compared to 0.914 for intensity-based registration. Additionally an improvement in the dice similarity coefficient (DSC) of 13.6% was observed for the multi-scale registration compared to intensity-based registration and an 1.26% DSC improvement compared to registration involving the best individual scale.

In this study we explore the ability of a novel machine learning approach, in conjunction with computer-extracted features describing prostate cancer morphology on pre-treatment MRI, to predict whether a patient will develop biochemical recurrence within ten years of radiation therapy. Biochemical recurrence, which is characterized by a rise in serum prostate-specific antigen (PSA) of at least 2 ng/mL above the nadir PSA, is associated with increased risk of metastasis and prostate cancer-related mortality. Currently, risk of biochemical recurrence is predicted by the Kattan nomogram, which incorporates several clinical factors to predict the probability of recurrence-free survival following radiation therapy (but has limited prediction accuracy). Semantic attributes on T2w MRI, such as the presence of extracapsular extension and seminal vesicle invasion and surrogate measure- ments of tumor size, have also been shown to be predictive of biochemical recurrence risk. While the correlation between biochemical recurrence and factors like tumor stage, Gleason grade, and extracapsular spread are well- documented, it is less clear how to predict biochemical recurrence in the absence of extracapsular spread and for small tumors fully contained in the capsule. Computer{extracted texture features, which quantitatively de- scribe tumor micro-architecture and morphology on MRI, have been shown to provide clues about a tumor's aggressiveness. However, while computer{extracted features have been employed for predicting cancer presence and grade, they have not been evaluated in the context of predicting risk of biochemical recurrence. This work seeks to evaluate the role of computer-extracted texture features in predicting risk of biochemical recurrence on a cohort of sixteen patients who underwent pre{treatment 1.5 Tesla (T) T2w MRI. We extract a combination of first-order statistical, gradient, co-occurrence, and Gabor wavelet features from T2w MRI. To identify which of these T2w MRI texture features are potential independent prognostic markers of PSA failure, we implement a partial least squares (PLS) method to embed the data in a low{dimensional space and then use the variable importance in projections (VIP) method to quantify the contributions of individual features to classification on the PLS embedding. In spite of the poor resolution of the 1.5 T MRI data, we are able to identify three Gabor wavelet features that, in conjunction with a logistic regression classifier, yield an area under the receiver operating characteristic curve of 0.83 for predicting the probability of biochemical recurrence following radiation therapy. In comparison to both the Kattan nomogram and semantic MRI attributes, the ability of these three computer-extracted features to predict biochemical recurrence risk is demonstrated.

Multi-modal image registration is needed to align medical images collected from different protocols or imaging sources, thereby allowing the mapping of complementary information between images. One challenge of multimodal image registration is that typical similarity measures rely on statistical correlations between image intensities to determine anatomical alignment. The use of alternate image representations could allow for mapping of intensities into a space or representation such that the multimodal images appear more similar, thus facilitating their co-registration. In this work, we present a spectral embedding based registration (SERg) method that uses non-linearly embedded representations obtained from independent components of statistical texture maps of the original images to facilitate multimodal image registration. Our methodology comprises the following main steps: 1) image-derived textural representation of the original images, 2) dimensionality reduction using independent component analysis (ICA), 3) spectral embedding to generate the alternate representations, and 4) image registration. The rationale behind our approach is that SERg yields embedded representations that can allow for very different looking images to appear more similar, thereby facilitating improved co-registration. Statistical texture features are derived from the image intensities and then reduced to a smaller set by using independent component analysis to remove redundant information. Spectral embedding generates a new representation by eigendecomposition from which only the most important eigenvectors are selected. This helps to accentuate areas of salience based on modality-invariant structural information and therefore better identifies corresponding regions in both the template and target images. The spirit behind SERg is that image registration driven by these areas of salience and correspondence should improve alignment accuracy. In this work, SERg is implemented using Demons to allow the algorithm to more effectively register multimodal images. SERg is also tested within the free-form deformation framework driven by mutual information. Nine pairs of synthetic T1-weighted to T2-weighted brain MRI were registered under the following conditions: five levels of noise (0%, 1%, 3%, 5%, and 7%) and two levels of bias field (20% and 40%) each with and without noise. We demonstrate that across all of these conditions, SERg yields a mean squared error that is 81.51% lower than that of Demons driven by MRI intensity alone. We also spatially align twenty-six ex vivo histology sections and in vivo prostate MRI in order to map the spatial extent of prostate cancer onto corresponding radiologic imaging. SERg performs better than intensity registration by decreasing the root mean squared distance of annotated landmarks in the prostate gland via both Demons algorithm and mutual information-driven free-form deformation. In both synthetic and clinical experiments, the observed improvement in alignment of the template and target images suggest the utility of parametric eigenvector representations and hence SERg for multimodal image registration.

Radical prostatectomy (RP) and radiation therapy (RT) are the most common treatment options for prostate cancer (PCa). Despite advancements in radiation delivery and surgical procedures, RP and RT can result in failure rates as high as 40% and >25%, respectively. Treatment failure is characterized by biochemical recurrence (BcR), which is defined in terms of prostate specific antigen (PSA) concentrations and its variation following treatment. PSA is expected to decrease following treatment, thereby its presence in even small concentrations (e.g 0.2 ng/ml for surgery or 2 ng/ml over the nadir PSA for radiation therapy) is indicative of treatment failure. Early identification of treatment failure may enable the use of more aggressive or neo-adjuvant therapies. Moreover, predicting failure prior to treatment may spare the patient from a procedure that is unlikely to be successful. Our goal is to identify differences on pre-treatment MRI between patients who have BcR and those who remain disease-free at 5 years post-treatment. Specifically, we focus on (1) identifying statistically significant differences in MRI intensities, (2) establishing morphological differences in prostatic anatomic structures, and (3) comparing these differences with the natural variability of prostatic structures. In order to attain these objectives, we use an anatomically constrained registration framework to construct BcR and non-BcR statistical atlases based on the pre-treatment magnetic resonance images (MRI) of the prostate. The patients included in the atlas either underwent RP or RT and were followed up for at least 5 years. The BcR atlas was constructed from a combined population of 12 pre-RT 1.5 Tesla (T) MRI and 33 pre-RP 3T MRI from patients with BcR within 5 years of treatment. Similarly, the non-BcR atlas was built based on a combined cohort of 20 pre-RT 1.5T MRI and 41 pre-RP 3T MRI from patients who remain disease-free 5 years post treatment. We chose the atlas framework as it enables the mapping of MR images from all subjects into the same canonical space, while constructing both an imaging and a morphological statistical atlas. Such co-registration allowed us to perform voxel-by-voxel comparisons of MRI intensities and capsule and central gland morphology to identify statistically significant differences between the BcR and non-BcR patient populations. To assess whether the morphological differences are valid, we performed an additional experiment where we constructed sub-population atlases by randomly sampling RT patients to construct the BcR and non-BcR atlases. Following these experiments we observed that: (1) statistically significant MRI intensity differences exist between BcR and non-BcR patients, specifically on the border of the central gland; (2) statistically significant morphological differences are visible in the prostate and central gland, specifically in the proximity of the apex, and (3) the differences between the BcR and non-BcR cohorts in terms of shape appeared to be consistent across these sub-population atlases as observed in our RT atlases.

We introduce a novel radiohistomorphometric method for quantitative correlation and subsequent discovery of imaging markers for aggressive prostate cancer (CaP). While this approach can be employed in the context any imaging modality and disease domain, we seek to identify quantitative dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) attributes that are highly correlated with density and architecture of tumor microvessels, surrogate markers of CaP aggressiveness. This retrospective study consisted of five Gleason score matched patients who underwent 3 Tesla multiparametric MRI prior to radical prostatectomy (RP). The excised gland was sectioned and quartered with a rotary knife. For each serial section, digitized images of individual quadrants were reconstructed into pseudo whole mount sections via previously developed stitching program. The individual quadrants were stained with vascular marker CD31 and annotated for CaP by an expert pathologist. The stained microvessel regions were quantitatively characterized in terms of density and architectural arrangement via graph algorithms, yielding a series of quantitative histomorphometric features. The reconstructed pseudo whole mount histologic sections were non-linearly co-registered with DCE MRI to identify tumor extent on MRI on a voxel-by-voxel basis. Pairwise correlations between kinetic and microvessel features within CaP annotated regions on the two modalities were computed to identify highly correlated attributes. Preliminary results of the radiohistomorphometric correlation identified 8 DCE MRI kinetic features that were highly and significantly (p

Mouse lung models facilitate the investigation of conditions such as chronic inflammation which are associated with common lung diseases. The multi-scale manifestation of lung inflammation prompted us to use multi-scale imaging - both in vivo, ex vivo MRI along with ex vivo histology, for its study in a new quantitative way. Some imaging modalities, such as MRI, are non-invasive and capture macroscopic features of the pathology, while others, e.g. ex vivo histology, depict detailed structures. Registering such multi-modal data to the same spatial coordinates will allow the construction of a comprehensive 3D model to enable the multi-scale study of diseases. Moreover, it may facilitate the identification and definition of quantitative of in vivo imaging signatures for diseases and pathologic processes. We introduce a quantitative, image analytic framework to integrate in vivo MR images of the entire mouse with ex vivo histology of the lung alone, using lung ex vivo MRI as conduit to facilitate their co-registration. In our framework, we first align the MR images by registering the in vivo and ex vivo MRI of the lung using an interactive rigid registration approach. Then we reconstruct the 3D volume of the ex vivo histological specimen by efficient group wise registration of the 2D slices. The resulting 3D histologic volume is subsequently registered to the MRI volumes by interactive rigid registration, directly to the ex vivo MRI, and implicitly to in vivo MRI. Qualitative evaluation of the registration framework was performed by comparing airway tree structures in ex vivo MRI and ex vivo histology where airways are visible and may be annotated. We present a use case for evaluation of our co-registration framework in the context of studying chronic inammation in a diseased mouse.

Statistical imaging atlases allow for integration of information from multiple patient studies collected across different image scales and modalities, such as multi-parametric (MP) MRI and histology, providing population statistics regarding a specific pathology within a single canonical representation. Such atlases are particularly valuable in the identification and validation of meaningful imaging signatures for disease characterization in vivo within a population. Despite the high incidence of prostate cancer, an imaging atlas focused on different anatomic structures of the prostate, i.e. an anatomic atlas, has yet to be constructed. In this work we introduce a novel framework for MRI atlas construction that uses an iterative, anatomically constrained registration (AnCoR) scheme to enable the proper alignment of the prostate (Pr) and central gland (CG) boundaries. Our current implementation uses endorectal, 1.5T or 3T, T2-weighted MRI from 51 patients with biopsy confirmed cancer; however, the prostate atlas is seamlessly extensible to include additional MRI parameters. In our cohort, radical prostatectomy is performed following MP-MR image acquisition; thus ground truth annotations for prostate cancer are available from the histological specimens. Once mapped onto MP-MRI through elastic registration of histological slices to corresponding T2-w MRI slices, the annotations are utilized by the AnCoR framework to characterize the 3D statistical distribution of cancer per anatomic structure. Such distributions are useful for guiding biopsies toward regions of higher cancer likelihood and understanding imaging profiles for disease extent in vivo. We evaluate our approach via the Dice similarity coefficient (DSC) for different anatomic structures (delineated by expert radiologists): Pr, CG and peripheral zone (PZ). The AnCoR-based atlas had a CG DSC of 90.36%, and Pr DSC of 89.37%. Moreover, we evaluated the deviation of anatomic landmarks, the urethra and veromontanum, and found 3.64 mm and respectively 4.31 mm. Alternative strategies that use only the T2-w MRI or the prostate surface to drive the registration were implemented as comparative approaches. The AnCoR framework outperformed the alternative strategies by providing the lowest landmark deviations.

Mouse lung models facilitate the study of the pathogenesis of various pulmonary diseases such as infections and inflammatory diseases. The co-registration of ex vivo histological data and pre-excised magnetic resonance imaging (MRI) in preclinical mouse models would allow for determination and validation of imaging signatures for different pathobiologies within the lung. While slice-based co-registration could be used, this approach assumes that (a) slice correspondences between the two different modalities exist, and (b) finding slice correspondences often requires the intervention of an expert and is time consuming. A more practical approach is to first reconstruct the 3D histological volume from individual slices, then perform 3D registration with the MR volume. Before the histological reconstruction, image registration is required to compensate for geometric differences between slices. Pairwise algorithms work by registering pairs of successive slices. However, even if successive slices are registered reasonably well, the propagation of registration errors over slices can yield a distorted volumetric reconstruction significantly different in shape from the shape of the true specimen. Groupwise registration can reduce the error propagation by considering more than two successive images during the registration, but existing algorithms are computationally expensive. In this paper, we present an efficient groupwise registration approach, which yields consistent volumetric reconstruction and yet runs equally fast as pairwise registration. The improvements are based on 1) natural gradient which speeds up the transform warping procedure and 2) efficient optimization of the cost function of our groupwise registration. The strength of the natural gradient technique is that it could help mitigate the impact of the uncertainties of the gradient direction across multiple template slices. Experiments on two mouse lung datasets show that compared to pairwise registration, our groupwise approach runs faster in terms of registration convergence, and yields globally more consistent reconstruction.