Description: Classical surgery is being overtaken by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm computed tomography (CT) and C-arm fluoroscopy are routinely used in clinical practice for intraoperative guidance. However, due to constraints regarding acquisition time and device configuration, intraoperative modalities have limited soft tissue image quality and reliable assessment of the cardiac anatomy typically requires contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a probabilistic sparse matching approach to fuse high-quality preoperative CT images and nongated, noncontrast intraoperative C-arm CT images by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the preoperative CT and mapped to the intraoperative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments on 95 clinical datasets demonstrate that our model-based fusion approach has an average execution time of 1.56 s, while the accuracy of 5.48 mm between the anchor anatomy in both images lies within expert user confidence intervals. In direct comparison with image-to-image registration based on an open-source state-of-the-art medical imaging library and a recently proposed quasi-global, knowledge-driven multi-modal fusion approach for thoracic-abdominal images, our model-based method exhibits superior performance in terms of registration accuracy and robustness with respect to both target anatomy and anchor anatomy alignment errors.

Description: Image guided interventions have seen growing interest in recent years. The use of X-rays for the procedure impels limiting the dose over time. Image sequences obtained thereby exhibit high levels of noise and very low contrasts. Hence, the development of efficient methods to enable optimal visualization of these sequences is crucial. We propose an original denoising method based on the curvelet transform. First, we apply a recursive temporal filter to the curvelet coefficients. As some residual noise remains, a spatial filtering is performed in the second step, which uses a magnitude-based classification and a contextual comparison of curvelet coefficients. This procedure allows to denoise the sequence while preserving low-contrasted structures, but does not improve their contrast. Finally, a third step is carried out to enhance the features of interest. For this, we propose a line enhancement technique in the curvelet domain. Indeed, thin structures are sparsely represented in that domain, allowing a fast and efficient detection. Quantitative and qualitative evaluations performed on synthetic and real low-dose sequences demonstrate that the proposed method enables a 50% dose reduction.

Description: Fluorescence lifetime imaging (FLIM) has demonstrated potential for robust assessment of atherosclerotic plaques biochemical composition and for complementing conventional intravascular ultrasound (IVUS), which provides information on plaque morphology. The success of such a bi-modal imaging modality depends on accurate segmentation of the IVUS images and proper angular registration between these two modalities. This paper reports a novel IVUS segmentation methodology addressing this issue. The image preprocessing consisted of denoising, using the Wiener filter, followed by image smoothing, implemented through the application of the alternating sequential filter on the edge separability metric images. Extraction of the lumen/intima and media/adventitia boundaries was achieved by tracing the gray-scale peaks over the A-lines of the IVUS preprocessed images. Cubic spline interpolation, in both cross-sectional and longitudinal directions, ensured boundary smoothness and continuity. The detection of the guide-wire artifact in both modalities is used for angular registration. Intraluminal studies were conducted in 13 ex vivo segments of human coronaries. The IVUS segmentation accuracy was assessed against independent manual tracings, providing 91.82% sensitivity and 97.55% specificity. The proposed methodology makes the bi-modal FLIM and IVUS approach feasible for comprehensive intravascular diagnosis by providing co-registered biochemical and morphological information of atherosclerotic plaques.

Description: C-arm computed tomography (CT) is an innovative technique that enables a C-arm system to generate 3-D images from a set of 2-D X-ray projections. This technique can reduce treatment-related complications and may improve interventional efficacy and safety. However, state-of-the-art C-arm systems rely on a circular short scan for data acquisition, which limits coverage in the axial direction. This limitation was reported as a problem in hepatic vascular interventions. To solve this problem, as well as to further extend the value of C-arm CT, axially extended-volume C-arm CT is needed. For example, such an extension would enable imaging the full aorta, the peripheral arteries or the spine in the interventional room, which is currently not feasible. In this paper, we demonstrate that performing long object imaging using a reverse helix is feasible in the interventional room. This demonstration involved developing a novel calibration method, assessing geometric repeatability, implementing a reconstruction method that applies to real reverse helical data, and quantitatively evaluating image quality. Our results show that: 1) the reverse helical trajectory can be implemented and reliably repeated on a multiaxis C-arm system; and 2) a long volume can be reconstructed with satisfactory image quality using reverse helical data.

Description: A common problem of imaging 3-D objects into image plane is superposition of the projected structures. In dynamic imaging, projection overlaps of organs and tissues complicate extraction of signals specific to individual structures with different dynamics. The problem manifests itself also in dynamic tomography as tissue mixtures are present in voxels. Separation of signals specific to dynamic structures belongs to the category of blind source separation. It is an underdetermined problem with many possible solutions. Existing separation methods select the solution that best matches their additional assumptions on the source model. We propose a novel blind source separation method based on probabilistic model of dynamic image sequences assuming each source dynamics as convolution of an input function and a source specific kernel (modeling organ impulse response or retention function). These assumptions are formalized as a Bayesian model with hierarchical prior and solved by the Variational Bayes method. The proposed prior distribution assigns higher probability to sparse source images and sparse convolution kernels. We show that the results of separation are relevant to selected tasks of dynamic renal scintigraphy. Accuracy of tissue separation with simulated and clinical data provided by the proposed method outperformed accuracy of previously developed methods measured by the mean square and mean absolute errors of estimation of simulated sources and the sources separated by an expert physician. MATLAB implementation of the algorithm is available for download.

Description: Tractography is a class of algorithms aiming at in vivo mapping the major neuronal pathways in the white matter from diffusion magnetic resonance imaging (MRI) data. These techniques offer a powerful tool to noninvasively investigate at the macroscopic scale the architecture of the neuronal connections of the brain. However, unfortunately, the reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality by nature. As a matter of fact, several techniques have been proposed in recent years to estimate, at the voxel level, intrinsic microstructural features of the tissue, such as axonal density and diameter, by using multicompartment models. In this paper, we present a novel framework to reestablish the link between tractography and tissue microstructure. Starting from an input set of candidate fiber-tracts, which are estimated from the data using standard fiber-tracking techniques, we model the diffusion MRI signal in each voxel of the image as a linear combination of the restricted and hindered contributions generated in every location of the brain by these candidate tracts. Then, we seek for the global weight of each of them, i.e., the effective contribution or volume, such that they globally fit the measured signal at best. We demonstrate that these weights can be easily recovered by solving a global convex optimization problem and using efficient algorithms. The effectiveness of our approach has been evaluated both on a realistic phantom with known ground-truth and in vivo brain data. Results clearly demonstrate the benefits of the proposed formulation, opening new perspectives for a more quantitative and biologically plausible assessment of the structural connectivity of the brain.

Description: In this work, we present a new methodology to facilitate prediction of recurrent prostate cancer (CaP) following radical prostatectomy (RP) via the integration of quantitative image features and protein expression in the excised prostate. Creating a fused predictor from high-dimensional data streams is challenging because the classifier must 1) account for the “curse of dimensionality” problem, which hinders classifier performance when the number of features exceeds the number of patient studies and 2) balance potential mismatches in the number of features across different channels to avoid classifier bias towards channels with more features. Our new data integration methodology, supervised Multi-view Canonical Correlation Analysis (sMVCCA), aims to integrate infinite views of highdimensional data to provide more amenable data representations for disease classification. Additionally, we demonstrate sMVCCA using Spearman’s rank correlation which, unlike Pearson’s correlation, can account for nonlinear correlations and outliers. Forty CaP patients with pathological Gleason scores 6–8 were considered for this study. 21 of these men revealed biochemical recurrence (BCR) following RP, while 19 did not. For each patient, 189 quantitative histomorphometric attributes and 650 protein expression levels were extracted from the primary tumor nodule. The fused histomorphometric/proteomic representation via sMVCCA combined with a random forest classifier predicted BCR with a mean AUC of 0.74 and a maximum AUC of 0.9286. We found sMVCCA to perform statistically significantly (p 〈 0.05) better than comparative state-of-the-art data fusion strategies for predicting BCR. Furthermore, Kaplan-Meier analysis demonstrated improved BCR-free survival prediction for the sMVCCA-fused classifier as compared to histology or proteomic features alone.

Description: Free-breathing cardiac diffusion tensor imaging (DTI) is a promising but challenging technique for the study of fiber structures of the human heart in vivo. This work proposes a clinically compatible and robust technique to provide three-dimensional (3-D) fiber architecture properties of the human heart. To this end, 10 short-axis slices were acquired across the entire heart using a multiple shifted trigger delay (TD) strategy under free breathing conditions. Interscan motion was first corrected automatically using a nonrigid registration method. Then, two post-processing schemes were optimized and compared: an algorithm based on principal component analysis (PCA) filtering and temporal maximum intensity projection (TMIP), and an algorithm that uses the wavelet-based image fusion (WIF) method. The two methods were applied to the registered diffusion-weighted (DW) images to cope with intrascan motion-induced signal loss. The tensor fields were finally calculated, from which fractional anisotropy (FA), mean diffusivity (MD), and 3-D fiber tracts were derived and compared. The results show that the comparison of the FA values $({rm FA}_{{{{rm PCATMIP}}}} =0.45 pm 0.10, {rm FA}_{{{{rm WIF}}}} =0.42 pm 0.05, {rm P}=0.06)$ showed no significant difference, while the MD values ( ${rm MD}_{{{{rm PCATMIP}}}}=0.83 pm 0.12times 10^{{{-3}}}$ mm $^{{{2}}}$ /s, ${rm MD}_{{{{rm WIF}}}}=0.74pm 0.05times 10^{{{-3}}}$ mm $^{{{2}}}$ /s, ${rm P}=0.028$ ) were significantly different. Improved helix angle variations through the myocardium wall reflecting the rotation characteristic of c- rdiac fibers were observed with WIF. This study demonstrates that the combination of multiple shifted TD acquisitions and dedicated post-processing makes it feasible to retrieve in vivo cardiac tractographies from free-breathing DTI acquisitions. The substantial improvements were observed using the WIF method instead of the previously published PCATMIP technique.

Description: The detection of newly appearing and changing pigmented skin lesions (PSLs) is essential for timely diagnosis of cutaneous melanoma. Total body skin examination (TBSE) procedures, currently practiced for this purpose, can be extremely time-consuming for patients with numerous lesions. In addition, these procedures are prone to subjectivity when selecting PSLs for baseline image comparison, increasing the risk of missing a developing cancer. To address this issue, we propose a new photogrammetry-based total body scanning system allowing for skin surface image acquisition using cross-polarized light. Equipped with 21 high-resolution cameras and a turntable, this scanner automatically acquires a set of overlapping images, covering 85%–90% of the patient's skin surface. These images are used for the automated mapping of PSLs and their change estimation between explorations. The maps produced relate images of individual lesions with their locations on the patient's body, solving the body-to-image and image-to-image correspondence problem in TBSEs. Currently, the scanner is limited to patients with sparse body hair and, for a complete skin examination, the scalp, palms, soles and inner arms should be photographed manually. The initial tests of the scanner showed that it can be successfully applied for automated mapping and temporal monitoring of multiple lesions: PSLs relevant for follow-up were repeatedly mapped in several explorations. Moreover, during the baseline image comparison, all lesions with artificially induced changes were correctly identified as “evolved.”