Real-time endoscopic optical properties imaging.

With almost 50% of all surgeries in the U.S. being performed as minimally invasive procedures, there is a need to develop quantitative endoscopic imaging techniques to aid surgical guidance. Recent developments in widefield optical imaging make endoscopic implementations of real-time measurement possible. In this work, we introduce a proof-of-concept endoscopic implementation of a functional widefield imaging technique called 3D single snapshot of optical properties (3D-SSOP) that provides quantitative maps of absorption and reduced scattering optical properties as well as surface topography with simple instrumentation added to a commercial endoscope. The system's precision and accuracy is validated using tissue-mimicking phantoms, showing a max error of 0.004 mm-1, 0.05 mm-1, and 1.1 mm for absorption, reduced scattering, and sample topography, respectively. This study further demonstrates video acquisition of a moving phantom and an in vivo sample with a framerate of approximately 11 frames per second.

Autofluorescence imaging for improved visualization of joint structures during arthroscopic surgery.

BACKGROUND: The purpose of our study is to develop the arthroscopic autofluorescence imaging (AFI) system to improve the visualization during arthroscopic surgery by real-time enhancing the contrast between joint structures with autofluorescence imaging. Its validity was evaluated around the arthroscopic anterior cruciate ligament (ACL) reconstruction, specifically improving the contrast between the femoral insertion site and its background. The feasibility of the AFI system was validated with bovine and human knees. The spectral responses of the femoral insertion site and its surrounding bone and cartilage were measured with a fluorospectrometer. A prototype of the AFI system was developed based on the spectral responses (SR) and test images of the insertion site. The accuracy was validated by evaluating the overlap between manually segmented insertion sites on the white light color images and on the corresponding spectral unmixed autofluorescence images. The final prototype of the AFI system was tested during arthroscopy in cadaveric knees. RESULTS: The results showed that the joint structures have different SRs. Spectral unmixing enabled separation of the SRs and improved the contrast between the joint structures. The agreement between visible light and autofluorescence ligament insertions had a mean Dice coefficient of 0.84 and the mean Dice coefficient of the interobserver variability for visible light imaging was 0.85. CONCLUSIONS: We have shown that the femoral insertion site can be accurately visualized with autofluorescence imaging combined with spectral unmixing. The AFI system demonstrates the feasibility of real-time and subject-specific visualization of the femoral insertion site which can facilitate anatomic ACL reconstruction. In addition, the AFI system can facilitate arthroscopic procedures in other joints and can also be used as a diagnostic tool.

Detection of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Longitudinal Brain MRI.

Mild Cognitive Impairment (MCI) is an intermediate stage between healthy and Alzheimer's disease (AD). To enable early intervention it is important to identify the MCI subjects that will convert to AD in an early stage. In this paper, we provide a new method to distinguish between MCI patients that either convert to Alzheimer's Disease (MCIc) or remain stable (MCIs), using only longitudinal T1-weighted MRI. Currently, most longitudinal studies focus on volumetric comparison of a few anatomical structures, thereby ignoring more detailed development inside and outside those structures. In this study we propose to exploit the anatomical development within the entire brain, as found by a non-rigid registration approach. Specifically, this anatomical development is represented by the Stationary Velocity Field (SVF) from registration between the baseline and follow-up images. To make the SVFs comparable among subjects, we use the parallel transport method to align them in a common space. The normalized SVF together with derived features are then used to distinguish between MCIc and MCIs subjects. This novel feature space is reduced using a Kernel Principal Component Analysis method, and a linear support vector machine is used as a classifier. Extensive comparative experiments are performed to inspect the influence of several aspects of our method on classification performance, specifically the feature choice, the smoothing parameter in the registration and the use of dimensionality reduction. The optimal result from a 10-fold cross-validation using 36 month follow-up data shows competitive results: accuracy 92%, sensitivity 95%, specificity 90%, and AUC 94%. Based on the same dataset, the proposed approach outperforms two alternative ones that either depends on the baseline image only, or uses longitudinal information from larger brain areas. Good results were also obtained when scans at 6, 12, or 24 months were used for training the classifier. Besides the classification power, the proposed method can quantitatively compare brain regions that have a significant difference in development between the MCIc and MCIs groups.

Real-time, profile-corrected single snapshot imaging of optical properties.

A novel acquisition and processing method that enables real-time, single snapshot of optical properties (SSOP) and 3-dimensional (3D) profile measurements in the spatial frequency domain is described. This method makes use of a dual sinusoidal wave projection pattern permitting to extract the DC and AC components in the frequency domain to recover optical properties as well as the phase for measuring the 3D profile. In this method, the 3D profile is used to correct for the effect of sample's height and angle and directly obtain profile-corrected absorption and reduced scattering maps from a single acquired image. In this manuscript, the 3D-SSOP method is described and validated on tissue-mimicking phantoms as well as in vivo, in comparison with the standard profile-corrected SFDI (3D-SFDI) method. On average, in comparison with 3D-SFDI method, the 3D-SSOP method allows to recover the profile within 1.2mm and profile-corrected optical properties within 12% for absorption and 6% for reduced scattering over a large field-of-view and in real-time.

Optical mammography using diffuse optical spectroscopy for monitoring tumor response to neoadjuvant chemotherapy in women with locally advanced breast cancer.

PURPOSE: Diffuse optical spectroscopy (DOS) has the potential to enable monitoring of tumor response during chemotherapy, particularly in the early stages of treatment. This study aims to assess feasibility of DOS for monitoring treatment response in HER2-negative breast cancer patients receiving neoadjuvant chemotherapy (NAC) and compare DOS with tumor response assessment by MRI. EXPERIMENTAL DESIGN: Patients received NAC in six cycles of 3 weeks. In addition to standard treatment monitoring by dynamic contrast enhanced MRI (DCE-MRI), DOS scans were acquired after the first, third, and last cycle of chemotherapy. The primary goal was to assess feasibility of DOS for early assessment of tumor response. The predictive value of DOS and DCE-MRI compared with pathologic response was assessed. RESULTS: Of the 22 patients, 18 patients had a partial or complete tumor response at pathologic examination, whereas 4 patients were nonresponders. As early as after the first chemotherapy cycle, a significant difference between responders and nonresponders was found using DOS (HbO2 86% ± 25 vs. 136% ± 25, P = 0.023). The differences between responders and nonresponders continued during treatment (halfway treatment, HbO2 68% ± 22 vs. 110% ± 10, P = 0.010). Using DCE-MRI, a difference between responders and nonresponders was found halfway treatment (P = 0.005) using tumor volume measurement calculations. CONCLUSIONS: DOS allows for tumor response assessment and is able to differentiate between responders and nonresponders after the first chemotherapy cycle and halfway treatment. In this study, DOS was equally effective in predicting tumor response halfway treatment compared with DCE-MRI. Therefore, DOS may be used as a novel imaging modality for (early) treatment monitoring of NAC.

Visualizing the spatial gene expression organization in the brain through non-linear similarity embeddings.

The Allen Brain Atlases enable the study of spatially resolved, genome-wide gene expression patterns across the mammalian brain. Several explorative studies have applied linear dimensionality reduction methods such as Principal Component Analysis (PCA) and classical Multi-Dimensional Scaling (cMDS) to gain insight into the spatial organization of these expression patterns. In this paper, we describe a non-linear embedding technique called Barnes-Hut Stochastic Neighbor Embedding (BH-SNE) that emphasizes the local similarity structure of high-dimensional data points. By applying BH-SNE to the gene expression data from the Allen Brain Atlases, we demonstrate the consistency of the 2D, non-linear embedding of the sagittal and coronal mouse brain atlases, and across 6 human brains. In addition, we quantitatively show that BH-SNE maps are superior in their separation of neuroanatomical regions in comparison to PCA and cMDS. Finally, we assess the effect of higher-order principal components on the global structure of the BH-SNE similarity maps. Based on our observations, we conclude that BH-SNE maps with or without prior dimensionality reduction (based on PCA) provide comprehensive and intuitive insights in both the local and global spatial transcriptome structure of the human and mouse Allen Brain Atlases.

User-guided compressed sensing for magnetic resonance angiography.

Compressed sensing (CS) magnetic resonance imaging (MRI) enables the reconstruction of MRI images with fewer samples in k-space. One requirement is that the acquired image has a sparse representation in a known transform domain. MR angiograms are already sparse in the image domain. They can be further sparsified through finite-differences. Therefore, it is a natural application for CS-MRI. However, low-contrast vessels are likely to disappear at high undersampling ratios, since the commonly used £(1) reconstruction tends to underestimate the magnitude of the transformed sparse coefficients. These vessels, however, are likely to be clinically important for medical diagnosis. To avoid the fading of low-contrast vessels, we propose a user-guided CS MRI that is able to mitigate the reduction of vessel contrast within a region of interest (ROI). Simulations show that these low-contrast vessels can be well maintained via our method which results in higher local quality compared to conventional CS.

Cardiac MR perfusion image processing techniques: a survey.

First-pass cardiac MR perfusion (CMRP) imaging has undergone rapid technical advancements in recent years. Although the efficacy of CMRP imaging in the assessment of coronary artery diseases (CAD) has been proven, its clinical use is still limited. This limitation stems, in part, from manual interaction required to quantitatively analyze the large amount of data. This process is tedious, time-consuming, and prone to operator bias. Furthermore, acquisition and patient related image artifacts reduce the accuracy of quantitative perfusion assessment. With the advent of semi- and fully automatic image processing methods, not only the challenges posed by these artifacts have been overcome to a large extent, but a significant reduction has also been achieved in analysis time and operator bias. Despite an extensive literature on such image processing methods, to date, no survey has been performed to discuss this dynamic field. The purpose of this article is to provide an overview of the current state of the field with a categorical study, along with a future perspective on the clinical acceptance of image processing methods in the diagnosis of CAD.

Fully automated attenuation measurement and motion correction in FLIP image sequences.

Fluorescence loss in photobleaching (FLIP) is a method to study compartment connectivity in living cells. A FLIP sequence is obtained by alternatively bleaching a spot in a cell and acquiring an image of the complete cell. Connectivity is estimated by comparing fluorescence signal attenuation in different cell parts. The measurements of the fluorescence attenuation are hampered by the low signal to noise ratio of the FLIP sequences, by sudden sample shifts and by sample drift. This paper describes a method that estimates the attenuation by modeling photobleaching as exponentially decaying signals. Sudden motion artifacts are minimized by registering the frames of a FLIP sequence to target frames based on the estimated model and by removing frames that contain deformations. Linear motion (sample drift) is reduced by minimizing the entropy of the estimated attenuation coefficients. Experiments on 16 in vivo FLIP sequences of muscle cells in Drosophila show that the proposed method results in fluorescence attenuations similar to the manually identified gold standard, but with standard deviations of approximately 50 times smaller. As a result of this higher precision, cell compartment edges and details such as cell nuclei become clearly discernible. The main value of this method is that it uses a model of the bleaching process to correct motion and that the model based fluorescence intensity and attenuation estimates can be interpreted easily. The proposed method is fully automatic, and runs in approximately one minute per sequence, making it suitable for unsupervised batch processing of large data series.

Robust motion correction in the frequency domain of cardiac MR stress perfusion sequences.

First-pass cardiac MR perfusion (CMRP) imaging allows identification of hypo-perfused areas in the myocardium and therefore helps in early detection of coronary artery disease (CAD). However, its efficacy is often limited by respiratory motion artifacts, especially in stress-induced sequences. These distortions lead to unreliable estimates of perfusion linked parameters, such as the myocardial perfusion reserve index (MPRI). We propose a novel, robust motion correction method that suppresses motion artifacts in the frequency domain. The method is validated using rest and stress perfusion datasets of 10 patients and is compared to a state-of-the-art independent component analysis based method. Contrary to the latter, the proposed method reduces the remaining motion to less than the pixel size and allows the reliable computation of the MPRI. The strong agreement between perfusion parameters based on expert contours and after applying the proposed method enables the near-automated quantitative analyses of stress MR perfusion sequences in a clinical setting.

Groupwise rigid registration of wrist bones.

We present an extension of the symmetric ICP algorithm that is unbiased for an arbitrary number (N > or = 2) of shapes, using rigid transformations and scaling. The method does not require the selection of a reference shape or registration order and hence it is unbiased towards any of the registered shapes. The functional to be minimized is non-linear in the transformation parameters and thus computationally complex. We therefore propose a first order approximation that estimates the transformation parameters in a closed form, with computational complexity (see text for symbol)(N2). Using a set of wrist bones, we show that the least-squares minimization and the proposed approximation converge to the same solution. Experiments also show that the proposed algorithms lead to smaller registration errors than algorithms that select a reference shape or register to an evolving mean shape. The low computational cost and trivial parallelization enable the alignment of large numbers of bones.

An efficient and robust algorithm for parallel groupwise registration of bone surfaces.

In this paper a novel groupwise registration algorithm is proposed for the unbiased registration of a large number of densely sampled point clouds. The method fits an evolving mean shape to each of the example point clouds thereby minimizing the total deformation. The registration algorithm alternates between a computationally expensive, but parallelizable, deformation step of the mean shape to each example shape and a very inexpensive step updating the mean shape. The algorithm is evaluated by comparing it to a state of the art registration algorithm. Bone surfaces of wrists, segmented from CT data with a voxel size of 0.3 x 0.3 x 0.3 mm3, serve as an example test set. The negligible bias and registration error of about 0.12 mm for the proposed algorithm are similar to those in. However, current point cloud registration algorithms usually have computational and memory costs that increase quadratically with the number of point clouds, whereas the proposed algorithm has linearly increasing costs, allowing the registration of a much larger number of shapes: 48 versus 8, on the hardware used.

A 4D statistical model of wrist bone motion patterns.

Direct imaging of ligament damage in the wrist remains a challenge. Still, such damage can be assessed indirectly through the analysis of changes in wrist pose and motion pattern. For this purpose we built a statistical reference model that describes healthy motion patterns. We show that such a model can also be used to detect and quantify pathologies. A model that only describes the global translations and rotations of the carpal bones is insufficiently accurate due to size and shape variations of the bones. We present a local statistical motion model that minimizes the influence of size and shape differences by analyzing the coordinate differences of pairs of points on adjacent bone surfaces. These differences are determined in a set of 14 healthy example wrists imaged in a range of poses by means of 4D-RX imaging. The distribution of the differences as a function of the pose form the local statistical motion model (LSMM). Translations of 2 mm and rotations of 20° with respect to the healthy example wrists are detected as outliers in the point pair distributions. An evaluation involving wrists with a damaged ligament between scaphoid and lunate shows that not only joint space widenings can be detected, but also shifts of congruent bone surfaces. The LSMM is also used to perform a virtual reconstruction of the most likely healthy wrist after a simulated perturbation of bones. The reconstruction precision is shown to be about 1 mm. Therefore, the presented 4D statistical model of wrist bone movement may become a valuable clinical tool for diagnosis and surgical planning.

Localized component analysis for arthritis detection in the trapeziometacarpal joint.

The trapeziometacarpal joint enables the prehensile function of the thumb. Unfortunately, this joint is vulnerable to osteoarthritis (OA) that typically affects the local shape of the trapezium. A novel, local statistical shape model is defined that employs a differentiable locality measure based on the weighted variance of point coordinates per mode. The simplicity of the function and the smooth derivative enable to quickly determine localized components for densely sampled surfaces. The method is employed to assess a set of 60 trapezia (38 healthy, 22 with OA). The localized components predominantly model regions affected by OA, contrary to shape variations found with PCA. Furthermore, identification of pathological trapezia based on the localized modes of variation is improved compared to PCA.

Statistical descriptions of scaphoid and lunate bone shapes.

Diagnosing of injuries of the wrist bones is problematic due to a highly complex and variable geometry. knowledge of variations of healthy bone shapes is essential to detect wrist pathologies, developing prosthetics and investigating biomechanical properties of the wrist joint. In previous literature various methods have been proposed to classify different scaphoid and lunate types. These classifications were mainly qualitative or were based on a limited number of manually determined surface points. The purposes of this study are to develop a quantitative, standardized description of the variations in the scaphoid and lunate and to investigate whether it is feasible to divide carpal bones in isolated shape categories based on statistical grounds. The shape variations of the scaphoid and lunate were described by constructing a statistical shape model (SSM) of healthy bones. SSM shape parameters were determined that describe the deviation of each shape from the mean shape. The first five modes of variation in the SSMs describe 60% of the total variance of the scaphoid and 57% of the lunate. Higher modes describe less than 5% of the variance per mode. The distributions of the parameters that characterize the bone shape variations along the modes do not significantly differ from a normal distribution. The SSM provides a description of possible shape variations and the distribution of scaphoid and lunate shapes in our population at an accuracy of approximately the voxel size (0.3x0.3x0.3mm(3)). The developed statistical shape model represents the previously qualitatively described variations of scaphoid and lunate. However, strict classifications based on shape differences are not feasible on statistical grounds.

Constrained registration of the wrist joint.

Comparing wrist shapes of different individuals requires alignment of these wrists into the same pose. Unconstrained registration of the carpal bones results in anatomically nonfeasible wrists. In this paper, we propose to constrain the registration using the shapes of adjacent bones, by keeping the width of the gap between adjacent bones constant. The registration is formulated as an optimization involving two terms. One term aligns the wrist bones by minimizing the distances between corresponding bone surfaces. The second term constrains the registration by minimizing the distances between adjacent sliding surfaces. The registration is based on the Iterative Closest Point algorithm. All bones are registered concurrently so that no bias is introduced towards any of the bones. The proposed registration method delivers anatomically correct configurations of the bones. The registration errors are in the order of the voxel size of the acquired CT data (0.3 x 0.3 x 0.3 mm(3)). The standard deviation in the widths of gaps between adjacent bones is in the order of 10% with an insignificant bias. This is a large improvement over the standard deviations of 30%-80% encountered in unconstrained registration. The value of this method is its capability of accurately registering joints in varying poses resulting in physiological joint configurations.