Optimizing 3D retinal vasculature imaging in diabetic retinopathy using registration and averaging of OCT-A

High resolution visualization of optical coherence tomography (OCT) and OCT angiography (OCT-A) data is required to fully take advantage of the imaging modality’s three-dimensional nature. However, artifacts induced by patient motion often degrade OCT-A data quality. This is especially true for patients with deteriorated focal vision, such as those with diabetic retinopathy (DR). We propose a novel methodology for software-based OCT-A motion correction achieved through serial acquisition, volumetric registration, and averaging. Motion artifacts are removed via a multi-step 3D registration process, and visibility is significantly enhanced through volumetric averaging. We demonstrate that this method permits clear 3D visualization of retinal pathologies and their surrounding features, 3D visualization of inner retinal capillary connections, as well as reliable visualization of the choriocapillaris layer.     

Effect of optical coherence tomography and angiography sampling rate towards diabetic retinopathy severity classification

Optical coherence tomography (OCT) and OCT angiography (OCT-A) may benefit the screening of diabetic retinopathy (DR). This study investigated the effect of laterally subsampling OCT/OCT-A en face scans by up to a factor of 8 when using deep neural networks for automated referable DR classification. There was no significant difference in the classification performance across all evaluation metrics when subsampling up to a factor of 3, and only minimal differences up to a factor of 8. Our findings suggest that OCT/OCT-A can reduce the number of samples (and hence the acquisition time) for a volume for a given field of view on the retina that is acquired for rDR classification.     

Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns

High speed Optical Coherence Tomography (OCT) has made it possible to rapidly capture densely sampled 3D volume data. One key application is the acquisition of high quality in vivo volumetric data sets of the human retina. Since the volume is acquired in a few seconds, eye movement during the scan process leads to distortion, which limits the accuracy of quantitative measurements using 3D OCT data. In this paper, we present a novel software based method to correct motion artifacts in OCT raster scans. Motion compensation is performed retrospectively using image registration algorithms on the OCT data sets themselves. Multiple, successively acquired volume scans with orthogonal fast scan directions are registered retrospectively in order to estimate and correct eye motion. Registration is performed by optimizing a large scale numerical problem as given by a global objective function using one dense displacement field for each input volume and special regularization based on the time structure of the acquisition process. After optimization, each volume is undistorted and a single merged volume is constructed that has superior signal quality compared to the input volumes. Experiments were performed using 3D OCT data from the macula and optic nerve head acquired with a high-speed ultra-high resolution 850 nm spectral OCT as well as wide field data acquired with a 1050 nm swept source OCT instrument. Evaluation of registration performance and result stability as well as visual inspection shows that the algorithm can correct for motion in all three dimensions and on a per A-scan basis. Corrected volumes do not show visible motion artifacts. In addition, merging multiple motion corrected and registered volumes leads to improved signal quality. These results demonstrate that motion correction and merging improves image quality and should also improve morphometric measurement accuracy from volumetric OCT data.     

Quantitative 3D-OCT motion correction with tilt and illumination correction,robust similarity measure and regularization

Variability in illumination, signal quality, tilt and the amount of motion pose challenges for post-processing based 3D-OCT motion correction algorithms. We present an advanced 3D-OCT motion correction algorithm using image registration and orthogonal raster scan patterns aimed at addressing these challenges. An intensity similarity measure using the pseudo Huber norm and a regularization scheme based on a pseudo L_0.5 norm are introduced. A two-stage registration approach was developed. In the first stage, only axial motion and axial tilt are coarsely corrected. This result is then used as the starting point for a second stage full optimization. In preprocessing, a bias field estimation based approach to correct illumination differences in the input volumes is employed. Quantitative evaluation was performed using a large set of data acquired from 73 healthy and glaucomatous eyes using SD-OCT systems. OCT volumes of both the optic nerve head and the macula region acquired with three independent orthogonal volume pairs for each location were used to assess reproducibility. The advanced motion correction algorithm using the techniques presented in this paper was compared to a basic algorithm corresponding to an earlier version and to performing no motion correction. Errors in segmentation-based measures such as layer positions, retinal and nerve fiber thickness, as well as the blood vessel pattern were evaluated. The quantitative results consistently show that reproducibility is improved considerably by using the advanced algorithm, which also significantly outperforms the basic algorithm. The mean of the mean absolute retinal thickness difference over all data was 9.9 um without motion correction, 7.1 um using the basic algorithm and 5.0 um using the advanced algorithm. Similarly, the blood vessel likelihood map error is reduced to 69% of the uncorrected error for the basic and to 47% of the uncorrected error for the advanced algorithm. These results demonstrate that our advanced motion correction algorithm has the potential to improve the reliability of quantitative measurements derived from 3D-OCT data substantially.OCIS codes: (170.4500) Optical coherence tomography, (170.4470) Ophthalmology, (100.2980) Image enhancement, (100.5010) Pattern recognition     

基于灰度的二维/三维图像配准方法及其在骨科导航手术中的实现

目的随着基于医学图像导航的手术对三维信息需求的加强,建立二维/三维的医学图像配准算法,在二维规划的基础上引入三维信息。方法首先,将获取的骨盆图像体数据集通过投影得到DRR图像,将获取的X线图像进行校正;然后,利用灰度相似性测度配准DRR图像和无失真X线图像。结果在初始位置相对坐标位移<20mm,角度<5°时配准精度小于1.47%,取得良好的配准结果。结论本文提出的基于灰度计算的配准技术能够实现术中二维医学图像与术前三维医学图像的配准,实现术中二维/三维手术规划相结合。     

Motion-compensated noninvasive periodontal health monitoring using handheld and motor-based photoacoustic-ultrasound imaging systems

Simultaneous visualization of the teeth and periodontium is of significant clinical interest for image-based monitoring of periodontal health. We recently reported the application of a dual-modality photoacoustic-ultrasound (PA-US) imaging system for resolving periodontal anatomy and periodontal pocket depths in humans. This work utilized a linear array transducer attached to a stepper motor to generate 3D images via maximum intensity projection. This prior work also used a medical head immobilizer to reduce artifacts during volume rendering caused by motion from the subject (e.g., breathing, minor head movements). However, this solution does not completely eliminate motion artifacts while also complicating the imaging procedure and causing patient discomfort. To address this issue, we report the implementation of an image registration technique to correctly align B-mode PA-US images and generate artifact-free 2D cross-sections. Application of the deshaking technique to PA phantoms revealed 80% similarity to the ground truth when shaking was intentionally applied during stepper motor scans. Images from handheld sweeps could also be deshaken using an LED PA-US scanner. In ex vivo porcine mandibles, pigmentation of the enamel was well-estimated within 0.1 mm error. The pocket depth measured in a healthy human subject was also in good agreement with our prior study. This report demonstrates that a modality-independent registration technique can be applied to clinically relevant PA-US scans of the periodontium to reduce operator burden of skill and subject discomfort while showing potential for handheld clinical periodontal imaging.     

Eye motion correction algorithm for OCT-based corneal topography

With its sequential image acquisition, OCT-based corneal topography is often susceptible to measurement errors due to eye motion. We have developed a novel algorithm to detect eye motion and minimize its impact on OCT topography maps. We applied the eye motion correction algorithm to corneal topographic scans acquired using a 70 kHz spectral-domain OCT device. OCT corneal topographic measurements were compared to those from a rotating Scheimpflug camera topographer. The motion correction algorithm provided a 2-4 fold improvement in the repeatability of OCT topography and its agreement with the standard Scheimpflug topographer. The repeatability of OCT Zernike-based corneal mean power, cardinal astigmatism, and oblique astigmatism after motion detection was 0.14 D, 0.28 D, and 0.24 D, respectively. The average differences between the two devices were 0.19 D for simulated keratometry-based corneal mean power, 0.23 D for cardinal astigmatism, and 0.25 D for oblique astigmatism. Our eye motion detection method can be applied to any OCT device, and it therefore represents a powerful tool for improving OCT topography.     

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time-frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32±12s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained time-intensity curves from .84±.19 before registration to .96±.06 after registration.     

Respiratory motion correction in dynamic MRI using robust data decomposition registration – Application to DCE-MRI

Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15–62% reduction following registration) in tissue time–intensity curves and improved areas under the curve (AUC₆₀) at early enhancement.     

3D非刚性图像运动校正在肝DCE-MRI定量成像中的应用

目的探讨3D非刚性图像运动校正在提高肝脏DCE-MRI图像质量和定量参数准确性的应用价值。方法 35例确诊肝细胞癌患者进行自由呼吸下全肝MRI灌注扫描,使用3D对比增强非刚性配准对多期动态增强扫描MRI图像进行图像运动配准,利用肝脏双血供模型和腹主动脉、门静脉时间-浓度曲线作为血管输入函数(VIF),分别获得肝癌、肝实质图像运动校正前后灌注参数(K~(trans)、K_(ep)、Ve、Vp、HPI)。比较图像运动校正前后各组定量参数差异。对运动校正前后图像质量分别进行客观和主观评价。测量病灶最大面积,比较原始图像上校正前后病灶面积的差异。结果使用3D图像运动校正后腹主动脉、门静脉时间-浓度曲线波动幅度缩小,平滑度更好。图像运动校正后肝癌K~(trans)、K_(ep)、Ve、Vp、HPI值均大于校正前,K~(trans)、Ve、Vp,值差异存在统计学差异(P0.05),K_(ep)值两者间无统计学差异(P0.05)。图像运动校正后肝实质K~(trans)、K_(ep)、Ve、Vp、HPI值均大于校正前,K~(trans)、Ve、Vp值差异存在统计学差异(P0.05);K_(ep)值两者间无统计学差异(P0.05);图像运动校正前后HPI值相近,且无统计学差异(P0.05)。肝癌在原始图像上面积平均值运动校正前大于运动校正后,但无统计学意义(P0.05)。校正后肝组织噪声低于矫正前,无统计学意义(P0.05),校正后图像主观评分明显高于矫正前,差异存在统计学意义(P0.05)。结论 3D非刚性运动校正能减少肝脏MRI多期动态增强图像运动伪影,有助于提高灌注定量参数的准确性。     

Endoscopic en-face optical coherence tomography and fluorescence imaging using correlation-based probe tracking

Forward-viewing endoscopic optical coherence tomography (OCT) provides 3D imaging in vivo, and can be combined with widefield fluorescence imaging by use of a double-clad fiber. However, it is technically challenging to build a high-performance miniaturized 2D scanning system with a large field-of-view. In this paper we demonstrate how a 1D scanning probe, which produces cross-sectional OCT images (B-scans) and 1D fluorescence T-scans, can be transformed into a 2D scanning probe by manual scanning along the second axis. OCT volumes are assembled from the B-scans using speckle decorrelation measurements to estimate the out-of-plane motion along the manual scan direction. Motion within the plane of the B-scans is corrected using image registration by normalized cross correlation. En-face OCT slices and fluorescence images, corrected for probe motion in 3D, can be displayed in real-time during the scan. For a B-scan frame rate of 250 Hz, and an OCT lateral resolution of approximately 20μm , the approach can handle out-of-plane motion at speeds of up to 4 mm/s.     

Vector of motion measurements in the living cochlea using a 3D OCT vibrometry system

Optical coherence tomography (OCT) has become an important tool for measuring the vibratory response of the living cochlea. It stands alone in its capacity to measure the intricate motion of the hearing organ through the surrounding otic capsule bone. Nevertheless, as an extension of phase-sensitive OCT, it is only capable of measuring motion along the optical axis. Hence, measurements are 1-D. To overcome this limitation and provide a measure of the 3-D vector of motion in the cochlea, we developed an OCT system with three sample arms in a single interferometer. Taking advantage of the long coherence length of our swept laser, we depth (frequency) encode the three channels. An algorithm to depth decode and coregister the three channels is followed by a coordinate transformation that takes the vibrational data from the experimental coordinate system to Cartesian or spherical polar coordinates. The system was validated using a piezo as a known vibrating element that could be positioned at various angles. The angular measurement on the piezo was shown to have an RMSE of ≤ 0.30° (5.2 mrad) with a standard deviation of the amplitude of ≤ 120 pm. Finally, we demonstrate the system for in vivo imaging by measuring the vector of motion over a volume image in the apex of the mouse cochlea.     

Motion correction of in vivo three-dimensional optical coherence tomography of human skin using a fiducial marker

This paper presents a novel method based on a fiducial marker for correction of motion artifacts in 3D, in vivo, optical coherence tomography (OCT) scans of human skin and skin scars. The efficacy of this method was compared against a standard cross-correlation intensity-based registration method. With a fiducial marker adhered to the skin, OCT scans were acquired using two imaging protocols: direct imaging from air into tissue; and imaging through ultrasound gel into tissue, which minimized the refractive index mismatch at the tissue surface. The registration methods were assessed with data from both imaging protocols and showed reduced distortion of skin features due to motion. The fiducial-based method was found to be more accurate and robust, with an average RMS error below 20 μm and success rate above 90%. In contrast, the intensity-based method had an average RMS error ranging from 36 to 45 μm, and a success rate from 50% to 86%. The intensity-based algorithm was found to be particularly confounded by corrugations in the skin. By contrast, tissue features did not affect the fiducial-based method, as the motion correction was based on delineation of the flat fiducial marker. The average computation time for the fiducial-based algorithm was approximately 21 times less than for the intensity-based algorithm.     

Generating large field of view en-face projection images from intra-acquisition motion compensated volumetric optical coherence tomography data

A technique to generate large field of view projection maps of arbitrary optical coherence tomography (OCT) data is described. The technique is divided into two stages - an image acquisition stage that features a simple to use fast and robust retinal tracker to get motion free retinal OCT volume scans - and a stitching stage where OCT data from different retinal locations is first registered against a reference image using a custom pyramid-based approach and finally stitched together into one seamless large field of view (FOV) image. The method is applied to data recorded with a polarization sensitive OCT instrument in healthy subjects and glaucoma patients. The tracking and stitching accuracies are quantified, and finally, large FOV images of retinal nerve fiber layer retardation that contain the arcuate nerve fiber bundles from the optic nerve head to the raphe are demonstrated.     

Joint CT/CBCT deformable registration and CBCT enhancement for cancer radiotherapy

This paper details an algorithm to simultaneously perform registration of computed tomography (CT) and cone-beam computed (CBCT) images, and image enhancement of CBCT. The algorithm employs a viscous fluid model which naturally incorporates two components: a similarity measure for registration and an intensity correction term for image enhancement. Incorporating an intensity correction term improves the registration results. Furthermore, applying the image enhancement term to CBCT imagery leads to an intensity corrected CBCT with better image quality. To achieve minimal processing time, the algorithm is implemented on a graphic processing unit (GPU) platform. The advantage of the simultaneous optimization strategy is quantitatively validated and discussed using a synthetic example. The effectiveness of the proposed algorithm is then illustrated using six patient datasets, three head-and-neck datasets and three prostate datasets.     

Analysis of macular OCT images using deformable registration

Optical coherence tomography (OCT) of the macula has become increasingly important in the investigation of retinal pathology. However, deformable image registration, which is used for aligning subjects for pairwise comparisons, population averaging, and atlas label transfer, has not been well–developed and demonstrated on OCT images. In this paper, we present a deformable image registration approach designed specifically for macular OCT images. The approach begins with an initial translation to align the fovea of each subject, followed by a linear rescaling to align the top and bottom retinal boundaries. Finally, the layers within the retina are aligned by a deformable registration using one-dimensional radial basis functions. The algorithm was validated using manual delineations of retinal layers in OCT images from a cohort consisting of healthy controls and patients diagnosed with multiple sclerosis (MS). We show that the algorithm overcomes the shortcomings of existing generic registration methods, which cannot be readily applied to OCT images. A successful deformable image registration algorithm for macular OCT opens up a variety of population based analysis techniques that are regularly used in other imaging modalities, such as spatial normalization, statistical atlas creation, and voxel based morphometry. Examples of these applications are provided to demonstrate the potential benefits such techniques can have on our understanding of retinal disease. In particular, included is a pilot study of localized volumetric changes between healthy controls and MS patients using the proposed registration algorithm.OCIS codes: (100.0100) Image processing, (170.4470) Ophthalmology, (170.4500) Optical coherence tomography     

Improved optimization for the robust and accurate linear registration and motion correction of brain images

Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.     

Master slave en-face OCT/SLO

Master Slave optical coherence tomography (MS-OCT) is an OCT method that does not require resampling of data and can be used to deliver en-face images from several depths simultaneously. As the MS-OCT method requires important computational resources, the number of multiple depth en-face images that can be produced in real-time is limited. Here, we demonstrate progress in taking advantage of the parallel processing feature of the MS-OCT technology. Harnessing the capabilities of graphics processing units (GPU)s, information from 384 depth positions is acquired in one raster with real time display of up to 40 en-face OCT images. These exhibit comparable resolution and sensitivity to the images produced using the conventional Fourier domain based method. The GPU facilitates versatile real time selection of parameters, such as the depth positions of the 40 images out of the set of 384 depth locations, as well as their axial resolution. In each updated displayed frame, in parallel with the 40 en-face OCT images, a scanning laser ophthalmoscopy (SLO) lookalike image is presented together with two B-scan OCT images oriented along rectangular directions. The thickness of the SLO lookalike image is dynamically determined by the choice of number of en-face OCT images displayed in the frame and the choice of differential axial distance between them.OCIS codes: (110.4500) Optical coherence tomography, (170.0110) Imaging systems, (330.4460) Ophthalmic optics and devices, (120.3890) Medical optics instrumentation, (200.4960) Parallel processing     

Robust motion correction for cardiac T1 and ECV mapping using a T1 relaxation model approach

T1 and ECV mapping are quantitative methods for myocardial tissue characterization using cardiac MRI, and are highly relevant for the diagnosis of diffuse myocardial diseases. Since the maps are calculated pixel-by-pixel from a set of MRI images with different T1-weighting, it is critical to assure exact spatial correspondence between these images. However, in practice, different sources of motion e.g. cardiac motion, respiratory motion or patient motion, hamper accurate T1 and ECV calculation such that retrospective motion correction is required. We propose a new robust non-rigid registration framework combining a data-driven initialization with a model-based registration approach, which uses a model for T1 relaxation to avoid direct registration of images with highly varying contrast. The registration between native T1 and enhanced T1 to obtain a motion free ECV map is also calculated using information from T1 model-fitting. The method was validated on three datasets recorded with two substantially different acquisition protocols (MOLLI (dataset 1 (n=15) and dataset 2 (n=29)) and STONE (dataset 3 (n = 210))), one in breath-hold condition and one free-breathing. The average Dice coefficient increased from 72.6 ± 12.1% to 82.3 ± 7.4% (P < 0.05) and mean boundary error decreased from 2.91 ± 1.51mm to 1.62 ± 0.80mm (P < 0.05) for motion correction in a single T1-weighted image sequence (3 datasets) while average Dice coefficient increased from 63.4 ± 22.5% to 79.2 ± 8.5% (P < 0.05) and mean boundary error decreased from 3.26 ± 2.64mm to 1.77 ± 0.86mm (P < 0.05) between native and enhanced sequences (dataset 1 and 2). Overall, the native T1 SD error decreased from 67.32 ± 32.57ms to 58.11 ± 21.59ms (P < 0.05), enhanced SD error from 30.15 ± 25ms to 22.74 ± 8.94ms (P < 0.05) and ECV SD error from 10.08 ± 9.59% to 5.42 ± 3.21% (P < 0.05) (dataset 1 and 2).     

Motion analysis and removal in intensity variation based OCT angiography

In this work, we investigated how bulk motion degraded the quality of optical coherence tomography (OCT) angiography that was obtained through calculating interframe signal variation, i.e., interframe signal variation based optical coherence angiography (isvOCA). We demonstrated theoretically and experimentally that the spatial average of isvOCA signal had an explicit functional dependency on bulk motion. Our result suggested that the bulk motion could lead to an increased background in angiography image. Based on our motion analysis, we proposed to reduce image artifact induced by transient bulk motion in isvOCA through adaptive thresholding. The motion artifact reduced angiography was demonstrated in a 1.3μm spectral domain OCT system. We implemented signal processing using graphic processing unit for real-time imaging and conducted in vivo microvasculature imaging on human skin. Our results clearly showed that the adaptive thresholding method was highly effective in the motion artifact removal for OCT angiography.OCIS codes: (110.4500) Optical coherence tomography, (170.2655) Functional monitoring and imaging, (100.2000) Digital image processing