Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation

Fang, Leyuan; Li, Shutao; McNabb, Ryan P.; Nie, Qing; Kuo, Anthony N.; Toth, Cynthia A.; Izatt, Joseph A.; Farsiu, Sina

doi:10.1109/tmi.2013.2271904

Cited by 200 publications

(82 citation statements)

References 63 publications

(115 reference statements)

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“…To obtain [ M ] we use ridge regression that mimimizes an objective function given by (Leyuan Fang et al, 2013; Jia et al, 2013):…”

Section: Methodsmentioning

confidence: 99%

“…Our method uses a combination of Proper Orthogonal Decomposition (POD)(Kerschen et al, 2005; Kosambi, 1943; Karhunen, 1947; Loeve, 1948) and ridge regression (Fang et al, 2013) to merge patient-specific CFD and 4D Flow MRI. Benchmark tests using a numerical flow phantom against state-of-the-art techniques indicates that our method is able to recover fine details in complex recirculating flows with error metrics that are substantially better.…”

Section: Introductionmentioning

confidence: 99%

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Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression

Bakhshinejad

Baghaie²,

Vali

et al. 2017

Journal of Biomechanics

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Time resolved phase-contrast magnetic resonance imaging 4D-PCMR (also called 4D Flow MRI) data while capable of non-invasively measuring blood velocities, can be affected by acquisition noise, flow artifacts, and resolution limits. In this paper, we present a novel method for merging 4D Flow MRI with computational fluid dynamics (CFD) to address these limitations and to reconstruct de-noised, divergence-free high-resolution flow-fields. Proper orthogonal decomposition (POD) is used to construct the orthonormal basis of the local sampling of the space of all possible solutions to the flow equations both at the low-resolution level of the 4D Flow MRI grid and the high-level resolution of the CFD mesh. Low-resolution, de-noised flow is obtained by projecting in-vivo 4D Flow MRI data onto the low-resolution basis vectors. Ridge regression is then used to reconstruct high-resolution de-noised divergence-free solution. The effects of 4D Flow MRI grid resolution, and noise levels on the resulting velocity fields are further investigated. A numerical phantom of the flow through a cerebral aneurysm was used to compare the results obtained using the POD method with those obtained with the state-of-the-art de-noising methods. At the 4D Flow MRI grid resolution, the POD method was shown to preserve the small flow structures better than the other methods, while eliminating noise. Furthermore, the method was shown to successfully reconstruct details at the CFD mesh resolution not discernible at the 4D Flow MRI grid resolution. This method will improve the accuracy of the clinically relevant flow-derived parameters, such as pressure gradients and wall shear stresses, computed from in-vivo 4D Flow MRI data.

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“…To obtain [ M ] we use ridge regression that mimimizes an objective function given by (Leyuan Fang et al, 2013; Jia et al, 2013):…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression

Bakhshinejad

Baghaie²,

Vali

et al. 2017

Journal of Biomechanics

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“…9,12 The images were post-processed with the sparsity based simultaneous denoising and interpolation method trained specifically for these imaging sytems. 17 Sparsity based simultaneous denoising and interpolation has previously been used for reliable denoising of OCT images in human and murine eyes without distorting image features. 17,18 The location of the scans was confirmed with the scanning laser ophthalmoscope infrared images from the Spectralis or the summed voxel projection from the intraoperative images.…”

Section: Methodsmentioning

confidence: 99%

“…17 Sparsity based simultaneous denoising and interpolation has previously been used for reliable denoising of OCT images in human and murine eyes without distorting image features. 17,18 The location of the scans was confirmed with the scanning laser ophthalmoscope infrared images from the Spectralis or the summed voxel projection from the intraoperative images. The locations of preoperative connecting strands for each subject were matched and compared with corresponding locations in intraoperative and postoperative images.…”

Section: Methodsmentioning

confidence: 99%

Intraoperative Spectral Domain Optical Coherence Tomography Imaging After Internal Limiting Membrane Peeling in Idiopathic Epiretinal Membrane With Connecting Strands

Nam

DeSouza

Hahn

et al. 2015

Retina

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Purpose To report the intraoperative optical coherence tomography findings in idiopathic epiretinal membrane (ERM) with connecting strands and to describe the postoperative outcomes. Methods A retrospective, case series study within a prospective observational intraoperative optical coherence tomography imaging study was performed. Epiretinal membranes with connecting strands were characterized on preoperative spectral domain optical coherence tomography images and assessed against corresponding intraoperative (after internal limiting membrane [ILM] peeling) and postoperative spectral domain optical coherence tomography images. Results Eleven locations of the connecting strands in 7 eyes were studied. The connecting strands had visible connections from the inner retinal surface to the ERM in all locations, and the reflectivity was moderate in 8 locations and high in 3 locations. After ERM and ILM peeling, disconnected strands were identified in all of the intraoperative optical coherence tomography images. The reflectivity of the remaining intraoperative strands was higher than that of the preoperative lesions and appeared as “finger-like” and branching projections. The remaining disconnected lesions were contiguous with the inner retinal layers. Postoperatively, the intraoperative lesions disappeared completely in all locations, and recurrent formation of ERM was not identified in any eyes. Conclusion In ERM eyes with connecting strands, intraoperative spectral domain optical coherence tomography imaging showed moderately to highly reflective sub-ILM finger-like lesions that persist immediately after membrane and ILM peeling. Postoperatively, the hyperreflective lesions disappeared spontaneously without localized nerve fiber layer loss. The sub-ILM connecting strands may represent glial retinal attachments.

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“…We began the segmentation process by denoising the 8-bit grayscale OCT images using a previously described sparsitybased denoising technique. 32 Next, we calculated three gradient images, two horizontal gradient images, and one vertical gradient image by convolving the image with [1;1; 1;1; 1;0; −1; −1; −1; −1; −1], [−1; −1; −1; −1; −1;0; 1;1; 1;1; 1], and [1; 1; 1; 1; 1; 0; −1; −1; −1; −1; −1] (MATLAB notation) filters. We then linearly normalized the pixel values in these three gradient images to be between 0 and 1.…”

Section: Representative Application In Retinal Optical Coherence Tomomentioning

confidence: 99%

Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images

et al. 2016

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We introduce a metric in graph search and demonstrate its application for segmenting retinal optical coherence tomography (OCT) images of macular pathology. Our proposed “adjusted mean arc length” (AMAL) metric is an adaptation of the lowest mean arc length search technique for automated OCT segmentation. We compare this method to Dijkstra’s shortest path algorithm, which we utilized previously in our popular graph theory and dynamic programming segmentation technique. As an illustrative example, we show that AMAL-based length-adaptive segmentation outperforms the shortest path in delineating the retina/vitreous boundary of patients with full-thickness macular holes when compared with expert manual grading.

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Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation

Cited by 200 publications

References 63 publications

Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression

Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression

Intraoperative Spectral Domain Optical Coherence Tomography Imaging After Internal Limiting Membrane Peeling in Idiopathic Epiretinal Membrane With Connecting Strands

Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images

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