Fast Mesh-Based Medical Image Registration

Baghaie, Ahmadreza; Yu, Zeyun; D’Souza, Roshan

doi:10.1007/978-3-319-14364-4_1

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…In order to achieve accurate fusion results, the TOF and 4D Flow MRI data has to be spatially registered. While a plethora of various 3D image registration methods can be found in the literature [18, 19, 20], here, since the focus is on brain data, it is best to choose methodologies specifically designed for brain image registration. For this purpose, the seminal approach of Jenkinson et al [21] named FLIRT (FMRIB’s Linear Image Registration Tool) is utilized.…”

Section: Methodsmentioning

confidence: 99%

Curvelet Transform-based volume fusion for correcting signal loss artifacts in Time-of-Flight Magnetic Resonance Angiography data

Baghaie¹,

Schnell

Bakhshinejad

et al. 2018

Computers in Biology and Medicine

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Flow fields in cerebral aneurysms can be measured in vivo with phase-contrast MRI (4D Flow MRI), providing 3D anatomical magnitude images as well as 3-directional velocities through the cardiac cycle. The low spatial resolution of the 4D Flow MRI data, however, requires the images to be co-registered with higher resolution angiographic data for better segmentation of the blood vessel geometries to adequately quantify relevant flow descriptors such as wall shear stress or flow residence time. Time-of-Flight Magnetic Resonance Angiography (TOF MRA) is a non-invasive technique for visualizing blood vessels without the need to administer contrast agent. Instead TOF uses the blood flow-related enhancement of unsaturated spins entering into an imaging slice as means to generate contrast between the stationary tissue and the moving blood. Because of the higher resolutions, TOF data are often used to assist with the segmentation process needed for the flow analysis and Computational Fluid Dynamics (CFD) modeling. However, presence of slow moving and recirculating blood flow such as in brain aneurysms, especially regions where the blood flow is not perpendicular to the image plane, causes signal loss in these regions. In this work a 3D Curvelet Transform-based image fusion approach is proposed for signal loss artifact reduction of TOF volume data. Experiments show the superiority of the proposed approach in comparison to other multi-resolution 3D Wavelet-based image fusion methodologies. The proposed approach can further facilitate model-based fluid analysis and pre/post-operative treatment of patients with brain aneurysms.

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Section: Methodsmentioning

confidence: 99%

Curvelet Transform-based volume fusion for correcting signal loss artifacts in Time-of-Flight Magnetic Resonance Angiography data

Baghaie¹,

Schnell

Bakhshinejad

et al. 2018

Computers in Biology and Medicine

Self Cite

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“…In any image registration technique, the transformation is found between two images; one as a reference image which is kept unchanged and one as a moving image (or template) which is deformed assuming some constraints dictating the extent and type of deformation (rigid, non-rigid, deformable etc) (Baghaie and Yu (2014), Baghaie et al (2014)). Having a reference image of the retina with minimal motion artifacts, it is possible to correct the OCT data.…”

Section: Methodsmentioning

confidence: 99%

Involuntary eye motion correction in retinal optical coherence tomography: Hardware or software solution?

Baghaie

D’Souza

2017

Medical Image Analysis

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In this paper, we review state-of-the-art techniques to correct eye motion artifacts in Optical Coherence Tomography (OCT) imaging. The methods for eye motion artifact reduction can be categorized into two major classes: 1) hardware-based techniques and 2) software-based techniques. In the first class, additional hardware is mounted onto the OCT scanner to gather information about the eye motion patterns during OCT data acquisition. This information is later processed and applied to the OCT data for creating an anatomically correct representation of the retina, either in an offline or online manner. In software based techniques, the motion patterns are approximated either by comparing the acquired data to a reference image, or by considering some prior assumptions about the nature of the eye motion. Careful investigations done on the most common methods in the field provides invaluable insight regarding future directions of the research in this area. The challenge in hardware-based techniques lies in the implementation aspects of particular devices. However, the results of these techniques are superior to those obtained from software-based techniques because they are capable of capturing secondary data related to eye motion during OCT acquisition. Software-based techniques on the other hand, achieve moderate success and their performance is highly dependent on the quality of the OCT data in terms of the amount of motion artifacts contained in them. However, they are still relevant to the field since they are the sole class of techniques with the ability to be applied to legacy data acquired using systems that do not have extra hardware to track eye motion.

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“…There are three sub-steps in the extraction process: pre-processing, segmentation, and visualization. Pre-processing step involves image alignment (parametric/deformable) for compensation of the movements be-tween consecutive slices and extracting the correspondence between image features [36], slice interpolation for asymmetry correction of different resolutions along the three axes [37], noise reduction [38] etc. For segmentation step, due to different variables like, anatomical objects of interest, large variation of their properties in images, different medical imaging modalities there is no general and unique solution.…”

Section: Imagingmentioning

confidence: 99%

A short review on techniques for processes and process simulation of scaffold-free tissue engineering

Bakhshinejad

2015

Preprint

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The invention of three-dimensional printers has led to major innovations in tissue engineering. They have enabled the printing of complex geometries such as those that occur in natural tissues, that were not possible with traditional manufacturing techniques. Tissue engineering in particular deals with printing bio-compatible material that may be infused with live cells. Thus additional complexity is incurred because the live cells can migrate and proliferate and thus change the printed geometry. One of the important issues is the prediction of geometry and possibly mechanical properties of the steady state tissue. In this short review, we will provide an overview of different tissue engineering processes that are currently available. Furthermore, we will review two important techniques, namely, Cellular Potts Model (CPM), and Cellular Particle Dynamics (CPD) that have been used to predict the steady state of printed tissue.

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Fast Mesh-Based Medical Image Registration

Cited by 6 publications

References 15 publications

Curvelet Transform-based volume fusion for correcting signal loss artifacts in Time-of-Flight Magnetic Resonance Angiography data

Curvelet Transform-based volume fusion for correcting signal loss artifacts in Time-of-Flight Magnetic Resonance Angiography data

Involuntary eye motion correction in retinal optical coherence tomography: Hardware or software solution?

A short review on techniques for processes and process simulation of scaffold-free tissue engineering

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