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Robust Image Registration for Improved Clinical Efficiency: Using Local Structure Analysis and Model-Based Processing
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0003-0908-9470
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Medical imaging plays an increasingly important role in modern healthcare. In medical imaging, it is often relevant to relate different images to each other, something which can prove challenging, since there rarely exists a pre-defined mapping between the pixels in different images. Hence, there is a need to find such a mapping/transformation, a procedure known as image registration. Over the years, image registration has been proved useful in a number of clinical situations. Despite this, current use of image registration in clinical practice is rather limited, typically only used for image fusion. The limited use is, to a large extent, caused by excessive computation times, lack of established validation methods/metrics and a general skepticism toward the trustworthiness of the estimated transformations in deformable image registration.

This thesis aims to overcome some of the issues limiting the use of image registration, by proposing a set of technical contributions and two clinical applications targeted at improved clinical efficiency. The contributions are made in the context of a generic framework for non-parametric image registration and using an image registration method known as the Morphon. 

In image registration, regularization of the estimated transformation forms an integral part in controlling the registration process, and in this thesis, two regularizers are proposed and their applicability demonstrated. Although the regularizers are similar in that they rely on local structure analysis, they differ in regard to implementation, where one is implemented as applying a set of filter kernels, and where the other is implemented as solving a global optimization problem. Furthermore, it is proposed to use a set of quadrature filters with parallel scales when estimating the phase-difference, driving the registration. A proposal that brings both accuracy and robustness to the registration process, as shown on a set of challenging image sequences. Computational complexity, in general, is addressed by porting the employed Morphon algorithm to the GPU, by which a performance improvement of 38-44x is achieved, when compared to a single-threaded CPU implementation.

The suggested clinical applications are based upon the concept paint on priors, which was formulated in conjunction with the initial presentation of the Morphon, and which denotes the notion of assigning a model a set of properties (local operators), guiding the registration process. In this thesis, this is taken one step further, in which properties of a model are assigned to the patient data after completed registration. Based upon this, an application using the concept of anatomical transfer functions is presented, in which different organs can be visualized with separate transfer functions. This has been implemented for both 2D slice visualization and 3D volume rendering. A second application is proposed, in which landmarks, relevant for determining various measures describing the anatomy, are transferred to the patient data. In particular, this is applied to idiopathic scoliosis and used to obtain various measures relevant for assessing spinal deformity. In addition, a data analysis scheme is proposed, useful for quantifying the linear dependence between the different measures used to describe spinal deformities.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. , 120 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1514
Keyword [en]
Image registration, deformable models, scoliosis, visualization, volume rendering, adaptive regularization, GPGPU, CUDA
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-91116ISBN: 978-91-7519-637-4 (print)OAI: oai:DiVA.org:liu-91116DiVA: diva2:616544
Public defence
2013-05-31, Eken, Campus US, Linköping University, Linköping, 09:15 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2007-4786
Available from: 2013-05-08 Created: 2013-04-17 Last updated: 2014-10-08Bibliographically approved
List of papers
1. Adaptive anisotropic regularization of deformation fields for non-rigid registration using the Morphon framework
Open this publication in new window or tab >>Adaptive anisotropic regularization of deformation fields for non-rigid registration using the Morphon framework
2010 (English)In: IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE , 2010, 473-476 p.Conference paper, Published paper (Refereed)
Abstract [en]

Image registration is a crucial task in many applications and applied in a variety of different areas. In addition to the primary task of image alignment, the deformation field is valuable when studying structural/volumetric changes in the brain. In most applications a regularizing term is added to achieve a smoothly varying deformation field. This can sometimes cause conflicts in situations of local complex deformations. In this paper we present a new regularizer, which aims at handling local complex deformations while maintaining an overall smooth deformation field. It is based on an adaptive anisotropic regularizer and its usefulness is demonstrated by two examples, one synthetic and one with real MRI data from a pre- and post-op situation with normal pressure hydrocephalus.

Place, publisher, year, edition, pages
IEEE, 2010
Series
IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149
National Category
Medical and Health Sciences Biomedical Laboratory Science/Technology
Identifiers
urn:nbn:se:liu:diva-56398 (URN)10.1109/ICASSP.2010.5495704 (DOI)000287096000116 ()978-1-4244-4296-6 (ISBN)978-1-4244-4295-9 (ISBN)
Conference
35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2010), 14-19 March 2010, Dallas, Texas, USA
Funder
Swedish Research Council, 2007-4786
Available from: 2010-05-10 Created: 2010-05-10 Last updated: 2014-09-25
2. Globally Optimal Displacement Fields Using Local Tensor Metric
Open this publication in new window or tab >>Globally Optimal Displacement Fields Using Local Tensor Metric
2012 (English)In: Image Processing (ICIP), 2012 19th IEEE International Conference on, 2012, 2957-2960 p.Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

In this paper, we propose a novel algorithm for regularizing displacement fields in image registration. The method uses the local structure tensor and gradients of the displacement field to impose a local metric, which is then used optimizing a global cost function. The method allows for linear operators, such as tensors and differential operators modeling the underlying physical anatomy of the human body in medical images. The algorithm is tested using output from the Morphon image registration algorithm on MRI data as well as synthetic test data and the result is compared to the initial displacement field. The results clearly demonstrate the power of the method and the unique features brought forth through the global optimization approach.

Keyword
Image Processing, Image Registration, Regularization, Optimization, Tensor
National Category
Medical Image Processing Signal Processing
Identifiers
urn:nbn:se:liu:diva-81947 (URN)10.1109/ICIP.2012.6467520 (DOI)978-1-4673-2534-9 (ISBN)
Conference
2012 IEEE International Conference on Image Processing, September 30 - October 3, 2012, Orlando, Florida, USA
Projects
Dynamic Context Atlases for Image Denoising and Patient Safety
Funder
Swedish Research Council, 2011-5176Swedish Research Council, 2007-4786
Available from: 2012-09-26 Created: 2012-09-26 Last updated: 2015-04-17Bibliographically approved
3. Parallel Scales for More Accurate Displacement Estimation in Phase-Based Image Registration
Open this publication in new window or tab >>Parallel Scales for More Accurate Displacement Estimation in Phase-Based Image Registration
2010 (English)In: Pattern Recognition (ICPR), 2010, IEEE Computer Society, 2010, 2329-2332 p.Conference paper, Published paper (Refereed)
Abstract [en]

Phase-based methods are commonly applied in image registration. When working with phase-difference methods only a single is employed, although the algorithms are normally iterated over multiple scales, whereas phase-congruency methods utilize the phase from multiple scales simultaneously. This paper presents an extension to phase-difference methods employing parallel scales to achieve more accurate displacements. Results are also presented clearly favouring the use of parallel scales over single scale in more than 95% of the 120 tested cases. 

Place, publisher, year, edition, pages
IEEE Computer Society, 2010
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-59332 (URN)10.1109/ICPR.2010.570 (DOI)
Conference
20th International Conference on pattern Recognition (ICPR 2010), 23-26 August 2010, Istanbul, Turkey
Funder
Swedish Research Council, 2007-4786
Available from: 2012-06-27 Created: 2010-09-13 Last updated: 2013-09-12Bibliographically approved
4. Phase-Based Non-Rigid 3D Image Registration - From Minutes to Seconds Using CUDA
Open this publication in new window or tab >>Phase-Based Non-Rigid 3D Image Registration - From Minutes to Seconds Using CUDA
2011 (English)Conference paper, Published paper (Other academic)
Abstract [en]

Image registration is a well-known concept within the medical image domain and has been shown to be useful in a number of dierent tasks. However, due to sometimes long processing times, image registration is not fully utilized in clinical workows, where time is an important factor. During the last couple of years, a number of signicant projects have been introduced to make the computational power of GPUs available to a wider audience, where the most well known is CUDA. In this paper we present, with the aid of CUDA, a speedup in the range of 38-44x (from 29 minutes to 40 seconds) when implementing a phasebased non-rigid image registration algorithm, known as the Morphon, on a single GPU. The achieved speedup is in the same magnitude as the speedups reported from other non-rigid registration algorithms fully ported to the GPU. Given the impressive speedups, both reported in this paper and other papers, we therefore consider that it is now feasible to eectively integrate image registration into various clinical workows, where time is a critical factor.

National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-75387 (URN)
Conference
Joint MICCAI Workshop on High Performance and Distributed Computing for Medical Imaging, HP-MICCAI, September 22nd, Toronto, Canada
Funder
Swedish Research Council, 2007-4786
Available from: 2012-03-01 Created: 2012-02-28 Last updated: 2013-09-12Bibliographically approved
5. Model-Based Transfer Functions for Efficient Visualization of Medical Image Volumes
Open this publication in new window or tab >>Model-Based Transfer Functions for Efficient Visualization of Medical Image Volumes
2011 (English)In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings, Springer Berlin/Heidelberg, 2011, Vol. 6688/2011, 592-603 p.Conference paper, Published paper (Refereed)
Abstract [en]

The visualization of images with a large dynamic range is a difficult task and this is especially the case for gray-level images. In radiology departments, this will force radiologists to review medical images several times, since the images need to be visualized with several different contrast windows (transfer functions) in order for the full information content of each image to be seen. Previously suggested methods for handling this situation include various approaches using histogram equalization and other methods for processing the image data. However, none of these utilize the underlying human anatomy in the images to control the visualization and the fact that different transfer functions are often only relevant for disjoint anatomical regions. In this paper, we propose a method for using model-based local transfer functions. It allows the reviewing radiologist to apply multiple transfer functions simultaneously to a medical image volume. This provides the radiologist with a tool for making the review process more efficient, by allowing him/her to review more of the information in a medical image volume with a single visualization. The transfer functions are automatically assigned to different anatomically relevant regions, based upon a model registered to the volume to be visualized. The transfer functions can be either pre-defined or interactively changed by the radiologist during the review process. All of this is achieved without adding any unfamiliar aspects to the radiologist’s normal work-flow, when reviewing medical image volumes.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2011
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 6688
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-71679 (URN)10.1007/978-3-642-21227-7_55 (DOI)000308543900055 ()978-3-642-21226-0 (ISBN)
Conference
17th Scandinavian Conference on Image Analysis, SCIA 2011, Ystad, Sweden, May 2011.
Funder
Swedish Research Council, 2007-4786
Available from: 2012-06-27 Created: 2011-10-31 Last updated: 2015-10-08Bibliographically approved
6. Fully automatic measurements of axial vertebral rotation for assessment of spinal deformity in idiopathic scoliosis
Open this publication in new window or tab >>Fully automatic measurements of axial vertebral rotation for assessment of spinal deformity in idiopathic scoliosis
Show others...
2013 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 58, no 6, 1775-1787 p.Article in journal (Refereed) Published
Abstract [en]

Reliable measurements of spinal deformities in idiopathic scoliosis are vital, since they are used for assessing the degree of scoliosis, deciding upon treatment and monitoring the progression of the disease. However, commonly used two dimensional methods (e.g. the Cobb angle) do not fully capture the three dimensional deformity at hand in scoliosis, of which axial vertebral rotation (AVR) is considered to be of great importance. There are manual methods for measuring the AVR, but they are often time-consuming and related with a high intra- and inter-observer variability. In this paper, we present a fully automatic method for estimating the AVR in images from computed tomography. The proposed method is evaluated on four scoliotic patients with 17 vertebrae each and compared with manual measurements performed by three observers using the standard method by Aaro-Dahlborn. The comparison shows that the difference in measured AVR between automatic and manual measurements are on the same level as the inter-observer difference. This is further supported by a high intraclass correlation coefficient (0.971-0.979), obtained when comparing the automatic measurements with the manual measurements of each observer. Hence, the provided results and the computational performance, only requiring approximately 10 to 15 s for processing an entire volume, demonstrate the potential clinical value of the proposed method.

Place, publisher, year, edition, pages
Institute of Physics (IOP), 2013
National Category
Medical Image Processing Orthopedics
Identifiers
urn:nbn:se:liu:diva-89619 (URN)10.1088/0031-9155/58/6/1775 (DOI)000315735400007 ()
Funder
Swedish Foundation for Strategic Research , SM10-0022
Available from: 2013-02-28 Created: 2013-02-28 Last updated: 2017-12-06
7. Model-based registration for assessment of spinal deformities in idiopathic scoliosis
Open this publication in new window or tab >>Model-based registration for assessment of spinal deformities in idiopathic scoliosis
2014 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 59, no 2, 311-326 p.Article in journal (Refereed) Published
Abstract [en]

Detailed analysis of spinal deformity is important within orthopaedic healthcare, in particular for assessment of idiopathic scoliosis. This paper addresses this challenge by proposing an image analysis method, capable of providing a full three-dimensional spine characterization. The proposed method is based on the registration of a highly detailed spine model to image data from computed tomography. The registration process provides an accurate segmentation of each individual vertebra and the ability to derive various measures describing the spinal deformity. The derived measures are estimated from landmarks attached to the spine model and transferred to the patient data according to the registration result. Evaluation of the method provides an average point-to-surface error of 0.9 mm ± 0.9 (comparing segmentations), and an average target registration error of 2.3 mm ± 1.7 (comparing landmarks). Comparing automatic and manual measurements of axial vertebral rotation provides a mean absolute difference of 2.5° ± 1.8, which is on a par with other computerized methods for assessing axial vertebral rotation. A significant advantage of our method, compared to other computerized methods for rotational measurements, is that it does not rely on vertebral symmetry for computing the rotational measures. The proposed method is fully automatic and computationally efficient, only requiring three to four minutes to process an entire image volume covering vertebrae L5 to T1. Given the use of landmarks, the method can be readily adapted to estimate other measures describing a spinal deformity by changing the set of employed landmarks. In addition, the method has the potential to be utilized for accurate segmentations of the vertebrae in routine computed tomography examinations, given the relatively low point-to-surface error.

Place, publisher, year, edition, pages
Institute of Physics and Engineering in Medicine, 2014
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-91233 (URN)10.1088/0031-9155/59/2/311 (DOI)000332842000005 ()
Funder
Swedish Research Council, 2007-4786Swedish Foundation for Strategic Research , SM10-0022
Available from: 2013-04-17 Created: 2013-04-17 Last updated: 2017-12-06Bibliographically approved

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