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Fully automatic measurements of axial vertebral rotation for assessment of spinal deformity in idiopathic scoliosis
Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0003-0908-9470
Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0002-9368-0177
Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Neurokirurgi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Ortopedkliniken i Linköping.
Vise andre og tillknytning
2013 (engelsk)Inngår i: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 58, nr 6, s. 1775-1787Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Institute of Physics (IOP), 2013. Vol. 58, nr 6, s. 1775-1787
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-89619DOI: 10.1088/0031-9155/58/6/1775ISI: 000315735400007OAI: oai:DiVA.org:liu-89619DiVA, id: diva2:608527
Forskningsfinansiär
Swedish Foundation for Strategic Research , SM10-0022Tilgjengelig fra: 2013-02-28 Laget: 2013-02-28 Sist oppdatert: 2018-01-11
Inngår i avhandling
1. Robust Image Registration for Improved Clinical Efficiency: Using Local Structure Analysis and Model-Based Processing
Åpne denne publikasjonen i ny fane eller vindu >>Robust Image Registration for Improved Clinical Efficiency: Using Local Structure Analysis and Model-Based Processing
2013 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2013. s. 120
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1514
Emneord
Image registration, deformable models, scoliosis, visualization, volume rendering, adaptive regularization, GPGPU, CUDA
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-91116 (URN)978-91-7519-637-4 (ISBN)
Disputas
2013-05-31, Eken, Campus US, Linköping University, Linköping, 09:15 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Swedish Research Council, 2007-4786
Tilgjengelig fra: 2013-05-08 Laget: 2013-04-17 Sist oppdatert: 2019-12-03bibliografisk kontrollert

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