Point-based elastic registration of medical image data using approximating thin-plate splines

Rohr, Karl; Stiehl, H. Siegfried; Sprengel, Rainer; Beil, Wolfgang; Buzug, Thorsten M.; Weese, Jürgen; Kühn, Michael

doi:10.1007/bfb0046967

Cited by 104 publications

(84 citation statements)

References 15 publications

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“…Two different spline families that have been used extensively in the literature to model 3D transformations are thin-plate splines and B-splines. Kim et al [55] and Rohr et al [56] have reported methods based on thin-plate splines to perform deformable image registration. However, one major drawback of thin-plate splines is that they have infinite support.…”

Section: Deformable Modelsmentioning

confidence: 99%

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

Dandekar¹

2008

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Minimally invasive image-guided interventions (IGIs) are time and cost efficient, minimize unintended damage to healthy tissues, and lead to faster patient recovery. Advanced three-dimensional (3D) image processing is a critical need for navigation during IGIs. However, achieving on-demand performance, as required by IGIs, for these image processing operations using software-only implementations is challenging because of the sheer size of the 3D images, and memory and compute intensive nature of the operations. This dissertation, therefore, is geared toward developing high-performance 3D image processing architectures, which will enable improved intraprocedural visualization and navigation capabilities during IGIs.In this dissertation we present an architecture for real-time implementation of 3D filtering operations that are commonly employed for preprocessing of medical images. This architecture is approximately two orders of magnitude faster than corresponding software implementations and is capable of processing 3D medical images at their acquisition speeds. Report Documentation PageCombining complementary information through registration between pre-and intraprocedural images is a fundamental need in the IGI workflow. Intensity-based deformable registration, which is completely automatic and locally accurate, is a promising approach to achieve this alignment. These algorithms, however, are extremely compute intensive, which has prevented their clinical use. We present an FPGA-based architecture for accelerated implementation of intensity-based deformable image registration. This high-performance architecture achieves over an order of magnitude speedup when compared with a corresponding software implementation and reduces the execution time of deformable registration from hours to minutes while offering comparable image registration accuracy.Furthermore, we present a framework for multiobjective optimization of finite-precision implementations of signal processing algorithms that takes into account multiple conflicting objectives such as implementation accuracy and hardware resource consumption. The evaluation that we have performed in the context of FPGA-based image registration demonstrates that such an analysis can be used to enhance automated hardware design processes, and efficiently identify a system configuration that meets given design constraints. In addition, we also outline two novel clinical applications that can directly benefit from these developments and demonstrate the feasibility of our approach in the context of these applications. These advances will ultimately enable integration of 3D image processing into clinical workflow.

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Section: Deformable Modelsmentioning

confidence: 99%

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

Dandekar¹

2008

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“…Methods can be divided in two classes : sparse and dense. In the first one, deformation is evaluated from a few set of control points with a deformation model such as thin-plate spline [7,8]. In the second one, a displacement vector is computed at each point leading to a dense deformation field.…”

Section: State Of the Artmentioning

confidence: 99%

Lung Deformation Estimation with Non-rigid Registration for Radiotherapy Treatment

Boldea

Sarrut

Clippe

2003

Lecture Notes in Computer Science

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Abstract.A main challenge in radiotherapy is to precisely take into account organs deformation and motion in order to adapt the treatment to each patient. This is particularly important in lung cancer where breathing leads to large displacements. In this work, breath holding techniques (with Active Breath Control device -ABC) were used to reduce movements during treatment. We study thorax and lung deformation between different CT scans acquired at same and different breathing stages. We developed non-rigid registration tools to evaluate for each patient the reproducibility of ABC and to extract motion information for subsequent dosimetric and modeling studies. First results show that ABC has a good reproducibility, that vector fields can be used to detect pathological situations and that deformations due to breathing can be estimated.

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“…In this way, features with low-levels of saliency influence the internal deformation to a lesser extent. The implementation involves minimizing the functional [16]: (5) where λ is the regularisation parameter that controls the amount of smoothness in the deformation and σ i 2 are the uncertainty terms.…”

Section: Final Registrationmentioning

confidence: 99%

MRI – Mammography 2D/3D Data Fusion for Breast Pathology Assessment

Behrenbruch

Marias

Armitage

et al. 2000

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000

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Abstract. Increasing use is being made of contrast-enhanced MagneticResonance Imaging (Gd-DTPA) for breast cancer assessment since it provides 3D functional information via pharmacokinetic interaction between contrast agent and tumour vascularity, and because it is applicable to women of all ages. Contrast-enhanced MRI (CE-MRI) is complimentary to conventional Xray mammography since it is a relatively low-resolution functional counterpart of a comparatively high-resolution 2D structural representation. However, despite the additional information provided by MRI, mammography is still an extremely important diagnostic imaging modality, particularly for several common conditions such as ductal carcinoma in-situ (DCIS) where it has been shown that there is a strong correlation between microcalcification clusters and malignancy [1]. Pathological indicators such as calcifications and fine spiculations are not visible in CE-MRI and therefore there is clinical and diagnostic value to fusing the high-resolution structural information available from mammography with the functional data acquired from MRI imaging. This paper presents a novel data fusion technique whereby medio-lateral (ML) and cranio-caudal (CC) mammograms (2D data) are registered to 3D contrastenhanced MRI volumes. We utilise a combination of pharmacokinetic modelling, projection geometry, wavelet-based landmark detection and thinplate spline non-rigid registration to transform the coordinates of regions of interest (ROIs) from the 2D mammograms to the spatial reference frame of the contrast-enhanced MRI volume.

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Point-based elastic registration of medical image data using approximating thin-plate splines

Cited by 104 publications

References 15 publications

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

Lung Deformation Estimation with Non-rigid Registration for Radiotherapy Treatment

MRI – Mammography 2D/3D Data Fusion for Breast Pathology Assessment

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