Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach

Metz, Coert; Klein, Stefan; Schaap, Michiel; Walsum, Theo van; Niessen, Wiro J.

doi:10.1016/j.media.2010.10.003

Cited by 205 publications

(234 citation statements)

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“…Their algorithm was recently extended in two different directions. First, Metz et al (2011) extended the output dimension of the transformation model to 3D+t. They used the sum of intensity variances over time as a groupwise similarity metric.…”

Section: Temporal Consistency In Image Registrationmentioning

confidence: 99%

“…They referred to this metric as accumulated pairwise estimates. These three algorithms (Ledesma-Carbayo et al (2005); Metz et al (2011);Yigitsoy et al (2011)) guarantee to recover smooth transformations in time by representing the displacement as a sum of smooth and continuous kernels. This displacement is expressed in the space of coordinates of a fixed reference frame.…”

Section: Temporal Consistency In Image Registrationmentioning

confidence: 99%

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Temporal diffeomorphic free-form deformation: Application to motion and strain estimation from 3D echocardiography

Craene

Piella

Cámara

et al. 2012

Medical Image Analysis

126

120

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This paper presents a new registration algorithm, called Temporal Diffeomorphic Free Form Deformation (TDFFD), and its application to motion and strain quantification from a sequence of 3D ultrasound (US) images. The originality of our approach resides in enforcing time consistency by representing the 4D velocity field as the sum of continuous spatiotemporal B-Spline kernels. The spatiotemporal displacement field is then recovered through forward Eulerian integration of the non-stationary velocity field. The strain tensor is computed locally using the spatial derivatives of the reconstructed displacement field. The energy functional considered in this paper weighs two terms: the image similarity and a regularization term. The image similarity metric is the sum of squared differences between the intensities of each frame and a reference one. Any frame in the sequence can be chosen as reference. The regularization term is based on the incompressibility of myocardial tissue. TDFFD was compared to pairwise 3D FFD and 3D+t FFD, both on displacement and velocity fields, on a set of synthetic 3D US images with different noise levels. TDFFD showed increased robustness to noise compared to these two state-of-the-art algorithms. TDFFD also proved to be more resistant to a reduced temporal resolution when decimating this synthetic sequence. Finally, this synthetic dataset was used to determine optimal settings of the TDFFD algorithm. Subsequently, TDFFD was applied to a database of cardiac 3D US images of the left ventricle acquired from 9 healthy volunteers and 13 patients treated by Cardiac Resynchronization Therapy (CRT). On healthy cases, uniform strain patterns were observed over all myocardial segments, as physiologically expected. On all CRT patients, the improvement in synchrony of regional longitudinal strain correlated with CRT clinical outcome as quantified by the reduction of end-systolic left ventricular volume at follow-up (6 and 12 months), showing the potential of the proposed algorithm for the assessment of CRT.

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Section: Temporal Consistency In Image Registrationmentioning

confidence: 99%

Section: Temporal Consistency In Image Registrationmentioning

confidence: 99%

Temporal diffeomorphic free-form deformation: Application to motion and strain estimation from 3D echocardiography

Craene

Piella

Cámara

et al. 2012

Medical Image Analysis

126

120

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“…For this task, we employ the latest state-of-art registration method of Metz et al [10] that enforces both spatial and temporal smoothness on sought deformation fields; the latter in particular would help decrease the sensitivity of registration to sporadic disappearances of image features of the tongue due to US wave reflectances, etc. (Fig.…”

Section: Explicit Characterization Of Dynamic Gestures Via 2d+time Rementioning

confidence: 99%

“…To develop robust descriptors, we make use of the accompanying audio files to perform temporal alignment of the US sequences. We then employ a well-validated registration algorithm [10] to reliably obtain a set of spatial correspondences that explicitly represent motion and subsequently extract a set of descriptors to capture dynamics of the tongue from the set of correspondences. In evaluating the effectiveness of the descriptors, we performed three clinically driven classification tasks.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Tongue Motion Analysis in 2D Ultrasound Image Sequences

Tang

Hamarneh

Bressmann

2011

Machine Learning in Medical Imaging

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Abstract. Analysis of tongue motions as captured in dynamic ultrasound (US) images has been an important tool in speech research. Previous studies generally required semi-automatic tongue segmentations to perform data analysis. In this paper, we adopt a machine learning approach that does not require tongue segmentation. Specifically, we employ advanced normalization procedures to temporally register the US sequences using their corresponding audio files. To explicitly encode motion, we then register the image frames spatio-temporally to compute a set of deformation fields from which we construct the velocity-based and spatiotemporal gestural descriptors, where the latter explicitly encode tongue dynamics during speech. Next, making use of the recently proposed Histogram Intersection Kernel, we perform support vector machine classification to evaluate the extracted descriptors with a set of clinical measures. We applied our method to speech abnormality and tongue gestures prediction. Overall, differentiating tongue motion, as produced by patients with or without speech impediments on a dataset of 24 US sequences, was achieved with classification accuracy of 94%. When applied to another dataset of 90 US sequences for two other classification tasks, accuracies were 86% and 84%.

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“…Metz et al in Ref. [20] propose a method for groupwise registration of dynamic lung data using both spatial and temporal constraints where groupwise optimization of B-splines is used.…”

Section: Introductionmentioning

confidence: 99%

Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information

Mahapatra

2012

J Digit Imaging

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We propose a joint segmentation and groupwise registration method for dynamic cardiac perfusion images that uses temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registration aims to maximize the smoothness of the intensity signal while segmentation minimizes a pixel's dissimilarity with other pixels having the same segmentation label. The cost function is optimized in an iterative fashion using B-splines. Tests on real patient datasets show that compared with two other methods, our method shows lower registration error and higher segmentation accuracy. This is attributed to the use of temporal information for groupwise registration and mutual complementary registration and segmentation information in one framework while other methods solve the two problems separately.

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Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach

Cited by 205 publications

References 50 publications

Temporal diffeomorphic free-form deformation: Application to motion and strain estimation from 3D echocardiography

Temporal diffeomorphic free-form deformation: Application to motion and strain estimation from 3D echocardiography

A Machine Learning Approach to Tongue Motion Analysis in 2D Ultrasound Image Sequences

Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information

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