Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition

Tirunagari, Santosh; Poh, Norman; Wells, Kevin; Bober, Miroslaw; Gorden, Isky; Windridge, David

doi:10.1007/s00138-017-0835-5

Cited by 36 publications

(20 citation statements)

References 30 publications

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“…In our previous studies [17], we presented a novel automated, registration-free movement correction approach based on windowed and reconstruction variants of Dynamic Mode Decomposition (WR-DMD) to suppress unwanted complex organ motion in DCE-MRI image sequences caused due to respiration.…”

Section: Methodsmentioning

confidence: 99%

Automatic Delineation of Kidney Region in DCE-MRI

Tirunagari,

Poh,

Wells

et al. 2019

Preprint

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Delineation of the kidney region in dynamic contrast enhanced magnetic resonance Imaging (DCE-MRI) is required during post-acquisition analysis in order to quantify various aspects of renal function, such as filtration and perfusion or blood flow. However, this can be obfuscated by the Partial Volume Effect (PVE), caused due to mixing of any single voxel with two or more signal intensities from adjacent regions such as liver region and other tissues. To avoid this problem, firstly, a kidney region of interest (ROI) needs to be defined for the analysis. A clinician may choose to select a region avoiding edges where PV mixing is likely to be significant. However, this approach is time-consuming and labour intensive. To address this issue, we present Dynamic Mode Decomposition (DMD) coupled with thresholding and blob analysis as a framework for automatic delineation of the kidney region. This method is first validated on synthetically generated data with ground-truth available and then applied to ten healthy volunteers' kidney DCE-MRI datasets. We found that the result obtained from our proposed framework is comparable to that of a human expert. For example, while our result gives an average Root Mean Square Error (RMSE) of 0.0097, the baseline achieves an average RMSE of 0.1196 across the 10 datasets. As a result, we conclude automatic modelling via DMD framework is a promising approach.

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Section: Methodsmentioning

confidence: 99%

Automatic Delineation of Kidney Region in DCE-MRI

Tirunagari,

Poh,

Wells

et al. 2019

Preprint

Self Cite

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“…DMD has the potential to transform the analysis of such neural recordings, as evidenced in a recent study by B. Brunton et al [45] that identified dynamically relevant features in ECOG data of sleeping patients, shown in Figure 3.6. Since then, several works have applied DMD to neural recordings or suggested possible implementation in hardware [6,39,337].…”

Section: Neurosciencementioning

confidence: 99%

Modern Koopman Theory for Dynamical Systems

Brunton¹,

Budišić²,

Kaiser³

et al. 2021

Preprint

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The field of dynamical systems is being transformed by the mathematical tools and algorithms emerging from modern computing and data science. First-principles derivations and asymptotic reductions are giving way to data-driven approaches that formulate models in operator theoretic or probabilistic frameworks. Koopman spectral theory has emerged as a dominant perspective over the past decade, in which nonlinear dynamics are represented in terms of an infinitedimensional linear operator acting on the space of all possible measurement functions of the system. This linear representation of nonlinear dynamics has tremendous potential to enable the prediction, estimation, and control of nonlinear systems with standard textbook methods developed for linear systems. However, obtaining finite-dimensional coordinate systems and embeddings in which the dynamics appear approximately linear remains a central open challenge. The success of Koopman analysis is due primarily to three key factors: 1) there exists rigorous theory connecting it to classical geometric approaches for dynamical systems, 2) the approach is formulated in terms of measurements, making it ideal for leveraging big-data and machine learning techniques, and 3) simple, yet powerful numerical algorithms, such as the dynamic mode decomposition (DMD), have been developed and extended to reduce Koopman theory to practice in real-world applications. In this review, we provide an overview of modern Koopman operator theory, describing recent theoretical and algorithmic developments and highlighting these methods with a diverse range of applications. We also discuss key advances and challenges in the rapidly growing field of machine learning that are likely to drive future developments and significantly transform the theoretical landscape of dynamical systems.

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“…Commonly used methods for kidney images using dynamic contrast enhanced magnetic resonance renogrpahy (DCE-MRR) involve motion artifacts that prevents objective assessment of kindey's function. Tirunagari et al [21] have used a used windowed and reconstruction variants of DMD (WR-DMD) for movement correction to overcome the short comings of the more frequently used intensity-based image registration techniques. The authors have eliminated 99% of the motion artifacts as compared to the original datasets, proving the viability of the proposed technique.…”

Section: Related Workmentioning

confidence: 99%

Automatic Seizure Detection Using Multi-Resolution Dynamic Mode Decomposition

et al. 2019

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Epilepsy is one of the most prevalent neurological issues faced by a large population around the globe. Epilepsy is marked by intermittent seizures, the detection of which can be a challenging problem. Therefore, reliably detecting the onset of seizures has evoked the interest of researchers over the last few years. Major leaps in the domain of machine learning, signal processing methods, and computational capabilities have made it a tractable task. In this paper, we apply multi-resolution dynamic mode decomposition (MRDMD), which is a data-driven dimensionality reduction technique, on the problem of epileptic seizure detection. This method can effectively separate a complex non-linear system into a collection of timescale components at different resolutions. We have applied this algorithm on two different scalp EEG datasets, i.e., CHB-MIT and KU Leaven datasets. We have applied necessary post-processing steps to reduce the false alarm rate and boost the sensitivity and specificity. A detailed analysis of the results has been presented for the proposed method applied to both the datasets. The algorithm achieves a sensitivity of 0.937 and 0.96, a specificity of 0.99 and 0.99, a false alarm rate of 0.587 and 0.413 per hour, and a latency of 3.12 and 2.75 s for CHB-MIT and KU Leuven, respectively. The results indicate that the multi-resolution analysis yields a significant improvement in sensitivity compared with the basic dynamic mode decomposition.

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Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition

Cited by 36 publications

References 30 publications

Automatic Delineation of Kidney Region in DCE-MRI

Automatic Delineation of Kidney Region in DCE-MRI

Modern Koopman Theory for Dynamical Systems

Automatic Seizure Detection Using Multi-Resolution Dynamic Mode Decomposition

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