Identification of Reduced-Order Thermal Therapy Models Using Thermal MR Images: Theory and Validation

Niu, Ran; Skliar, Mikhail

doi:10.1109/tmi.2012.2194720

Cited by 3 publications

(3 citation statements)

References 29 publications

(23 reference statements)

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“…As a model-order reduction technique, we use the Proper Orthogonal Decomposition (POD) method [23,24]. POD is a very commonly used model order reduction technique in many engineering fields, especially in fluid mechanics, with real-time implementations [25,26,27].…”

Section: Borehole Dynamicsmentioning

confidence: 99%

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A convex approach to a class of non-convex building HVAC control problems: Illustration by two case studies

Atam

Helsen

2015

Energy and Buildings

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In this paper, a convexification approach is presented for a class of non-convex optimal/model predictive control problems more specifically applied to building HVAC control problems. The original non-convex problems are convexified using a convex envelope approach. The approach is tested on two case studies: a benchmark building HVAC system control problem from the literature and control of a hybrid ground-coupled heat pump (HybGCHP) system. For the first application, convexified model predictive control was used and results were compared with fuzzy and adaptive control results. For the HybGCHP system, convexified optimal control was applied and the results were compared with dynamic programming based optimal control. In the first case superior performance was observed over the corresponding fuzzy and adaptive control results from the literature. For the HybGCHP system the associated convexified optimal control gave almost global optimal results in terms of responses and cost criteria. The suggested method is especially useful for optimal building HVAC control/design problems which include non-convex bilinear or fractional terms. Since a polynomial expression can be recursively expressed as a system of bilinear equations, the proposed method, in principle, can be applied to all systems where polynomial non-convexities exist.

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Section: Borehole Dynamicsmentioning

confidence: 99%

“…The reason for such transformation is the fact that this formulation allows an easy use of an existing dynamic programming toolbox [32]. Then, the reduced-order transformed borehole state-space dynamics becomesx r (k + 1) =Ã rxr (k) +B r u net (k), (27) …”

mentioning

confidence: 99%

A convex approach to a class of non-convex building HVAC control problems: Illustration by two case studies

Atam

Helsen

2015

Energy and Buildings

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“…Compare the new set of KL basis functions with that in [12], the shape of the first basis is similar, which is not the case of the higher-order basis functions. This demonstrates that the first basis function is identified to explain the maximized spatial correlation in thermal images, thus capturing the slow change of the process.…”

Section: A Validation With Simulationsmentioning

confidence: 99%

Compression of MR-Thermometry images using reduced-order Karhunen-Loève basis

Niu¹,

Skliar

2012

Proceedings of the 10th World Congress on Intelligent Control and Automation

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In this paper, we develop an approach to achieve MR thermal image compression by exploiting the spatial correlations of image voxels during thermal treatment. A set of reduced-order basis was identified using Karhunen-Loève (KL) decomposition of MR thermal images. Each image can be compressed with a relative small number of identified KL basis functions. The proposed approach can be used in real-time compression of imaging data in order to minimize a potentially massive amount of information that must be exchanged between the MR scanner and the treatment controller, and reduce computer storage requirements during thermal treatment. Simulation and MR thermometry experimental results demonstrate that the proposed image compression method is also effective in suppressing high spatial-frequency MR measurement noises.

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Identification of Controlled-Complexity Thermal Therapy Models Derived from Magnetic Resonance Thermometry Images

Niu

Skliar

2011

PLoS ONE

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Medical imaging provides information valuable in diagnosis, planning, and control of therapies. In this paper, we develop a method that uses a specific type of imaging—the magnetic resonance thermometry—to identify accurate and computationally efficient site and patient-specific computer models for thermal therapies, such as focused ultrasound surgery, hyperthermia, and thermally triggered targeted drug delivery. The developed method uses a sequence of acquired MR thermometry images to identify a treatment model describing the deposition and dissipation of thermal energy in tissues. The proper orthogonal decomposition of thermal images is first used to identify a set of empirical eigenfunctions, which captures spatial correlations in the thermal response of tissues. Using the reduced subset of eigenfunction as a functional basis, low-dimensional thermal response and the ultrasound specific absorption rate models are then identified. Once identified, the treatment models can be used to plan, optimize, and control the treatment. The developed approach is validated experimentally using the results of MR thermal imaging of a tissue phantom during focused ultrasound sonication. The validation demonstrates that our approach produces accurate low-dimensional treatment models and provides a convenient tool for balancing the accuracy of model predictions and the computational complexity of the treatment models.

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Identification of Reduced-Order Thermal Therapy Models Using Thermal MR Images: Theory and Validation

Cited by 3 publications

References 29 publications

A convex approach to a class of non-convex building HVAC control problems: Illustration by two case studies

A convex approach to a class of non-convex building HVAC control problems: Illustration by two case studies

Compression of MR-Thermometry images using reduced-order Karhunen-Loève basis

Identification of Controlled-Complexity Thermal Therapy Models Derived from Magnetic Resonance Thermometry Images

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Identification of Reduced-Order Thermal Therapy Models Using Thermal MR Images: Theory and Validation

Cited by 3 publications

References 29 publications

A convex approach to a class of non-convex building HVAC control problems: Illustration by two case studies

A convex approach to a class of non-convex building HVAC control problems: Illustration by two case studies

Compression of MR-Thermometry images using reduced-order Karhunen-Lo&#x00E8;ve basis

Identification of Controlled-Complexity Thermal Therapy Models Derived from Magnetic Resonance Thermometry Images

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Compression of MR-Thermometry images using reduced-order Karhunen-Loève basis