The Comparison Study of Quadratic Infinite Beam Program on Optimization Instensity Modulated Radiation Therapy Treatment Planning (IMRTP) between Threshold and Exponential Scatter Method with CERR® In The Case of Lung Cancer

Hardiyanti, Yati; Haekal, Mohammad; Waris, Abdul; Haryanto, Freddy

doi:10.1088/1742-6596/739/1/012144

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…We irradiate the box using four beams at 0°, 120°, 180°and 240°and a total amount of N B = 64 beamlets (16 beamlets/beam). The influence matrices (A red and A orange ) are obtained using the CERR's dose calculation algorithm QIB (Hardiyanti et al 2016) with the default settings; the dimensions of the beamlets are set to (1.0× 1.0) cm 2 .…”

Section: Spherementioning

confidence: 99%

Optimizing radiotherapy plans for cancer treatment with Tensor Networks

Cavinato¹,

Felser²,

Fusella³

et al. 2021

Phys. Med. Biol.

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We present a novel application of Tensor Network methods in cancer treatment as a potential tool to solve the dose optimization problem in radiotherapy. In particular, the intensity-modulated radiation therapy technique—that allows treating irregular and inhomogeneous tumors while reducing the radiation toxicity on healthy organs—is based on the optimization problem of the beamlets intensities that shall result in a maximal delivery of the therapy dose to cancer while avoiding the organs at risk of being damaged by the radiation. The resulting optimization problem is expressed as a cost function to be optimized. Here, we map the cost function into an Ising-like Hamiltonian, describing a system of long-range interacting qubits. Finally, we solve the dose optimization problem by finding the ground-state of the Hamiltonian using a Tree Tensor Network algorithm. In particular, we present an anatomical scenario exemplifying a prostate cancer treatment. A similar approach can be applied to future hybrid classical–quantum algorithms, paving the way for the use of quantum technologies in future medical treatments.

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Section: Spherementioning

confidence: 99%

Optimizing radiotherapy plans for cancer treatment with Tensor Networks

Cavinato¹,

Felser²,

Fusella³

et al. 2021

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

“…We irradiate the box using four beams at 0 • , 120 • , 180 • and 240 • and a total amount of N B = 64 beamlets (16 beamlets/beam). The influence matrices (A red and A orange ) are obtained using the CERR's dose calculation algorithm QIB [52] with the default settings; the dimensions of the beamlets are set (1.0 × 1.0) cm 2 .…”

Section: B Toy Modelsmentioning

confidence: 99%

Optimizing Radiotherapy Plans for Cancer Treatment with Tensor Networks

Cavinato,

Felser,

Fusella

et al. 2020

Preprint

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We present a novel application of Tensor Network methods in cancer treatment as a potential tool to solve the dose optimization problem in radiotherapy. In particular, the Intensity-Modulated Radiation Therapy (IMRT) technique -that allows treating irregular and inhomogeneous tumors while reducing the radiation toxicity on healthy organs -is based on the optimization of the radiation beamlets intensities. The optimization aims to maximize the delivery of the therapy dose to cancer while avoiding the organs at risk to prevent their damage by the radiation. Here, we map the dose optimization problem into the search of the ground state of an Ising-like Hamiltonian, describing a system of long-range interacting qubits. Finally, we apply a Tree Tensor Network algorithm to find the ground-state of the Hamiltonian. In particular, we present an anatomical scenario exemplifying a prostate cancer treatment. A similar approach can be applied to future hybrid classical-quantum algorithms, paving the way for the use of quantum technologies in future medical treatments.

show abstract

The Comparison Study of Quadratic Infinite Beam Program on Optimization Instensity Modulated Radiation Therapy Treatment Planning (IMRTP) between Threshold and Exponential Scatter Method with CERR® In The Case of Lung Cancer

Cited by 2 publications

References 1 publication

Optimizing radiotherapy plans for cancer treatment with Tensor Networks

Optimizing radiotherapy plans for cancer treatment with Tensor Networks

Optimizing Radiotherapy Plans for Cancer Treatment with Tensor Networks

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