Use of the Metal Deletion Technique for Radiotherapy Planning in Patients with Cardiac Implantable Devices

Odložilíková, Anna; Kurzyukova, Anastasia; Sepši, Milan; Pospíšil, David; Šlampa, Pavel

doi:10.14735/amko2018434

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Among them, the Orthopaedics Metal Artifact Reduction (O-MAR, Philips Health System) and the iterative Metal Artifact Reduction (iMAR, Siemens Healthcare) algorithms were widely investigated for the improvement of radiation therapy planning [13][14][15][16][17][18][19][20][21]. Among the research-based MAR algorithms, Metal Deletion Technique (MDT, ReVision Radiology) [22] was the one most often compared to the commercially available algorithms [23][24][25][26]. All these algorithms typically suffer from improper restoration of HU values, from distortion of anatomical structures, and even from creation of secondary artifacts [11].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based ultrasound transducer induced CT metal artifact reduction using generative adversarial networks for ultrasound-guided cardiac radioablation

Puvanasunthararajah

Camps²,

Wille

et al. 2023

Preprint

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In US-guided cardiac radioablation, a possible workflow includes simultaneous US and planning CT acquisitions, which can result in US transducer-induced metal artifacts on the planning CT scans. To reduce the impact of these artifacts, a metal artifact reduction (MAR) algorithm has been developed based on a deep learning Generative Adversarial Network (CycleGAN) called Cycle-MAR, and compared with iMAR (Siemens), O-MAR (Philips) and MDT (ReVision Radiology), and CCS-MAR (Combined Clustered Scan-based MAR). Cycle-MAR was trained with a supervised learning scheme using sets of paired clinical CT scans with and without simulated artifacts. It was then evaluated on CT scans with real artifacts of an anthropomorphic phantom, and on sets of clinical CT scans with simulated artifacts which were not used for Cycle-MAR training. Image quality metrics and HU value-based analysis were used to evaluate the performance of Cycle-MAR compared to the other algorithms. The proposed Cycle-MAR network effectively reduces the negative impact of the metal artifacts. For example, the calculated HU value improvement percentage for the cardiac structures in the clinical CT scans was 59.58%, 62.22%, and 72.84% after MDT, CCS-MAR, and Cycle-MAR application, respectively. The application of MAR algorithms reduces the impact of US transducer-induced metal artifacts on CT scans. In comparison to iMAR, O-MAR, MDT, and CCS-MAR, the application of developed Cycle-MAR network on CT scans performs better in reducing these metal artifacts.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based ultrasound transducer induced CT metal artifact reduction using generative adversarial networks for ultrasound-guided cardiac radioablation

Puvanasunthararajah

Camps²,

Wille

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Deep learning-based ultrasound transducer induced CT metal artifact reduction using generative adversarial networks for ultrasound-guided cardiac radioablation

Puvanasunthararajah,

Camps,

Wille

et al. 2023

Phys Eng Sci Med

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In US-guided cardiac radioablation, a possible workflow includes simultaneous US and planning CT acquisitions, which can result in US transducer-induced metal artifacts on the planning CT scans. To reduce the impact of these artifacts, a metal artifact reduction (MAR) algorithm has been developed based on a deep learning Generative Adversarial Network called Cycle-MAR, and compared with iMAR (Siemens), O-MAR (Philips) and MDT (ReVision Radiology), and CCS-MAR (Combined Clustered Scan-based MAR). Cycle-MAR was trained with a supervised learning scheme using sets of paired clinical CT scans with and without simulated artifacts. It was then evaluated on CT scans with real artifacts of an anthropomorphic phantom, and on sets of clinical CT scans with simulated artifacts which were not used for Cycle-MAR training. Image quality metrics and HU value-based analysis were used to evaluate the performance of Cycle-MAR compared to the other algorithms. The proposed Cycle-MAR network effectively reduces the negative impact of the metal artifacts. For example, the calculated HU value improvement percentage for the cardiac structures in the clinical CT scans was 59.58%, 62.22%, and 72.84% after MDT, CCS-MAR, and Cycle-MAR application, respectively. The application of MAR algorithms reduces the impact of US transducer-induced metal artifacts on CT scans. In comparison to iMAR, O-MAR, MDT, and CCS-MAR, the application of developed Cycle-MAR network on CT scans performs better in reducing these metal artifacts.

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Assessment of the knowledge level of radiographers and CT technologists regarding computed tomography parameters in Iran

Kazemi

Hajimiri

Saghatchi

et al. 2023

Radiation Medicine and Protection

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Use of the Metal Deletion Technique for Radiotherapy Planning in Patients with Cardiac Implantable Devices

Cited by 3 publications

References 7 publications

Deep learning-based ultrasound transducer induced CT metal artifact reduction using generative adversarial networks for ultrasound-guided cardiac radioablation

Deep learning-based ultrasound transducer induced CT metal artifact reduction using generative adversarial networks for ultrasound-guided cardiac radioablation

Deep learning-based ultrasound transducer induced CT metal artifact reduction using generative adversarial networks for ultrasound-guided cardiac radioablation

Assessment of the knowledge level of radiographers and CT technologists regarding computed tomography parameters in Iran

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