Predictive online 3D target tracking with population-based generative networks for image-guided radiotherapy

Romaguera, Liset Vázquez; Mezheritsky, Tal; Mansour, Rihab; Tanguay, William; Kadoury, Samuel

doi:10.1007/s11548-021-02425-x

Cited by 11 publications

(11 citation statements)

References 27 publications

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“…The representation of teaching information in space is expressed in world coordinates, but when teachers run the platform to display teaching information, the coordinate system will be transformed into an observation coordinate system, which needs to be transformed between the two coordinate systems. The transformation from world coordinates to observation coordinates can be realized through translation, rotation, and scaling, which is called information coordinate transformation [13][14][15]. When observing the teaching information of three-dimensional space, twodimensional images from different angles can be obtained by model transformation.…”

Section: Research On the Application Of Interactivementioning

confidence: 99%

The Application of Interactive Visualization and Computer Vision in Intelligent Education Based on Big Data AI Technology

2023

Wireless Communications and Mobile Computing

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Considering the problem that teachers’ command gesture tracking error is due to their poor detection ability in the intelligent education of big data or technology, the application method of interactive visualization and computer vision in the intelligent education of big data or technology is designed. Three-dimensional coordinate transformations of teaching content information and visual processing of teaching information. Use computer vision to detect teachers’ actions and complete computer vision detection of interactive teaching. Using the rendering algorithm, the AI education environment is optimized without affecting computer vision detection. So far, the interactive visualization and computer vision application method design in intelligent big data technology education has been completed. The experimental results show that combining these two technologies can significantly improve the tracking effect of teachers’ instruction gestures, optimize the design effect of teaching content, and raise teaching levels.

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Section: Research On the Application Of Interactivementioning

confidence: 99%

The Application of Interactive Visualization and Computer Vision in Intelligent Education Based on Big Data AI Technology

2023

Wireless Communications and Mobile Computing

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“…Nodule detection visibility could also be improved with a combination of machine learning and pattern recognition algorithms. [27][28][29][30] However, none of the systems was able to provide real-time bonesuppressed images from 3D computed tomography (CT) data for TMM.…”

Section: Introductionmentioning

confidence: 99%

“…tested RapidTrack software (Varian Medical Systems, Palo Alto, CA, USA) in a phantom and patient study and reported a decrease in tracking range error when BS images were used for image registration. Nodule detection visibility could also be improved with a combination of machine learning and pattern recognition algorithms 27–30 . However, none of the systems was able to provide real‐time bone‐suppressed images from 3D computed tomography (CT) data for TMM.…”

Section: Introductionmentioning

confidence: 99%

A novel bone suppression algorithm in intensity‐based 2D/3D image registration for real‐time tumor motion monitoring: Development and phantom‐based validation

et al. 2022

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Background Real‐time tumor motion monitoring (TMM) is a crucial process for intra‐fractional respiration management in lung cancer radiotherapy. Since the tumor can be partly or fully located behind the ribs, the TMM is challenging. Purpose The aim of this work was to develop a bone suppression (BS) algorithm designed for real‐time 2D/3D marker‐less TMM to increase the visibility of the tumor when overlapping with bony structures and consequently to improve the accuracy of TMM. Method A BS method was implemented in the in‐house developed software for ultrafast intensity‐based 2D/3D tumor registration (Fast Image‐based Registration [FIRE]). The method operates on both, digitally reconstructed radiograph (DRR) and intra‐fractional X‐ray images. The bony structures are derived from computed tomography data by thresholding during ray‐casting, and the resulting bone DRR is subtracted from intra‐fractional X‐ray images to obtain a soft‐tissue‐only image for subsequent tumor registration. The accuracy of TMM utilizing BS was evaluated within a retrospective phantom study with nine different 3D‐printed tumor phantoms placed in the in‐house developed Advanced Radiation DOSimetry (ARDOS) breathing phantom. A 24 mm craniocaudal tumor motion, including rib eclipses, was simulated, and X‐ray images were acquired on the Elekta Versa HD Linac in the lateral and posterior–anterior directions. An error assessment for BS images was evaluated with respect to the ground truth tumor position. Results A total error (root mean square error) of 0.87 ± 0.23 mm and 1.03 ± 0.26 mm was found for posterior–anterior and lateral imaging; the mean time for BS was 8.03 ± 1.54 ms. Without utilizing BS, TMM failed in all X‐ray images since the registration algorithm focused on the rib position due to the predominant intensity of this tissue within DRR and X‐ray images. Conclusion The BS algorithm developed and implemented improved the accuracy, robustness, and stability of real‐time TMM in lung cancer in a phantom study, even in the case of rib interlude where normal tumor registration fails.

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“…In the system, there are 5 independent variables. Vital capacity, airflow, and the 3-dimensional reference position are the five variables [8].…”

Section: Introductionmentioning

confidence: 99%

Biomedical image restoration using machine learning GPU acceleration approach for precision improvement

Hazela

Aarti

Khandelwal

et al. 2022

ijhs

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Focusing the image in a single x-ray projection, an algorithm proposed for actual time volumetric image rebuilt and 3-dimensional location of the lump. Using the Principal Component Analysis (PCA) initialize the parameterized Deformation Vector Fields (DVF) of pulmonary movement. By adjusting the PCA coefficients, applied the DVF applied to optimize the reference image so that, the simulated projection of the rebuilt image matches the observed projection. The digital phantom & patient information was used to evaluate the technique. The phantom has an average relative image rebuilt error of 7.5 percent and a 3-dimensional location of the lump inaccuracy of 0.9 mm, correspondingly. The patient's location of the lump inaccuracy is less than 2 mm. On a GPU NVIDIA C1060, recreating a single volumetric image from every projection takes about 0.2 & 0.3 secs for patient and phantom. From a single image, clinical relevance could lead to reliable 3-dimensional lump tracking.

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Predictive online 3D target tracking with population-based generative networks for image-guided radiotherapy

Cited by 11 publications

References 27 publications

The Application of Interactive Visualization and Computer Vision in Intelligent Education Based on Big Data AI Technology

The Application of Interactive Visualization and Computer Vision in Intelligent Education Based on Big Data AI Technology

A novel bone suppression algorithm in intensity‐based 2D/3D image registration for real‐time tumor motion monitoring: Development and phantom‐based validation

Biomedical image restoration using machine learning GPU acceleration approach for precision improvement

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