Dose Super-Resolution in Prostate Volumetric Modulated Arc Therapy Using Cascaded Deep Learning Networks

Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with fine structural details are preferred for visual task analysis. Recognizing this significance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In this paper, we propose an efficient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-specific heads to the trained Hydra trunk for simultaneous learning of multiple visual tasks in medical images. The Hydra is evaluated on publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classification. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.

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Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Medical Physics ◽

10.1002/mp.13925 ◽

2019 ◽

Vol 47 (2) ◽

pp. 371-379 ◽

Cited By ~ 1

Author(s):

Yuhei Koike ◽

Shingo Ohira ◽

Yuichi Akino ◽

Tomohiro Sagawa ◽

Masashi Yagi ◽

...

Keyword(s):

Deep Learning ◽

Volumetric Modulated Arc Therapy ◽

Dual Energy ◽

Dual Energy Ct ◽

Therapy Planning ◽

Arc Therapy ◽

Noncontrast Ct

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Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning

Life ◽

10.3390/life11121305 ◽

2021 ◽

Vol 11 (12) ◽

pp. 1305

Author(s):

Patiparn Kummanee ◽

Wares Chancharoen ◽

Kanut Tangtisanon ◽

Todsaporn Fuangrod

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Dose Distribution ◽

Learning Algorithm ◽

Volumetric Modulated Arc Therapy ◽

Dose Calculation ◽

Generative Adversarial Network ◽

Percentage Difference ◽

Prediction Time ◽

Arc Therapy

Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.

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Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model

Physics and Imaging in Radiation Oncology ◽

10.1016/j.phro.2021.07.008 ◽

2021 ◽

Vol 19 ◽

pp. 112-119

Author(s):

Michael Lempart ◽

Hunor Benedek ◽

Mikael Nilsson ◽

Niklas Eliasson ◽

Sven Bäck ◽

...

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Cancer Patients ◽

Treatment Plan ◽

Volumetric Modulated Arc Therapy ◽

Learning Model ◽

Plan Generation ◽

Dose Prediction ◽

Arc Therapy ◽

Deep Learning Model

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Systematic method for a deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance of volumetric modulated arc therapy

Medical Physics ◽

10.1002/mp.14682 ◽

2020 ◽

Author(s):

Seiji Tomori ◽

Noriyuki Kadoya ◽

Tomohiro Kajikawa ◽

Yuto Kimura ◽

Kakutarou Narazaki ◽

...

Keyword(s):

Quality Assurance ◽

Deep Learning ◽

Prediction Model ◽

Volumetric Modulated Arc Therapy ◽

Patient Specific ◽

Systematic Method ◽

Gamma Evaluation ◽

Arc Therapy

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Content-aware frame interpolation (CAFI): Deep Learning-based temporal super-resolution for fast bioimaging

10.1101/2021.11.02.466664 ◽

2021 ◽

Author(s):

Martin Priessner ◽

David C.A. Gaboriau ◽

Arlo Sheridan ◽

Tchern Lenn ◽

Jonathan R. Chubb ◽

...

Keyword(s):

Deep Learning ◽

Light Exposure ◽

Super Resolution ◽

Single Particle Tracking ◽

Video Frame ◽

Learning Networks ◽

Frame Interpolation ◽

Cellular Processes ◽

Image Pairs ◽

Content Aware

The development of high-resolution microscopes has made it possible to investigate cellular processes in 4D (3D over time). However, observing fast cellular dynamics remains challenging as a consequence of photobleaching and phototoxicity. These issues become increasingly problematic with the depth of the volume acquired and the speed of the biological events of interest. Here, we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo (ZS) and Depth-Aware Video Frame Interpolation (DAIN), based on combinations of recurrent neural networks, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series as a post-acquisition analysis step. We show that CAFI predictions are capable of understanding the motion context of biological structures to perform better than standard interpolation methods. We benchmark CAFI's performance on six different datasets, obtained from three different microscopy modalities (point-scanning confocal, spinning-disc confocal and confocal brightfield microscopy). We demonstrate its capabilities for single-particle tracking methods applied to the study of lysosome trafficking. CAFI therefore allows for reduced light exposure and phototoxicity on the sample and extends the possibility of long-term live-cell imaging. Both DAIN and ZS as well as the training and testing data are made available for use by the wider community via the ZeroCostDL4Mic platform.

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