Conceptual Design of PET-linac System for Molecular-guided Radiotherapy

Ishikawa, Makoto; Yamaguchi, S; Tanabe, Soichi; Bengua, Gerard; Sutherland, Keith; Suzuki, Ryusuke; Miyamoto, Naoki; Nishijima, Ken ichi; Katoh, Norio; Shirato, Hiroki

doi:10.1016/j.ijrobp.2010.07.1566

Cited by 4 publications

(3 citation statements)

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“…compared a semiautomated segmentation vs experts contouring method, and presented the intra‐ and interoperator standard deviations that were significantly lower for the semi‐automated segmentation. Also, contouring capability is expected to improve the efficiency of molecular image guided radiotherapy when used in concert with a PET‐CT Linac system in which fast, accurate contouring is critical for adaptive replanning on each fraction. The developed deep fully convolutional network based segmentation can also be applied to current kV‐cone‐beam, CT based, adaptive replanning approaches.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

et al. 2019

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Purpose To investigate the use and efficiency of 3‐D deep learning, fully convolutional networks (DFCN) for simultaneous tumor cosegmentation on dual‐modality nonsmall cell lung cancer (NSCLC) and positron emission tomography (PET)‐computed tomography (CT) images. Methods We used DFCN cosegmentation for NSCLC tumors in PET‐CT images, considering both the CT and PET information. The proposed DFCN‐based cosegmentation method consists of two coupled three‐dimensional (3D)‐UNets with an encoder‐decoder architecture, which can communicate with the other in order to share complementary information between PET and CT. The weighted average sensitivity and positive predictive values denoted as Scores, dice similarity coefficients (DSCs), and the average symmetric surface distances were used to assess the performance of the proposed approach on 60 pairs of PET/CTs. A Simultaneous Truth and Performance Level Estimation Algorithm (STAPLE) of 3 expert physicians’ delineations were used as a reference. The proposed DFCN framework was compared to 3 graph‐based cosegmentation methods. Results Strong agreement was observed when using the STAPLE references for the proposed DFCN cosegmentation on the PET‐CT images. The average DSCs on CT and PET are 0.861 ± 0.037 and 0.828 ± 0.087, respectively, using DFCN, compared to 0.638 ± 0.165 and 0.643 ± 0.141, respectively, when using the graph‐based cosegmentation method. The proposed DFCN cosegmentation using both PET and CT also outperforms the deep learning method using either PET or CT alone. Conclusions The proposed DFCN cosegmentation is able to outperform existing graph‐based segmentation methods. The proposed DFCN cosegmentation shows promise for further integration with quantitative multimodality imaging tools in clinical trials.

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Section: Discussionmentioning

confidence: 99%

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

et al. 2019

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“…3c). Due to anatomical changes stemming from patient movement (such as breathing or digestion), imaging is ideally conducted in real time 229 ; in fact, the use of advanced real-time IGRT is rapidly growing through the development of MR-LINAC [230][231][232][233] and PET-LINAC 234 systems.…”

Section: Imaging and Radiotherapymentioning

confidence: 99%

Multiplexed imaging for diagnosis and therapy

et al. 2017

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Complex molecular and metabolic phenotypes depict cancers as a constellation of different diseases with common themes. Precision imaging of such phenotypes requires flexible and tuneable modalities capable of identifying phenotypic fingerprints by using a restricted number of parameters whilst ensuring sensitivity to dynamic biological regulation. Common phenotypes can be detected by in vivo imaging technologies, and effectively define the emerging standards for disease classification and patient stratification in radiology. However, for the imaging data to accurately represent a complex fingerprint, the individual imaging parameters need to be measured and analysed in relation to their wider spatial and molecular context. In this respect, targeted palettes of molecular imaging probes facilitate the detection of heterogeneity in oncogene-driven alterations and their response to treatment, and lead to the expansion of rational-design elements for the combination of imaging experiments. In this Review, we evaluate criteria for conducting multiplexed imaging, and discuss its opportunities for improving patient diagnosis and the monitoring of therapy.

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“…Treatment plans based on multimodality images and daily imageguided radiotherapy (IGRT), based on volumetric images, are both becoming routine. Advanced examples of this trend are combinations of magnetic resonance imaging (MRI) [4] as well as positron emission tomography (PET) [5] scanners with treatment devices, such as linear accelerators or radioactive sources [6]. These new solutions claim the advantage of excellent imaging of the target and OARs using morphological/functional information prior or during treatment.…”

Section: Introductionmentioning

confidence: 99%

Excellent image quality or superior dose conformity?

Stancanello

2011

Cureus

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Conceptual Design of PET-linac System for Molecular-guided Radiotherapy

Cited by 4 publications

References 0 publications

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

Multiplexed imaging for diagnosis and therapy

Excellent image quality or superior dose conformity?

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