Evidence map›Paper›PMID 37399367›Full record

ArticleMedical physics2023

Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study.

Yabo Fu, Qiyong Fan, Weixing Cai, Feifei Li, Xiuxiu He, John Cuaron, Laura Cervino, Jean M Moran, Tianfang Li, Xiang Li

Abstract read
In one paragraph

Article in Medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yabo FuDepartment of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Qiyong FanDepartment of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Weixing CaiDepartment of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Feifei LiDepartment of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Xiuxiu HeDepartment of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
John CuaronDepartment of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Laura CervinoDepartment of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Jean M MoranDepartment of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Tianfang LiDepartment of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Xiang LiDepartment of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

backgroundIntrafraction motion monitoring in External Beam Radiation Therapy (EBRT) is usually accomplished by establishing a correlation between the tumor and the surrogates such as an external infrared reflector, implanted fiducial markers, or patient skin surface. These techniques either have unstable surrogate-tumor correlation or are invasive. Markerless real-time onboard imaging is a noninvasive alternative that directly images the target motion. However, the low target visibility due to overlapping tissues along the X-ray projection path makes tumor tracking challenging. PURPOSE: To enhance the target visibility in projection images, a patient-specific model was trained to synthesize the Target Specific Digitally Reconstructed Radiograph (TS-DRR).

methodsPatient-specific models were built using a conditional Generative Adversarial Network (cGAN) to map the onboard projection images to TS-DRR. The standard Pix2Pix network was adopted as our cGAN model. We synthesized the TS-DRR based on the onboard projection images using phantom and patient studies for spine tumors and lung tumors. Using previously acquired CT images, we generated DRR and its corresponding TS-DRR to train the network. For data augmentation, random translations were applied to the CT volume when generating the training images. For the spine, separate models were trained for an anthropomorphic phantom and a patient treated with paraspinal stereotactic body radiation therapy (SBRT). For lung, separate models were trained for a phantom with a spherical tumor insert and a patient treated with free-breathing SBRT. The models were tested using Intrafraction Review Images (IMR) for the spine and CBCT projection images for the lung. The performance of the models was validated using phantom studies with known couch shifts for the spine and known tumor deformation for the lung.

resultsBoth the patient and phantom studies showed that the proposed method can effectively enhance the target visibility of the projection images by mapping them into synthetic TS-DRR (sTS-DRR). For the spine phantom with known shifts of 1 mm, 2 mm, 3 mm, and 4 mm, the absolute mean errors for tumor tracking were 0.11 ± 0.05 mm in the x direction and 0.25 ± 0.08 mm in the y direction. For the lung phantom with known tumor motion of 1.8 mm, 5.8 mm, and 9 mm superiorly, the absolute mean errors for the registration between the sTS-DRR and ground truth are 0.1 ± 0.3 mm in both the x and y directions. Compared to the projection images, the sTS-DRR has increased the image correlation with the ground truth by around 83% and increased the structural similarity index measure with the ground truth by around 75% for the lung phantom.

conclusionsThe sTS-DRR can greatly enhance the target visibility in the onboard projection images for both the spine and lung tumors. The proposed method could be used to improve the markerless tumor tracking accuracy for EBRT.

Indexed as

Cone-Beam Computed TomographyLung NeoplasmsHumansLungMotionPhantoms, ImagingRadiography

Identifiers

PMID37399367
PMCPMC11313213

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.