Imaging study of pseudo-CT images of superposed ultrasound deformation fields acquired in radiotherapy based on step-by-step local registration

Sun, Haitao; Lin, Tao; Xie, Kai; Gao, Liugang; Sui, Jian-feng; Ni, Xinye

doi:10.1007/s11517-018-1912-2

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Based on the research of Van der Meer et al, we defined different regions of interest (ROIs) for the US sim and US tx and then performed local registration to obtain several deformation fields. These fields were then applied to the CT sim to obtain pseudo-CT. We verified that the pseudo-CT images obtained by US deformation based on stepwise local registration were similar to cone beam computed tomography (CBCT) images [11]. However, the deformation fields based on multiple ROIs are discontinuous, resulting in inaccurate connection of tissues and organs at the critical positions of different ROIs, which can be solved by smoothing, which reduces image clarity.…”

Section: Introductionmentioning

confidence: 67%

Stepwise Local Synthetic Pseudo-CT Imaging Based on Anatomical Semantic Guidance

Sun

Zhang²,

Fan

et al. 2019

IEEE Access

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In this study, an anatomic semantic guided neural style transfer (ASGNST) algorithm was developed and pseudo-computed tomography (CT) images synthesized in steps. CT images and ultrasound (US) images of 20 cervical cancer patients to be treated were selected. The foreground (FG) and background (BG) regions of the US images were segmented by the region growth method, and three objective functions for content, style, and contour loss were defined. Based on the two types of regions, a local pseudo-CT image synthesis model based on a convolution neural network was established. Then, global 2D pseudo-CT images were obtained using the weighted average fusing algorithm, and the final pseudo-CT images were obtained through 3D reconstruction. US phantom and data of five additional cervical cancer patients were used for prediction. Furthermore, three image synthesis algorithms-global deformation field (GDF), stepwise local deformation field (SLDF), and neural style transfer (NST)-were selected for comparative verification. The pseudo-CT images synthesized by the four algorithms were compared with the ground-truth CT images obtained during treatment. The structural similarity index between the ground-truth CT and pseudo-CT synthesized by the improved algorithm significantly differed from those synthesized by the other three algorithms (t GDF_bg = 7.175, t SLDF_bg = 4.513, t NST_bg = 3.228, t GDF_fg = 10.518, t SLDF_fg = 5.522, t NST_fg = 2.869, p < 0.05). Further, the mean absolute error and peak signal-to-noise ratio values prove that the pseudo-CT synthesized by the ASGNST algorithm is similar to the ground-truth CT. The improved algorithm can obtain pseudo-CT images with high precision and provides a novel direction for image guidance in cervical cancer brachytherapy. INDEX TERMS Neural style transfer, pseudo-CT, radiotherapy, ultrasound.

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Section: Introductionmentioning

confidence: 67%

Stepwise Local Synthetic Pseudo-CT Imaging Based on Anatomical Semantic Guidance

Sun

Zhang²,

Fan

et al. 2019

IEEE Access

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“…Recent studies have shown that acquiring image for positioning verification in each fraction is beneficial to improve positioning accuracy. But these image acquisition technologies not only make patients suffer from additional radiation, but also cause extra time consumption, burdening those countries and regions with insufficient radiotherapy resources ( 5 , 10 , 11 ). Taking China as an example, there were 4.57 million new cancer cases in 2020, accounting for 23% of the global new cancer cases (19.29 million cases) ( 12 , 13 ).…”

Section: Introductionmentioning

confidence: 99%

Setup error assessment based on “Sphere-Mask” Optical Positioning System: Results from a multicenter study

Zhang

Zhou²,

Chu³

et al. 2022

Front. Oncol.

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BackgroundThe setup accuracy plays an extremely important role in the local control of tumors. The purpose of this study is to verify the feasibility of "Sphere-Mask" Optical Positioning System (S-M_OPS) for fast and accurate setup.MethodsFrom 2016 to 2021, we used S-M_OPS to supervise 15441 fractions in 1981patients (with the cancer in intracalvarium, nasopharynx, esophagus, lung, liver, abdomen or cervix) undergoing intensity-modulated radiation therapy (IMRT), and recorded the data such as registration time and mask deformation. Then, we used S-M_OPS, laser line and cone beam computed tomography (CBCT) for co-setup in 277 fractions, and recorded laser line-guided setup errors and S-M_OPS-guided setup errors with CBCT-guided setup result as the standard.ResultsS-M_OPS supervision results: The average time for laser line-guided setup was 31.75s. 12.8% of the reference points had an average deviation of more than 2 mm and 5.2% of the reference points had an average deviation of more than 3 mm. Co-setup results: The average time for S-M_OPS-guided setup was 7.47s, and average time for CBCT-guided setup was 228.84s (including time for CBCT scan and manual verification). In the LAT (left/right), VRT (superior/inferior) and LNG (anterior/posterior) directions, laser line-guided setup errors (mean±SD) were -0.21±3.13mm, 1.02±2.76mm and 2.22±4.26mm respectively; the 95% confidence intervals (95% CIs) of laser line-guided setup errors were -6.35 to 5.93mm, -4.39 to 6.43mm and -6.14 to 10.58mm respectively; S-M_OPS-guided setup errors were 0.12±1.91mm, 1.02±1.81mm and -0.10±2.25mm respectively; the 95% CIs of S-M_OPS-guided setup errors were -3.86 to 3.62mm, -2.53 to 4.57mm and -4.51 to 4.31mm respectively.ConclusionS-M_OPS can greatly improve setup accuracy and stability compared with laser line-guided setup. Furthermore, S-M_OPS can provide comparable setup accuracy to CBCT in less setup time.

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“…The conventional methods for obtaining pseudo CT images based on US images are mainly divided into two categories, the deformation-field registration method, and the image-synthesis method. The deformation field is obtained via registration between the US images scanned in the simulation stage, and those scanned in the treatment stage, and it is applied to the CT images scanned in the simulation stage to obtain the virtual CT image (10)(11)(12). However, the deformation field obtained using this method is discontinuous, resulting in the inaccurate connection of tissues and organs at the critical points of different regions of interest (ROIs).…”

Section: Introductionmentioning

confidence: 99%

Research on obtaining pseudo CT images based on stacked generative adversarial network

Sun¹,

Fan³

et al. 2021

Quant Imaging Med Surg

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Background: To investigate the feasibility of using a stacked generative adversarial network (sGAN) to synthesize pseudo computed tomography (CT) images based on ultrasound (US) images. Methods:The pre-radiotherapy US and CT images of 75 patients with cervical cancer were selected for the training set of pseudo-image synthesis. In the first stage, labeled US images were used as the first conditional GAN input to obtain low-resolution pseudo CT images, and in the second stage, a superresolution reconstruction GAN was used. The pseudo CT image obtained in the first stage was used as an input, following which a high-resolution pseudo CT image with clear texture and accurate grayscale information was obtained. Five cross validation tests were performed to verify our model. The mean absolute error (MAE) was used to compare each pseudo CT with the same patient's real CT image. Also, another 10 cases of patients with cervical cancer, before radiotherapy, were selected for testing, and the pseudo CT image obtained using the neural style transfer (NSF) and CycleGAN methods were compared with that obtained using the sGAN method proposed in this study. Finally, the dosimetric accuracy of pseudo CT images was verified by phantom experiments. Results:The MAE metric values between the pseudo CT obtained based on sGAN, and the real CT in five-fold cross validation are 66.82±1.59 HU, 66.36±1.85 HU, 67.26±2.37 HU, 66.34±1.75 HU, and 67.22±1.30 HU, respectively. The results of the metrics, namely, normalized mutual information (NMI), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR), between the pseudo CT images obtained using the sGAN method and the ground truth CT (CTgt) images were compared with those of the other two methods via the paired t-test, and the differences were statistically significant. The dice similarity coefficient (DSC) measurement results showed that the pseudo CT images obtained using the sGAN method were more similar to the CTgt images of organs at risk. The dosimetric phantom experiments also showed that the dose distribution between the pseudo CT images synthesized by the new method was similar to that of the CTgt images.Conclusions: Compared with NSF and CycleGAN methods, the sGAN method can obtain more accurate pseudo CT images, thereby providing a new method for image guidance in radiotherapy for cervical cancer.

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Imaging study of pseudo-CT images of superposed ultrasound deformation fields acquired in radiotherapy based on step-by-step local registration

Cited by 5 publications

References 20 publications

Stepwise Local Synthetic Pseudo-CT Imaging Based on Anatomical Semantic Guidance

Stepwise Local Synthetic Pseudo-CT Imaging Based on Anatomical Semantic Guidance

Setup error assessment based on “Sphere-Mask” Optical Positioning System: Results from a multicenter study

Research on obtaining pseudo CT images based on stacked generative adversarial network

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