Cone-beam computed tomography(CBCT) is mostly used for position verification during the treatment process. However,severe image artifacts in CBCT hinder its direct use in dose calculation and adaptive radiation therap...Cone-beam computed tomography(CBCT) is mostly used for position verification during the treatment process. However,severe image artifacts in CBCT hinder its direct use in dose calculation and adaptive radiation therapy re-planning for proton therapy. In this study, an improved U-Net neural network named CBAM-U-Net was proposed for CBCT noise reduction in proton therapy, which is a CBCT denoised U-Net network with convolutional block attention modules. The datasets contained 20 groups of head and neck images. The CT images were registered to CBCT images as ground truth. The original CBCT denoised U-Net network, sCTU-Net, was trained for model performance comparison. The synthetic CT(SCT) images generated by CBAM-U-Net and the original sCTU-Net are called CBAM-SCT and U-Net-SCT images, respectively. The HU accuracies of the CT, CBCT, and SCT images were compared using four metrics: mean absolute error(MAE), root mean square error(RMSE), peak signal-to-noise ratio(PSNR), and structure similarity index measure(SSIM). The mean values of the MAE, RMSE, PSNR, and SSIM of CBAM-SCT images were 23.80 HU, 64.63 HU, 52.27 dB, and 0.9919, respectively,which were superior to those of the U-Net-SCT images. To evaluate dosimetric accuracy, the range accuracy was compared for a single-energy proton beam. The γ-index pass rates of a 4 cm × 4 cm scanned field and simple plan were calculated to compare the effects of the noise reduction capabilities of the original U-Net and CBAM-U-Net on the dose calculation results. CBAM-U-Net reduced noise more effectively than sCTU-Net, particularly in high-density tissues. We proposed a CBAM-U-Net model for CBCT noise reduction in proton therapy. Owing to the excellent noise reduction capabilities of CBAM-U-Net, the proposed model provided relatively explicit information regarding patient tissues. Moreover, it maybe be used in dose calculation and adaptive treatment planning in the future.展开更多
The wide availability, low radiation dose and short acquisition time of Cone-Beam CT (CBCT) scans make them an attractive source of data for compiling databases of anatomical structures. However CBCT has higher noise ...The wide availability, low radiation dose and short acquisition time of Cone-Beam CT (CBCT) scans make them an attractive source of data for compiling databases of anatomical structures. However CBCT has higher noise and lower contrast than helical slice CT, which makes segmentation more challenging and the optimal methods are not yet known. This paper evaluates several methods of segmenting airway geometries (nares, nasal cavities and pharynx) from typical dental quality head and neck CBCT data. The nasal cavity has narrow and intricate passages and is separated from the paranasal sinuses by thin walls, making it is susceptible to either over- or under-segmentation. The upper airway was split into two: the nasal cavity and the pharyngeal region (nasopharynx to larynx). Each part was segmented using global thresholding, multi-step level-set, and region competition methods (the latter using thresholding, clustering and classification initialisation and edge attraction techniques). The segmented 3D surfaces were evaluated against a reference manual segmentation using distance-, overlap- and volume-based metrics. Global thresholding, multi-step level-set, and region competition all gave satisfactory results for the lower part of the airway (nasopharynx to larynx). Edge attraction failed completely. A semi-automatic region-growing segmentation with multi-thresholding (or classification) initialization offered the best quality segmentation. With some minimal manual editing, it resulted in an accurate upper airway model, as judged by the similarity and volumetric indices, while being the least time consuming of the semi-automatic methods, and relying the least on the operator’s expertise.展开更多
目的:探讨CBCT在正畸临床诊断和治疗中的应用。方法:选取深圳市儿童医院口腔正畸科就诊的正畸患者25例(男12例,女13例),年龄11～16岁,平均13.8岁。入选病例均是因采用传统平片无法确诊,需要拍摄CBCT来辅助诊断和治疗的病例。CBCT影像使...目的:探讨CBCT在正畸临床诊断和治疗中的应用。方法:选取深圳市儿童医院口腔正畸科就诊的正畸患者25例(男12例,女13例),年龄11～16岁,平均13.8岁。入选病例均是因采用传统平片无法确诊,需要拍摄CBCT来辅助诊断和治疗的病例。CBCT影像使用NEW TOM VGi(QRs.r.l.Corp,Verona,Italy)大视野锥形束计算机体层X线摄影机拍摄,采用NEW TOMNNT软件进行图像的重建和分析。结果:CBCT可以清晰的显示阻生牙、异位牙的牙根形态、长度,弯曲牙根与牙长轴所成的角度;三维显示异位牙与邻牙的空间位置关系,邻牙的牙根吸收情况;上颌后牙牙根与上颌窦的位置关系等。结论:CBCT三维重建技术可大大提高正畸病例诊断的准确性,同时也为治疗计划的制定和实施提供了有利指导。展开更多
基金Digital Medical Equipment Research and Development Project,Ministry of Science and Technology,China:The development of Synchrotron-based proton therapy system(2016YFC0105400).
文摘Cone-beam computed tomography(CBCT) is mostly used for position verification during the treatment process. However,severe image artifacts in CBCT hinder its direct use in dose calculation and adaptive radiation therapy re-planning for proton therapy. In this study, an improved U-Net neural network named CBAM-U-Net was proposed for CBCT noise reduction in proton therapy, which is a CBCT denoised U-Net network with convolutional block attention modules. The datasets contained 20 groups of head and neck images. The CT images were registered to CBCT images as ground truth. The original CBCT denoised U-Net network, sCTU-Net, was trained for model performance comparison. The synthetic CT(SCT) images generated by CBAM-U-Net and the original sCTU-Net are called CBAM-SCT and U-Net-SCT images, respectively. The HU accuracies of the CT, CBCT, and SCT images were compared using four metrics: mean absolute error(MAE), root mean square error(RMSE), peak signal-to-noise ratio(PSNR), and structure similarity index measure(SSIM). The mean values of the MAE, RMSE, PSNR, and SSIM of CBAM-SCT images were 23.80 HU, 64.63 HU, 52.27 dB, and 0.9919, respectively,which were superior to those of the U-Net-SCT images. To evaluate dosimetric accuracy, the range accuracy was compared for a single-energy proton beam. The γ-index pass rates of a 4 cm × 4 cm scanned field and simple plan were calculated to compare the effects of the noise reduction capabilities of the original U-Net and CBAM-U-Net on the dose calculation results. CBAM-U-Net reduced noise more effectively than sCTU-Net, particularly in high-density tissues. We proposed a CBAM-U-Net model for CBCT noise reduction in proton therapy. Owing to the excellent noise reduction capabilities of CBAM-U-Net, the proposed model provided relatively explicit information regarding patient tissues. Moreover, it maybe be used in dose calculation and adaptive treatment planning in the future.
文摘The wide availability, low radiation dose and short acquisition time of Cone-Beam CT (CBCT) scans make them an attractive source of data for compiling databases of anatomical structures. However CBCT has higher noise and lower contrast than helical slice CT, which makes segmentation more challenging and the optimal methods are not yet known. This paper evaluates several methods of segmenting airway geometries (nares, nasal cavities and pharynx) from typical dental quality head and neck CBCT data. The nasal cavity has narrow and intricate passages and is separated from the paranasal sinuses by thin walls, making it is susceptible to either over- or under-segmentation. The upper airway was split into two: the nasal cavity and the pharyngeal region (nasopharynx to larynx). Each part was segmented using global thresholding, multi-step level-set, and region competition methods (the latter using thresholding, clustering and classification initialisation and edge attraction techniques). The segmented 3D surfaces were evaluated against a reference manual segmentation using distance-, overlap- and volume-based metrics. Global thresholding, multi-step level-set, and region competition all gave satisfactory results for the lower part of the airway (nasopharynx to larynx). Edge attraction failed completely. A semi-automatic region-growing segmentation with multi-thresholding (or classification) initialization offered the best quality segmentation. With some minimal manual editing, it resulted in an accurate upper airway model, as judged by the similarity and volumetric indices, while being the least time consuming of the semi-automatic methods, and relying the least on the operator’s expertise.
文摘目的:探讨CBCT在正畸临床诊断和治疗中的应用。方法:选取深圳市儿童医院口腔正畸科就诊的正畸患者25例(男12例,女13例),年龄11～16岁,平均13.8岁。入选病例均是因采用传统平片无法确诊,需要拍摄CBCT来辅助诊断和治疗的病例。CBCT影像使用NEW TOM VGi(QRs.r.l.Corp,Verona,Italy)大视野锥形束计算机体层X线摄影机拍摄,采用NEW TOMNNT软件进行图像的重建和分析。结果:CBCT可以清晰的显示阻生牙、异位牙的牙根形态、长度,弯曲牙根与牙长轴所成的角度;三维显示异位牙与邻牙的空间位置关系,邻牙的牙根吸收情况;上颌后牙牙根与上颌窦的位置关系等。结论:CBCT三维重建技术可大大提高正畸病例诊断的准确性,同时也为治疗计划的制定和实施提供了有利指导。
