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Correction of motion artifacts in cone-beam CT using a patient-specific respiratory motion model.

机译:使用特定于患者的呼吸运动模型校正锥形束CT中的运动伪影。

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PURPOSE: Respiratory motion adversely affects CBCT image quality and limits its localization accuracy for image-guided radiation treatment. Motion correction methods in CBCT have focused on the thorax because of its higher soft tissue contrast, whereas low-contrast tissue in abdomen remains a challenge. The authors report on a method to correct respiration-induced motion artifacts in 1 min CBCT scans that is applicable in both thorax and abdomen, using a motion model adapted to the patient from a respiration-correlated image set. METHODS: Model adaptation consists of nonrigid image registration that maps each image to a reference image in the respiration-correlated set, followed by a principal component analysis to reduce errors in the nonrigid registration. The model parametrizes the deformation field in terms of observed surrogate (diaphragm or implanted marker) position and motion (inhalation or exhalation) between the images. In the thorax, the model is obtained from the same CBCT images that are to be motion-corrected, whereas in the abdomen, the model uses respiration-correlated CT (RCCT) images acquired prior to the treatment session. The CBCT acquisition is a single 360 degrees rotation lasting 1 min, while simultaneously recording patient breathing. The approximately 600 projection images are sorted into six (in thorax) or ten (in abdomen) subsets and reconstructed to obtain a set of low-quality respiration-correlated RC-CBCT images. Application of the motion model deforms each of the RC-CBCT images to a chosen reference image in the set; combining all images yields a single high-quality CBCT image with reduced blurring and motion artifacts. Repeated application of the model with different reference images produces a series of motion-corrected CBCT images over the respiration cycle, for determining the motion extent of the tumor and nearby organs at risk. The authors also investigate a simpler correction method, which does not use PCA and correlates motion state with respiration phase, thus assuming repeatable breathing patterns. Comparison of contrast-to-noise ratios of pixel intensities within anatomical structures relative to surrounding background tissue provides a quantitative assessment of relative organ visibility. RESULTS: Evaluation in lung phantom, two patient cases in thorax and two in upper abdomen, shows that blurring and streaking artifacts are visibly reduced with motion correction. The boundaries of tumors in the thorax, liver, and kidneys are sharper and more discernible. Repeat application of the method in one thorax case, with reference images chosen at end expiration and end inspiration, indicates its feasibility for observing tumor motion extent. Phase-based motion correction without PCA reduces blurring less effectively; in addition, implanted markers appear broken up, indicating inconsistencies in the phase-based correction. In structures showing 1 cm or more motion excursion, PCA-based motion correction shows the highest contrast-to-noise ratios in the cases examined. CONCLUSIONS: Motion correction of CBCT is feasible and yields observable improvement in the thorax and abdomen. The PCA-based model is an important component: First, by reducing deformation errors caused by the nonrigid registration and second, by relating deformation to surrogate position rather than phase, thus accommodating breathing pattern changes between imaging sessions. The accuracy of the method requires confirmation in further patient studies.
机译:目的:呼吸运动对CBCT图像质量产生不利影响,并限制了其在图像引导放射治疗中的定位精度。 CBCT中的运动校正方法由于其较高的软组织对比度而集中在胸腔,而腹部的低对比度组织仍然是一个挑战。作者报告了一种方法,该方法使用适合于患者的与呼吸相关的图像集中的运动模型,在1分钟的CBCT扫描中校正呼吸引起的运动伪影,该方法适用于胸部和腹部。方法:模型适应包括非刚性图像配准,该配准将每个图像映射到与呼吸相关的集合中的参考图像,然后进行主成分分析以减少非刚性配准中的错误。该模型根据观察到的替代物(膜片或植入的标记)位置和图像之间的运动(吸气或呼气)对变形场进行参数化。在胸部,该模型是从要进行运动校正的相同CBCT图像中获得的,而在腹部,该模型则使用在治疗之前获取的与呼吸相关的CT(RCCT)图像。 CBCT采集是一个持续1分钟的360度旋转,同时记录了患者的呼吸情况。将大约600幅投影图像分类为六个(胸部)或十个(腹部)子集,并进行重构以获得一组低质量的与呼吸相关的RC-CBCT图像。运动模型的应用将每个RC-CBCT图像变形为集合中的选定参考图像。合并所有图像可生成具有减少的模糊和运动伪像的单个高质量CBCT图像。重复使用具有不同参考图像的模型,可以在呼吸周期内生成一系列经运动校正的CBCT图像,以确定肿瘤和附近有风险的器官的运动程度。作者还研究了一种更简单的校正方法,该方法不使用PCA,而是将运动状态与呼吸阶段相关联,从而假定了可重复的呼吸方式。相对于周围背景组织的解剖结构内像素强度的对比度噪声比的比较,提供了相对器官可见性的定量评估。结果:对肺部幻象进行评估,其中2例患者胸廓和2例上腹部病变,通过运动矫正明显减少了模糊和条纹痕迹。胸部,肝脏和肾脏中的肿瘤边界更加清晰可见。在一种胸腔情况下重复应用该方法,并在呼气末期和吸气末期选择参考图像,表明该方法可用于观察肿瘤运动程度。没有PCA的基于相位的运动校正无法有效地减少模糊;此外,植入的标记物似乎破裂,表明基于相位的校正不一致。在显示出1 cm或更多运动偏移的结构中,基于PCA的运动校正在所检查的情况下显示出最高的对比度噪声比。结论:CBCT的运动矫正是可行的,并且可以显着改善胸腔和腹部。基于PCA的模型是重要的组成部分:首先,通过减少由非刚性配准引起的变形误差,其次,通过将变形与替代位置而不是相位相关联,从而适应成像会话之间的呼吸模式变化。该方法的准确性需要进一步的患者研究证实。

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