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

Abstract

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 marker͒ position and motion ͑inhalation or exhalation͒ 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 ͑RCCT͒ images acquired prior to the treatment session. The CBCT acquisition is a single 360°rotation lasting 1 min, while simultaneously recording patient breathing. The approximately 600 projection images are sorted into six ͑in thorax͒ or ten ͑in abdomen͒ 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...

Cone beam CT image artefacts related to head motion simulated by a robot skull: visual characteristics and impact on image quality

“…[8][9][10][11][12][13][14] One study model was related to respiratory motion. 9,11,13,17 The fundamental aspect of this respiratory motion artefact correction model is that it requires a respiratory signal, which provides a temporal guideline to the acquisition of images, allowing a correction for the problem during image reconstruction. 13,17 A similar approach might be possible for oral and maxillofacial CBCT imaging if we can obtain more detailed information on how (and if) patients move during the examination.…”

“…9,11,13,17 The fundamental aspect of this respiratory motion artefact correction model is that it requires a respiratory signal, which provides a temporal guideline to the acquisition of images, allowing a correction for the problem during image reconstruction. 13,17 A similar approach might be possible for oral and maxillofacial CBCT imaging if we can obtain more detailed information on how (and if) patients move during the examination. The suggested selection of motion sets was based on the movement of the C-arm of the CBCT units and the effect that this might cause in the patients because of fear or subconscious reflections.…”

Four‐dimensional cone beam CT reconstruction and enhancement using a temporal nonlocal means method

“…It has also been investigated to enhance the CBCT image by first deforming images at all phases into a single one and superimposing them together. 11,12 The efficacy of these approaches, however, largely depends on the accuracy of the algorithms involved, such as deformable image registration algorithms.…”

Evaluating the four-dimensional cone beam computed tomography with varying gantry rotation speed

“…Respiratory motion creates artefacts in CBCT images, such as blurring, doubling, streaking and distortions, which greatly degrade the image quality and hence affect the target localization accuracy. [17][18][19] Nowadays, the four-dimensional CT (4DCT) scan is increasingly used in clinics for radiotherapy planning of thoracic and upperabdomen sites 20,21 owing to its superiority in eliminating the respiration-induced artefacts, which also enables a more accurate description of tumour motion. For a true four-dimensional (4D) treatment, ideally the verification imaging should also include a fourth dimension.…”