Correction of patient motion in cone-beam CT using 3D–2D registration

Ouadah, S; Jacobson, M.; Stayman, J. Webster; Ehtiati, Tina; Weiss, Clifford R.; Siewerdsen, J. H.

doi:10.1088/1361-6560/aa9254

Cited by 28 publications

(19 citation statements)

References 34 publications

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“…In particular CBCT suffers from strong movement influences due to long scanning times ranging between approx. 5 and 60 s . The amount of significant patient motion was estimated to be at least 20% .…”

Section: Introductionmentioning

confidence: 99%

Projection‐based improvement of 3D reconstructions from motion‐impaired dental cone beam CT data

et al. 2019

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Purpose Computed tomography (CT) and, in particular, cone beam CT (CBCT) have been increasingly used as a diagnostic tool in recent years. Patient motion during acquisition is common in CBCT due to long scan times. This results in degraded image quality and may potentially increase the number of retakes. Our aim was to develop a marker‐free iterative motion correction algorithm that works on the projection images and is suitable for local tomography. Methods We present an iterative motion correction algorithm that allows the patient's motion to be detected and taken into account during reconstruction. The core of our method is a fast GPU‐accelerated three‐dimensional reconstruction algorithm. Assuming rigid motion, motion correction is performed by minimizing a pixel‐wise cost function between all captured x‐ray images and parameterized projections of the reconstructed volume. Results Our method is marker‐free and requires only projection images. Furthermore, it can deal with local tomography data. We demonstrate the effectiveness of our approach on both simulated and real motion‐beset patient images. The results show that our new motion correction algorithm leads to accurate reconstructions with sharper edges, better contrasts and more detail. Conclusions The presented method allows for correction of patient motion with observable improvements in image quality compared to uncorrected reconstructions. Potentially, this may reduce the number of retakes caused by corrupted reconstructions due to patient movements.

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Section: Introductionmentioning

confidence: 99%

Projection‐based improvement of 3D reconstructions from motion‐impaired dental cone beam CT data

et al. 2019

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“…Recently, image reconstruction algorithms have taken patient movements into account during the volume reconstruction process and suggested software‐based, automated motion‐artefact correction (Ouadah et al . 2016, Spin‐Neto & Wenzel 2016, Spin‐Neto et al . 2018a,b, Niebler et al .…”

Section: Discussionmentioning

confidence: 99%

“…2015, Ouadah et al . 2016). Whilst the first is highly dependent on the accuracy of the fiducial‐tracking system, the second may be affected by the innate voxel value variation present in CBCT data sets, leading to inaccuracies in movement tracking (Horn & Schunck 1981, Barron & Khurana 1997, Spin‐Neto et al .…”

Section: Discussionmentioning

confidence: 99%

Impact of motion artefacts and motion‐artefact correction on diagnostic accuracy of apical periodontitis in CBCT images: an ex vivo study in human cadavers

et al. 2020

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AimTo assess the impact of motion artefacts and motion‐artefact correction on diagnostic accuracy of apical periodontitis (AP) in CBCT images.MethodologyBased on clinical and radiographic inspection of 40 formalin‐fixated human jaw specimens, 77 roots in 45 teeth (molars and premolars), with various disease and treatment state, were selected. The specimens were mounted on a robot simulating 3‐mm movement types (nodding, lateral rotation and tremor). CBCT images with and without (controls) movements were acquired in four CBCT units: without motion‐artefact correction in Cranex 3Dx, Orthophos SL 3D, and Promax 3D Mid, and with motion‐artefact correction in Promax 3D Mid and X1. Three observers blindly assessed (i) whether the images were interpretable and (ii) if AP was present (5‐step probability index). Histopathology provided the reference standard for presence of AP. Weighted Kappa statistics described inter‐observer agreement. Estimates of diagnostic accuracy were assessed by means of receiver operator characteristic (ROC) curve analysis. Area under the curve (AUC) provided a measure of accuracy, and paired‐sample AUC difference tests compared differences amongst the CBCT units and movement types.ResultsObserver agreement was substantial for control images, moderate for motion‐artefact corrected images and fair for images without motion‐artefact correction. When movement was present, motion‐artefact correction reduced the percentage of images scored as noninterpretable or with uncertain disease state (score 3 in the 5‐step probability index). Control images were not perfectly accurate (both false‐positive and false‐negative results were present; AUC 0.750–0.799). Images acquired with movement and without motion‐artefact correction (AUC 0.541–0.709) were associated with significantly lower accuracy than control images (P < 0.05). With motion‐artefact correction, accuracy was comparable to that observed in control images (AUC 0.732–0.790).ConclusionsDiagnostic accuracy of apical periodontitis in CBCT images was dependent on the presence of motion artefacts (i.e. lower accuracy associated with the presence of movement). Motion‐artefact correction systems positively influenced image interpretability and diagnostic accuracy.

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“…Differences in spatiotemporal sampling between CT scanner designs lead to important differences in image quality, particularly in the presence of patient motion, which interacts dynamically with sampling. The effects of patient motion on CT image quality have been investigated in many previous studies for both diagnostic FBCT 6–9 and interventional CBCT 10–13 . Active motion compensation strategies that seek to eliminate motion are also used routinely for radiotherapy planning and delivery, including deep inspiration breathe hold, 14,15 abdominal compression, 16 and gating 17 .…”

Section: Introductionmentioning

confidence: 99%

“…The effects of patient motion on CT image quality have been investigated in many previous studies for both diagnostic FBCT [6][7][8][9] and interventional CBCT. [10][11][12][13] Active motion compensation strategies that seek to eliminate motion are also used routinely for radiotherapy planning and delivery, including deep inspiration breathe hold, 14,15 abdominal compression, 16 and gating. 17 However, these strategies are associated with other operational challenges and uncertainties and may not be consistently employed for motion below ±2.5 mm (5-mm peak-to-peak).…”

Section: Introductionmentioning

confidence: 99%

Effect of subject motion and gantry rotation speed on image quality and dose delivery in CT‐guided radiotherapy

et al. 2022

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To investigate the effects of subject motion and gantry rotation speed on computed tomography (CT) image quality over a range of image acquisition speeds for fan-beam (FB) and cone-beam (CB) CT scanners, and quantify the geometric and dosimetric errors introduced by FB and CB sampling in the context of adaptive radiotherapy. Methods: Images of motion phantoms were acquired using four CT scanners with gantry rotation speeds of 0.5 s/rotation (denoted FB-0.5), 1.9 s/rotation (FB-1.9), 16.6 s/rotation (CB-16.6), and 60.0 s/rotation (CB-60.0). A phantom presenting various tissue densities undergoing motion with 4-s period and ranging in amplitude from ±0.5 to ±10.0 mm was used to characterize motion artifacts (streaks), motion blur (edge-spread function, ESF), and geometric inaccuracy (excursion of insert centroids and distortion of known shape). An anthropomorphic abdomen phantom undergoing ±2.5-mm motion with 4-s period was used to simulate an adaptive radiotherapy workflow, and relative geometric and dosimetric errors were compared between scanners. Results: At ±2.5-mm motion, phantom measurements demonstrated mean ± SD ESF widths of 0.6 ± 0.0, 1.3 ± 0.4, 2.0 ± 1.1, and 2.9 ± 2.0 mm and geometric inaccuracy (excursion) of 2.7 ± 0.4, 4.1 ± 1.2, 2.6 ± 0.7, and 2.0 ± 0.5 mm for the FB-0.5, FB-1.9, CB-16.6, and CB-60.0 scanners, respectively. The results demonstrated nonmonotonic trends with scanner speed for FB and CB geometries. Geometric and dosimetric errors in adaptive radiotherapy plans were largest for the slowest (CB-60.0) scanner and similar for the three faster systems (CB-16.6, FB-1.9, and FB-0.5). Conclusions: Clinically standard CB-60.0 demonstrates strong image quality degradation in the presence of subject motion, which is mitigated through faster CBCT or FBCT. Although motion blur is minimized for FB-0.5 and FB-1.9, such systems suffer from increased geometric distortion compared to CB-16.6. Each system reflects tradeoffs in image artifacts and geometric inaccuracies that affect treatment delivery/dosimetric error and should be considered in the design of next-generation CT-guided radiotherapy systems.

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Correction of patient motion in cone-beam CT using 3D–2D registration

Cited by 28 publications

References 34 publications

Projection‐based improvement of 3D reconstructions from motion‐impaired dental cone beam CT data

Projection‐based improvement of 3D reconstructions from motion‐impaired dental cone beam CT data

Impact of motion artefacts and motion‐artefact correction on diagnostic accuracy of apical periodontitis in CBCT images: an ex vivo study in human cadavers

Effect of subject motion and gantry rotation speed on image quality and dose delivery in CT‐guided radiotherapy

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