Factors affecting patient movement and re-exposure in cone beam computed tomography examination

Spin‐Neto, Rubens; Matzen, Louise Hauge; Schropp, Lars; Gotfredsen, E; Wenzel, Ann

doi:10.1016/j.oooo.2015.01.011

Cited by 46 publications

(81 citation statements)

References 21 publications

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“…17 CBCT examinations were performed using a Scanora ® 3D unit (Soredex Oy, Tuusula, Finland) by one of the three experienced operators (radiographic technicians). In this unit, patients are seated, with a chinrest used to stabilize the mandible and two vertical plastic bars (one in each side) to support the position of the head.…”

Section: Basic Study Populationmentioning

confidence: 99%

“…To fit the CBCT examination protocol of the Section of Oral Radiology, Department of Dentistry, Aarhus University, the patients were video-recorded during CBCT examination as previously described. 17,18 Briefly, a high-definition camera (Canon Legria HSF21, Canon, Japan) was located on each side (right and left) of the patient, at 45°i n relation to the patient and with approximately 1-m distance to the patient's face. These videos were later cropped to fit the examination time (in which the CBCT arm containing the X-ray tube moves around the patient), synchronized (to show the exact same period of the examination and to last exactly the same time) and saved as audio video interleaved files, keeping the native resolution and speed.…”

Section: Basic Study Populationmentioning

confidence: 99%

“…17 In this way, one can speculate that the group of patients, who are more susceptible to ionizing radiation, is also the group in which patient motion artefacts could be more often present, leading to a re-exposure. 17 Until now, no studies have assessed the relationship between the various types and characteristics (number, duration, complexity and distance) of patient movement and the relationship between these characteristics and the severity of degradation in CBCT image quality. An understanding of this relationship could be used to define "thresholds" for when these movements are nocuous to image quality.…”

Section: Introductionmentioning

confidence: 99%

“…[13][14][15] The impact of patient motion artefacts on the various diagnostic tasks performed based on CBCT imaging is yet unknown. 16,17 If patient motion artefacts are present in an image, leading to compromised image quality, a re-exposure of the patient may be necessary. A recent study 17 has shown that being a child (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…16,17 If patient motion artefacts are present in an image, leading to compromised image quality, a re-exposure of the patient may be necessary. A recent study 17 has shown that being a child (i.e. younger than 16 years) is the major factor related to patient movement.…”

Section: Introductionmentioning

confidence: 99%

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Movement characteristics in young patients and the impact on CBCT image quality

Spin‐Neto¹,

Matzen²,

Schropp³

et al. 2016

Dentomaxillofacial Radiology

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Objectives: To assess patient movement characteristics in children and young adults and the impact on CBCT image quality. Methods: During 33 CBCT examinations, the patients (age: average, 14 years; range, 9-25 years) who had moved were identified by video observation [movement group (MG)]. The CBCT data sets were matched with those of 33 non-moving patients according to age, diagnostic task, examined region, field of view and voxel resolution [non-movement group (N-MG)]. Three observers scored the videos of MG, regarding motional state second by second (moving/non-moving), and movement characteristics: duration (in seconds), complexity (uniplanar or multiplanar) and distance (,3/$3 # 10/.10 mm). The observers blindly assessed axial sections of the 66 examinations individually, categorizing the image quality (appropriate/acceptable/inappropriate). Next, the observers blindly assessed axial sections of the matched-pairs images simultaneously, deciding which image in the pair had the highest image quality or if it was impossible to decide. The relationship between image quality and movement/movement characteristics was evaluated. Results: When the 66 CBCT images were evaluated individually, no relationship between image quality and movement was found. However, based on the matched-pairs assessment, accumulated number (#2 vs $3, p 5 0.039), duration (#5 s vs $6 s, p 5 0.024) and complexity (uniplanar vs multiplanar, p 5 0.046) of movements had an impact on image quality; the more severe the movement, the more often the image quality was assessed lower in the MG. Conclusions: Axial CBCT images of young patients who moved during examination did not always present lower quality than images originating from non-moving patients. Image quality was, however, significantly lower in the moving patients when movement occurred several times, had a long duration or was multiplanar.

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Section: Basic Study Populationmentioning

confidence: 99%

Section: Basic Study Populationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Movement characteristics in young patients and the impact on CBCT image quality

Spin‐Neto¹,

Matzen²,

Schropp³

et al. 2016

Dentomaxillofacial Radiology

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View full text Add to dashboard Cite

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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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Motion correction for separate mandibular and cranial movements in cone beam CT reconstructions

et al. 2023

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Background: Patient motions are a repeatedly reported phenomenon in oral and maxillofacial cone beam CT scans, leading to reconstructions of limited usability. In certain cases, independent movements of the mandible induce unpredictable motion patterns. Previous motion correction methods are not able to handle such complex cases of patient movements. Purpose: Our goal was to design a combined motion estimation and motion correction approach for separate cranial and mandibular motions, solely based on the 2D projection images from a single scan. Methods: Our iterative three-step motion correction algorithm models the two articulated motions as independent rigid motions. First of all, we segment cranium and mandible in the projection images using a deep neural network. Next, we compute a 3D reconstruction with the poses of the object's trajectories fixed. Third, we improve all poses by minimizing the projection error while keeping the reconstruction fixed.Step two and three are repeated alternately. Results: We find that our marker-free approach delivers reconstructions of up to 85% higher quality, with respect to the projection error, and can improve on already existing techniques, which model only a single rigid motion. We show results of both synthetic and real data created in different scenarios. The reconstruction of motion parameters in a real environment was evaluated on acquisitions of a skull mounted on a hexapod, creating a realistic, easily reproducible motion profile. Conclusions: The proposed algorithm consistently enhances the visual quality of motion impaired cone beam computed tomography scans, thus eliminating the need for a re-scan in certain cases, considerably lowering radiation dosage for the patient. It can flexibly be used with differently sized regions of interest and is even applicable to local tomography.

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Factors affecting patient movement and re-exposure in cone beam computed tomography examination

Cited by 46 publications

References 21 publications

Movement characteristics in young patients and the impact on CBCT image quality

Movement characteristics in young patients and the impact on CBCT image quality

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

Motion correction for separate mandibular and cranial movements in cone beam CT reconstructions

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