Faculdade de Odontologia de Piracicaba
GUSTAVO MACHADO SANTAELLA
O impacto na qualidade de imagem e interpretabilidade com artefatos de movimento em exames de tomografia computadorizada de feixe cônico adquiridos com geometria total e
parcial de exposição
PIRACICABA 2019
O impacto na qualidade de imagem e interpretabilidade com artefatos de movimento em exames de tomografia computadorizada de feixe cônico adquiridos com geometria total e
parcial de exposição
Tese apresentada à Faculdade de Odontologia de Piracicaba da Universidade Estadual de Campinas como parte dos requisitos exigidos para a obtenção do título de Doutor em Radiologia Odontológica, na Área de Radiologia Odontológica
Orientador: Prof. Dr. Pedro Luiz Rosalen
Este trabalho corresponde à versão final da tese defendida pelo aluno Gustavo Machado Santaella, e orientada pelo Prof. Dr. Pedro Luiz Rosalen.
PIRACICABA 2019
- ORCID: 0000-0002-0884-2443
de Pessoal de Nível Superior – Brasil (CAPES) - Código de Financiamento 001.
Agradeço ao meu orientador Prof. Dr. Pedro Luiz Rosalen, que topou o desafio de orientar um aluno de uma outra área e o fez com maestria, dando liberdade e muito incentivo para que o trabalho fosse executado da forma como foi.
Aos professores Dr. Rubens Spin-Neto e Ann Wenzel, Dr. Odont., e à
Universidade de Aarhus, na Dinamarca, por me receberem tão bem e, principalmente, por
ajudarem e cederem a metodologia desenvolvida por eles para que esse trabalho pudesse ser desenvolvido. Com certeza a execução desse não seria possível sem essa colaboração.
À Universidade Estadual de Campinas, na pessoa do Magnífico Reitor Prof. Dr.
Marcelo Knobel.
À Faculdade de Odontologia de Piracicaba, na pessoa do Senhor Diretor Prof. Dr.
Francisco Haiter Neto.
Ao Programa de Pós-Graduação em Radiologia Odontológica da FOP-UNICAMP, na pessoa da Senhora Coordenadora Profa. Dra. Deborah Queiroz de Freitas França.
Aos meus pais, José Francisco e Maria de Fátima, por tudo que me proporcionaram nessa caminhada. Dificilmente essa jornada seria feita sem o apoio emocional e financeiro que tenho recebido, e serei eternamente grato por tudo.
Aos meus avós paternos e padrinhos Miguel e Maria de Lourdes que foram a minha família mais próxima nos últimos anos, sempre de portas abertas para me receber. Muito obrigado!
Aos meus avós maternos Sebastiana e José (que Deus o tenha), por todo o amor e apoio recebido mesmo à distância.
já realizados quanto os que estão ainda a serem alcançados.
Ao professor Dr. Francisco Haiter Neto, por estar sempre disposto a ajudar e pelo contato inicial com os professores da Dinamarca, que levou à realização deste trabalho.
Aos professores Dra. Deborah Queiroz de Freitas França e Dr. Matheus Lima de
Oliveira, por todo o esforço que fazem pelo programa e pelos alunos para que possamos
crescer juntos. Com certeza seremos sempre gratos por tudo que vocês nos proporcionam. Aos professores Dr. Frab Norberto Boscolo e Dra. Solange Maria de Almeida
Boscolo pelos valiosos ensinamentos compartilhados.
Aos professores Dra. Anne Caroline Costa Oenning, Dr. Claudio Costa, Dra.
Deborah Queiroz de Freitas França e Dr. Luiz Roberto Coutinho Manhães Junior pelas
contribuições dadas a esse trabalho no exame de defesa de tese de doutorado.
Aos professores Dr. Matheus Lima de Oliveira, Dr. Yuri Martins Costa e Dr. Yuri
Nejaim pelas considerações e colaborações dadas a este trabalho no exame de qualificação.
Aos funcionários da Área de Radiologia Odontológica, Luciane Sattolo, Fernando
Andrade, Waldeck Moreira e Sarah do Amaral Bacchim pela dedicação ao trabalho na
faculdade.
À grande amiga e colega Polyane Mazucatto Queiroz, vulgo “dupla de doutorado”, por todo o incentivo, apoio e troca de ideias nessa busca pelo título de Doutor. A ela devo muito do que conquistei cientificamente.
Aos amigos de turma de mestrado e doutorado Eliana Dantas Da Costa, Leonardo
Vieira Peroni, Luciana Jácome Lopes e Mayra Cristina Yamasaki por todo o companheirismo
nessa caminhada.
A todos os amigos do programa de Radiologia Odontológica e colegas de pós-graduação da FOP-UNICAMP, pelo tempo convivido e aprendizado compartilhado. Com certeza, de uma maneira ou de outra, vocês ajudaram muito nessa jornada.
contendo uma fonte e um detector de raios X. A forma como este feixe incide no detector define se a exposição é alinhada com o campo de visão (FOV) e o detector, ou se o detector é deslocado para um dos lados, configurando uma geometria parcial de exposição. É também bem descrito na literatura que movimentos realizados pelo paciente durante a aquisição destas imagens podem influenciar negativamente na imagem final, resultando na formação de artefatos. Além disso, novos aparelhos possuem algoritmos para correção destes artefatos de movimento. Desta forma, este estudo objetivou avaliar a aquisição de imagens com detector alinhado e deslocado e a influência na qualidade e na interpretabilidade de imagens de TCFC com artefatos de movimento. Além disso, testou-se a eficácia de dois métodos de redução de artefatos de movimento nas diferentes geometrias de exposição. Para isso, foram utilizados um fantoma de cera utilidade, um crânio humano afixado em um robô programado para executar diferentes movimentos, e diversos equipamentos de TCFC com protocolos de geometria total e parcial de exposição, como o Cranex 3Dx, Ortophos SL, Picasso Trio, Promax 3D Mid, Scanora 3D e X1. Dois desses equipamentos apresentavam ferramentas de redução de artefatos de movimento (Promax 3D Mid e X1). As imagens técnicas do fantoma foram avaliadas no software ImageJ por um único avaliador, onde foram obtidos a média e o desvio padrão dos valores de voxel de treze regiões de interesse em diferentes posições dentro do volume. As imagens clínicas com o crânio foram aleatorizadas e avaliadas por 3 avaliadores experientes no software OnDemand3D, onde foram descritas a presença de artefatos de movimento, perda de nitidez e interpretabilidade dessas imagens em três regiões de interesse. Quando comparados com protocolos com o detector alinhado, as imagens adquiridas por protocolos de TCFC com geometria parcial apresentaram variações na distribuição dos valores de voxel dentro do campo de visão, e os artefatos de movimento foram percebidos apenas parcialmente no campo visão, afetando principalmente as regiões sendo adquiridas no momento da movimentação. As ferramentas de redução de artefatos de movimentos testadas foram eficazes na interpretabilidade em 97,2% dos casos para protocolos de detector alinhado, porém para detectores deslocados essa eficácia foi menor (42,6%). Desta forma, a aquisição de imagens de TCFC utilizando uma geometria de exposição parcial pode alterar a distribuição dos valores de voxel dentro do FOV e afeta diretamente a forma como os artefatos de movimento se apresentam dentro da imagem e sua interpretabilidade em tarefas diagnósticas. Além disso, ela compromete a eficácia da ferramenta de compensação de artefatos de movimentos presente em um dos aparelhos testados (Promax 3D Mid).
Palavras-chave: tomografia computadorizada de feixe cônico, artefatos, intensificação de imagem radiográfica
source and a detector. The way the beam is exposed defines whether the exposure is aligned with the field of view (FOV) and the detector, or if the detector is offset to one side by configuring a partial exposure geometry. It is also well described in the literature that movements performed by the patient during the acquisition of these images can negatively influence the final image, resulting in the formation of artifacts. In addition, new devices have algorithms for correction of these movement artifacts. In this way, this study aimed to evaluate the acquisition of images with aligned and lateral-offset detectors and the influence on the quality and the interpretability of CBCT images with motion artefacts. In addition, the effectiveness of methods of reducing movement artefacts in different exposure geometries was tested. To do this, we used a utility wax phantom, and a human skull affixed in a robot programmed to perform different movements, and several CBCT equipment with aligned and partial geometry exposure protocols, such as Cranex 3Dx, Ortophos SL, Picasso Trio, Promax 3D Mid, Scanora 3D and X1. Two of these devices featured motion artefacts reduction tools (Promax 3D Mid and X1). The phantom images were evaluated in the ImageJ software by a single evaluator, where the mean and standard deviation of the voxel values of thirteen regions of interest were obtained at different positions within the volume. The clinical images with the skull were randomized and evaluated by 3 experienced evaluators in the software OnDemand3D, where they were described the presence of movement artifacts, loss of sharpness and interpretability of these images in three regions of interest. When compared with protocols with the aligned detector, the images acquired by protocols with an offset detector showed variations in the distribution of voxel values within the field of view, and the motion artefacts were only partially observed in the FOV, affecting mainly the regions being acquired at the moment of the movement. The artefact reduction tools tested were effective in interpretability in 97.2% of cases for aligned detector protocols, but for offset detectors this efficacy was lower (42.6%). Thus, the acquisition of CBCT images using a partial exposure geometry can alter the distribution of voxel values within the FOV and directly affects the way motion artefacts appear within the image and their interpretability in diagnostic tasks. In addition, it compromises the effectiveness of the motion artefact compensation tool present in one of the tested devices (Promax 3D Mid).
2 ARTIGOS...13
2.1ARTIGO:QUANTITATIVE ASSESSMENT OF CBCT IMAGE QUALITY VARIATION RELATED TO CBCT-DETECTOR LATERAL-OFFSET POSITION ...13
2.2ARTIGO:THE IMPACT OF MOVEMENT ON IMAGE QUALITY AND INTERPRETABILITY IN CBCT DEVICES WITH ALIGNED AND LATERAL-OFFSET DETECTORS ...27
3 DISCUSSÃO ...46
4 CONCLUSÃO ...48
REFERÊNCIAS* ...49
ANEXOS ...52
ANEXO 1–CARTA DE ISENÇÃO DE NECESSIDADE DE APROVAÇÃO EM COMITÊ DE ÉTICA (DINAMARCA) ...52
ANEXO 2–CARTA DE ISENÇÃO DE NECESSIDADE DE APROVAÇÃO EM COMITÊ DE ÉTICA (BRASIL) ...53
ANEXO 3–VERIFICAÇÃO DE ORIGINALIDADE E PREVENÇÃO DE PLÁGIO ...54
1 INTRODUÇÃO
A tomografia computadorizada de feixe cônico (TCFC), desenvolvida na década de 90 (Mozzo et al., 1998; Arai et al., 1999), se baseia no uso de um aparelho contendo uma fonte de raios X, que emite um feixe em formato cônico ou piramidal, e um detector de raios X. O paciente é posicionado entre essas duas partes, que rotacionam ao seu redor. Essa rotação pode ser total (360o) ou parcial (~180o - 210o) dependendo do aparelho e do protocolo de aquisição. O feixe resultante ao atingir o detector é convertido em diversas imagens base, que são imagens radiográficas bidimensionais. Estas, por sua vez, passam por um algoritmo e são reconstruídas em imagens axiais do paciente, para que possam ser avaliadas posteriormente em um software (Scarfe e Farman, 2008; Pauwels et al., 2015a).
As imagens digitais são formadas por pixels. Em imagens radiográficas, um pixel pode representar apenas uma tonalidade de cinza. Um fator importante em imagens digitais é a quantidade de tons de cinza que uma imagem pode apresentar, isto é, a quantidade de pixels com diferentes tons de cinza cada um. Por isso, diz-se que quanto mais tons de cinza disponíveis em uma imagem, representada pela profundidade de bits desta, diz-se que tem uma maior resolução de contraste. E quanto menor o tamanho de pixel da imagem, maior a sua resolução espacial, e com isso maior a capacidade de representação de detalhes de uma imagem. Em TCFC, após o processo de reconstrução das imagens, estas imagens axiais bidimensionais são sobrepostas por um software para serem avaliadas tridimensionalmente, e por isso passam a ser chamadas de voxel (Pauwels et al., 2012; Scarfe et al., 2017).
Antes da aquisição das imagens, um fator importante a ser determinado é o tamanho do FOV (field of view – campo de visão) que será utilizado. A área de interesse do exame deve estar contida dentro das imagens adquiridas. Para isso, pode-se optar por um volume maior, contendo toda a região de região de interesse, ou mais de um volume com FOV menores (Pauwels et al., 2015a). Para aquisição de volumes maiores, o tamanho do detector pode ser um fator limitante, pois é necessário que toda informação a ser reconstruída esteja contida nas projeções base para que seja possível a reconstrução. Porém, como uma forma de se obter FOV maiores com a utilização de receptores menores, o equipamento pode ter um deslocamento do feixe de projeção e do detector para fora do eixo central do FOV (Figuras 1 e 2). Desta forma, durante uma aquisição com 360o de rotação, o centro do FOV é irradiado
durante toda a rotação, enquanto que as periferias por apenas metade da rotação (Scarfe e Farman, 2008; Molteni, 2013; White e Pharoah, 2013).
Figura 1. Esquema de aquisição em projeção total (esquerda) e parcial (direita) com um
detector de mesmo tamanho. (Fonte: Scarfe e Farman, 2008)
Figura 2. Aquisição em geometria parcial para o equipamento NewTom VGi. (Fonte: Molteni,
2013)
O processo de reconstrução das imagens base em axiais vem de um algoritmo conhecido como retroprojeção filtrada, que foi adaptado para imagens obtidas por meio de feixe cônico por Feldkamp, Davis e Kress (Feldkamp et al., 1984; Schulze et al., 2011). Essa fórmula exige que o objeto a ser reconstruído se mantenha estático durante toda aquisição das imagens, para que os artefatos na imagem reconstruída sejam reduzidos (Schulze et al., 2011).
Artefatos são alterações na imagem que não representam corretamente o objeto escaneado. São resultado de discrepâncias entre o processo de aquisição das imagens base e o algoritmo utilizado para reconstrução matemática. Dentre os diferentes tipos de artefatos observados nas imagens de TCFC, temos os de ausência de sinal, endurecimento do feixe de
radiação, efeito do volume parcial, subamostragem, em anel e os de movimento (Barrett e Keat, 2004; Schulze et al., 2011).
Os artefatos de movimento são causados por um movimento do paciente durante a aquisição das imagens base, o que leva a um erro geométrico de exposição. Desta forma, viola-se o princípio da retroprojeção filtrada, onde o objeto deve permanecer completamente estático e alinhado entre as múltiplas projeções base. Com isso há uma perda de nitidez, ou uma informação duplicada nas imagens reconstruídas (Schulze et al., 2011; Spin-Neto et al., 2013).
Alguns métodos foram desenvolvidos para detectar movimentos durante a aquisição. Estes podem ser pela utilização de câmeras de precisão que percebem movimentações, ou por dispositivos com acelerômetro fixados ao paciente (Spin-Neto et al., 2017b), ou pela detecção dos movimentos a partir da avaliação das imagens base (Schulze et al., 2015). Com isso, alguns algoritmos conseguem implementar correções para estes movimentos durante a reconstrução dessas imagens, resultando em imagens com qualidade adequada para interpretação em algumas tarefas diagnósticas (Spin-Neto et al., 2018b).
Sabendo da prevalência desses tipos de artefatos, o quanto comprometem a imagem e da relação direta desses com fatores geométricos de aquisição e reconstrução da imagem tomográfica, é necessário avaliar aspectos que possam afetar na formação desses artefatos em imagens de diferentes equipamentos de TCFC. Os objetivos do presente estudo são avaliar a influência da aquisição de imagens de TCFC em geometria parcial ou total na qualidade e na interpretabilidade de imagens adquiridas com diferentes tipos de movimentação em momentos distintos durante o exame. Além disso, testou-se a eficácia de métodos de redução de artefatos de movimento nas diferentes geometrias de exposição.
2 ARTIGOS
2.1 Artigo: Quantitative assessment of CBCT image quality variation related to CBCT-detector lateral-offset position
Authors: Gustavo Machado Santaella, Pedro Luiz Rosalen, Polyane Mazucatto Queiroz,
Francisco Haiter-Neto, Ann Wenzel, Rubens Spin-Neto
Abstract
Objectives: To assess the effect of CBCT detector position (aligned or lateral-offset) on image
quality parameters, by the mean voxel value difference/MVVD and standard deviation of voxel value/SDVV.
Methods:Forty CBCT volumes of a cylindrical utility wax phantom centralized in the field-of-view (FOV) were acquired in six units with aligned and offset detectors: Cranex 3Dx (CRA), Ortophos SL (ORT), Picasso Trio (PIC), Promax 3D Mid (PRO), Scanora 3D (SCA), and X1. Eight image-acquisition protocols were selected to provide four protocols with the detector aligned (CRA, ORT, PRO, X1), and four protocols with the offset detector (CRA, PIC, PRO, SCA). In all volumes, thirteen regions-of-interest (ROIs) inside the FOV were evaluated and MVVD (the percentage of difference to the central ROI) and SDVV were obtained.
Results:MVVD for units with aligned detectors ranged from -32.8% to 22.8%, and from -20.7% to 69.5% for units with offset detectors. SDVV for most aligned detectors was lower near the FOV centre, while for the units with offset detectors it was lower for the peripheral ROIs, except for one unit (PIC).
Conclusion: The use of an offset detector to acquire CBCT images lead to increased MVVD
ranges and modified SDVV distribution inside the FOV compared to the use of an aligned detector.
Keywords: cone beam computed tomography, artefacts, radiographic image enhancement
Introduction
Cone beam computed tomography (CBCT) volumes are acquired using an X-ray source and a detector that rotates around the patient, acquiring multiple two-dimensional images, often called basis images (or raw projections), that are posteriorly reconstructed into a three-dimensional volume.1–3 Due to the divergence of the X-rays, the cone- or pyramidal-shaped beam1 emitted by the source hits the detector (i.e. image receptor) with different angulations. The central beam is perpendicular (90º) to the detector and increases the accuracy of the method used for image reconstruction,4 while the peripheral beams have more acute angles, which cause variations in the reconstructed image and may degrade image quality (i.e. introduction of noise and artefacts).1,5,6
Multiple studies have been conducted considering the variations in voxel values depending on the position of the patient/object in the field-of–view (FOV).7–11 However, little attention has been given on how the basis images are acquired, focusing mainly on the final reconstructed volume. This can be related to the fact that data available for these projections is rarely provided by the manufacturers, and to difficulties accessing such images. One can speculate that the central region in the volume (i.e. its midplane), is reconstructed based on the region where the central X-ray beam hits the detector perpendicularly, while the edges of the volume are related to the peripheral beams. But this is not always the case. Some manufacturers do use a detector aligned with the X-ray source, in which the central beam is aligned with the central part of the detector and with the midplane of the FOV. This results in basis images in which the entire FOV is seen, in all images. In a 15x13 cm FOV, for example, the detector must have dimensions larger than those, considering the divergence of the X-ray beam and the distance between the patient and the detector. There is a limiting factor, however, mostly related to the costs of the unit, and the image detector is not always large enough to fit the entire FOV, both in width and height.
Two methods have been developed to acquire FOVs that are actually larger than the detector. One is volume stitching,2,12 in which multiple cylindrical volumes are acquired, reconstructed, and then stitched together, and the other is the use of offset detectors.1,6 In this method, during the acquisition of the basis images, the detector is horizontally offset, and not centrally aligned with the FOV (Figure 1). Only the central part of the FOV is present in all basis images, and this area is used by the algorithm to blend the images during reconstruction,
while the peripheral regions of the FOV are present in just some projections.6 When a lateral offset detector is used, the resulting volume is a single cylinder, not based on stitched volumes.
Figure 1. Schematic representation of aligned (A) and lateral-offset (B and C) detector setups. In
(A) the central X-ray beam passes through the centre of the FOV and hits the central part of the detector. In (B), it does not pass the centre of the FOV, but still hits the central part of the detector. In (C) it passes the centre of the FOV but hits the edge of the detector.
The aim of the present study was to assess two image quality parameters (mean voxel value difference/MVVD and standard deviation of voxel values/SDVV), in multiple regions-of-interest (ROIs) of CBCT images acquired using units with aligned and laterally offset detectors.
Methods and materials
Phantom
A cylindrical phantom developed and described in a previous study8 was used. The phantom (depicted in figure 2) was made of utility wax, had 98 mm in diameter and a height of 50 mm, and a cylindrical metal alloy (aluminium-copper) sample (5 mm in diameter and 5 mm in height) at the centre.
Figure 2. Utility wax phantom used to acquire the volumes.
Image acquisition
Six CBCT units were used: Cranex 3Dx (CRA, Soredex Oy, Finland), Ortophos SL (ORT, Sirona Dental Systems GmbH, Germany), Picasso Trio (PIC, Vatech, South Korea), Promax 3D Mid (PRO, Planmeca Oy, Finland), Scanora 3D (SCA, Soredex Oy, Finland), and X1 (3Shape, Denmark). Eight image-acquisition protocols were selected, providing four protocols with an aligned detector, and four with an offset detector. CRA and PRO were able to acquire both aligned and offset setups. Image protocols are presented in Table 1.
Table 1. CBCT units and protocol used for volume acquisition
Name Field-of-view
(cm)
Detector position (offset type)
Voxel size
(mm) kVp mA
Cranex 3Dx (CRA) 8 x 8 Aligned 0.20 89.8 6.0
Cranex 3Dx (CRA) 15 x 8 Offset (2 partial rotations) 0.25 89.8 5.0
Orthophos SL (ORT) 11 x 10 Aligned 0.16 85.0 6.0
Picasso Trio (PIC) 12 x 8.5 Offset (360°) 0.20 80.0 3.7
ProMax 3D Mid (PRO) 10 x 10 Aligned 0.15 90.0 10.0
ProMax 3D Mid (PRO) 16 x 10 Offset (360°) 0.20 90.0 10.0
Scanora 3D (SCA) 10 x 8 Offset (360°) 0.30 90.0 13.0
X1 8 x 8 Aligned 0.15 90.0 12.0
To acquire the images, the phantom was positioned centralized in the FOV, and the position was not changed between acquisitions. If available in the unit, tools for metal artefact correction were de-activated. The X1 required the use of the motion tracking device,13 and
therefore it was set-up above the phantom and the X-ray beam trajectory. The motion correction tool was active in X1, since it was a requirement for image acquisition.
For each unit and for each protocol, five image volumes were acquired. The basis images of each acquisition were obtained to confirm the centralized position of the phantom within the FOV and the position of the detector in relation to the FOV (Figure 3).
Figure 3. Single basis image of each protocol used to show the alignment or offset of
images acquired before the reconstruction.
Data management
The acquired CBCT volumes were exported as “digital imaging and communications in medicine” (DICOM) multi-files. Three axial sections of each volume were selected. The central axial section of the metal cylinder included in the phantom was used for ROI determination, and two sections, one 5 mm above (“upper”) and one 5 mm below (“lower”) the metal cylinder were selected for further evaluation.
To select the ROIs for evaluation, in the central axial section showing the metal sample, a central quadrangular ROI (6x6 mm) was chosen around the sample. Based on this ROI, 12 other ROIs were selected in four directions around the metal sample (left, right, anterior, and posterior) and in three distances from the central ROI (touching the central ROI – Near; 15 mm from the central ROI – Middle; and 30 mm from the central ROI – Far) for the upper and lower axial section as shown in Figure 4. The ROIs were grouped into latero-lateral (LL) and antero-posterior (AP) samples.
Figure 4. Axial sections of PIC in the centre of the metal sample used for
ROI selection, and above the metal sample used for evaluation.
To calculate MVVD and SDVV, the mean and the standard deviation of voxel values for each ROI was measured using ImageJ 1.52e (U.S. National Institutes of Health, Bethesda, Maryland, USA), in the “upper” and the “lower” sections. The values were then grouped according to region for each unit: LL Far, LL Middle, LL Near, Central, AP Near, AP Middle, and AP Far. For each of these seven groups, there were 20 values (2 ROIs x 2 sections x 5 volumes) per unit, while there were 10 values for the central (1 ROI x 2 sections x 5 volumes).
MVVD was calculated comparing the mean voxel value of each group to the mean voxel value of the central ROI, while SDVV was the mean standard deviation value for each group. Descriptive statistics and graphics depicting MVVD and SDVV values were performed using Prism 7.05 (GraphPad Software, La Jolla, California, USA). The mean voxel values and SDVV were compared with the central ROI using ANOVA One-Way with Dunnet as post-hoc (α = 5%).
Results
The mean voxel values and SDVV obtained are presented in Table 2. The central ROI showed, for all units and protocols, a relatively large range for the mean voxel values, also with large standard deviation. For example, for SCA it was the ROI with the highest standard deviation.
MVVD and SDVV for aligned and offset detectors are presented in Figures 5-8, showing the ROIs that had statistically significant differences with the central ROI. MVVD observed for units with aligned detectors ranged from -32.8% to 22.8%, while for the units with offset detectors it ranged from -20.7% to 69.5%. For units with aligned detectors, ROIs, which were
farther from the central region, were those with the largest MVVD. For units with offset detectors the same was seen for CRA and PIC, but not for PRO and SCA.
SDVV for aligned detectors was lower in the ROI near the FOV centre, except for PRO. For the units with offset detectors, SDVV was lower for the ROIs farther from the centre, except for PIC.
Figure 5. Percentage of mean voxel value
difference (MVVD) for imaging protocols based on the use of aligned detectors.
Figure 6. Percentage of mean voxel value
difference (MVVD) for imaging protocols based on the use of latera-offset detectors.
Figure 7. Standard deviation of voxel value
(SDVV) for imaging protocols based on the use of
aligned detectors.
Figure 8. Standard deviation of voxel value
(SDVV) for imaging protocols based on the use of
lateral-offset detectors.
Discussion
This study focused on presenting a feature that some dental CBCT units use to acquire volumes which are larger in diameter than the imaging detector used: changing the location of the centre, allowing it to be laterally offset from the mid-plane of the FOV. The use of a
laterally-positioned detector in dental CBCT units can be implemented in two manners.1,4,6 In some units, the detector is always offset to the same side, and a full rotation (i.e. 360º) around the patient is needed. Some units with this trajectory are the Picasso Trio (Vatech, South Korea) and Scanora 3D (Soredex Oy, Finland) for all the FOV sizes, and Promax 3D Mid (Planmeca Oy, Finland) for larger FOVs. We now present a second variation in dental CBCT units, used by some manufacturers, where the machine does a first clockwise partial rotation (180º + the beam angle)2 with the detector offset to one side, then the entire C-arm (with the X-ray source and the detector) moves leaving the detector offset to the opposite side, and rotates back counter clockwise to the initial position. Some units with this trajectory are the Cranex 3Dx (Soredex Oy, Finland) and the OP300 Maxio (Instrumentarium Dental, Finland) in larger FOVs. We analysed images of a utility wax phantom to evaluate some technical characteristics of image quality among different units (i.e. mean and standard deviation of voxel values within an image volume).
The detector being positioned with a lateral offset might affect the resulting image as the number of projections available for reconstructing an image is different depending on the region of interest covered in each offset acquisition. In other words, the central region of the FOV is covered during the full examination, while peripheral regions are present only in some basis images, during some specific periods of the partial rotations. Therefore, the number of projections available for reconstruction is different among different regions of the volume. The central cylindrical part of the volume is seen in all projections, to be able to blend the projections before reconstruction, essentially almost doubling the number of images of this region compared to the others, in both types of laterally offset detectors. The area between these two parts can show some ring artefacts in the reconstructed images.6
Adding to that, the central X-ray beam may be aligned with the centre of the detector, and not with centre of the FOV. With this, using laterally offset detectors, the midplane of the projections, the region where the central (and most perpendicular) X-ray beam hits the detector, might not represent the midplane of the reconstructed volume. And it is known that the overall image quality (i.e. noise and resolution) is reduced with increasing cone angles due to the algorithm’s quality being guaranteed only for the central plane, and the quality degrading as a function to the distance from it.5,14
The central region of the tested phantom was used as reference for statistical comparisons and for MVVD calculation based on this assumption that it corresponds to the midplane section where the photons hit the detector perpendicularly, and therefore should present more ideal image quality values than regions farther from it.4,11 At least for SDVV, which in this study was used as a method to evaluate the homogeneity of the image,15 it is expected that the central region presents lower values than the others due to the acuter cone angles in other regions.
These two changes related to laterally offset detectors (the different number of projections within regions of the FOV, and the offset of the central beam in relation to the mid-plane of the FOV), were observed for the units CRA 15x8 and PRO 16x10, where the ROI, which was further from the FOV centre, had lower SDVV values than those of ROI near the centre, while the central region, that had more regions available for reconstruction, had a SDVV lower than those of ROI near the centre. This can be explained by the fact that the entire C-arm moves to the side when acquiring images with the offset detector in these units, therefore it is understandable that the central beam is still aligned with the central area of the detector, which is not centrally aligned to the FOV. Although only PRO 16x10 presented statistically significant differences in the aforementioned ROI, it is important to note the change in SDVV distribution observed between aligned and laterally offset detector positions for CRA, where the CRA 8x8 protocol presented statistically significant higher SDVV for the ROI far from the centre.
The other units with an aligned detector setup also had the highest SDVV for the ROIs further from the centre, except for PRO 10x10. This behaviour was also observed for PIC, that has an offset detector, but a standard deviation in the evaluated ROI that resembles the ones observed in the aligned detector units. One can speculate that, even though the detector is offset for all FOV, the central beam is aligned with edge of the detector, making it aligned with the midplane of the reconstructed volume as well.
Different studies have evaluated the variations in mean pixel intensities and noise in different positions of an object inside the field of view.7–10 Oliveira et al. (2013)7 observed variations in CT numbers in ROI located in different teeth regions using a NewTom 3G and a 5G with different FOV. No information was provided on the geometry of projections. Molteni et al. (2013)4 listed the NewTom VGi as a unit that has an offset detector, but that can not be
transferred with certainty to the other units by this brand. As our results showed no consistency among MVVD between aligned and offset detectors, viewing the basis images is needed to define which type of projection was used. Some of the MVVD observed in our study were higher than the 10% suggested by SEDENTEXCT,16 when considering the variation in image density values compared to a baseline image. Interesting to notice, the differences found in the present study are higher than the acceptable values within the same acquisitions.
The results found by Machado et al. (2018)10 evaluating the artefact formation in implants using the voxel value SD are consistent with the ones of the present study for most aligned detectors, if considered that the anterior region is usually closer to the edges of the field of view than the posterior regions in dental scans.9 No information is given on the projection geometry, but the device used has a 20 x 25 cm amorphous silicon flat panel detector,17 large enough to cover in all projections the entire FOV used. Another study8 observed similar results than ours for PIC, even though different methods of positioning the phantom were used. Higher SDVV were observed around the metal object when closer to the edge of the phantom. One of the studies found in dental CBCT units also evaluated the basis images.9 The aligned detector of the unit used in the study presented similar SDVV increase in the edges of the field of view, as observed in our study for aligned detectors.
An important consideration is related to the radiation dose the patient is exposed on these different geometries. Even though it was not evaluated in this study, compared to a full rotation with aligned detectors, a dose reduction is expected in acquisitions with an offset detector, as only the central region will be exposed the entire time. However, in acquisitions with partial rotation, the central region will probably be exposed twice more than in aligned geometries. Further studies should focus on the influence of these different image acquisition geometries on different diagnostic tasks, and how the patient is affected considering radiation burden and diagnostic outcome.
Conclusion
The use of an offset detector to acquire CBCT images lead to increased MVVD ranges and modified the SDVV distribution inside the FOV in some units compared to the use of an aligned detector.
References
1. Scarfe WC, Farman AG. What is Cone-Beam CT and How Does it Work? Dent Clin North Am 2008; 52: 707–30. doi: https://doi.org/10.1016/j.cden.2008.05.005 2. Pauwels R, Araki K, Siewerdsen JH, Thongvigitmanee SS. Technical aspects of dental
CBCT: state of the art. Dentomaxillofacial Radiol 2015; 44: 20140224. doi: https://doi.org/10.1259/dmfr.20140224
3. Scarfe W, Azevedo B, Toghyani S, Farman A. Cone Beam Computed Tomographic imaging in orthodontics. Aust Dent J 2017; 62: 33–50. doi:
https://doi.org/10.1111/adj.12479
4. Molteni R. Prospects and challenges of rendering tissue density in Hounsfield units for cone beam computed tomography. Oral Surg Oral Med Oral Pathol Oral Radiol 2013; 116: 105–19. doi: https://doi.org/10.1016/j.oooo.2013.04.013
5. Feldkamp LA, Davis LC, Kress JW. Practical cone-beam algorithm. J Opt Soc Am A 1984; 1: 612. doi: https://doi.org/10.1364/JOSAA.1.000612
6. Schulze R, Heil U, Groß D, Bruellmann DD, Dranischnikow E, Schwanecke U, et al. Artefacts in CBCT: A review. Dentomaxillofacial Radiol 2011; 40: 265–73. doi: https://doi.org/10.1259/dmfr/30642039
7. Oliveira ML, Tosoni GM, Lindsey DH, Mendoza K, Tetradis S, Mallya SM. Influence of anatomical location on CT numbers in cone beam computed tomography. Oral Surg Oral Med Oral Pathol Oral Radiol 2013; 115: 558–64. doi:
https://doi.org/10.1016/j.oooo.2013.01.021
8. Queiroz PM, Santaella GM, da Paz TDJ, Freitas DQ. Evaluation of a metal artefact reduction tool on different positions of a metal object in the FOV. Dentomaxillofacial Radiol 2017; 46: 20160366. doi: https://doi.org/10.1259/dmfr.20160366
9. Pauwels R, Jacobs R, Bogaerts R, Bosmans H, Panmekiate S. Reduction of scatter-induced image noise in cone beam computed tomography: Effect of field of view size and position. Oral Surg Oral Med Oral Pathol Oral Radiol 2016; 121: 188–95. doi: https://doi.org/10.1016/j.oooo.2015.10.017
10. Machado AH, Fardim KAC, de Souza CF, Sotto-Maior BS, Assis NMSP, Devito KL. Effect of anatomical region on the formation of metal artefacts produced by dental implants in cone beam computed tomographic images. Dentomaxillofacial Radiol 2018; 47: 20170281. doi: https://doi.org/10.1259/dmfr.20170281
11. Hwang JJ, Park H, Jeong H-G, Han S-S. Change in Image Quality According to the 3D Locations of a CBCT Phantom. Bencharit S, organizador. PLoS One 2016; 11: e0153884. doi: https://doi.org/10.1371/journal.pone.0153884
12. Ozemre MO, Gulsahi A. Comparison of the accuracy of full head cone beam CT images obtained using a large field of view and stitched images. Dentomaxillofacial Radiol 2018; 20170454. doi: https://doi.org/10.1259/dmfr.20170454
13. Spin-Neto R, Matzen LH, Schropp LW, Sørensen TS, Wenzel A. An ex vivo study of automated motion artefact correction and the impact on cone beam CT image quality and interpretability. Dentomaxillofacial Radiol 2018; 47: 20180013. doi:
https://doi.org/10.1259/dmfr.20180013
14. Kalender WA, Kyriakou Y. Flat-detector computed tomography (FD-CT). Eur Radiol 2007; 17: 2767–79. doi: https://doi.org/10.1007/s00330-007-0651-9
15. Pauwels R, Stamatakis H, Manousaridis G, Walker A, Michielsen K, Bosmans H, et al. Development and applicability of a quality control phantom for dental cone-beam CT. J Appl Clin Med Phys 2011; 12: 3478. doi: https://doi.org/10.1120/jacmp.v12i4.3478 16. SEDENTEXCT. Radiation protection no. 172: cone beam CT for dental and maxillofacial
radiology (evidence-based guidelines). 2012.
17. Davies J, Johnson B, Drage N. Effective doses from cone beam CT investigation of the jaws. Dentomaxillofacial Radiol 2012; 41: 30–6. doi:
2.2 Artigo: The impact of movement on image quality and interpretability in CBCT devices with aligned and lateral-offset detectors
Authors: Gustavo Machado Santaella, Pedro Luiz Rosalen, Francisco Haiter-Neto, Ann
Wenzel, Rubens Spin-Neto
Abstract
Objectives: To evaluate the formation of motion artefacts when using a lateral-offset detector
on image quality and interpretability of simulated diagnostic tasks and the effect of two motion correction algorithms in reducing these artefacts.
Methods: A human skull with three different conditions, 2 implant planning regions and 1
furcation problem, was mounted on a robot simulating intense movement patterns (anteroposterior translation, lifting, anteroposterior translation + lifting, lateral rotation, tremor for 2s and continuous tremor). Four CBCT units were used: Cranex 3Dx (CRA), Ortophos SL (ORT), Promax 3D Mid (PRO), and X1. Protocols with aligned (CRA, ORT, PRO and X1) and lateral-offset (CRA and PRO) detectors, and three protocols with motion correction were tested (PRO and X1). Movements were executed in 3 different timings for units with a lateral-offset detector and 1 timing for units with an aligned detector. In total, 98 volumes were acquired and evaluated. Images were scored by three blinded evaluators for the presence of motion stripes artefacts, overall unsharpness, and interpretability. Cohen’s kappa coefficient was used to score interrater agreement, and the results were summarized and described as percentages.
Results: Interobserver agreement was good for all evaluated aspects (0.67 to 0.70 on
average). Regarding aligned detectors, images were considered not-interpretable in all tasks for most protocols without motion correction, and the motion reduction algorithm of X1 and PRO greatly enhanced interpretability for most protocols. Protocols with a lateral-offset detector presented differences in interpretability of the different task regions depending on which moment the movement happened. Task 2 interpretability was most compromised in timings 2 and 3, task 3 in timing 1 for PRO and CRA and 3 for CRA, and task 1 in timings 1 and 3 for CRA and 1 and 2 for PRO. The motion artefact compensation of PRO was less effective as its aligned counterpart.
Conclusion: A lateral-offset detector resulted in motion artefacts being formed in different
regions of the FOV, depending on the timing of the movements, which might result in images not having to be reacquired if the region of interest was not affected. The motion correction algorithms greatly enhanced image quality and interpretability for aligned detector units but were less effective for images acquired with a lateral-offset detector.
Keywords: cone beam computed tomography, patient movement; motion artifacts;
radiographic image enhancement.
Introduction
Cone beam computed tomography images in dental exams are deeply affected by movements during the acquisition, which can result in motion artefacts in the reconstructed volumes. These are more prone to happen due to the sometimes long acquisition times of CBCT, especially in children and elderly patients with systemic alterations, such as Parkinson’s disease.1
The problem lies in the characteristics of the reconstruction algorithm, usually being a variation of the filtered backprojection for CBCT proposed by Feldkamp et al. (1984)2 which assumes a complete stationary geometry in all the basis images. But with the movement between the projections, the intensities representing the same area are backprojected into different positions, resulting in multiple inconsistencies, or artefacts, that may be present in the reconstructed images.3 Artefacts are changes in the image that do not correctly represent the scanned object. They are the result of discrepancies between the acquisition process of the basis images and the algorithm used for reconstruction.3,4 In case of motion artefacts, these can be the presence of double contours or overall unsharpness, for example.5
Using a lateral-offset detector in relation to the source-to-rotation-centre axis is a method used by some units’ manufacturers to acquire volumes larger in diameter with a smaller detector when compared to units with an aligned detector, mainly related to the higher cost of larger sized detectors.3,6,7 This way, a single volume is acquired, where the centre of the volume is acquired in all projections, while the edges are viewed in only some of the basis images. Also, movements known to cause motion artefacts in the images affect different regions of the FOV depending on which region was being acquired.7
Methods of detecting the movements during the acquisition have been proposed, either by using a head tracking device8,9 or by detecting the movements in the basis images.10 Then, a correction can be applied in some units through an iterative reconstruction that adjusts for the movements during the backprojection, reducing the motion artefacts visible in the final image.8 But the algorithms available were tested in a setup with an aligned detector. With this, the aims of this study were to evaluate the influence on some clinical tasks evaluation of acquiring CBCT images with a lateral-offset detector in the formation of artefacts
with different types of movement at different moments during the examination and the effect of two motion correction algorithms in reducing these artefacts.
Methods and materials
CBCT units
Four CBCT units were selected to be included in this study: Cranex 3Dx (CRA, Soredex Oy, Finland), Ortophos SL (ORT, Sirona Dental Systems GmbH, Germany), Promax 3D Mid (PRO, Planmeca Oy, Finland) and X1 (3Shape, Denmark). All units had in common the fact that images are usually acquired with the patient standing up, to better reproduce the movement protocols of the robot used among the different devices.
In total, the devices were separated into eight protocols, described in Table 1. The parameters were selected as the default mA and kVp for each device, and the smallest voxel size available for that field-of-view (FOV). Two devices had offset detector protocols (Cranex 3Dx and Promax 3D Mid), while the other two were all with aligned detectors. Both devices with offset detector had a similar approach, in which they have FOVs with an aligned detector (up to 8x8 in CRA and 10x10 in PRO), and FOVs larger than those used an offset detector to acquire the images. CRA did two partial-rotations, with the detector offset to the right side first, then to the left side in the second rotation, while PRO did a 360o rotation with the detector offset to the right side.
Table 1. Units and acquisition protocols used in this study.
Unit Field-of-view
(cm)
Offset detector Voxel size (mm)
kVp mA Motion Artefact
Correction Aligned detector
Soredex Cranex 3Dx 8 x 8 No 0.20 89.8 6 No
3Shape X1 8 x 8 No 0.15 90.0 12 Yes (head tracker)
Sirona Orthophos SL 8 x 8 No 0.16 85.0 6 No
Planmeca ProMax 3D Mid 10 x 10 No 0.15 90.0 10 No
Planmeca ProMax 3D Mid 10 x 10 No 0.15 90.0 10 Yes (CALM)
Offset detector
Soredex Cranex 3Dx 15 x 8 2x rotations 0.25 89.8 5 No
Planmeca ProMax 3D Mid 16 x 10 360º 0.20 90.0 10 No
Planmeca ProMax 3D Mid 16 x 10 360º 0.20 90.0 10 Yes (CALM)
Two of the devices tested had movement artefact correction methods (PRO and X1). In PRO it was possible to disable the compensation and, therefore, images were reconstructed
and evaluated with it turned ON and OFF. X1 requires the use of the tracking device and does not allow images to be acquired without it, so only volumes with motion correction activated were obtained.
Experimental setup and robot programming
A human skull embedded in wax to simulate soft-tissues11 and partially dentate was used in this study. Three regions of interest with different conditions were present in the skull and were selected due to their position in the FOV. They were edentulous anterior maxilla, defined as a region evaluated for an implant or graft placement, and mandible left and right sides, in which there was an edentulous region for implant placement and a furcation lesion in the first molar, respectively.
The skull was mounted on a robot (UR10, Universal Robots, Odense, Denmark) that executes pre-defined movements with precise control of angular position, velocity and acceleration.8 Six different movement types (described in Table 2) were selected based on a previous study8 as movements considered of high intensity, to better identify the regions affected, with a non-returning to the initial position pattern, a distance of 3 mm and a speed of 5 mm/s. Two movements were categorized as uniplanar or non-complex (head anteroposterior translation and lifting) and four as multiplanar or complex (head anteroposterior translation + lifting, lateral rotation, 2s tremor, and continuous tremor). In the devices with an aligned detector geometry, the movements were executed only when the X-ray source was behind the skull, while in devices with an offset geometry, the movements were executed in 3 different timings: timing 1 (T1 - when the source was behind the skull), timing 2 (T2 - source in front of skull for PRO, due to the 360o rotation, and behind the skull in the second rotation for CRA) and timing 3 (T3 - movements executed both in timings, first to the final motion position, than back to the initial position the second time). All movement timings were adjusted for each unit and programmed in the robot to assure that the movements were consistent among the acquisitions. The basis images showing the moment the movements started in all units are seen in Figure 1. The continuous tremor was an exception as it run during the full acquisition for aligned detectors, or during the entire first, last and both timings for offset detector units.
Table 2. Movements executed by the robot. Motion pattern
1 Head antero-posterior translation 2 Head lifting
3 Head antero-posterior translation + head lifting (2 separate movements) 4 Head lateral rotation
5 Tremor lasting 2s
6 Continuous tremor (6 Hz) 0 Still – no movement (control)
Figure 1. Basis images showing the timing where the movements started in most protocols for all the
units.
Image acquisition
In total, 98 images were acquired and evaluated (Table 3). The FOV chosen were based on: first, if the device had offset and aligned projections, to keep the same height, and second to choose a similar FOV for all devices, the exception being PRO, in which the smallest height for offset acquisitions is 16x10, so a FOV of 10x10 for aligned acquisitions was chosen, following criteria #1. PRO works with a compensation of movement artefacts based on an iterative reconstruction of the projections and does not need an external apparatus to work, and then images were acquired only once and reconstructed with and without the motion compensation, resulting in 4 sets of volumes (2 for aligned, and 2 for offset). X1, on the other hand, needs the tracking device for motion correction,8 and cannot be used without it, so only one group of volumes was obtained.
Table 3. Number of volumes acquired per protocol and timing. Units
Timing Number of volumes
Aligned detector CRA 1 7 ORT 1 7 PROwo 1 7 PROwi 1 7 X1 1 7 Offset detector CRA 1 7 2 7 3 7 PROwo 1 7 2 7 3 7 PROwi 1 7 2 7 3 7 Total 98 Image evaluation
All the obtained volumes were anonymized and blinded to the evaluators. OnDemand3D was used to evaluate the images, in a low light room and using a large screen size (60’) and FullHD resolution (1920x1080 pixels) monitor.
Three different tasks of clinical relevance in dental exams were evaluated. Task 1 was an edentulous region for implant and bone graft planning in the maxilla, located in the anterior part of the FOV, and focused on the contours of the bone tissue and visibility of the nasopalatine canal. Task 2 was for implant planning in an edentulous region in the mandible located in the left side of the FOV, focusing also on the contours of the bone tissue and visibility of the mandibular canal. And task 3 was a diagnosis for a furcation problem in the lower first molar on the right side of the FOV, where the correct limits of bone contours and soft-tissue simulator should be identifiable. This way, the tasks combined formed a triangle, with its vertices in each task, with most movements happening at the exact time the rotation centre intersected the task 1 region (X-ray source behind the skull).
Three evaluators with experience in movement artefacts evaluation based on previous studies did the images screening together, but scored separately for presence of movement stripes (0 = No stripes or enamel stripes / 1 = Movement stripes), unsharpness (0 = None or mild - Bony and dental contours are easily discernible / 1 = Moderate to severe - bony and
dental contours are not discernible, sometimes with double contours) and image interpretability (0 = Interpretable / 1 = Not interpretable) for each diagnostical task. The evaluators were blinded entirely to the acquisition protocol used. The regions chosen did not have any metal or other high-density materials that could generate other types of artefacts. After opening the volumes, images of the first task region were observed in all three main reconstruction planes (Axial, Sagittal, and Coronal), while the observers gave an overall score for the three evaluated aspects. Then, they readjusted the planes to evaluate task 2 and then task 3. This was repeated for every volume, with a limit of 15 volumes per session to avoid observer fatigue.
Data treatment
The scores of the three evaluators were tabulated and evaluated in a commercially available software (IBM Corp., New York, NY; formerly SPSS Inc., Chicago, IL). Interobserver agreement was measured by Cohen’s Kappa coefficient. The findings for all observers were summarized and a cross-table was made showing the consensus of the findings between the presence of movement stripes/presence of unsharpness/not interpretable images and the CBCT unit/movement characteristics (i.e. movement type, detector offset, and timing) for each task. Data were reported as percentage and agreement values.
Results
The interobserver agreements in the acquired volumes were good for all the evaluated aspects,12 such as the presence of movement stripe artefacts (0.68 on average, 0.60 – 0.82 range), overall unsharpness (0.67 on average, 0.63 – 0.72 range) and image interpretability (0.70 on average, 0.63 – 0.78 range).
All the images from the still groups were scored as interpretable and absent of movement artefacts stripes or overall unsharpness, for all evaluators and protocols. Therefore, only the images which had movements registered during the acquisition are considered in the following results.
The task 1, which was for implant and bone graft planning, located in the anterior part of the FOV, in acquisitions with an aligned detector, was scored for presence of movement artefact stripes and overall unsharpness and non-interpretable images, respectively, in 100 – 100 – 88.9% for CRA, 100 – 94.4 – 88.9% for ORT and 94.4 – 88.9 – 83.3% for PROWO for the
images without movement artefacts correction, and 88.9 – 44.4 – 33.3% for PROWI and 5.6 – 0 – 0% for X1 for the images with correction. The offset detector groups were scored for movement timing 1 in 100 – 94.4 – 94.4% for CRA, 88.9 – 94.4 – 77.8% for PROWO and 100 – 94.4 – 61.1% for PROWi. For timing 2 in 66.7 – 33.3 – 16.7% for CRA, 66.7 – 61.1 – 61.1% for PROWO and 94.4 – 77.8 – 55.6% for PROWi, and for timing 3 100 – 94.4 – 88.9% for CRA, 83.3 – 22.2 – 22.2% for PROWO and 77.8 – 38.89 – 33.3% for PROWI.
Regarding the task 2, which was for implant planning in a edentulous region, located in the left side of the FOV, in acquisitions with an aligned detector, it was scored for presence of movement artefact stripes and overall unsharpness and non-interpretable images, respectively, in 88.9 – 77.8 – 55.6% for CRA, 83.3 – 83.3 – 61.1% for ORT and 94.4 – 72.2 – 55.6% for PROWO for the images without movement artefacts correction, and 61.1 – 44.4 – 16.67% for PROWI and 11.1 – 0 – 0% for X1. The offset detector groups were scored for movement timing 1 in 66.7 – 22.2 – 5.6% for CRA, 61.1 – 44.4 – 22.2% for PROWO and 72.2 – 38.9 – 11.1% for PROWI. For timing 2 in 77.8 – 50 – 22.2% for CRA, 83.3 – 77.8 – 77.8% for PROWO and 94.4 – 66.7 – 50% for PROWI, and for timing 3, 100 – 83.3 – 72.2% for CRA, 88.9 – 61.1 – 61.1% for PROWO and 83.3 – 55.6 – 16.7% for PROWI.
Task 3 was a diagnosis for a furcation problem in the lower first molar on the right side of the FOV, and in acquisitions with an aligned detector it was scored for presence of movement artefact stripes and overall unsharpness and non-interpretable images, respectively, in 94.4 – 94.4 – 83.3% for CRA, 88.9 – 83.3 – 55.6% for ORT and 100 – 94.4 – 66.7% for PROWO for the images without movement artefacts correction, and 83.3 – 50 – 16.67% for PROWI and 11.1 – 0 – 0% for X1. The offset detector groups were scored for movement timing 1 in 100 – 100 – 77.8% for CRA, 100 – 94.4 – 66.7% for PROWO and 100 – 83.3 – 61.1% for PROWI. For timing 2 in 5.6 – 11.1 – 0% for CRA, 83.3 – 77.8 – 55.6% for PROWO and 94.4 – 72.2 – 61.1% for PROWI, and for timing 3, 100 – 94.4 – 72.2% for CRA, 88.9 – 33.3 – 27.8% for PROWO and 61.1 – 38.9 – 22.2% for PROWI.
The scores regarding the different tasks and different detector positions are presented in Tables 4 and 5, while Figures 2, 3 and 4 show examples of the images.
Table 5. The observers’ consensus scores regarding the presence of movement stripes (filled circle, present; empty circle, absent), overall unsharpness (filled circle, present; empty circle, absent), and image
interpretability (filled circle, non-interpretable; empty circle, interpretable), according to movement pattern and tasks for protocols with an offset detector.
Task 1 – Anterior region
Type Timing Movement stripes Unsharpness Non-interpretable
CRA PROwo PROwi CRA PROwo PROwi CRA PROwo PROwi
AP translation 1 ● ● ● ● ● ● ● ● ● 2 ○ ○ ● ○ ○ ● ○ ○ ○ 3 ● ● ○ ● ○ ○ ● ○ ○ Head lifting 1 ● ● ● ● ● ● ● ● ● 2 ○ ○ ● ○ ○ ● ○ ○ ● 3 ● ● ● ● ○ ○ ● ○ ○ AP translation + Head lifting 1 ● ● ● ● ● ● ● ● ● 2 ● ● ● ○ ● ● ○ ● ● 3 ● ● ● ● ○ ● ● ○ ● Lateral rotation 1 ● ● ● ● ● ● ● ● ○ 2 ● ● ● ● ● ● ○ ● ○ 3 ● ● ● ● ○ ○ ● ○ ○ Tremor 1 ● ● ● ● ● ● ● ● ● 2 ● ● ● ○ ● ● ○ ● ● 3 ● ● ● ● ○ ○ ● ○ ○ Continuous tremor 1 ● ● ● ● ● ● ● ● ○ 2 ● ● ● ● ● ○ ● ● ○ 3 ● ● ● ● ● ● ● ● ● Still 1 ○ ○ ○ ○ ○ ○ ○ ○ ○ 2 ○ ○ ○ ○ ○ ○ ○ ○ ○ 3 ○ ○ ○ ○ ○ ○ ○ ○ ○
Task 2 – Left side
Type Timing Movement stripes Unsharpness Non-interpretable
CRA PROwo PROwi CRA PROwo PROwi CRA PROwo PROwi
AP translation 1 ● ● ● ○ ● ● ○ ○ ○ 2 ○ ○ ● ○ ○ ○ ○ ○ ○ 3 ● ● ● ○ ○ ○ ○ ○ ○ Head lifting 1 ● ● ● ○ ● ● ○ ● ● 2 ● ● ● ● ● ● ○ ● ● 3 ● ● ● ● ● ○ ● ● ○ AP translation + Head lifting 1 ● ● ● ● ● ○ ○ ○ ○ 2 ● ● ● ● ● ● ○ ● ● 3 ● ● ● ● ○ ● ● ○ ○ Lateral rotation 1 ● ● ● ● ● ○ ○ ○ ○ 2 ● ● ● ● ● ● ○ ● ○ 3 ● ● ● ● ● ● ○ ● ○ Tremor 1 ● ● ● ○ ○ ○ ○ ○ ○ 2 ● ● ● ○ ● ● ○ ● ● 3 ● ● ● ● ● ● ● ● ○ Continuous tremor 1 ○ ○ ● ○ ○ ● ○ ○ ○ 2 ● ● ● ● ● ○ ● ● ○ 3 ● ● ● ● ● ● ● ● ● Still 1 ○ ○ ○ ○ ○ ○ ○ ○ ○ 2 ○ ○ ○ ○ ○ ○ ○ ○ ○ 3 ○ ○ ○ ○ ○ ○ ○ ○ ○
Table 5 (continued). The observers’ consensus scores regarding the presence of movement stripes
(filled circle, present; empty circle, absent), overall unsharpness (filled circle, present; empty circle, absent), and image interpretability (filled circle, non-interpretable; empty circle, interpretable),
according to movement pattern and tasks for protocols with an offset detector. Task 3 – Right side
Type Timin
g
Movement stripes Unsharpness Non-interpretable
CRA PROw o PROwi CRA PROw o PROwi CRA PROw o PROwi AP translation 1 ● ● ● ● ● ● ○ ○ ● 2 ○ ○ ● ○ ○ ● ○ ○ ● 3 ● ● ○ ● ○ ○ ○ ○ ○ Head lifting 1 ● ● ● ● ● ● ● ● ● 2 ○ ● ● ○ ● ● ○ ● ● 3 ● ● ○ ● ● ○ ● ● ○ AP translation + Head lifting 1 ● ● ● ● ● ● ● ● ● 2 ○ ● ● ○ ● ● ○ ● ● 3 ● ● ● ● ○ ● ● ○ ○ Lateral rotation 1 ● ● ● ● ● ○ ● ○ ○ 2 ○ ● ● ○ ● ● ○ ○ ○ 3 ● ● ○ ● ○ ○ ○ ○ ○ Tremor 1 ● ● ● ● ● ● ● ● ○ 2 ○ ● ● ○ ● ● ○ ● ● 3 ● ● ● ● ○ ○ ● ○ ○ Continuous tremor 1 ● ● ● ● ● ● ● ● ● 2 ○ ● ● ● ● ○ ○ ○ ○ 3 ● ● ● ● ● ● ● ● ● Still 1 ○ ○ ○ ○ ○ ○ ○ ○ ○ 2 ○ ○ ○ ○ ○ ○ ○ ○ ○ 3 ○ ○ ○ ○ ○ ○ ○ ○ ○
Discussion
The presence of motion artefacts in dental CBCT images is a well-documented challenge.1,3,5,7,13,14 The causes for such problem are established as discrepancies in the acquired images, when a patient moves during the exam, changing the position of the intensities used by the algorithm for the image reconstruction,3 causing stripe like artefacts to show, and the structures to appear either blurry (i.e. overall unsharpness) or with double contours.1,15
To this date, two studies evaluated automated methods of detecting movements during the acquisition in dental CBCT units.9,10 One used a head tracking device detected by cameras positioned above the patient to precisely detect movement in all axis,9 while the other presented an algorithm to detect the movements in the basis projections of a unit that does not have any extra apparatus for motion evaluation.10 A unit with the head tracking device and a motion correction algorithm was tested later, showing excellent results compared to other devices and protocols without the correction, both with uniplanar and multiplanar movements.8 The present study tested two units that had algorithms to reduce motion artefacts, one with the head tracking device (X1) and the other based on movements detected in the basis images (PRO). Due to the nature of the algorithms applied during the reconstruction, where X1 had the exact information of the movements acquired by the head tracker, while PRO obtain this information less precisely by analysing the basis images, the authors chose to name the algorithms as motion correction for X1 and motion compensation for PRO.
The detector offset method used by some units to acquire volumes with a diameter larger than the width of the detector used is described in some other studies,3,6,16,17 but without many considerations on how it affects the evaluation of different diagnostic tasks. A previous study6 described the two types of detector offset used by the units in this study: the full 360º rotation with the detector offset to the right applied by PRO and the double partial rotation of CRA, with the detector offset to the right side in the first rotation, and to the left side in the second rotation. An image quality evaluation was done, but in a utility wax phantom, not directly related to clinical evaluations.
One study7 used a unit that is said to have an offset-axis geometry of acquisition (NewTom™ 5G; QR srl, Verona, Italy)16 to evaluate different movements and the resulting motion artefacts. The angular exposition of 180º used in one of the protocols by the authors, which corresponds to the X-ray source behind the skull employed in the present study, showed a similar amount of artefacts formed in the right and left arches. In the present study, this result was observed in the units that had an aligned detector, while the ones with offset presented differences between left and right sides for most protocols (Figure 5). An explanation for this would be if the unit used by the authors is similar to the ones used in the present study, where depending on the FOV size an aligned or offset protocol is used. The detector size listed (20 x 25 cm) is large enough to accommodate the FOV used (12 x 8 cm). In our study, we evaluated the basis images to be sure that the lateral-offset protocol was being used and that the movements happened in the planned timing.
Figure 5. Axial and coronal images of the “continuous tremor” group of
the CRA protocol with the offset detector in the three timings tested.
The movements protocols used in the present study are considered intense, and most are multiplanar, adapted from previous studies to provide images with more artefacts. The only distance used was 3 mm, which highly increases the risk of obtaining images considered
