Effect of dental distance from implant and MAR algorithm on the detection of vertical root fracture in CBCT images

Three-dimensional cone-beam computed tomography (CBCT) has an important role in the detection of vertical root fractures (VRFs). The effect of artifact generation by high-density objects like dental implants on image quality was well documented. This study aimed to assess the effect of tooth-implant distance and the application of metal artifact reduction (MAR) algorithm on the detection of VRFs on CBCT scans. This study was conducted on 20 endodontically treated single-rooted teeth. VRFs were induced in 10 teeth, while the other 10 remained intact. The implant was inserted in the right second premolar socket area, and two teeth were inserted in right canine and right first premolar sockets area randomly and underwent CBCT with and without the application of MAR algorithm. SPSS 21 was used to analyze the results (alpha=0.05). According to the findings of this study, all four variables of sensitivity, specificity, accuracy, and positive predictive values in diagnosis were higher in cases without MAR software at both close(roots in first premolar sockets) and far distances (roots in canine sockets) from the implant. However, the highest rate of diagnosis accuracy of the first and second radiologists was in the far distance group from the implant without MAR, and the lowest rate of diagnosis accuracy in the first and second radiologists was in the close distance to the implant. Applying MAR algorithm had no positive effect on detection of VRFs on CBCT scans in both close and distant scenarios.

La tomografía computarizada de haz cónico tridimensional (CBCT) tiene un papel importante en la detección de fracturas radiculares verticales (VRF). El efecto de la generación de artefactos por objetos de alta densidad como los implantes dentales en la calidad de la imagen está bien documentado. Este estudio tuvo como objetivo evaluar el efecto de la distancia entre el diente y el implante y la aplicación del algoritmo de reducción de artefactos metálicos (MAR) en la detección de VRF en escaneos CBCT. Este estudio se realizó en 20 dientes uniradiculares tratados endodónticamente. Se indujeron VRF en 10 dientes, mientras que los otros 10 permanecieron intactos. El implante se insertó en el área del alveolo del segundo premolar derecho, y dos dientes se insertaron en el canino derecho y en el área del alvéolo del primer premolar derecho al azar y se sometieron a CBCT con y sin la aplicación del algoritmo MAR. Se utilizó SPSS 21 para analizar los resultados (alfa=0,05). De acuerdo con los hallazgos de este estudio, las cuatro variables de sensibilidad, especificidad, precisión y valores predictivos positivos en el diagnóstico fueron más altas en los casos sin el software MAR tanto en distancias cercanas (raíces en las cavidades de los primeros premolares) como lejanas (raíces en las cavidades de los caninos) del implante. Sin embargo, la tasa más alta de precisión diagnóstica del primer y segundo radiólogo fue en el grupo de mayor distancia al implante sin MAR, y la tasa más baja de precisión diagnóstica en el primer y segundo radiólogo fue en la distancia cercana al implante. La aplicación del algoritmo MAR no tuvo un efecto positivo en la detección de VRF en escaneos CBCT en escenarios cercanos y distantes.

Laplace-Based Interpolation Method in Reduction of Metal Artifact in Computed Tomography Imaging

@#Introduction: Metal artifacts can degrade the image quality of computed tomography (CT) images which lead to errors in diagnosis. This study aims to evaluate the performance of Laplace interpolation (LI) method for metal artifacts reduction (MAR) in CT images in comparison with cubic spline (CS) interpolation. Methods: In this study, the proposed MAR algorithm was developed using MATLAB platform. Firstly, the virtual sinogram was acquired from CT image using Radon transform function. Then, dual-adaptive thresholding detected and segmented the metal part within the CT sinogram. Performance of the two interpolation methods to replace the missing part of segmented sinogram were evaluated. The interpolated sinogram was reconstructed, prior to image fusion to obtain the final corrected image. The qualitative and quantitative evaluations were performed on the corrected CT images (both phantom and clinical images) to evaluate the effectiveness of the proposed MAR technique. Results: From the findings, LI method had successfully replaced the missing data on both simple and complex thresholded sinogram as compared to CS method (p-value = 0.17). The artifact index was significantly reduced by LI method (p-value = 0.02). For qualitative analysis, the mean scores by radiologists for LI-corrected images were higher than original image and CS-corrected images. Conclusion: In conclusion, LI method for MAR produced better results as compared to CS interpolation method, as it worked more effective by successfully interpolated all the missing data within sinogram in most of the CT images.

U-net based metal segmentation on projection domain for metal artifact reduction in dental CT

Unlike medical computed tomography (CT), dental CT often suffers from severe metal artifacts stemming from high-density materials employed for dental prostheses. Despite the many metal artifact reduction (MAR) methods available for medical CT, those methods do not sufficiently reduce metal artifacts in dental CT images because MAR performance is often compromised by the enamel layer of teeth, whose X-ray attenuation coefficient is not so different from that of prosthetic materials. We propose a deep learning-based metal segmentation method on the projection domain to improve MAR performance in dental CT. We adopted a simplified U-net for metal segmentation on the projection domain without using any information from the metal-artifacts-corrupted CT images. After training the network with the projection data of five patients, we segmented the metal objects on the projection data of other patients using the trained network parameters. With the segmentation results, we corrected the projection data by applying region filling inside the segmented region. We fused two CT images, one from the corrected projection data and the other from the original raw projection data, and then we forward-projected the fused CT image to get the fused projection data. To get the final corrected projection data, we replaced the metal regions in the original projection data with the ones in the fused projection data. To evaluate the efficacy of the proposed segmentation method on MAR, we compared the MAR performance of the proposed segmentation method with a conventional MAR method based on metal segmentation on the CT image domain. For the MAR performance evaluation, we considered the three primary MAR performance metrics: the relative error (REL), the sum of square difference (SSD), and the normalized absolute difference (NAD). The proposed segmentation method improved MAR performances by around 5.7% for REL, 6.8% for SSD, and 8.2% for NAD. The proposed metal segmentation method on the projection domain showed better MAR performance than the conventional segmentation on the CT image domain. We expect that the proposed segmentation method can improve the performance of the existing MAR methods that are based on metal segmentation on the CT image domain.

Metal Artifact Reduction for Orthopedic Implants: Brain CT Angiography in Patients with Intracranial Metallic Implants

BACKGROUND: The purpose of this study was to qualitatively and quantitatively evaluate the effects of a metal artifact reduction for orthopedic implants (O-MAR) for brain computed tomographic angiography (CTA) in patients with aneurysm clips and coils. METHODS: The study included 36 consecutive patients with 47 intracranial metallic implants (42 aneurysm clips, 5 coils) who underwent brain CTA. The computed tomographic images with and without the O-MAR were independently reviewed both quantitatively and qualitatively by two reviewers. For quantitative analysis, image noises near the metallic implants of non-O-MAR and O-MAR images were compared. For qualitative analysis, image quality improvement and the presence of new streak artifacts were assessed. RESULTS: Image noise was significantly reduced near metallic implants (P < 0.01). Improvement of implant-induced streak artifacts was observed in eight objects (17.0%). However, streak artifacts were aggravated in 11 objects (23.4%), and adjacent vessel depiction was worsened in eight objects (17.0%). In addition, new O-MAR-related streak artifacts were observed in 32 objects (68.1%). New streak artifacts were more prevalent in cases with overlapping metallic implants on the same axial plane than in those without (P = 0.018). Qualitative assessment revealed that the overall image quality was not significantly improved in O-MAR images. CONCLUSION: In conclusion, the use of the O-MAR in patients with metallic implants significantly reduces image noise. However, the degree of the streak artifacts and surrounding vessel depiction were not significantly improved on O-MAR images.

Value and Clinical Application of Orthopedic Metal Artifact Reduction Algorithm in CT Scans after Orthopedic Metal Implantation

OBJECTIVE: To evaluate orthopedic metal artifact reduction algorithm (O-MAR) in CT orthopedic metal artifact reduction at different tube voltages, identify an appropriate low tube voltage for clinical practice, and investigate its clinical application. MATERIALS AND METHODS: The institutional ethical committee approved all the animal procedures. A stainless-steel plate and four screws were implanted into the femurs of three Japanese white rabbits. Preoperative CT was performed at 120 kVp without O-MAR reconstruction, and postoperative CT was performed at 80–140 kVp with O-MAR. Muscular CT attenuation, artifact index (AI) and signal-to-noise ratio (SNR) were compared between preoperative and postoperative images (unpaired t test), between paired O-MAR and non-O-MAR images (paired Student t test) and among different kVp settings (repeated measures ANOVA). Artifacts' severity, muscular homogeneity, visibility of inter-muscular space and definition of bony structures were subjectively evaluated and compared (Wilcoxon rank-sum test). In the clinical study, 20 patients undertook CT scan at low kVp with O-MAR with informed consent. The diagnostic satisfaction of clinical images was subjectively assessed. RESULTS: Animal experiments showed that the use of O-MAR resulted in accurate CT attenuation, lower AI, better SNR, and higher subjective scores (p < 0.010) at all tube voltages. O-MAR images at 100 kVp had almost the same AI and SNR as non-O-MAR images at 140 kVp. All O-MAR images were scored ≥ 3. In addition, 95% of clinical CT images performed at 100 kVp were considered satisfactory. CONCLUSION: O-MAR can effectively reduce orthopedic metal artifacts at different tube voltages, and facilitates low-tube-voltage CT for patients with orthopedic metal implants.