Artifact Reduction Research Articles

Three-dimensional printed custom-made modular talus prosthesis in patients with talus malignant tumor resection

BackgroundTalar malignant tumor is extremely rare. Currently, there are several alternative management options for talus malignant tumor including below-knee amputation, tibio-calcaneal arthrodesis, and homogenous bone transplant while their shortcomings limited the clinical application. Three-dimensional (3D) printed total talus prosthesis in talus lesion was reported as a useful method to reconstruct talus, however, most researches are case reports and its clinical effect remains unclear. Therefore, the current study was to explore the application of 3D printed custom-made modular prosthesis in talus malignant tumor.MethodsWe retrospectively analyzed the patients who received the 3D printed custom-made modular prosthesis treatment due to talus malignant tumor in our hospital from February 2016 to December 2021. The patient's clinical data such as oncology outcome, operation time, and volume of blood loss were recorded. The limb function was evaluated with the Musculoskeletal Tumor Society 93 (MSTS-93) score, The American Orthopedic Foot and Ankle Society (AOFAS) score; the ankle joint ranges of motion as well as the leg length discrepancy were evaluated. Plain radiography and Tomosynthesis-Shimadzu Metal Artefact Reduction Technology (T-SMART) were used to evaluate the position of prosthesis and the osseointegration. Postoperative complications were recorded.ResultsThe average patients’ age and the follow-up period were respectively 31.5 ± 13.1 years; and 54.8 months (range 26–72). The medium operation time was 2.4 ± 0.5 h; the intraoperative blood loss was 131.7 ± 121.4 ml. The mean MSTS-93 and AOFAS score was 26.8 and 88.5 respectively. The average plantar flexion, dorsiflexion, varus, and valgus were 32.5, 9.2, 10.8, and 5.8 degree respectively. One patient had delayed postoperative wound healing. There was no leg length discrepancy observed in any patient and good osseointegration was observed on the interface between the bone and talus prosthesis in all subjects.ConclusionThe modular structure of the prosthesis developed in this study seems to be convenient for prosthesis implantation and screws distribution. And the combination of solid and porous structure improves the initial stability and promotes bone integration. Therefore, 3D printed custom-made modular talus prosthesis could be an alternative option for talus reconstruction in talus malignant tumor patients.

Quad-Net: Quad-Domain Network for CT Metal Artifact Reduction.

Metal implants and other high-density objects in patients introduce severe streaking artifacts in CT images, compromising image quality and diagnostic performance. Although various methods were developed for CT metal artifact reduction over the past decades, including the latest dual-domain deep networks, remaining metal artifacts are still clinically challenging in many cases. Here we extend the state-of-the-art dual-domain deep network approach into a quad-domain counterpart so that all the features in the sinogram, image, and their corresponding Fourier domains are synergized to eliminate metal artifacts optimally without compromising structural subtleties. Our proposed quad-domain network for MAR, referred to as Quad-Net, takes little additional computational cost since the Fourier transform is highly efficient, and works across the four receptive fields to learn both global and local features as well as their relations. Specifically, we first design a Sinogram-Fourier Restoration Network (SFR-Net) in the sinogram domain and its Fourier space to faithfully inpaint metal-corrupted traces. Then, we couple SFR-Net with an Image-Fourier Refinement Network (IFR-Net) which takes both an image and its Fourier spectrum to improve a CT image reconstructed from the SFR-Net output using cross-domain contextual information. Quad-Net is trained on clinical datasets to minimize a composite loss function. Quad-Net does not require precise metal masks, which is of great importance in clinical practice. Our experimental results demonstrate the superiority of Quad-Net over the state-of-the-art MAR methods quantitatively, visually, and statistically. The Quad-Net code is publicly available at https://github.com/longzilicart/Quad-Net.

Stereotactic Body Radiation Therapy for Clinically Localized Prostate Cancer in Men With Hip Prostheses: A Cautionary Note.

Stereotactic body radiation therapy (SBRT) has been established as a safe and effective treatment for prostate cancer. SBRT requires high accuracy to reduce treatment margins. Metal hip prostheses create artifacts that distort pelvic imaging and potentially decrease the accuracy of target/organ at risk (OAR) identification and radiation dose calculations. Data on the safety and efficacy of SBRT after hip replacement is limited. This single-institution study sought to evaluate the safety and local control following SBRT for prostate cancer in men with hip replacements. 23 patients treated with localized prostate cancer and a history of pre-treatment hip replacement, treated with SBRT from 2007 to 2017 at MedStar Georgetown University Hospital were included in this retrospective analysis. Treatment was administered with the CyberKnife® (Accuray Incorporated, Sunnyvale, CA) at doses of 35 Gy or 36.25 Gy in 5 fractions. The targets and OARs were identified and contoured by a single experienced Radiation Oncologist (SPC).The adequacy of the CT and T2W MRI images for treatment planning was assessed with a three-point scale (good, adequate, or suboptimal).During treatment planning, care was taken to avoid treatment beams that directly traversed the hip prosthesis. Toxicities were recorded and scored using the Common Terminology Criteria for Adverse Events version 4.0 (CTCAE v.4.0). Local recurrence was confirmed by magnetic resonance imaging and/or prostate biopsy. The median follow-up was seven years. The patients were elderly (median age = 71 years) with a high rate of comorbidities (Charlson Comorbidity Index > 2 in 25%). Four patients had bilateral hip replacements. The majority of patients were low to intermediate risk per the D'Amico classification. Around 13% received upfront ADT. In total, 13 patients were treated with 35 Gy, and 10 were treated with 36.25 Gy.The rates of late > Grade 3 GU toxicity and> Grade 2 GI toxicity were 8.6% and 4.3%, respectively. There were no Grade 4 or 5 toxicities.Six patients (26%) developed a local recurrence at a median time of 7.5 years. Of these six patients, four had unilateral hip replacements and two had bilateral.Three underwent salvage cryotherapy and three received salvage ADT. In the general population, high-grade toxicities and local recurrences are uncommon following prostate SBRT.However, in this cohort of patients with prior hip replacements, prostate SBRT had higher than expected rates of late toxicity and local recurrence. In the opinion of the authors, such patients should be counseled regarding an elevated risk of late toxicity and local recurrence with prostate SBRT. With its ultrasound guidance, brachytherapy would have the advantage of circumventing the need for MRI/CT-based imaging and thus may represent a preferable radiation alternative in this patient population. If these patients are treated with SBRT, they should be monitored closely for local recurrence so early salvage can be performed. We hopethat recent advances in metal artifact reduction techniques and dose-calculation algorithms will improve future outcomes.

1186: Evaluation of the Direct Density Algorithm for Metal Artifact Reduction.

1186: Evaluation of the Direct Density Algorithm for Metal Artifact Reduction.

Photon-counting detector computed tomography for metal artifact reduction: a comparative study of different artifact reduction techniques in patients with orthopedic implants

PurposeArtifacts caused by metallic implants remain a challenge in computed tomography (CT). We investigated the impact of photon-counting detector computed tomography (PCD-CT) for artifact reduction in patients with orthopedic implants with respect to image quality and diagnostic confidence using different artifact reduction approaches.Material and methodsIn this prospective study, consecutive patients with orthopedic implants underwent PCD-CT imaging of the implant area. Four series were reconstructed for each patient (clinical standard reconstruction [PCD-CTStd], monoenergetic images at 140 keV [PCD-CT140keV], iterative metal artifact reduction (iMAR) corrected [PCD-CTiMAR], combination of iMAR and 140 keV monoenergetic [PCD-CT140keV+iMAR]). Subsequently, three radiologists evaluated the reconstructions in a random and blinded manner for image quality, artifact severity, anatomy delineation (adjacent and distant), and diagnostic confidence using a 5-point Likert scale (5 = excellent). In addition, the coefficient of variation [CV] and the relative quantitative artifact reduction potential were obtained as objective measures.ResultsWe enrolled 39 patients with a mean age of 67.3 ± 13.2 years (51%; n = 20 male) and a mean BMI of 26.1 ± 4 kg/m2. All image quality measures and diagnostic confidence were significantly higher for the iMAR vs. non-iMAR reconstructions (all p < 0.001). No significant effect of the different artifact reduction approaches on CV was observed (p = 0.26). The quantitative analysis indicated the most effective artifact reduction for the iMAR reconstructions, which was higher than PCD-CT140keV (p < 0.001).ConclusionPCD-CT allows for effective metal artifact reduction in patients with orthopedic implants, resulting in superior image quality and diagnostic confidence with the potential to improve patient management and clinical decision making.

Marker-based C-arm self-calibration with unknown calibration pattern.

Accurate tomographic reconstructions require the knowledge of the actual acquisition geometry. Many mobile C-arm CT scanners have poorly reproducible acquisition geometries and thus need acquisition-specific calibration procedures. Most of geometric self-calibration methods based on projection data either need prior information or are limited to the estimation of a low number of geometric calibration parameters. Other self-calibration methods generally use a calibration pattern with known geometry and are hardly implementable in practice for clinicalapplications. We present a three-step marker based self-calibration method which does not require the prior knowledge of the calibration pattern and thus enables the use of calibration patterns with arbitrary markerspositions. The first step of the method aims at detecting the set of markers of the calibration pattern in each projection of the CT scan and is performed using the YOLO (You Only Look Once) Convolutional Neural Network. The projected marker trajectories are then estimated by a sequential projection-wise marker association scheme based on the Linear Assignment Problem which uses Kalman filters to predict the markers 2D positions in the projections. The acquisition geometry is finally estimated from the marker trajectories using the Bundle-adjustmentalgorithm. The calibration method has been tested on realistic simulated images of the ICRP (International Commission on Radiological Protection) phantom, using calibration patterns with 10 and 20 markers. The backprojection error was used to evaluate the self-calibration method and exhibited sub-millimeter errors. Real images of two human knees with 10 and 30 markers calibration patterns were then used to perform a qualitative evaluation of the method, which showed a remarkable artifacts reduction and bone structures visibilityimprovement. The proposed calibration method gave promising results that pave the way to patient-specific geometric self-calibrations inclinics.

Awareness for artifacts in fluorescence microscopy of β-TCP

Fluorescence analysis of β-TCP ceramics is often used to describe cells found on said ceramics. However, we found, to our knowledge, so far undescribed artifacts which might sometimes be hard to differentiate from cells due to shape and fluorescence behavior. We tried prolonged ultrasound washing as well as Technovit 9100 fixation to reduce these artifacts. While untreated dowels showed no reduction in artifacts no matter the further treatment, Technovit fixation reduced the artifacts with even further reduction achieved by mechanical cleaning. As a consequence, scientists working with these dowels and likely even other types should try to avoid creating false positive results by considering the existence of these artifacts, checking additional filters for unusual fluorescence and by reducing them by using Technovit fixation when possible.

Effect of different cone beam computed tomography settings on artifact production in titanium and zirconia dental implants: An in vitro study.

The use of dental implants in the treatment of edentulous patients is increasing. Zirconia implants are an alternative to titanium implants, offering advantages in terms of aesthetics and biological compatibility. However, the number of artifacts observed on radiographic images with zirconia implants compared to titanium implants is yet to be determined. The purpose of this study was to evaluate the impact of different cone-beam computed tomography (CBCT) parameters on the production of artifacts in zirconia and titanium implants. A dry human mandible was coated with wax to simulate human soft tissues and examined. Subsequently, titanium and zirconia implants were placed at the same points in the posterior region of the mandible. The production of artifacts on CBCT scans was evaluated using 2 parameters. The first parameter, the standard deviation within the region of interest (SDROI), is based on a comparison of the gray levels at implant and control areas. The second parameter was the contrast-to-noise ratio (CNR), which was evaluated for different protocols created by various combinations of the field of view (FOV) area, milliampere [mA] intensity and metal artifact reduction (MAR) programs. The study found that zirconia implants produced more artifacts than titanium implants. However, the production of artifacts in zirconia implants could be significantly reduced by increasing the mA values, performing CBCT scans with smaller FOV areas, and enabling MAR programs. The production of artifacts is a disadvantage of zirconia implants, but this drawback can be mitigated by selecting appropriate protocols for the CBCT device.

Enhanced Video Super-Resolution Network towards Compressed Data

Video super-resolution (VSR) algorithms aim at recovering a temporally consistent high-resolution (HR) video from its corresponding low-resolution (LR) video sequence. Due to the limited bandwidth during video transmission, most available videos on the internet are compressed. Nevertheless, few existing algorithms consider the compression factor in practical applications. In this paper, we propose an enhanced VSR model towards compressed videos, termed as ECVSR, to simultaneously achieve compression artifacts reduction and SR reconstruction end-to-end. ECVSR contains a motion-excited temporal adaption network (METAN) and a multi-frame SR network (SRNet). The METAN takes decoded LR video frames as input and models inter-frame correlations via bidirectional deformable alignment and motion-excited temporal adaption, where temporal differences are calculated as motion prior to excite the motion-sensitive regions of temporal features. In SRNet, cascaded recurrent multi-scale blocks (RMSB) are employed to learn deep spatio-temporal representations from adapted multi-frame features. Then, we build a reconstruction module for spatio-temporal information integration and HR frame reconstruction, which is followed by a detail refinement module for texture and visual quality enhancement. Extensive experimental results on compressed videos demonstrate the superiority of our method for compressed VSR. Code will be available at https://github.com/lifengcs/ECVSR .

Potential Benefits of Photon-Counting CT in Dental Imaging: A Narrative Review.

Background/Objectives: Advancements in oral imaging technology are continually shaping the landscape of dental diagnosis and treatment planning. Among these, photon-counting computed tomography (PCCT), introduced in 2021, has emerged as a promising, high-quality oral technology. Dental imaging typically requires a resolution beyond the standard CT systems achievable with the specialized cone-beam CT. PCCT can offer up to 100 µm resolution, improve soft-tissue contrast, and provide faster scanning times, which are crucial for detailed dental diagnosis and treatment planning. Using semiconductor detectors, PCCT produces sharper images and can potentially reduce the number of scans required, thereby decreasing patient radiation exposure. This review aimed to explore the potential benefits of PCCT in dental imaging. Methods: This review analyzed the literature on PCCT in dental imaging from January 2010 to February 2024, sourced from PubMed, Scopus, and Web of Science databases, focusing on high-resolution, patient safety, and diagnostic efficiency in dental structure assessment. We included English-language articles, case studies, letters, observational studies, and randomized controlled trials while excluding duplicates and studies unrelated to PCCT's application in dental imaging. Results: Studies have highlighted the superiority of PCCT in reducing artifacts, which are often problematic, compared to conventional CBCT and traditional CT scans, due to metallic dental implants, particularly when used with virtual monoenergetic imaging and iterative metal artifact reduction, thereby improving implant imaging. This review acknowledges limitations, such as the potential for overlooking other advanced imaging technologies, a narrow study timeframe, the lack of real-world clinical application data in this field, and costs. Conclusions: PCCT represents a promising advancement in dental imaging, offering high-resolution visuals, enhanced contrast, and rapid scanning with reduced radiation exposure.

Assessment of SPECT Image Reconstruction in Liver Scanning Using 99mTc/ EDDA/ HYNIC-TOC

Purpose: Given that the Single Photon Emission Computed Tomography (SPECT) image quality is defined experimentally, developing a specialized scanning technique for each procedure is necessary to increase the diagnosis accuracy. This study aims to determine the optimal algorithm for liver scan reconstruction using 99mTc/SPECT. Materials and Methods: The Filtered Back-Projection (FBP) reconstruction method was used in liver scanning using 99mTc-EDDA/HYNIC-TOC (Tektrotyd) for SPECT images of 30 patients which were acquired with a dual-head EvoExel detector system. Using different types of filters in SPECT imaging, various optimal results can be achieved in the processed images, such as artifact reduction, noise reduction, or signal enhancement and recovery. To evaluate the effect of different filters on image quality, Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR), and contrast parameters were calculated. Results: Applying filters enhanced contrast in the images in most cases as well as CNR and SNR. Metz (power = 2), Shepp-Logan (Cut-off frequency = 0.67) and Metz (power = 2) filters increase the CNR, contrast and SNR in images more than the other filters, respectively. The maximum improvement for CNR, contrast and SNR was from 0.62 to 2.35, 0.99 to 1.31, and 8.48 to 14.70, respectively. Conclusion: Based on the results, the Hamming filter, due to providing high-quality images for visual analysis of liver SPECT, and the Butterworth filter, due to balancing the image quality and noise for quantitative analysis, are recommended.

A cardiac MRI motion artifact reduction method based on edge enhancement network

Cardiac magnetic resonance imaging (MRI) usually requires a long acquisition time. The movement of the patients during MRI acquisition will produce image artifacts. Previous studies have shown that clear MR image texture edges are of great significance for pathological diagnosis. In this paper, a motion artifact reduction method for cardiac MRI based on edge enhancement network is proposed. Firstly, the four-plane normal vector adaptive fractional differential mask is applied to extract the edge features of blurred images. The four-plane normal vector method can reduce the noise information in the edge feature maps. The adaptive fractional order is selected according to the normal mean gradient and the local Gaussian curvature entropy of the images. Secondly, the extracted edge feature maps and blurred images are input into the de-artifact network. In this network, the edge fusion feature extraction network and the edge fusion transformer network are specially designed. The former combines the edge feature maps with the fuzzy feature maps to extract the edge feature information. The latter combines the edge attention network and the fuzzy attention network, which can focus on the blurred image edges. Finally, extensive experiments show that the proposed method can obtain higher peak signal-to-noise ratio and structural similarity index measure compared to state-of-art methods. The de-artifact images have clear texture edges.

Automated detection and removal of artifacts from sEMG signals based on fuzzy inference system and signal decomposition methods

Surface electromyography (sEMG) signal quality decreases when it is contaminated by different types of artifacts. Detection and removal of the contaminants from sEMG signals were a major and interesting task for several research works in recent years. In this paper, a novel automatic approach was designed for the detection and removal of artifacts from sEMG signals. The proposed method consists of a combined model that based on a fuzzy inference system (FIS), a signal decomposition method, and a denoising technique. Based on three spectral statistical features, which are calculated in frequency domain using power spectral density: composite multiscale entropy (CMSE), kurtosis (K), and skewness (S), FIS detects the artifactual epochs of the sEMG signal. Thereafter, those epochs are decomposed into their components by a signal decomposition. In this work, we have tested the performance of two decomposition methods namely stationary wavelet transform (SWT) and variational mode decomposition (VMD). A second FIS is used to select the contaminated components. Depending on the type of artifact, two procedures are considered, the first aims to denoise motion artifact (MOA), electrocardiography interference (ECG) and power line interference (PLI), whereas the second procedure aims to eliminate the additive white gaussian noise (AWGN). Finally, the denoised signal is reconstructed. The evaluation performance of the proposed method is compared with the performance of some existing methods using the following metrics: SNR improvement (ΔSNR), the reduction in artifact (λ(%)), improvement in signal to noise and distortion ratio (ΔSNDR) and improvement in the root mean square error (ΔRMSE). The results emphazed the superiority of our method over others in terms of all the used metrics and computational time.

Metal Artifact Correction in Industrial CT Images Based on a Dual-Domain Joint Deep Learning Framework

Industrial computed tomography (CT) images reconstructed directly from projection data using the filtered back projection (FBP) method exhibit strong metal artifacts due to factors such as beam hardening, scatter, statistical noise, and deficiencies in the reconstruction algorithms. Traditional correction approaches, confined to either the projection domain or the image domain, fail to fully utilize the rich information embedded in the data. To leverage information from both domains, we propose a joint deep learning framework that integrates UNet and ResNet architectures for the correction of metal artifacts in CT images. Initially, the UNet network is employed to correct the imperfect projection data (sinograms), the output of which serves as the input for the CT image reconstruction unit. Subsequently, the reconstructed CT images are fed into the ResNet, with both networks undergoing a joint training process to optimize image quality. We take the projection data obtained by analytical simulation as the data set. The resulting optimized industrial CT images show a significant reduction in metal artifacts, with the average Peak Signal-to-Noise Ratio (PSNR) reaching 36.13 and the average Structural Similarity Index (SSIM) achieving 0.953. By conducting simultaneous correction in both the projection and image domains, our method effectively harnesses the complementary information from both, exhibiting a marked improvement in correction results over the deep learning-based single-domain corrections. The generalization capability of our proposed method is further verified in ablation experiments and multi-material phantom CT artifact correction.

Applications of dual-energy CT in acute musculoskeletal and trauma imaging-a review.

Recent advances in computed tomography have resulted in new applications of CT scans in musculoskeletal imaging. Dual-energy CT technology involves the acquisition of data at high and low kilovolts, allowing differentiation and quantification of materials with different X-ray absorption. Newer CT scanners with a variety of post-processing options allow interesting applications of dual-energy CT in musculoskeletal and trauma imaging. This article provides an overview of the basic principles and physics of DECT. We review applications of DECT in the evaluation of the acute painful joint with suspicion of gout, metal artefact reduction in the prosthetic joint and in imaging of patients following major trauma. We present a review of literature and case examples to illustrate the strengths and limitations of this modality in the diagnosis of acute musculoskeletal conditions.

Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning.

Patient head motion is a common source of image artifacts in computed tomography (CT) of the head, leading to degraded image quality and potentially incorrect diagnoses. The partial angle reconstruction (PAR) means dividing the CT projection into several consecutive angular segments and reconstructing each segment individually. Although motion estimation and compensation using PAR has been developed and investigated in cardiac CT scans, its potential for reducing motion artifacts in head CT scans remains unexplored. To develop a deep learning (DL) model capable of directly estimating head motion from PAR images of head CT scans and to integrate the estimated motion into an iterative reconstruction process to compensate for the motion. Head motion is considered as a rigid transformation described by six time-variant variables, including the three variables for translation and three variables for rotation. Each motion variable is modeled using a B-spline defined by five control points (CP) along time. We split the full projections from 360° into 25 consecutive PARs and subsequently input them into a convolutional neural network (CNN) that outputs the estimated CPs for each motion variable. The estimated CPs are used to calculate the object motion in each projection, which are incorporated into the forward and backprojection of an iterative reconstruction algorithm to reconstruct the motion-compensated image. The performance of our DL model is evaluated through both simulation and phantom studies. The DL model achieved high accuracy in estimating head motion, as demonstrated in both the simulation study (mean absolute error (MAE) ranging from 0.28 to 0.45mm or degree across different motion variables) and the phantom study (MAE ranging from 0.40 to 0.48mm or degree). The resulting motion-corrected image, , exhibited a significant reduction in motion artifacts when compared to the traditional filtered back-projection reconstructions, which is evidenced both in the simulation study (image MAE drops from 178 33HU to 37 9HU, structural similarity index (SSIM) increases from 0.60 0.06 to 0.98 0.01) and the phantom study (image MAE drops from 117 17HU to 42 19HU, SSIM increases from 0.83 0.04 to 0.98 0.02). We demonstrate that using PAR and our proposed deep learning model enables accurate estimation of patient head motion and effectively reduces motion artifacts in the resulting head CT images.

IBVC: Interpolation-driven B-frame video compression

Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. However, previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation or video frame interpolation. They suffer from inaccurate quantized motions and inefficient motion compensation. To address these issues, we propose a simple yet effective structure called Interpolation-driven B-frame Video Compression (IBVC). Our approach only involves two major operations: video frame interpolation and artifact reduction compression. IBVC introduces a bit-rate free MEMC based on interpolation, which avoids optical-flow quantization and additional compression distortions. Later, to reduce duplicate bit-rate consumption and focus on unaligned artifacts, a residual guided masking encoder is deployed to adaptively select the meaningful contexts with interpolated multi-scale dependencies. In addition, a conditional spatio-temporal decoder is proposed to eliminate location errors and artifacts instead of using MEMC coding in other methods. The experimental results on B-frame coding demonstrate that IBVC has significant improvements compared to the relevant state-of-the-art methods. Meanwhile, our approach can save bit rates compared with the random access (RA) configuration of H.266 (VTM). The code will be available at https://github.com/ruhig6/IBVC.

Optimized raw data selection for artifact reduction of breathing controlled four-dimensional sequence scanning

Background and purposeEven with most breathing-controlled four-dimensional computed tomography (4DCT) algorithms image artifacts caused by single significant longer breathing still occur, resulting in negative consequences for radiotherapy. Our study presents first phantom examinations of a new optimized raw data selection and binning algorithm, aiming to improve image quality and geometric accuracy without additional dose exposure. Materials and methodsTo validate the new approach, phantom measurements were performed to assess geometric accuracy (volume fidelity, root mean square error, Dice coefficient of volume overlap) for one- and three-dimensional tumor motion trajectories with and without considering motion hysteresis effects. Scans without significantly longer breathing cycles served as references. ResultsMedian volume deviations between optimized approach and reference of at maximum 1% were obtained considering all movements. In comparison, standard reconstruction yielded median deviations of 9%, 21% and 12% for one-dimensional, three-dimensional, and hysteresis motion, respectively. Measurements in one- and three-dimensional directions reached a median Dice coefficient of 0.970 ± 0.013 and 0.975 ± 0.012, respectively, but only 0.918 ± 0.075 for hysteresis motions averaged over all measurements for the optimized selection. However, for the standard reconstruction median Dice coefficients were 0.845 ± 0.200, 0.868 ± 0.205 and 0.915 ± 0.075 for one- and three-dimensional as well as hysteresis motions, respectively. Median root mean square errors for the optimized algorithm were 30 ± 16HU2 and 120 ± 90HU2 for three-dimensional and hysteresis motions, compared to 212 ± 145HU2 and 130 ± 131HU2 for the standard reconstruction. ConclusionsThe algorithm was proven to reduce 4DCT-related artifacts due to missing projection data without further dose exposure. An improvement in radiotherapy treatment planning due to better image quality can be expected.

The added effect of artifact reduction algorithm and ultra-low dose protocols on the accuracy of measurements obtained using Planmeca Viso G7 cone beam computed tomography

Objective The aim of this study is to evaluate the accuracy of linear and volumetric measurements using Planmeca Viso G7 cone beam computed tomography (CBCT) examinations acquired with ultra-low dose (ULD) and metal artifact reduction algorithm (ARA) protocols. Materials and methods Eight metallic spherical objects different in size with pre scanning diameter measurements using a calibrated digital caliper were fixed on a five dry human mandible. The real physical diameters and linear measurements between the metallic objects were measured. A CBCT acquisition was obtained four times with the same proper field of view, 150 μm and 100 Kv, in four scans using the Planmeca Viso G7 CBCT, machine acquisition was done with and without ARA, ULD, which is ARA, and ULD. Both acquisition protocols are inherited options in the Planmeca Viso G7 CBCT machine. All images were interpreted with the Planmeca Romexis software. Results Linear and volumetric measurements were found to have a nonstatistically significant difference with real measurements in the acquisition with ARA and ULD, but with a statistically significant difference with the other three acquisition sequences. Conclusions Using the ARA in combination with the ULD as an acquisition tool in the CBCT machine results in accurate linear and volumetric measurements, enhances image quality, and reduces metallic artifact with the lowest radiation dose.

The effect of MATLAB-based metal artifact reduction software on radiotherapy dose distribution

The effect of MATLAB-based metal artifact reduction software on radiotherapy dose distribution