Development of a stereoscopic CT metal artifact management algorithm using gantry angle tilts for head and neck patients

Abstract

Dental amalgams are a common source of artifacts in head and neck (HN) images. Commercial artifact reduction techniques have been offered, but are substantially ineffectual at reducing artifacts from dental amalgams, can produce additional artifacts, provide inaccurate HU information, or require extensive computation time, and thus offer limited clinically utility. The goal of this work was to define and validate a novel algorithm and provide a phantom-based testing as proof of principle. An initial clinical comparison to a vendor's current solution was also performed. The algorithm uses two-angled CT scans in order to generate a single image set with minimal artifacts posterior to the metal implants. The algorithm was evaluated using a phantom simulating a HN patient with dental fillings. Baseline (no artifacts) geometrical measurements of the phantom were taken in the anterior-posterior, leftright, and superior-inferior directions and compared to the metal-corrected images using our algorithm to evaluate possible distortion from application of the algorithm. Mean HU numbers were also compared between the baseline scan and corrected image sets. A similar analysis was performed on the vendor's algorithm for comparison. The algorithm developed in this work successfully preserved the image geometry and HU and corrected the CT metal artifacts in the region posterior to the metal. The average total distortion for all gantry angles in the AP, LR, and SI directions was 0.17, 0.12, and 0.14 mm, respectively. The HU measurements showed significant consistency throughout the different reconstructed images when compared to the baseline image sets. The vendor's algorithm also showed no geometrical distortion but performed inferiorly in the HU number analysis compared to our technique. Our novel metal artifact management algorithm, using CT gantry angle tilts, provides a promising technique for clinical management of metal artifacts from dental amalgam.

Computed Tomography Image Texture under Feature Extraction Algorithm in the Diagnosis of Effect of Specific Nursing Intervention on Mycoplasma Pneumonia in Children

“…is was similar to the findings of Hata et al [22], indicating that specific nursing intervention can more effectively promote the recovery of children with MP compared with routine nursing. In addition, there was no considerable difference between the observation group and the control group in the proportion of small patchy shadow, large patchy consolidation shadow, and bronchiole dilation (P > 0.05), which was different from the research of Branco et al [23]. e reason may be that the time of review in this study was relatively short and there was not a considerable difference in longterm outcomes in children.…”

Which failures do patient‐specific quality assurance systems need to catch?

“…[5][6][7][8] International Radiation Oncology auditing groups (the USA's IROC-Houston, the UK's National Radiotherapy Trials Quality Assurance Group, and the Australian Clinical Dosimetry Service) provided data on errors they had observed, including their severity and frequency. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] We included relevant errors from published FMEAs and general literature, including several AAPM task group reports. 1,3,42,[49][50][51][52][53] including both incident learning systems and FMEAs are essential for producing a complete collection of failure modes.…”

“…Note that we are using TG‐100′s definition of error, which encompasses “failures consisting of acts of commission or omission that incorrectly execute the intended action required by the process.” We reviewed entries from national/international incident learning systems including entries in the SAFRON/ROSEIS database and publications from RO‐ILS 5–8 . International Radiation Oncology auditing groups (the USA's IROC‐Houston, the UK's National Radiotherapy Trials Quality Assurance Group, and the Australian Clinical Dosimetry Service) provided data on errors they had observed, including their severity and frequency 9–25 . We included relevant errors from published FMEAs and general literature, 26–48 including several AAPM task group reports 1,3,42,49–53 .…”

Evaluation of image quality of a novel computed tomography metal artifact management technique on an anthropomorphic head and neck phantom

“…We calculated the percentage of bad pixels inside the circle that defined the phantom cylinder, which was considered the region of interest in the phantom that the algorithm aimed to improve. Since AMPP was previously shown to not introduce any bias into HU values [17] , all pixels with a CT number error above +20 HU or below −20 HU were considered to be bad pixels. The 20-HU threshold was based on the HU standard deviation obtained in the baseline scan.…”