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

A Comprehensive Review of Advancements in Diagnostic Imaging: Unveiling Oral Cavity Malignancies Using Computed Tomography

Early detection of oral cavity malignancies is essential for improving treatment outcomes and patient survival rates. Diagnostic imaging, particularly computed tomography (CT), plays a pivotal role in the early identification and detailed assessment of these malignancies. This comprehensive review explores the advancements in CT imaging and its application in diagnosing oral cavity cancers. It discusses the anatomy and physiology of the oral cavity, the clinical characteristics of common malignancies, and the principles and protocols of CT imaging. The review highlights the diagnostic features of oral malignancies on CT, including distinguishing benign from malignant lesions and staging criteria. Emerging technologies, such as higher-resolution imaging, integration with other modalities, and the potential of artificial intelligence, are examined for their role in enhancing diagnostic accuracy. The clinical implications, challenges, and future directions in the use of CT imaging for oral cavity malignancies are also discussed. This review underscores the importance of continued research and technological advancements in optimizing the use of CT for early detection and effective management of oral cavity cancers.

Iterative Metal Artifact Reduction in Head and Neck CT Facilitates Tumor Visualization of Oral and Oropharyngeal Cancer Obscured by Artifacts From Dental Hardware

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate the diagnostic utility of iterative metal artifact reduction (iMAR) in computed tomography (CT)-imaging of oral and oropharyngeal cancers when obscured by dental hardware artifacts and to determine the most appropriate iMAR settings for this purpose.

MATERIALS AND METHODS

The study retrospectively enrolled 27 patients (8 female, 19 male; mean age 64±12.7years) with histologically confirmed oral or oropharyngeal cancer obscured by dental artifacts in contrast-enhanced CT. Raw CT data were reconstructed with ascending iMAR strengths (levels 1/2/3/4/5) and one reconstruction without iMAR (level 0). For subjective analysis, two blinded radiologists rated tumor visualization and artifact severity on a five-point Likert scale. For objective analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) were determined.

RESULTS

iMAR reconstructions improved the subjective image quality of tumor edge and contrast, and the objective parameters of tumor SNR and CNR, reaching their optimum at iMAR levels 4 and 5 (P<.001). AI decreased with iMAR reconstructions reaching its minimum at iMAR level 5 (P<.001). Tumor detection rates increased 2.4-fold with iMAR 5, 2.1-fold with iMAR 4, and 1.9-fold with iMAR 3 compared to reconstructions without iMAR. Disadvantages such as algorithm-induced artifacts increased significantly with higher iMAR strengths (P<.05), reaching a maximum with iMAR 5.

CONCLUSION

iMAR significantly improves CT imaging of oral and oropharyngeal cancers, as confirmed by both subjective and objective measures, with best results at highest iMAR strengths.

Verification of multi-structure targeting in chronic microelectrode brain recordings from CT scans

BACKGROUND

Large-scale microelectrode recordings offer a unique opportunity to study neurophysiological processes at the network level with single cell resolution. However, in the small brains of many experimental animals, it is often technically challenging to verify the correct targeting of the intended structures, which inherently limits the reproducibility of acquired data.

NEW METHOD

To mitigate this problem, we have developed a method to programmatically segment the trajectory of electrodes arranged in larger arrays from acquired CT-images and thereby determine the position of individual recording tips with high spatial resolution, while also allowing for coregistration with an anatomical atlas, without pre-processing of the animal samples or post-imaging histological analyses.

RESULTS

Testing the technical limitations of the developed method, we found that the choice of scanning angle influences the achievable spatial resolution due to shadowing effects caused by the electrodes. However, under optimal acquisition conditions, individual electrode tip locations within arrays with 250 µm inter-electrode spacing were possible to reliably determine.

COMPARISON TO EXISTING METHODS

Comparison to a histological verification method suggested that, under conditions where individual wires are possible to track in slices, a 90% correspondence could be achieved in terms of the number of electrodes groups that could be reliably assigned to the same anatomical structure.

CONCLUSIONS

The herein reported semi-automated procedure to verify anatomical targeting of brain structures in the rodent brain could help increasing the quality and reproducibility of acquired neurophysiological data by reducing the risk of assigning recorded brain activity to incorrectly identified anatomical locations.

DATA AVAILABILITY

The tools developed in this study are freely available as a software package at: https://github.com/NRC-Lund/ct-tools.

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

To evaluate the effect of specific nursing intervention in children with mycoplasma pneumonia (MP), a feature extraction algorithm based on gray level co-occurrence matrix (GLCM) was proposed and combined with computed tomography (CT) image texture features. Then, 98 children with MP were rolled into the observation group with 49 cases (specific nursing) and the control group with 49 cases (routine nursing). CT images based on feature extraction algorithm of optimized GLCM were used to examine the children before and after nursing intervention, and the recovery of the two groups of children was discussed. The results showed that the proportion of lung texture increase, rope shadow, ground glass shadow, atelectasis, and pleural effusion in the observation group (24.11%, 3.86%, 8.53%, 15.03%, and 3.74%) was significantly lower than that in the control group (28.53%, 10.23%, 13.34%, 21.15%, and 8.13%) after nursing (P < 0.05). There were no significant differences in the proportion of small patchy shadows, large patchy consolidation shadows, and bronchiectasis between the observation group and the control group (P > 0.05). In the course of nursing intervention, in the observation group, the disappearance time of cough, normal temperature, disappearance time of lung rales, and absorption time of lung shadow (2.15 ± 0.86 days, 4.81 ± 1.14 days, 3.64 ± 0.55 days, and 5.96 ± 0.62 days) were significantly shorter than those in the control group (2.87 ± 0.95 days, 3.95 ± 1.06 days, 4.51 ± 1.02 days, and 8.14 ± 1.35 days) (P < 0.05). After nursing intervention, the proportion of satisfaction and total satisfaction in the experimental group (67.08% and 28.66%) was significantly higher than that in the control group (40.21% and 47.39%), while the proportion of dissatisfaction (4.26%) was significantly lower than that in the control group (12.4%) (P < 0.05). To sum up, specific nursing intervention was more beneficial to improve the progress of characterization recovery and the overall recovery effect of children with MP relative to conventional nursing. CT image based on feature extraction algorithm of optimized GLCM was of good adoption value in the diagnosis and treatment of MP in children.

Geometric and dosimetric impact of 3D generative adversarial network-based metal artifact reduction algorithm on VMAT and IMPT for the head and neck region

Background

We investigated the geometric and dosimetric impact of three-dimensional (3D) generative adversarial network (GAN)-based metal artifact reduction (MAR) algorithms on volumetric-modulated arc therapy (VMAT) and intensity-modulated proton therapy (IMPT) for the head and neck region, based on artifact-free computed tomography (CT) volumes with dental fillings.

Methods

Thirteen metal-free CT volumes of the head and neck regions were obtained from The Cancer Imaging Archive. To simulate metal artifacts on CT volumes, we defined 3D regions of the teeth for pseudo-dental fillings from the metal-free CT volumes. HU values of 4000 HU were assigned to the selected teeth region of interest. Two different CT volumes, one with four (m4) and the other with eight (m8) pseudo-dental fillings, were generated for each case. These CT volumes were used as the Reference. CT volumes with metal artifacts were then generated from the Reference CT volumes (Artifacts). On the Artifacts CT volumes, metal artifacts were manually corrected for using the water density override method with a value of 1.0 g/cm³ (Water). By contrast, the CT volumes with reduced metal artifacts using 3D GAN model extension of CycleGAN were also generated (GAN-MAR). The structural similarity (SSIM) index within the planning target volume was calculated as quantitative error metric between the Reference CT volumes and the other volumes. After creating VMAT and IMPT plans on the Reference CT volumes, the reference plans were recalculated for the remaining CT volumes.

Results

The time required to generate a single GAN-MAR CT volume was approximately 30 s. The median SSIMs were lower in the m8 group than those in the m4 group, and ANOVA showed a significant difference in the SSIM for the m8 group (p < 0.05). Although the median differences in D_98%, D_50% and D_2% were larger in the m8 group than the m4 group, those from the reference plans were within 3% for VMAT and 1% for IMPT.

Conclusions

The GAN-MAR CT volumes generated in a short time were closer to the Reference CT volumes than the Water and Artifacts CT volumes. The observed dosimetric differences compared to the reference plan were clinically acceptable.

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

Background and purpose

Artefacts caused by dental amalgam implants present a common challenge in computed tomography (CT) and therefore treatment planning dose calculations. The goal was to perform a quantitative image quality analysis of our Artifact Management for Proton Planning (AMPP) algorithm which used gantry tilts for managing metal artefacts on Head and Neck (HN) CT scans and major vendors’ commercial approaches.

Materials and methods

Metal artefact reduction (MAR) algorithms were evaluated using an anthropomorphic phantom with a removable jaw for the acquisition of images with and without (baseline) metal artifacts. AMPP made use of two angled CT scans to generate one artifact-reduced image set. The MAR algorithms from four vendors were applied to the images with artefacts and the analysis was performed with respective baselines. Planar HU difference maps and volumetric HU differences were analyzed.

Results

AMPP algorithm outperformed all vendors’ commercial approaches in the elimination of artefacts in the oropharyngeal region, showing the lowest percent of pixels outside +− 20 HU criteria, 4%; whereas those in the MAR-corrected images ranged from 26% to 67%. In the region of interest within the affected slices, the commercial MAR algorithms showed inconsistent performance, whereas the AMPP algorithm performed consistently well throughout the phantom’s posterior region.

Conclusions

A novel MAR algorithm was evaluated and compared to four commercial algorithms using an anthropomorphic phantom. Unanimously, the analysis showed the AMPP algorithm outperformed vendors’ commercial approaches, showing the potential to be broadly implemented, improve visualizations in patient anatomy and provide accurate HU information.

Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow

Purpose:

We conducted our study to develop a tool capable of automatically detecting dental artifacts in a CT scan on a slice-by-slice basis and to assess the dosimetric impact of implementing the tool into the Radiation Planning Assistant (RPA), a web-based platform designed to fully automate the radiation therapy treatment planning process.

Methods:

We developed an automatic dental artifact identification tool and assessed the dosimetric impact of its use in the RPA. Three users manually annotated 83,676 head-and-neck (HN) CT slices (549 patients). Majority-voting was applied to the individual annotations to determine the presence or absence of dental artifacts. The patients were divided into train, cross-validation, and test data sets (ratio: 3:1:1, respectively). A random subset of images without dental artifacts was used to balance classes (1:1) in the training data set. The Inception-V3 deep learning model was trained with the binary cross-entropy loss function. With use of this model, we automatically identified artifacts on 15 RPA HN plans on a slice-by-slice basis and investigated three dental artifact management methods applied before and after volumetric modulated arc therapy (VMAT) plan optimization. The resulting dose distributions and target coverage were quantified.

Results:

Per-slice accuracy, sensitivity, and specificity were 99 %, 91 %, and 99 %, respectively. The model identified all patients with artifacts. Small dosimetric differences in total plan dose were observed between the various density-override methods (±1 Gy). For the pre- and post-optimized plans, 90 % and 99 %, respectively, of dose comparisons resulted in normal structure dose differences of ±1 Gy. Differences in the volume of structures receiving 95 % of the prescribed dose (V95[%]) were ≤0.25 % for 100 % of plans.

Conclusion:

The dosimetric impact of applying dental artifact management before and after artifact plan optimization was minor. Our results suggest that not accounting for dental artifacts in the current RPA workflow (where only post-optimization dental artifact management is possible) may result in minor dosimetric differences. If RPA users choose to override CT densities as a solution to managing dental artifacts, our results suggest segmenting the volume of the artifact and overriding its density to water is a safe option.

Dosimetric impact of commercial CT metal artifact reduction algorithms and a novel in-house algorithm for proton therapy of head and neck cancer

PURPOSE

To compare the dosimetric impact of all major commercial vendors' metal artifact reduction (MAR) algorithms to one another, as well as to a novel in-house technique (AMPP) using an anthropomorphic head phantom.

MATERIALS AND METHODS

The phantom was an Alderson phantom, modified to allow for artifact-filled and baseline (no artifacts) computed tomography (CT) scans using teeth capsules made with metal amalgams or bone-equivalent materials. It also included a cylindrical insert that was accessible from the bottom of the neck and designed to introduce soft tissue features into the phantom that were used in the analysis. The phantom was scanned with the metal teeth in place using each respective vendor's MAR algorithm: OMAR (Philips), iMAR (Siemens), SEMAR (Canon), and SmartMAR (GE); the AMPP algorithm was designed in-house. Uncorrected and baseline (bone-equivalent teeth) image sets were also acquired using a Siemens scanner. Proton spot scanning treatment plans were designed on the baseline image set for five targets in the phantom. Once optimized, the proton beams were copied onto the different artifact-corrected image sets, with no reoptimization of the beams' parameters, to evaluate dose distribution differences in the different MAR-corrected and -uncorrected image sets. Dose distribution differences were evaluated by comparing dose-volume histogram (DVH) metrics, including planning target volume D95 and clinical target volume D99 coverages, V100, D0.03cc, and heterogeneity indexes, along with a qualitative and water equivalent thickness (WET) analysis.

RESULTS

Uncorrected CT metal artifacts and commercial MAR algorithms negatively impacted the proton dose distributions of all five target shapes and locations in an inconsistent manner, sometimes overdosing by as much as 11.1% (D0.03) or underdosing by as much as 11.7% (V100) the planning target volumes. The AMPP-corrected images, however, provided dose distributions that consistently agreed with the baseline dose distribution. The dosimetry results also suggest that the commercial MAR algorithms' performances varied more with target location and less with target shape. Once relocated further from the metal, the target showed dose distributions that agreed more with the baseline for all commercial solutions, improving the overdosing by as much as 6%, implying inadequate HU correction from commercial MAR algorithms. In comparison to the baseline, HU profile shapes were considerably altered by commercial algorithms and reference values showed differences that represent stopping power percentage differences of 2.7-10%. The AMPP algorithm plans showed the smallest WET differences with the baseline (0.06 cm on average), while the commercial image sets created differences that ranged from 0.11 to 0.54 cm.

CONCLUSIONS

Computed tomography metal artifacts negatively impacted proton dose distributions on all five targets analyzed. The commercial MAR solutions performed inconsistently throughout all targets compared to the metal-free baseline. A lack of CTV coverage and an increased number of hotspots were observed throughout all commercial solutions. Dose distribution errors were related to the proximity to the artifacts, demonstrating the inability of commercial techniques to adequately correct severe artifacts. In contrast, AMPP consistently showed dose distributions that best matched the baseline, likely because it makes use of accurate HU information, as opposed to interpolated data like commercial algorithms.