Neck Computed Tomography Images Research Articles

Endotracheal tube cuff position in relationship to the walls of the trachea: A retrospective computed tomography-based analysis

Background: The use of cuffed endotracheal tubes (ETTs) has become the standard of care in pediatric practice. The rationale for the use of a cuffed ETT is to minimize pressure around the cricoid while providing an effective airway seal. However, safe care requires that the cuff lie distal to the cricoid ring following endotracheal intubation. The current study demonstrates the capability of computed tomography (CT) imaging in identifying the position of the cuff of the ETT in intubated patients. Methods: The study included patients ranging in age from 1 month to 10 years who underwent neck and chest CT imaging that required general anesthesia and endotracheal intubation. The location of the ETT and of the cuff within the airway was determined from axial CT images at three levels (proximal, middle, and distal). Anatomical orientations were tabulated, and percent chances of each orientation were determined for the ETT and the cuff. Results: The study cohort included 42 patients ranging in age from 1 to 114 months. An ETT with a polyvinylchloride cuff was used in 24 patients, and an ETT with a polyurethane cuff was used in 18 patients. The ETT was located near the posterior wall of the trachea in approximately 24–38% of patients, being most likely to be centrally located at the proximal end and at its mid-portion. The middle part of the cuff was most likely to be positioned in the mid-portion of the trachea but tended to skew anteriorly at both the proximal and distal ends. Conclusion: This is the first study using CT imaging to identify the uniformity of cuff inflation within the trachea in children. With commonly used cuffed ETTs, cuff inflation and the final position of ETT cuff within the tracheal lumen were not uniform. Future investigations are needed to determine the reasons for this asymmetry and its clinical implications.

VOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images.

Accurate and consistent contouring of organs-at-risk (OARs) from medical images is a key step of radiotherapy (RT) cancer treatment planning. Most contouring approaches rely on computed tomography (CT) images, but the integration of complementary magnetic resonance (MR) modality is highly recommended, especially from the perspective of OAR contouring, synthetic CT and MR image generation for MR-only RT, and MR-guided RT. Although MR has been recognized as valuable for contouring OARs in the head and neck (HaN) region, the accuracy and consistency of the resulting contours have not been yet objectively evaluated. To analyze the interobserver and intermodality variability in contouring OARs in the HaN region, performed by observers with different level of experience from CT and MR images of the samepatients. In the final cohort of 27 CT and MR images of the same patients, contours of up to 31 OARs were obtained by a radiation oncology resident (junior observer, JO) and a board-certified radiation oncologist (senior observer, SO). The resulting contours were then evaluated in terms of interobserver variability, characterized as the agreement among different observers (JO and SO) when contouring OARs in a selected modality (CT or MR), and intermodality variability, characterized as the agreement among different modalities (CT and MR) when OARs were contoured by a selected observer (JO or SO), both by the Dice coefficient (DC) and 95-percentile Hausdorff distance (HD ). The mean (±standard deviation) interobserver variability was 69.0 ± 20.2% and 5.1 ± 4.1mm, while the mean intermodality variability was 61.6 ± 19.0% and 6.1 ± 4.3mm in terms of DC and HD , respectively, across all OARs. Statistically significant differences were only found for specific OARs. The performed MR to CT image registration resulted in a mean target registration error of 1.7 ± 0.5mm, which was considered as valid for the analysis of intermodalityvariability. The contouring variability was, in general, similar for both image modalities, and experience did not considerably affect the contouring performance. However, the results indicate that an OAR is difficult to contour regardless of whether it is contoured in the CT or MR image, and that observer experience may be an important factor for OARs that are deemed difficult to contour. Several of the differences in the resulting variability can be also attributed to adherence to guidelines, especially for OARs with poor visibility or without distinctive boundaries in either CT or MR images. Although considerable contouring differences were observed for specific OARs, it can be concluded that almost all OARs can be contoured with a similar degree of variability in either the CT or MR modality, which works in favor of MR images from the perspective of MR-only and MR-guidedRT.

Inter-rater reliability of a novel objective endpoint for benign central airway stenosis interventions: Segmentation-based volume rendering of computed tomography scans.

To evaluate the reliability of a novel segmentation-based volume rendering approach for quantification of benign central airway obstruction (BCAO). A retrospective single-center cohort study. Data were ascertained using electronic health records at a tertiary academic medical center in the United States. Patients with airway stenosis located within the trachea on two-dimensional (2D) computed tomography (CT) imaging and documentation of suspected benign etiology were included. Four readers with varying expertise in quantifying tracheal stenosis severity were selected to manually segment each CT using a volume rendering approach with the available free tools in the medical imaging viewing software OsiriX (Bernex, Switzerland). Three expert thoracic radiologists were recruited to quantify the same CTs using traditional subjective methods on a continuous and categorical scale. The interrater reliability for continuous variables was calculated by the intraclass correlation coefficient (ICC) using a two-way mixed model with 95% confidence intervals (CI). Thirty-eight patients met the inclusion criteria, and fifty CT scans were selected for measurement. The most common etiology of BCAO was iatrogenic in 22 patients (58%). There was an even distribution of chest and neck CT imaging within our cohort. The average ICC across all four readers for the volume rendering approach was 0.88 (95% CI, 0.84 to 0.93), suggesting good to excellent agreement. The average ICC for thoracic radiologists for subjective methods on the continuous scale was 0.38 (95% CI, 0.20 to 0.55), suggesting poor to fair agreement. The kappa for the categorical approach was 0.26, suggesting a slight to fair agreement amongst the raters. In this retrospective cohort study, agreement was good to excellent for raters with varying expertise in airway cross-sectional imaging using a novel segmentation-based volume rendering approach to quantify BCAO. This proposed measurement outperformed our expert thoracic radiologists using conventional subjective grading methods.

Assessment of the effectiveness of Saba shielding with the composition of Cu–Bi in neck CT imaging: a phantom and patient study

The use of computed tomography (CT) is a very well-established medical diagnostic imaging modality, however, the high radiation dose due to this imaging method is a major concern. Therefore, dose reduction methods are necessary, especially for superficial radiosensitive organs like the thyroid. The aim of this study is to construct and assess a CT shield with composition of 90% Cu and 10% Bi (Saba shield) with regard to dose reduction and image quality. The efficiency of the constructed shields for dose reduction was assessed by measuring entrance skin dose (ESD), using thermoluminescence dosimeters placed on an anthropomorphic phantom. Image quality was assessed quantitatively based on image noise and CT number accuracy by drawing regions of interest on CT images of the anthropomorphic phantom. Image quality was further investigated qualitatively in a patient study. Application of the Saba shield and 100% Bi shield with the thickness of one thickness (1T) reduced ESD by 50.2% and 51.7%, respectively, and using a three-fold thickness reduced ESD by 64.6% and 65.1%, respectively. Saba shield with thickness of 1T had no significant change in image noise in the anterior part, and image noise and mean CT number in the posterior part (P > 0.05). The statistical analysis performed did not find any meaningful difference between the study and control groups in image quality assessment of the patient study (P > 0.05). The 1T Saba shield reduced thyroid dose efficiently during neck CT imaging without causing unwanted effects on image quality.

Using Texture Analysis of Neck Computed Tomography Images to Differentiate Primary Hyperparathyroidism From Normal Controls.

To investigate the utility of texture analysis in detecting osseous changes associated with hyperparathyroidism on neck CT examinations compared with control patients and to explore the best regions in the head and neck to evaluate changes in the trabecular architecture secondary to hyperparathyroidism. Patients with hyperparathyroidism who underwent a 4D CT of the neck with contrast were included in this study. Age-matched control patients with no history of hyperparathyroidism who underwent a contrast-enhanced neck CT were also included. Mandibular condyles, bilateral mandibular bodies, the body of the C4 vertebra, the manubrium of the sternum, and bilateral clavicular heads were selected for analysis, and oval-shaped regions of interest were manually placed. These segmented areas were imported into an in-house developed texture analysis program, and 41 texture analysis features were extracted. A mixed linear regression model was used to compare differences in the texture analysis features contoured at each of the osseous structures between patients with hyperparathyroidism and age-matched control patients. A total of 30 patients with hyperparathyroidism and 30 age-matched control patients were included in this study. Statistically significant differences in texture features between patients with hyperparathyroidism and control patients in all 8 investigated osseous regions. The sternum showed the greatest number of texture features with statistically significant differences between these groups. Some CT texture features demonstrated statistically significant differences between patients with hyperparathyroidism and control patients. The results suggest that texture features may discriminate changes in the osseous architecture of the head and neck in patients with hyperparathyroidism.

SABOS‐Net: Self‐supervised attention based network for automatic organ segmentation of head and neck CT images

AbstractThe segmentation of Organs At Risk (OAR) in Computed Tomography (CT) images is an essential part of the planning phase of radiation treatment to avoid the adverse effects of cancer radiotherapy treatment. Accurate segmentation is a tedious task in the head and neck region due to a large number of small and sensitive organs and the low contrast of CT images. Deep learning‐based automatic contouring algorithms can ease this task even when the organs have irregular shapes and size variations. This paper proposes a fully automatic deep learning‐based self‐supervised 3D Residual UNet architecture with CBAM(Convolution Block Attention Mechanism) for the organ segmentation in head and neck CT images. The Model Genesis structure and image context restoration techniques are used for self‐supervision, which can help the network learn image features from unlabeled data, hence solving the annotated medical data scarcity problem in deep networks. A new loss function is applied for training by integrating Focal loss, Tversky loss, and Cross‐entropy loss. The proposed model outperforms the state‐of‐the‐art methods in terms of dice similarity coefficient in segmenting the organs. Our self‐supervised model could achieve a 4% increase in the dice score of Chiasm, which is a small organ that is present only in a very few CT slices. The proposed model exhibited better accuracy for 5 out of 7 OARs than the recent state‐of‐the‐art models. The proposed model could simultaneously segment all seven organs in an average time of 0.02 s. The source code of this work is made available at https://github.com/seeniafrancis/SABOSNet.

Weaving attention U-net: A novel hybrid CNN and attention-based method for organs-at-risk segmentation in head and neck CT images.

In radiotherapy planning, manual contouring is labor-intensive and time-consuming. Accurate and robust automated segmentation models improve the efficiency and treatment outcome. We aim to develop a novel hybrid deep learning approach, combining convolutional neural networks (CNNs) and the self-attention mechanism, for rapid and accurate multi-organ segmentation on head and neck computed tomography (CT) images. Head and neck CT images with manual contours of 115 patients were retrospectively collected and used. We set the training/validation/testing ratio to 81/9/25 and used the 10-fold cross-validation strategy to select the best model parameters. The proposed hybrid model segmented 10 organs-at-risk (OARs) altogether for each case. The performance of the model was evaluated by three metrics, that is, the Dice Similarity Coefficient (DSC), Hausdorff distance 95% (HD95), and mean surface distance (MSD). We also tested the performance of the model on the head and neck 2015 challenge dataset and compared it against several state-of-the-art automated segmentation algorithms. The proposed method generated contours that closely resemble the ground truth for 10 OARs. On the head and neck 2015 challenge dataset, the DSC scores of these OARs were 0.91 0.02, 0.73 0.10, 0.95 0.03, 0.76 0.08, 0.79 0.05, 0.87 0.05, 0.86 0.08, 0.87 0.03, and 0.87 0.07 for brain stem, chiasm, mandible, left/right optic nerve, left/right submandibular, and left/right parotid, respectively. Our results of the new weaving attention U-net (WAU-net) demonstrate superior or similar performance on the segmentation of head and neck CT images. We developed a deep learning approach that integrates the merits of CNNs and the self-attention mechanism. The proposed WAU-net can efficiently capture local and global dependencies and achieves state-of-the-art performance on the head and neck multi-organ segmentation task.

Study on the assignment method of intensity modulated radiotherapy plan for nasopharyngeal carcinoma based on MR images

Objective: To discuss the influence of different computed tomography (CT) value assignment methods on dose calculation of intensity modulated radiotherapy (IMRT) plan which designed for nasopharyngeal carcinoma (NPC) and the value assignment methods of IMRT plan for NPC based on magnetic resonance (MR) images. Methods: Simulation CT and MR image of 32 NPC patients in Shandong Cancer Hospital from March 2018 to November 2018 were selected for this study. Populate CT values were obtained by contouring and analyzing the simulation CT of patients' tissue, including bone, air, brain, eyeball, optic-nerve, lens, parotid, masseter, skin. Pseudo-CT were generated by different CT value assignment methods: CT1: CT value of all tissues was set to 0HU; CT2: CT value of air cavity was set to populate CT value based on CT1; CT3: CT value of Bone was set to populate CT value based on CT2; CT4: CT value of each soft tissue were set to populate CT value based on CT3. The IMRT plan for NPC as Plan0 was designed base on simulation CT. Then Plan0 was transplanted to four pseudo-CT to recalculate the dose and obtain Plan1, Plan2, Plan3 and Plan4, the differences of dosimetric parameters were compared with Plan0. NPC-IMRT plan was designed base on MR images by using the assignment method with CT value of each tissue were set to populate CT value. Results: In the head and neck CT images, the average populate CT values of bone and cavity were 621 HU and -720 HU, respectively. The populate CT values of other soft tissue ranges from -20 HU to 70 HU. The differences of dosimetric indexes of Plan1, Plan2, Plan3, Plan4 decreased sequentially compare to Plan0, the difference of the dosimetry parameters of Plan4 and Plan0 was the smallest. The differences of PTV D(99), PTV D(95), isocenter dose, D(mean) of all tissues, D(max) of bilateral eye balls, D(max) of bilateral lens, D(max) of bilateral optic nerves, D(mean) of bilateral parotid, V(20) of bilateral parotid, D(50) of bilateral parotid, D(max) of spinal cord, D(max) of brainstem, D(5) of brainstem between Plan4 and Plan0 were all less than 1%. The difference of V(30) in bilateral parotid between Plan4 and Plan0 was less than 1.5%. In the comparison of the pixel dose distribution, the regions of dose distribution difference greater than 1% mainly distributed in the air cavity, bone periphery and the skin. The target area of the IMRT plan for NPC based on MR images met 95% of the prescribed dose, and the dose of each organ at risk was within the dose limit. Conclusions: The assignment method of each tissue and organs set to populate CT value compared with other methods has the least influence on the dose calculation of NPC-IMRT plan, which could meet the clinical requirements. Therefore, it should be the first choice of assignment method when designing NPC-IMRT plan based on MR image.

Masticatory muscle index for indicating skeletal muscle mass in patients with head and neck cancer.

BackgroundA typical assessment for sarcopenia involves the use of abdominal computed tomography (CT) for calculating the skeletal muscle index (SMI) at the level of the third lumbar vertebra (L3). However, abdominal CT is not regularly performed on patients with head and neck cancer (HNC). We investigated whether masticatory SMI (M-SMI) measurements based on head and neck CT scans can be used to conduct sarcopenia assessments by evaluating whether M-SMI is correlated with L3-SMI.MethodsAbdominal and head and neck CT images of patients with trauma (n = 50) and HNC (n = 52) were analyzed retrospectively. Both manual delineation and threshold selection methods were used to measure cross-sectional areas of masticatory muscles and those of muscles at the L3 level on CT images. Muscle cross-sectional areas were normalized to height squared to calculate SMI, and a multivariate linear regression model was established to evaluate the correlation between the M-SMI and L3-SMI. Receiver operating characteristic curve analysis was used to assess the ability of the M-SMI to identify sarcopenia, and Cox logistic regression was used to identify predictors of sarcopenia.ResultsPatients with HNC had significantly lower M-SMI and L3-SMI than did patients with trauma (p = 0.011 and 0.03, respectively). M-SMI and L3-SMI were strongly correlated (r = 0.901, p < 0.001); in the multivariate model that included sex, the correlation was stronger (r = 0.913, p < 0.001). The associations of sarcopenia with a lower M-SMI (p < 0.001), male sex (p = 0.028), and advanced age (p = 0.011) were significant, and multivariate logistic analysis demonstrated that an M-SMI of <5.5 was an independent predictor of sarcopenia (hazard ratio = 5.37, p < 0.001).ConclusionsM-SMI assessment in routine head and neck CT scans is feasible and can be an alternative for detecting sarcopenia in patients with HNC.

An investigation of the morphometry and localization of the foramen ovale and rotundum in asymptomatic individuals and patients with trigeminal neuralgia

To compare the morphometric characteristics of the foramen ovale (FO) and foramen rotundum (FR), and their localization in the middle cranial fossa on the head and neck computed tomography images of patients with trigeminal neuralgia (TN) and asymptomatic individuals. The FO and FR length, width, and area parameters were examined in 158 asymptomatic individuals and 19 patients with TN. Their localization in the middle cranial fossa was determined according to the sagittal (y) axis passing through the middle of the dorsum sella and connecting the foremost point and the rearmost point of the skull and the transverse (x) axis passing through the middle of this axis. In the comparison with asymptomatic individuals, data on the painful side of patients with trigeminal neuralgia were used. The mean width of the FO and its distance from the transverse axis were determined to be 3.36 ± 0.79 mm and 1.44 ± 0.39 mm in asymptomatic individuals and 2.88 ± 0.83 mm and 1.23 ± 0.45 in TN patients, respectively. These FO parameters were statistically significantly smaller in patients with TN. In asymptomatic individuals, while the mean width of the FR was determined to be 2.05 ± 0.48 mm, the mean length was 2.14 ± 0.47 mm, and its distances to the transverse axis and sagittal axis were found to be 2.65 ± 0.35 mm and 1.96 ± 0.25 mm, respectively. In patients with TN, while the mean width of the FR was revealed to be 1.77 ± 0.46 mm, the mean length was 1.78 ± 0.42 mm, and its distances to the transverse axis and sagittal axis were found to be 2.33 ± 0.40 mm and 1.87 ± 0.16 mm, respectively. These FR parameters were statistically significantly smaller in patients with TN. Statistically significantly smaller dimensions of the FO and FR and their distances to the sagittal and transverse axes in patients with TN suggested that they might be among the causes of TN.

Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers

Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions. To explore clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy. This quality improvement study used a multicenter imaging data set comprising 519 pelvic and 242 head and neck computed tomography (CT) scans from 8 distinct clinical sites and patients diagnosed either with prostate or head and neck cancer. The scans were acquired as part of treatment dose planning from patients who received intensity-modulated radiation therapy between October 2013 and February 2020. Fifteen different OARs were manually annotated by expert readers and radiation oncologists. The models were trained on a subset of the data set to automatically delineate OARs and evaluated on both internal and external data sets. Data analysis was conducted October 2019 to September 2020. The autocontouring solution was evaluated on external data sets, and its accuracy was quantified with volumetric agreement and surface distance measures. Models were benchmarked against expert annotations in an interobserver variability (IOV) study. Clinical utility was evaluated by measuring time spent on manual corrections and annotations from scratch. A total of 519 participants' (519 [100%] men; 390 [75%] aged 62-75 years) pelvic CT images and 242 participants' (184 [76%] men; 194 [80%] aged 50-73 years) head and neck CT images were included. The models achieved levels of clinical accuracy within the bounds of expert IOV for 13 of 15 structures (eg, left femur, κ = 0.982; brainstem, κ = 0.806) and performed consistently well across both external and internal data sets (eg, mean [SD] Dice score for left femur, internal vs external data sets: 98.52% [0.50] vs 98.04% [1.02]; P = .04). The correction time of autogenerated contours on 10 head and neck and 10 prostate scans was measured as a mean of 4.98 (95% CI, 4.44-5.52) min/scan and 3.40 (95% CI, 1.60-5.20) min/scan, respectively, to ensure clinically accepted accuracy. Manual segmentation of the head and neck took a mean 86.75 (95% CI, 75.21-92.29) min/scan for an expert reader and 73.25 (95% CI, 68.68-77.82) min/scan for a radiation oncologist. The autogenerated contours represented a 93% reduction in time. In this study, the models achieved levels of clinical accuracy within expert IOV while reducing manual contouring time and performing consistently well across previously unseen heterogeneous data sets. With the availability of open-source libraries and reliable performance, this creates significant opportunities for the transformation of radiation treatment planning.

Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

BackgroundImpaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model with a commercial atlas-based model for fast auto-segmentation of the masticatory muscles on head and neck computed tomography (CT) images.Material and methodsPaired masseter (M), temporalis (T), medial and lateral pterygoid (MP, LP) muscles were manually segmented on 56 CT images. CT images were randomly divided into training (n = 27) and validation (n = 29) cohorts. Two methods were used for automatic delineation of masticatory muscles (MMs): Deep learning auto-segmentation (DLAS) and atlas-based auto-segmentation (ABAS). The automatic algorithms were evaluated using Dice similarity coefficient (DSC), recall, precision, Hausdorff distance (HD), HD95, and mean surface distance (MSD). A consolidated score was calculated by normalizing the metrics against interobserver variability and averaging over all patients. Differences in dose (∆Dose) to MMs for DLAS and ABAS segmentations were assessed. A paired t-test was used to compare the geometric and dosimetric difference between DLAS and ABAS methods.ResultsDLAS outperformed ABAS in delineating all MMs (p < 0.05). The DLAS mean DSC for M, T, MP, and LP ranged from 0.83 ± 0.03 to 0.89 ± 0.02, the ABAS mean DSC ranged from 0.79 ± 0.05 to 0.85 ± 0.04. The mean value for recall, HD, HD95, MSD also improved with DLAS for auto-segmentation. Interobserver variation revealed the highest variability in DSC and MSD for both T and MP, and the highest scores were achieved for T by both automatic algorithms. With few exceptions, the mean ∆D98%, ∆D95%, ∆D50%, and ∆D2% for all structures were below 10% for DLAS and ABAS and had no detectable statistical difference (P > 0.05). DLAS based contours had dose endpoints more closely matched with that of the manually segmented when compared with ABAS.ConclusionsDLAS auto-segmentation of masticatory muscles for the head and neck radiotherapy had improved segmentation accuracy compared with ABAS with no qualitative difference in dosimetric endpoints compared to manually segmented contours.

Machine Learning for Identification of Craniomaxillofacial Radiographic Lesions

Machine Learning for Identification of Craniomaxillofacial Radiographic Lesions

Development of size-specific institutional diagnostic reference levels for computed tomography protocols in neck imaging

Purpose: To develop size-specific institutional diagnostic reference levels (DRLs) for computed tomography (CT) protocols used in neck CT imaging (cervical spine CT, cervical CT angiography (CTA) and cervical staging CT) and to compare institutional to national DRLs. Materials and methods: Cervical CT examinations (spine, n = 609; CTA, n = 505 and staging CT, n = 184) performed between 01/2016 and 06/2017 were included in this retrospective study. For each region and examination, the volumetric CT dose index (CTDIvol) and dose-length product (DLP) were determined and binned into size bins according to patient water-equivalent diameter (dw). Linear regression analysis was performed to calculate size-specific institutional DRLs for CTDIvol and DLP, applying the 75th percentile as the upper limit for institutional DRLs. The mean institutional CTDIvol and DLP were compared to national DRLs (CTDIvol 20 mGy for cervical spine CT (DLP 300 mGycm) and cervical CTA (DLP 600 mGycm), and CTDIvol 15 mGy for cervical staging CT (DLP 330 mGycm)). Results: The mean CTDIvol and DLP (±standard deviation) were 15.2 ± 4.1 mGy and 181.5 ± 88.3 mGycm for cervical spine CT; 8.1 ± 4.3 mGy and 280.2 ± 164.3 mGycm for cervical CTA; 8.6 ± 1.9 mGy and 162.8 ± 85.0 mGycm for cervical staging CT. For all CT protocols, there was a linear increase in CTDIvol and DLP with increasing dw. For the CTDIvol, size-specific institutional DRLs increased with dw from 14 to 29 mGy for cervical spine CT, from 5 to 17 mGy for cervical CTA and from 8 to 13 mGy for cervical staging CT. For the DLP, size-specific institutional DRLs increased with dw from 130 to 510 mGycm for cervical spine CT, from 140 to 640 mGycm for cervical CTA and from 140 to 320 mGycm for cervical staging CT. Institutional DRLs were lower than national DRLs by 81% and 67% for cervical spine CT (dw = 17.8 cm), 43% and 51% for cervical CTA (dw = 19.5 cm) and 59% and 53% for cervical staging CT (dw = 18.8 cm) for CTDIvol and DLP, respectively. Conclusion: Size-specific institutional DRLs were generated for neck CT examinations. The mean institutional CTDIvol and DLP values were well below national DRLs.

Determination of the Radiation Dose Level in Different Slice Computerized Tomography

Today, many imaging tools have emerged in the medical field using ionizing radiation. Computed tomography (CT) is regarded the most creative and common modality among the other imaging devices. In CT imaging, radiation may scatter to the environment from the patient and the interaction medium. In this study, two CT scanners with different slice numbers were used to measure the scatter radiation dose to the environment. Alderson rando phantom was utilized in the acquired CT exams as human body simulation. Radiation dose levels at different distances were determined by placing thermoluminescence dosimeters in the surrounding environment (at different distances) during phantom’s head-neck and thorax CT imaging. As a result, it has been found that the radiation dose varies between 13.90±1.96 µSv and 96.79±11.12 µSv in head and neck CT imaging. While, in thorax CT scan, the radiation dose varies between 5.28±0.84 µSv and 20.63±1.76 µSv.

Regularized Three-Dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction in Head and Neck CT Images

The reduction of metal artifacts in computed tomography (CT) images, specifically for strong artifacts generated from multiple metal objects, is a challenging issue in medical imaging research. Although there have been some studies on supervised metal artifact reduction through the learning of synthesized artifacts, it is difficult for simulated artifacts to cover the complexity of the real physical phenomena that may be observed in X-ray propagation. In this paper, we introduce metal artifact reduction methods based on an unsupervised volume-to-volume translation learned from clinical CT images. We construct three-dimensional adversarial nets with a regularized loss function designed for metal artifacts from multiple dental fillings. The results of experiments using 915 CT volumes from real patients demonstrate that the proposed framework has an outstanding capacity to reduce strong artifacts and to recover underlying missing voxels, while preserving the anatomical features of soft tissues and tooth structures from the original images.

Efficacy of head and neck computed tomography for skeletal muscle mass estimation in patients with head and neck cancer

ObjectivesDiminished skeletal muscle mass (SMM) is a reliable marker of poor survival outcomes in patients with cancer. SMM or body composition is generally assessed at the third lumbar vertebra (L3) by abdominal computed tomography (CT) scans, not routinely evaluated in patients with head and neck squamous cell carcinoma (HNSCC). Therefore, we evaluated the effectiveness of head and neck CT images to assess SMM in patients with HNSCC for predicting their overall survival. Materials and MethodsSMM was assessed in 305 consecutive patients with stage III–IV HNSCC by measuring the cross-sectional area (CSA) at the third lumbar and cervical (C3) vertebrae levels. A formula for predicting the L3 SMM was established using linear regression analysis obtained from C3 CSA and other clinical factors. The actual SMM CSAs measured at L3 level and those obtained from the prediction model were compared using correlation analysis. The predictive power of our formula for estimating overall survival was compared using C-index. ResultsMedian SMM CSAs at the L3 and C3 levels were 174.5 cm2 and 56.3 cm2, respectively, and were not strongly correlated (adjusted R2 = 0.421). Prediction model 2 included the strongest predictive factors including sex, age, weight, and C3 SMM CSA, and significantly increased the L3 SMM correlation power (adjusted R2 = 0.721). The C-index of the prediction model was 0.713 (95% confidence interval 0.692–0.747). ConclusionsHead and neck CT imaging might be useful to estimate L3 SMM and predict overall survival in HNSCC patients.

Age-dependent reference values of the thyroid gland in pediatric population; from routine computed tomography data

PurposeTo evaluate age-dependent reference values for measurements of the thyroid gland in pediatric patients. MethodIn total 425 chest and neck computed tomography images of pediatric patients without pathology of the thyroid gland were retrospectively reviewed. Measurements of the thyroid gland were obtained in three dimensions (width, depth, length). The total volume was the sum of volumes of each lobe. Measurements were compared among the six groups according to age from 0 to 18 years. ResultsThere were moderate to strong positive linear correlations between age and all values (r = 0.58–0.82, all p < 0.001). The total volume of the thyroid gland showed a strong positive linear correlation with the three linear measurements of each lobe (all r > 0.7, all p < 0.001). Among these measurements, the depth of the right lobe showed the highest Pearson correlation coefficient with the total volume of the thyroid gland (r = 0.859). ConclusionThe depth of right lobe could be a representative measurement of total thyroid gland volume, similar to prior findings in adults.

Uncinate Process Area as a New Sensitive Morphological Parameter to Predict Cervical Neural Foraminal Stenosis

Background: Hypertrophy of the uncovertebral joint has been considered as a major cause of cervical neural foraminal stenosis (CNFS). The cross-sectional area of the uncinate process is a key morphologic parameter in the identification of uncovertebral joint hypertrophy. To evaluate the connection between CNFS and the uncinate process, we devised a new morphological parameter, the uncinate process area (UPA). Objective: We hypothesized that the UPA is an important morphologic parameter in the diagnosis of CNFS. Study Design: Retrospective observational study. Setting: The single center study in Incheon, Republic of Korea. Methods: UPA data were collected from 146 patients with CNFS and 197 control subjects who underwent neck computed tomography (CT) as part of a routine medical examination. Neck CT images were obtained from all subjects. The whole cross-sectional area of the bone margin of the uncinate process was measured at the C5-6 intervertebral disc level on CT scans using a picture archiving and communications system. Results: The average UPA was 15.52 mm2 in the control group and 29.97 mm2 in the CNFS group. The CNFS group displayed significantly greater UPA levels (P &lt; 0.001). Regarding the validity of the UPA as a predictor of CNFS, the receiver operating characteristic curve analysis revealed an optimal cut-off point for the UPA of 21.15 mm2 , with 91.8% sensitivity, 93.4% specificity, and an area under the curve of 0.972 (95% CI,0.956-0.989) in the CNFS group. Limitations: Anatomically, the UP is located on the superior lateral surfaces of the C3-7 cervical vertebral bodies. However, we focused on the C5-6 uncovertebral joint level, because many previous studies revealed C6 UP has the greatest height among UP and C5-6 uncovertebral joint hypertrophy is a primary cause of CNFS. Conclusions: The newly devised UPA is a sensitive parameter for assessing CNFS. A hypertrophied UPA is associated with an increased risk of CNFS. We think that this result will be helpful for diagnostic radiology in evaluating patients with CNFS. Institutional Review Board (IRB) approval number: IS16RISI0002 Key words: Uncinate process area, cervical neural foraminal stenosis, Uncovertebral joint hypertrophy, optimal cut-off point, cross- sectional area

Interobserver agreement of skeletal muscle mass measurement on head and neck CT imaging at the level of the third cervical vertebra

ObjectivesSkeletal muscle mass (SMM) is most often assessed in cancer patients on abdominal computed tomography (CT) imaging at the level of the third lumbar vertebra (L3). Abdominal CT imaging is not routinely performed in head and neck cancer (HNC) patients. Recently, a novel method to assess SMM on a single transversal CT slice at the level of the third cervical vertebra (C3) was published. The objective of this study was to assess the robustness of this novel C3 measurement method in terms of interobserver agreement.Patients and methodsPatients diagnosed with locally advanced head and neck squamous cell carcinoma (LA-HNSCC) at our center between 2007 and 2011 were evaluated. Fifty-four patients with were randomly selected for analysis. Six observers independently measured the cross-sectional muscle area (CSMA) at the level of C3 using a predefined, written protocol as instruction. Interobserver agreement was assessed using intraclass correlation coefficients (ICCs), a Bland–Altman plot and Fleiss’ kappa (κ).ResultsThe agreement in vertebra selection between all observers was excellent (Fleiss’ κ: 0.96). There was a substantial agreement between all observers in single slice selection (Fleiss’ κ: 0.61). For all CSMA measurements, ICCs were excellent (0.763–0.969; all p < 0.001). The Bland–Altman plot showed good agreement between measurements, with narrow limits of agreement.ConclusionInterobserver agreement for SMM measurement at the level of C3 was excellent. Assessment of SMM at the level of C3 is easy and robust and can performed on routinely available imaging in HNC patients.