Bone Imaging Research Articles

Photon-counting CT allows better visualization of temporal bone structures in comparison with current generation multi-detector CT

PurposeTo compare photon-counting CT (PCCT) and multi-detector CT (MDCT) for visualization of temporal bone anatomic structures.MethodsThirty-six exams of temporal bones without pathology were collected from consecutive patients on a MDCT, and another 35 exams on a PCCT scanner. Two radiologists independently scored visibility of 14 structures for the MDCT and PCCT dataset, using a 5-point Likert scale, with a 2-month wash-out period. For MDCT, the acquisition parameters were: 110 kV, 64 × 0.6 mm (slice thickness reconstructed to 0.4 mm), pitch 0.85, quality ref. mAs 150, and 1 s rotation time; for PCCT: 120 kV, 144 × 0.2 mm, pitch 0.35, IQ level 75, and 0.5 s rotation time. Patient doses were reported as dose length product values (DLP). Statistical analysis was done using the Mann–Whitney U test, visual grading characteristic (VGC) analysis, and ordinal regression.ResultsSubstantial agreement was found between readers (intraclass correlation coefficient 0.63 and 0.52 for MDCT and PCCT, resp.). All structures were scored higher for PCCT (p < 0.0001), except for Arnold’s canal (p = 0.12). The area under the VGC curve was 0.76 (95% CI, 0.73–0.79), indicating a significantly better visualization on PCCT. Ordinal regression showed the odds for better visualization are 354 times higher (95% CI, 75–1673) in PCCT (p < 0.0001). Average (range) of DLP was 95 (79–127) mGy*cm for MDCT and 74 (50–95) mGy*cm for PCCT (p < 0.001).ConclusionPCCT provides a better depiction of temporal bone anatomy than MDCT, at a lower radiation dose.Graphical Critical relevance statementPCCT provides a better depiction of temporal bone anatomy than MDCT, at a lower radiation dose.Key pointsPCCT allows high-resolution imaging of temporal bone structures.Compared to MDCT, the visibility of normal temporal bone structures is scored better with PCCT.PCCT allows to obtain high-quality CT images of the temporal bones at lower radiation doses than MDCT.

Age and gender classification with bone images using deep learning algorithms

In paediatrics, bone age is a crucial indicator of how a child's skeleton is developing. They have had great success ever since the creation of deep learning (DL)-based bone age prediction tools. Deep features learning, however, has a significant computing overhead problem. Deep convolution layers are used in this technique to learn representative features in the small yet useful regions that are extracted for feature learning. This work suggests using an extreme learning machine algorithm as the fundamental architecture in the final bone age assessment study to realise the rapid computation speed and feature interaction. The viability and efficacy of the suggested strategy have been verified by experiments using data that is openly accessible. The suggested model is explicitly trained using a cutting-edge end-to-end learning architecture employing bone scans to extract the most discriminative patches from the original high-resolution image. The bone picture is the foundation of the procedure. Our main objective is to categorize individuals by age using convolution neural network (CNN) classification models, such as the Xception and Mobile Net models. As a result, we have achieved results that are 90% and 94% accurate in classifying people by age using CNN models.

Generation of Synthetic Images of Trabecular Bone Based on Micro-CT Scans

Creating synthetic images of trabecular tissue provides an alternative for researchers to validate algorithms designed to study trabecular bone. Developing synthetic images requires baseline data, such as datasets of digital biological samples or templates, often unavailable due to privacy restrictions. Even when this baseline is available, the standard procedure combines the information to generate a single template as a starting point, reducing the variability in the generated synthetic images. This work proposes a methodology for building synthetic images of trabecular bone structure, creating a 3D network that simulates it. Next, the technical characteristics of the micro-CT scanner, the biomechanical properties of trabecular bones, and the physics of the imaging process to produce a synthetic image are simulated. The proposed methodology does not require biological samples, datasets, or templates to generate synthetic images. Since each synthetic image built is unique, the methodology is enabled to generate a vast number of synthetic images, useful in the performance comparison of algorithms under different imaging conditions. The created synthetic images were assessed using microarchitecture parameters of reference, and experimental results provided evidence that the obtained values match approaches requiring initial data. The scope of this methodology covers research aspects related to using synthetic images in further biomedical research or the development of educational training tools to understand the medical image.

Precision of bone mechanoregulation assessment in humans using longitudinal high-resolution peripheral quantitative computed tomography in vivo.

Local mechanical stimuli in the bone microenvironment are essential for the homeostasis and adaptation of the skeleton, with evidence suggesting that disruption of the mechanically-driven bone remodelling process may lead to bone loss. Longitudinal clinical studies have shown the combined use of high-resolution peripheral quantitative computed tomography (HR-pQCT) and micro-finite element analysis can be used to measure load-driven bone remodelling in vivo; however, quantitative markers of bone mechanoregulation and the precision of these analyses methods have not been validated in human subjects. Therefore, this study utilised participants from two cohorts. A same-day cohort (n=33) was used to develop a filtering strategy to minimise false detections of bone remodelling sites caused by noise and motion artefacts present in HR-pQCT scans. A longitudinal cohort (n=19) was used to develop bone imaging markers of trabecular bone mechanoregulation and characterise the precision for detecting longitudinal changes in subjects. Specifically, we described local load-driven formation and resorption sites independently using patient-specific odds ratios (OR) and 99% confidence intervals. Conditional probability curves were computed to link the mechanical environment to the remodelling events detected on the bone surface. To quantify overall mechanoregulation, we calculated a correct classification rate measuring the fraction of remodelling events correctly identified by the mechanical signal. Precision was calculated as root-mean-squared averages of the coefficient of variation (RMS-SD) of repeated measurements using scan-rescan pairs at baseline combined with a one-year follow-up scan. We found no significant mean difference (p<0.01) between scan-rescan conditional probabilities. RMS-SD was 10.5% for resorption odds, 6.3% for formation odds, and 1.3% for correct classification rates. Bone was most likely to be formed in high-strain and resorbed in low-strain regions for all participants, indicating a consistent, regulated response to mechanical stimuli. For each percent increase in strain, the likelihood of bone resorption decreased by 2.0±0.2%, and the likelihood of bone formation increased by 1.9±0.2%, totalling 38.3±1.1% of strain-driven remodelling events across the entire trabecular compartment. This work provides novel robust bone mechanoregulation markers and their precision for designing future clinical studies.

Automated model for identification on mastoid of temporal bone image

Mastoiditis occurs due to inflammation that can affect the structure of the mastoid bone. The mastoid bone consists of the mastoid air cell system (MACS) which protects the ear structures and regulates air pressure in the ear and has different sizes and characteristics, making it very difficult to identify precisely. This study aims to identify and find the right MACS size by developing an automatic identification model and obtaining the optimal threshold value in the segmentation process using the extended adaptive threshold (eAT) method. The research dataset uses computed tomography (CT)-scan images of 308 slices of 12 patients indicated for mastoiditis. The results of this study provide identification that has the right MACS accuracy and size. Overall, the optimal segmentation process obtained the smallest threshold value of 57 and the largest threshold value of 63, the smallest MACS size is 4.025 cm2 and the largest is 8.816 cm2 with an accuracy rate of 93.4%. The smaller MACS size indicates inflammation in the mastoid area and these patients require more intensive treatment.

Deep learning enhanced ultra-fast SPECT/CT bone scan in patients with suspected malignancy: quantitative assessment and clinical performance

Objectives. To evaluate the clinical performance of deep learning-enhanced ultrafast single photon emission computed tomography/computed tomography (SPECT/CT) bone scans in patients with suspected malignancy. Approach. In this prospective study, 102 patients with potential malignancy were enrolled and underwent a 20 min SPECT/CT and a 3 min SPECT scan. A deep learning model was applied to generate algorithm-enhanced images (3 min DL SPECT). The reference modality was the 20 min SPECT/CT scan. Two reviewers independently evaluated general image quality, Tc-99m MDP distribution, artifacts, and diagnostic confidence of 20 min SPECT/CT, 3 min SPECT/CT, and 3 min DL SPECT/CT images. The sensitivity, specificity, accuracy, and interobserver agreement were calculated. The lesion maximum standard uptake value (SUVmax) of the 3 min DL and 20 min SPECT/CT images was analyzed. The peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) were evaluated. Main results. The 3 min DL SPECT/CT images showed significantly superior general image quality, Tc-99m MDP distribution, artifacts, and diagnostic confidence than the 20 min SPECT/CT images (P < 0.0001). The diagnostic performance of the 20 min and 3 min DL SPECT/CT images was similar for reviewer 1 (paired X 2 = 0.333, P = 0.564) and reviewer 2 (paired X 2 = 0.05, P = 0.823). The diagnosis results for the 20 min (kappa = 0.822) and 3 min DL (kappa = 0.732) SPECT/CT images showed high interobserver agreement. The 3 min DL SPECT/CT images had significantly higher PSNR and SSIM than the 3 min SPECT/CT images (51.44 versus 38.44, P < 0.0001; 0.863 versus 0.752, P < 0.0001). The SUVmax of the 3 min DL and 20 min SPECT/CT images showed a strong linear relationship (r = 0.991; P < 0.0001). Significance. Ultrafast SPECT/CT with a 1/7 acquisition time can be enhanced by a deep learning method to achieve comparable image quality and diagnostic value to those of standard acquisition.

Computed Tomography 2.0: New Detector Technology, AI, and Other Developments.

Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.

Micron-resolution Imaging of Cortical Bone under 14 T Ultrahigh Magnetic Field.

Compact, mineralized cortical bone tissues are often concealed on magnetic resonance (MR) images. Recent development of MR instruments and pulse techniques has yielded significant advances in acquiring anatomical and physiological information from cortical bone despite its poor 1 H signals. This work demonstrates the first MR research on cortical bones under an ultrahigh magnetic field of 14 T. The 1 H signals of different mammalian species exhibit multi-exponential decays of three characteristic T2 or T2 * values: 0.1-0.5ms, 1-4ms, and 4-8ms. Systematic sample comparisons attribute these T2 /T2 * value ranges to collagen-bound water, pore water, and lipids, respectively. Ultrashort echo time (UTE) imaging under 14 T yielded spatial resolutions of 20-80 microns, which resolves the 3D anatomy of the Haversian canals. The T2 * relaxation characteristics further allow spatial classifications of collagen, pore water and lipids in human specimens. The study achieves a record of the spatial resolution for MR imaging in bone and shows that ultrahigh-field MR has the unique ability to differentiate the soft and organic compartments in bone tissues.

Frequency of congenital inner ear malformations among cochlear implant candidates at a tertiary care hospital Pakistan

Objectives: To determine the frequency and factors associated with inner ear anomalies among cochlear implant candidates. Method: The cross-sectional study was conducted at the Department of Otolaryngology, Aga Khan University Hospital, Karachi, from October 2021 to March 2022, and comprised patients of either gender aged &lt;18 years who received cochlear implants. All subjects underwent computed tomography and magnetic resonance imaging of the temporal bone to identify any inner ear anomaly. The data gathered included implantation age, gender, computed tomography and magnetic resonance imaging findings, and history of congenital anomaly, neurodevelopmental disorder, family hearing loss, consanguinity and brain-evoked response auditory result. Data was analysed using SPSS 23. Results: Of the 120 patients, 66(55%) were females and 54(45%) were males. The median age of the patients was 3 years (interquartile range: 2-5 years). Of the total, 7(5.8%) patients presented with inner ear malformations; 3(2.5%) each with enlarged vestibular aqueduct, Mondini malformation type II, and 1(0.8%) with semi-circular canal dysplasia. Conclusion: The frequency of inner ear anomalies amongst cochlear implant candidates was 5.8%, and the most common anomalies were enlarged vestibular aqueduct and Mondini malformation type II. Key Words: Cochlear implant, Inner ear anomalies, Inner ear malformations, Hearing loss, Frequency, Consanguinity, loss, Temporal bone, Tomography, Magnetic resonance, Neurodevelopmental disorders, Otolaryngology.

Primary care provider attitudes about and tendency to use non-recommended surveillance tests after curative breast cancer treatment.

Little is known about the factors contributing to the receipt of non-recommended surveillance testing among early-stage breast cancer survivors. We assessed primary care providers (PCP) attitudes about and tendency to order non-recommended surveillance testing for asymptomatic early-stage breast cancer survivors post-adjuvant chemotherapy. A stratified random sample of PCPs identified by early-stage breast cancer survivors were surveyed (N = 518, 61% response rate). PCPs were asked how likely they would be to order bone scans, imaging and/or tumor marker testing using a clinical vignette of an early-stage asymptomatic patient where these tests are non-recommended. A composite tendency to order score was created and categorized by tertiles (low, moderate, high). PCP-reported factors associated with high and moderate tendency to order non-recommended testing (vs. low) were estimated using multivariable, multinomial logistic regression. In this sample, 26% reported a high tendency to order non-recommended surveillance tests during survivorship for early-stage breast cancer survivors. PCPs who identified as family practice physicians and PCPs reporting more confidence in ordering surveillance testing were more likely to report a high tendency to order non-recommended testing (vs. low) ((aOR family practice 2.09, CI 1.2, 3.8; aOR more confidence 1.9, CI 1.1, 3.3). In this population-based sample of PCPs caring for breast cancer survivors, over a quarter of PCPs reported they would order non-recommended surveillance testing for asymptomatic early-stage breast cancer survivors. Efforts to better support PCPs and disseminate information about appropriate surveillance for cancer survivors are warranted.

The force awakens, but questions remain: The future of MGUS.

The force awakens, but questions remain: The future of MGUS.

Diagnosis of SPECT/CT bone imaging combined with two serum examinations in patients with bone metastases from pulmonary cancer.

To study the clinical diagnostic value of SPECT/CT bone imaging combined with two serum examinations in patients with bone metastases from pulmonary cancer. The clinical data of 120 patients consistent with pulmonary cancer admitted to the First Affiliated Hospitalof Hebei North University from March 2019 to December 2019 were selected for retrospective analysis, and they were divided into the bone metastasis group (n = 58) and non-bone metastasis group (n = 62) according to comprehensive evaluation result of X-ray, CT, MRI and clinical follow-up. The CT values of patients were obtained by SPECT/CT bone imaging to compare serum levels of ALP (alkaline phosphatase belongs to phosphoric monoester hydrolases, as a specific phosphatase, mainly in body tissues and body fluid) and BAP (bone alkaline phosphatase is formed by different modification and processing of alkaline phosphatase, and is mainly released by osteoblasts) and CT values of patients in both groups, using receiver operating characteristic (ROC) curve to evaluate the diagnostic efficacy of single detection and combined detection. SPECT/CT bone imaging in patients with bone metastasis from pulmonary cancer showed abnormal radioactive accumulation in spine, pelvis and bilateral ribs. Serum ALP, BAP and CT values in bone metastasis group were overtly higher than the non-bone metastasis group (P < 0.001). Logistic regression analysis showed that serum ALP, BAP and CT value were independent risk factors for bone metastasis from pulmonary cancer. The AUC value and Youden index of combined diagnosis were higher than those of single diagnosis. SPECT/CT bone imaging combined with serum detection of ALP and BAP in patients with pulmonary cancer is helpful for early diagnosis of bone metastasis, which provides more basis for the formulation and selection of clinical treatment options.

A primary and secondary feature interactive learning network for bone age assessment

Bone Age Assessment (BAA) is a method used to assess the degree of development of adolescents. Existing Bone Age Assessment deep learning models require additional manual annotation to locate several important regions. However, manual annotations are costly and subjective, and these regions are independent and lack mutual information exchange. In this paper, a primary and secondary feature interactive learning network (PSILNet) is proposed to locate important regions and learn their features from each other. PSILNet is specifically trained to place more attention to those bone age related regions in the X-ray image. Specifically, a backbone network with an attention erasure module is introduced to obtain primary and secondary features of hand bone images. Then, a feature self-learning module (FSLM) is designed to enhance both primary and secondary features. FSLM allows the network attention to focus on more than just the most noticeable regions. Furthermore, we designed a feature-mutual learning module (FMLM) to simulate the interaction of feature information, allowing the network to focus on the regions that are different from both. PSILNet adopts an end-to-end training method without any additional manual annotation. Experiments show that the mean absolute error of PSILNet is 4.09 months on the Radiological Society of North America (RSNA) dataset. It has competitive performance with existing deep learning models for BAA. In order to verify the generalizability of the proposed network. Experiments are conducted on The Asian Face Age Dataset (AFAD) and achieve the best result with a mean absolute error of 3.14 years.

Recognition and 3D Visualization of Human Body Parts and Bone Areas Using CT Images

Abstract The advent of medical imaging significantly assisted in disease diagnosis and treatment. This study introduces to a framework for detecting several human body parts in Computerised Tomography (CT) images formatted in DICOM files. In addition, the method can highlight the bone areas inside CT images and transform 2D slices into a visual 3D model to illustrate the structure of human body parts. Firstly, we leveraged shallow convolutional Neural Networks to classify body parts and detect bone areas in each part. Then, Grad-CAM was applied to highlight the bone areas. Finally, Insight and Visualization libraries were utilized to visualize slides in 3D of a body part. As a result, the classifiers achieved 98 % in F1-score in the classification of human body parts on a CT image dataset, including 1234 slides capturing body parts from a woman for the training phase and 1245 images from a male for testing. In addition, distinguishing between bone and non-bone images can reach 97 % in F1-score on the dataset generated by setting a threshold value to reveal bone areas in CT images. Moreover, the Grad-CAM-based approach can provide clear, accurate visualizations with segmented bones in the image. Also, we successfully converted 2D slice images of a body part into a lively 3D model that provided a more intuitive view from any angle. The proposed approach is expected to provide an interesting visual tool for supporting doctors in medical image-based disease diagnosis.

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning.

Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent of bone mineral density (BMD). Ultrashort echo time (UTE) MRI techniques of measuring cortical bone porosity and geometry have been extensively validated in preclinical studies and have recently been shown to detect impaired bone quality in vivo in patients with osteoporosis. However, these techniques rely on laborious image segmentation, which is clinically impractical. Additionally, UTE MRI porosity techniques typically require long scan times or external calibration samples and elaborate physics processing, which limit their translatability. To this end, the UTE MRI-derived Suppression Ratio has been proposed as a simple-to-calculate, reference-free biomarker of porosity which can be acquired in clinically feasible acquisition times. To explore whether a deep learning method can automate cortical bone segmentation and the corresponding analysis of cortical bone imaging biomarkers, and to investigate the Suppression Ratio as a fast, simple, and reference-free biomarker of cortical bone porosity. In this retrospective study, a deep learning 2D U-Net was trained to segment the tibial cortex from 48 individual image sets comprised of 46 slices each, corresponding to 2208 training slices. Network performance was validated through an external test dataset comprised of 28 scans from 3 groups: (1) 10 healthy, young participants, (2) 9 postmenopausal, non-osteoporotic women, and (3) 9 postmenopausal, osteoporotic women. The accuracy of automated porosity and geometry quantifications were assessed with the coefficient of determination and the intraclass correlation coefficient (ICC). Furthermore, automated MRI biomarkers were compared between groups and to dual energy X-ray absorptiometry (DXA)- and peripheral quantitative CT (pQCT)-derived BMD. Additionally, the Suppression Ratio was compared to UTE porosity techniques based on calibration samples. The deep learning model provided accurate labeling (Dice score 0.93, intersection-over-union 0.88) and similar results to manual segmentation in quantifying cortical porosity (R2≥0.97, ICC≥0.98) and geometry (R2≥0.82, ICC≥0.75) parameters in vivo. Furthermore, the Suppression Ratio was validated compared to established porosity protocols (R2≥0.78). Automated parameters detected age- and osteoporosis-related impairments in cortical bone porosity (P≤.002) and geometry (P values ranging from <0.001 to 0.08). Finally, automated porosity markers showed strong, inverse Pearson's correlations with BMD measured by pQCT (|R|≥0.88) and DXA (|R|≥0.76) in postmenopausal women, confirming that lower mineral density corresponds to greater porosity. This study demonstrated feasibility of a simple, automated, and ionizing-radiation-free protocol for quantifying cortical bone porosity and geometry in vivo from UTE MRI and deep learning.

A Computer Vision Algorithm to Classify Pneumatization of the Mastoid Process on Temporal Bone Computed Tomography Scans.

Pneumatization of the mastoid process is variable and of significance to the operative surgeon. Surgical approaches to the temporal bone require an understanding of pneumatization and its implications for surgical access. This study aims to determine the feasibility of using deep learning convolutional neural network algorithms to classify pneumatization of the mastoid process. De-identified petrous temporal bone images were acquired from a tertiary hospital radiology picture archiving and communication system. A binary classification mode in the pretrained convolutional neural network was used to investigate the utility of convolutional neural networks in temporal bone imaging. False positive and negative images were reanalyzed by the investigators and qualitatively assessed to consider reasons for inaccuracy. The overall accuracy of the model was 0.954. At a probability threshold of 65%, the sensitivity of the model was 0.860 (95% CI 0.783-0.934) and the specificity was 0.989 (95% CI 0.960-0.999). The positive predictive value was 0.973 (95% CI 0.904-0.993) and the negative predictive value was 0.935 (95% CI 0.901-0.965). The false positive rate was 0.006. The F1 number was 0.926 demonstrating a high accuracy for the model. The temporal bone is a complex anatomical region of interest to otolaryngologists. Surgical planning requires high-resolution computed tomography scans, the interpretation of which can be augmented with machine learning. This initial study demonstrates the feasibility of utilizing machine learning algorithms to discriminate anatomical variation with a high degree of accuracy. It is hoped this will lead to further investigation regarding more complex anatomical structures in the temporal bone.

Correlation of Bone Textural Parameters with Age in the Context of Orthopedic X-ray Studies

The aim of this study was to establish a relationship between the textural parameters observed in X-ray images of bones and the age of the individual. The study utilized a meticulous visual analysis of the images to identify significant correlations between textural features and age. Five distinct regions of interest, namely the Wing of the Ilium, Neck of the Femur, Greater Trochanter, Ischium, and Shaft of the Femur, were identified on both sides of the body. Textural parameters were then measured for each of these regions. The left femoral neck showed the most noteworthy associations, with the textures generated from the histogram of oriented gradients and gray-level co-occurrence matrix exhibiting the strongest correlations (ρ −0.52, p-value 4.95 × 10−14). The main finding of the current study is that correlation of age-dependent bone structure differences in the femoral neck area is higher than in other structures of the femur. This proposed methodology has the potential to aid in the early detection of osteoporosis, which is crucial for devising treatment plans and identifying potential risks associated with bone fragility.

Ultrasound Fusion: Applications in Musculoskeletal Imaging.

Ultrasound fusion is an established technique that pairs real time B-scan ultrasound (US) with other forms of cross-sectional imaging, including computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). Each of these imaging modalities has distinct advantages. CT provides superior anatomic resolution, with improved imaging of bone and calcified structures; MRI has superior contrast resolution; and PET provides physiologic information, identifying processes that are metabolically active (i.e., tumor, inflammatory conditions). However, these modalities are static. A key highlight of ultrasound is its capability of dynamic, real-time scanning. The ability to pair CT, MRI or PET with ultrasound can have significant advantages, both in diagnostic evaluation and when performing difficult or challenging image-guided interventions. Percutaneous interventions using ultrasound fusion have been described in the abdominal imaging literature; however, there have been very few musculoskeletal applications detailed in the literature. The purpose of this article is to review the basic concepts of real-time ultrasound fusion, and to detail, through the use of multiple case examples, its potential use as a safe and effective method for performing image-guided musculoskeletal interventions.

Characteristics of large vestibular aqueduct syndrome in wideband acoustic immittance.

To describe the characteristics of large vestibular aqueduct syndrome (LVAS) in wideband acoustic immittance (WAI) and to explore whether inner ear deformity has an impact on WAI results. Subjects with typical LVAS (LVAS group) and control subjects with a normal anatomical structure of the inner ear (control group) were screened from pediatric patients with cochlear implants using thin-slice computed tomography (CT) images of the temporal bone. With inflammation of the auditory canal and middle ear excluded by routine ear examination and 226 Hz acoustic immittance, WAI data were acquired. Then, the maximum absorbance as the major observation indicator on the mean tympanogram was compared between the LVAS group and control group, and a descriptive comparison of the mean tympanogram and frequency-absorbance curve at peak pressure was performed between the two groups. The LVAS group included 21 cases (38 ears), and the control group included 27 cases (45 ears). All LVAS subjects met the Valvassori criteria, and the VA at the horizontal semicircular canal displayed flared expansion. On the mean tympanogram, the maximum absorbance in the LVAS group (0.542 ± 0.087) was significantly higher than that in the control group (0.455 ± 0.087) (p < 0.001). The tympanogram in the LVAS group showed an overall elevation, and the absorbance at all pressure sampling points was significantly higher than that in the control group (p < 0.001). The frequency-absorbance curve at peak pressure first increased and then decreased in both groups, and the LVAS group showed higher absorbance than the control group in the frequency range below 2,828 Hz. The absorbance at 343-1,124 Hz was significantly different between the two groups (p < 0.001), and 343-1,124 Hz was the major frequency range at which the maximum absorbance on the mean tympanogram increased in the LVAS group. Large vestibular aqueduct syndrome (LVAS) shows increased absorbance in low and medium frequency ranges in WAI. The maximum absorbance on the mean tympanogram can serve as a reliable evaluation indicator. Inner ear factors must be considered when middle ear lesions are analyzed by WAI.

CT Imaging of Eustachian Tube Balloon Dilation: Method Development on Cadaver Heads.

Objective: To develop a methodology for the measurement of balloon dilation (BD) effects on Eustachian Tube (ET) structure using Computerized Tomography (CT) images. Methods: The BD of the ET was performed on three cadaver heads (five ears) through the nasopharyngeal orifice. The axial CT images of the temporal bones were obtained before dilation, while an inflated balloon was in the lumen of ET, and after balloon removal in each ear. Utilizing Dicom images captured by the ImageJ software 3D volume viewer function, the anatomical landmark coordinates of the ET were matched with their pre- and post-dilation counterparts, and the longitudinal axis of the ET was captured with serial images. The histograms of the regions of interest (ROI) and three different lumen width and length measurements were obtained from captured images. The densities of air, tissue, and bone were determined with histograms as a baseline to determine the BD rate as a function of increased air in the lumen. Results: The small ROI box included the area of prominently dilated ET lumen after BD and best represented the visually obvious changes in the lumen, compared to the ROIs that extended the wider areas (longest and longer). Air density was the outcome measure for comparison with each corresponding baseline value. The average increase in air density in the small ROI was 64%, while the longest and long ROI boxes showed 44 and 56% increases, respectively. Conclusion: This study describes a method to image the ET and quantify the outcomes of BD of the ET using anatomical landmarks.