BMC Medical Imaging

This study evaluated the use of an augmented reality navigation system that provides a markerless registration system using stereo vision in oral and maxillofacial surgery. A feasibility study was performed on a subject, wherein a stereo camera was used for tracking and markerless registration. The computed tomography data obtained from the volunteer was used to create an integral videography image and a 3-dimensional rapid prototype model of the jaw. The overlay of the subject’s anatomic site and its 3D-IV image were displayed in real space using a 3D-AR display. Extraction of characteristic points and teeth matching were done using parallax images from two stereo cameras for patient-image registration. Accurate registration of the volunteer’s anatomy with IV stereoscopic images via image matching was done using the fully automated markerless system, which recognized the incisal edges of the teeth and captured information pertaining to their position with an average target registration error of < 1 mm. These 3D-CT images were then displayed in real space with high accuracy using AR. Even when the viewing position was changed, the 3D images could be observed as if they were floating in real space without using special glasses. Teeth were successfully used for registration via 3D image (contour) matching. This system, without using references or fiducial markers, displayed 3D-CT images in real space with high accuracy. The system provided real-time markerless registration and 3D image matching via stereo vision, which, combined with AR, could have significant clinical applications.

Meningiomas typically manifest on magnetic resonance imaging (MRI) as iso- to hypointense on T1-weighted imaging and iso- to hyperintense on T2-weighted imaging. After contrast administration, they usually homogeneously enhance and exhibit a visible dural tail. Meningiomas with atypical findings may be misdiagnosed. We report a 50-year-old female patient with a pathologically diagnosed fibrous meningioma (World Health Organization grade I) that exhibited ring enhancement on MRI. Meningiomas may rarely present with ring enhancement on MRI. The natural history and mechanisms of cystic degeneration and enhancement in the various types of meningioma require further study.

Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature for the unaided human expert. Classifiers based on wavelet-derived features proved to be highly sensitive to a range of pathologies, as a result Type II errors were nearly eliminated. Pre-sorting the images changed the prior probability in the sorted database from 37% to 74%.

Hibernomas are rare benign soft-tissue tumours arising from brown fat tissue. Although imaging characteristics are not specific certain imaging features, common locations and patient demographics may suggest hibernoma as a differential diagnosis. We report on two 48-year-old male patients with hibernoma. The tumour presented with local swelling of the inguinal region in the first patient and was an incidental imaging finding in the second patient. Imaging included magnetic resonance imaging in both patients and computed tomography as well as 18 F-fluorodeoxyglucose positron emission tomography-computed tomography in the second patient. In both cases histological diagnosis was initially based on excisional and needle core biopsy, respectively. Complete surgical resection confirmed the diagnosis of hibernoma thereafter. In soft tissue tumours with fatty components hibernoma may be included into the differential diagnosis. Because of the risk of sampling errors in hibernoma-like tissue components of myxoid and well-differentiated liposarcoma, complete resection is mandatory. This article also reviews the current imaging literature of hibernomas.

To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia. A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC–AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful. The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia.

Myocardial strain is reported to be a sensitive indicator of myocardial mechanical changes in patients with hypertrophic cardiomyopathy (HCM). The changes in the mechanics of the myocardium of normal wall thickness (< 12 mm) have yet to be well studied. This study aimed to evaluate the function of myocardial segments of normal thickness in patients with HCM. Sixty-three patients with HCM and 30 controls were retrospectively enrolled in this retrospective study. Cine imaging, native and post-contrast T1 maps, T2 maps, and late gadolinium enhancement were performed. In addition, regional myocardial strain was assessed by cardiac magnetic resonance-tissue tracking. Strain parameters were compared between the controls and HCM patients with segments of the myocardium of normal thickness. Subgroup analysis was conducted in obstructive and non-obstructive HCM. Lastly, p < 0.05 was considered statistically significant. In normal-thickness myocardial segments of HCM (n = 716), diastolic peak strain rates (PSRs) were significantly lower than in the control group (n = 480) (radial, − 2.43 [− 3.36, − 1.78] vs. − 2.67 [− 3.58, − 1.96], p = 0.002; circumferential, 1.28 [1.01,1.60] vs. 1.39 [1.14, 1.78], p < 0.001; and longitudinal, 1.16 [0.75,1.51] vs. 1.28 [0.90, 1.71], p < 0.001). The normal-thickness segments showed no significant difference in systolic PSRs between HCM and the controls. In the subgroup analysis, significantly decreased diastolic PSRs were noted in both obstructive and non-obstructive HCM, compared with the controls (p < 0.05). Diastolic changes in myocardial mechanics were observed in normal-thickness segments of HCM, occurring before morphological remodeling and systolic dysfunction developed. This finding contributed to a better understanding of the mechanical pathophysiology of HCM with preserved left ventricular ejection fraction. It may potentially aid in predicting disease progression and risk stratification.