Quantifying the Tibiofemoral Joint Space Using X-ray Tomosynthesis

Purpose: Digital x-ray tomosynthesis (DTS) has the potential to provide 3D information about the knee joint in a load-bearing posture, which may improve diagnosis and monitoring of knee osteoarthritis compared with projection radiography, the current standard of care. Manually quantifying and visualizing the joint space width (JSW) from 3D tomosynthesis datasets may be challenging. This work developed a semiautomated algorithm for quantifying the 3D tibiofemoral JSW from reconstructed DTS images. The algorithm was validated through anthropomorphic phantom experiments and applied to three clinical datasets. Methods: A user-selected volume of interest within the reconstructed DTS volume was enhanced with 1D multiscale gradient kernels. The edge-enhanced volumes were divided by polarity into tibial and femoral edge maps and combined across kernel scales. A 2D connected components algorithm was performed to determine candidate tibial and femoral edges. A 2D joint space width map (JSW) was constructed to represent the 3D tibiofemoral joint space. To quantify the algorithm accuracy, an adjustable knee phantom was constructed, and eleven posterior–anterior (PA) and lateral DTS scans were acquired with the medial minimum JSW of the phantom set to 0–5 mm in 0.5 mm increments (VolumeRadTM, GE Healthcare, Chalfont St. Giles, United Kingdom). The accuracy of the algorithm was quantified by comparing the minimum JSW in a region of interest in the medial compartment of the JSW map to the measured phantom setting for each trial. In addition, the algorithm was applied to DTS scans of a static knee phantom and the JSW map compared to values estimated from a manually segmented computed tomography (CT) dataset. The algorithm was also applied to three clinical DTS datasets of osteoarthritic patients. Results: The algorithm segmented the JSW and generated a JSW map for all phantom and clinical datasets. For the adjustable phantom, the estimated minimum JSW values were plotted against the measured values for all trials. A linear fit estimated a slope of 0.887 (R2¼0.962) and a mean error across all trials of 0.34 mm for the PA phantom data. The estimated minimum JSW values for the lateral adjustable phantom acquisitions were found to have low correlation to the measured values (R2¼0.377), with a mean error of 2.13 mm. The error in the lateral adjustable-phantom datasets appeared to be caused by artifacts due to unrealistic features in the phantom bones. JSW maps generated by DTS and CT varied by a mean of 0.6 mm and 0.8 mm across the knee joint, for PA and lateral scans. The tibial and femoral edges were successfully segmented and JSW maps determined for PA and lateral clinical DTS datasets. Conclusions: A semiautomated method is presented for quantifying the 3D joint space in a 2D JSW map using tomosynthesis images. The proposed algorithm quantified the JSW across the knee joint to sub-millimeter accuracy for PA tomosynthesis acquisitions. Overall, the results suggest that x-ray tomosynthesis may be beneficial for diagnosing and monitoring disease progression or treatment of osteoarthritis by providing quantitative images of JSW in the load-bearing knee

Focal Spot, Summer/Fall 2009

https://digitalcommons.wustl.edu/focal_spot_archives/1112/thumbnail.jp

Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

Nipple discharge: the state of the art

Over 80% of females experience nipple discharge during their life. Differently from lactational (milk production) and physiological (white, green, or yellow), which are usually bilateral and involving multiple ducts, pathologic nipple discharge (PND) is a spontaneous commonly single-duct and unilateral, clear, serous, or bloody secretion. Mostly caused by intraductal papilloma(s) or ductal ectasia, in 5-33% of cases is due to an underlying malignancy. After clinical history and physical examination, mammography is the first step after 39, but its sensitivity is low (7–26%). Ultrasound shows higher sensitivity (63–100%). Nipple discharge cytology is limited by a false negative rate over 50%. Galactography is an invasive technique that may cause discomfort and pain; it can be performed only when the duct discharge is demonstrated at the time of the study, with incomplete/failed examination rate up to 15% and a difficult differentiation between malignant and benign lesions. Ductoscopy, performed under local anesthesia in outpatients, provides a direct visualization of intraductal lesions, allowing for directed excision and facilitating a targeted surgery. Its sensitivity reaches 94%; however, it is available in only few centers and most clinicians are unfamiliar with its use. PND has recently emerged as a new indication for contrast-enhanced breast MRI, showing sensitivity superior to galactography, with an overall sensitivity up to 96%, also allowing tailored surgery. Surgery no longer can be considered the standard approach to PND. We propose a state-of-the art flowchart for the management of nipple discharge, including ductoscopy and breast MRI as best options