Non-invasive measurement of knee implant loosening is important to provide a diagnostic tool for patients with recurrent complaints after a total knee arthroplasty (TKA). Displacement measurements are currently estimated between tibial implant and bone using a loading device, CT imaging and an advanced 3D image analysis workflow. However, user interaction is required within each step of this workflow, especially in the segmentation of implant and bone, increasing the complexity of this task and affecting its reproducibility. A deep learning-based segmentation model can alleviate the workload by increasing automation and reducing the variability of manual segmentation. In this work, we propose a segmentation algorithm for the tibial implant and tibial bone cortex. The automatically obtained segmentations are then introduced in the displacement calculation workflow and four displacement measurements are calculated, namely mean target registration error (mTRE), maximum total point motion (MTPM), magnitude of translation and rotation. Results show that the parameter distributions are similar to the manual approach, with intra-class correlation values ranging from 0.96 to 0.99 for the different displacement measurements. Moreover, the methodological error has a smaller or comparable distribution, showing the feasibility to increase automation in knee implant displacement assessment.
Mieke T.J. Bus, Paul Cernohorsky, Daniel de Bruin, Sybren Meijer, Geert Streekstra, Dirk Faber, Guido Kamphuis, Patricia Zondervan, Marcel van Herk, Maria Laguna-Pes, Maik Grundeken, Martin Brandt, Theo de Reijke, Jean J. M. C. H. de la Rosette, Ton van Leeuwen
Minimal invasive endoscopic treatment for upper urinary tract urothelial carcinoma (UUT-UC) is advocated in patients with low-risk disease and limited tumor volume. Diagnostic ureterorenoscopy combined with biopsy is the diagnostic standard. This study aims to evaluate two alternative diagnostic techniques for UUT-UC: optical coherence tomography (OCT) and endoluminal ultrasound (ELUS). Following nephroureterectomy, OCT, ELUS, and computed tomography (CT) were performed of the complete nephroureterectomy specimen. Visualization software (AMIRA®) was used for reconstruction and coregistration of CT, OCT, and ELUS. Finally, CT was used to obtain exact probe localization. Coregistered OCT and ELUS datasets were compared with histology. Coregistration with three-dimensional CT makes exact data matching possible in this ex-vivo setting to compare histology with OCT and ELUS. In OCT images of normal-appearing renal pelvis and ureter, urothelium, lamina propria, and muscularis were visible. With ELUS, all anatomical layers of the ureter could be distinguished, besides the urothelial layer. ELUS identified suspect lesions, although exact staging and differentiation between noninvasive and invasive lesions were not possible. OCT provides high-resolution imaging of normal ureter and ureter lesions. ELUS, however, is of limited value as it cannot differentiate between noninvasive and invasive tumors.
Rational and Objective: In CT systems, blurring is the main limiting factor for imaging in-stent restenosis. The aim of
this study is to systematically analyze the effect of blurring related biases on the quantitative assessment of in-stent
restenosis and to evaluate potential correction methods. Methods: 3D analytical models of a blurred, stented vessel are
presented to quantify blurring related artifacts in the stent diameter measurement. Two correction methods are presented
for an improved stent diameter measurement. We also examine the suitability of deconvolution techniques for correcting
blurring artifacts. Results: Blurring results in a shift of the maximum of the signal intensity towards the center position
of the stent, resulting in an underestimation of the stent diameter. This shift can be expressed as a function of the stent
radius and width of the point spread function. The correction for this phenomenon reduces the error with 75 percent.
Deconvolution reduces the blurring artifacts but introduces a ringing artifact. Conclusion: The analytical vessel models
are well suited to study the influence of various parameters on blurring-induced artifacts. The blurring-related
underestimation of the stent diameter can significantly be reduced using the presented corrections. Care should be taken
into choosing suitable deconvolution filters since they may introduce new artifacts.
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