Learning-based stochastic object models for characterizing anatomical variations

Noninvasive shape-fitting method quantifies cam morphology in femoroacetabular impingement syndrome: Implications for diagnosis and surgical planning

There are considerable limitations associated with the standard 2D imaging currently used for the diagnosis and surgical planning of cam-type femoroacetabular impingement syndrome (FAIS). The aim of this study was to determine the accuracy of a new patient-specific shape-fitting method that quantifies cam morphology in 3D based solely on preoperative MRI imaging. Preoperative and postoperative 1.5T MRI scans were performed on n = 15 patients to generate 3D models of the proximal femur, in turn used to create the actual and the virtual cam. The actual cams were reconstructed by subtracting the postoperative from the preoperative 3D model and used as reference, while the virtual cams were generated by subtracting the preoperative 3D model from the virtual shape template produced with the shape-fitting method based solely on preoperative MRI scans. The accuracy of the shape-fitting method was tested on all patients by evaluating the agreement between the metrics of height, surface area, and volume that quantified virtual and actual cams. Accuracy of the shape-fitting method was demonstrated obtaining a 97.8% average level of agreement between these metrics. In conclusion, the shape-fitting technique is a noninvasive and patient-specific tool for the quantification and localization of cam morphology. Future studies will include the implementation of the technique within a clinically based software for diagnosis and surgical planning for cam-type FAIS.

Task-based image quality assessment in radiation therapy: initial characterization and demonstration with computer-simulation study

In the majority of current radiation therapy (RT) applications, image quality is still assessed subjectively or by utilizing physical measures. A novel theory that applies objective task-based image quality assessment in radiation therapy (IQA-in-RT) was recently proposed, in which the area under the therapeutic operating characteristic curve (AUTOC) was employed as the figure-of-merit (FOM) for evaluating RT effectiveness. Although theoretically more appealing than conventional subjective or physical measures, a comprehensive implementation and evaluation of this novel task-based IQA-in-RT theory is required for its further application in improving clinical RT. In this work, a practical and modular IQA-in-RT framework is presented for implementing this theory for the assessment of imaging components on the basis of RT treatment outcomes. Computer-simulation studies are conducted to demonstrate the feasibility and utility of the proposed IQA-in-RT framework in optimizing x-ray computed tomography (CT) pre-treatment imaging, including the optimization of CT imaging dose and image reconstruction parameters. The potential advantages of optimizing imaging components in the RT workflow by use of the AUTOC as the FOM are also compared against those of other physical measures. The results demonstrate that optimization using the AUTOC leads to selecting different parameters from those indicated by physical measures, potentially improving RT performance. The sources of systemic randomness and bias that affect the determination of the AUTOC are also analyzed. The presented work provides a practical solution for the further investigation and analysis of the task-based IQA-in-RT theory and advances its applications in improving RT clinical practice and cancer patient care.