How specific are patient-specific simulations? Analyzing the accuracy of 3D-printing and modeling to create patient-specific rehearsals for complex urological procedures

PURPOSE

In the field of urology, 3D printing and modeling are now regularly utilized to enhance pre-operative planning, surgical training, patient-specific rehearsals (PSR), and patient education and counseling. Widespread accessibility and affordability of such technologies necessitates development of quality control measures to confirm the anatomical accuracy of these tools. Herein, we present three methods utilized to evaluate the anatomical accuracy of hydrogel PSR, developed using 3D printing and molding for pre-operative surgical rehearsals, of robotic-assisted partial nephrectomy (RAPN) and percutaneous nephrolithotomy (PCNL).

METHODS

Virtual computer-aided designs (CADs) of patient anatomy were created through segmentation of patient CT scan images. Ten patient-specific RAPN and PCNL hydrogel models were CT scanned and segmented to create a corresponding model CAD. The part compare tool (3-matic, Materialize), point-to-point measurements, and Dice similarity coefficient (DSC) analyzed surface geometry, alignment, and volumetric overlap of each model component.

RESULTS

Geometries of the RAPN parenchyma, tumor, artery, vein, and pelvicalyceal system lay within an average deviation of 2.5 mm (DSC = 0.70) of the original patient geometry and 5 mm (DSC = 0.45) of the original patient alignment. Similarly, geometries of the PCNL pelvicalyceal system and stone lay within 2.5 mm (DSC = 0.6) and within 15 mm (16% deviation) in alignment. This process enabled the refinement of our modeling process to fabricate anatomically accurate RAPN and PCNL PSR.

CONCLUSION

As 3D printing and modeling continues to have a greater impact on patient care, confirming anatomical accuracy should be introduced as a quality control measure prior to use for patient care.

Multi-resolution geometric modeling of the mitral heart valve leaflets

An essential element of cardiac function, the mitral valve (MV) ensures proper directional blood flow between the left heart chambers. Over the past two decades, computational simulations have made marked advancements toward providing powerful predictive tools to better understand valvular function and improve treatments for MV disease. However, challenges remain in the development of robust means for the quantification and representation of MV leaflet geometry. In this study, we present a novel modeling pipeline to quantitatively characterize and represent MV leaflet surface geometry. Our methodology utilized a two-part additive decomposition of the MV geometric features to decouple the macro-level general leaflet shape descriptors from the leaflet fine-scale features. First, the general shapes of five ovine MV leaflets were modeled using superquadric surfaces. Second, the finer-scale geometric details were captured, quantified, and reconstructed via a 2D Fourier analysis with an additional sparsity constraint. This spectral approach allowed us to easily control the level of geometric details in the reconstructed geometry. The results revealed that our methodology provided a robust and accurate approach to develop MV-specific models with an adjustable level of spatial resolution and geometric detail. Such fully customizable models provide the necessary means to perform computational simulations of the MV at a range of geometric accuracies in order to identify the level of complexity required to achieve predictive MV simulations.

False positive control of activated voxels in single fMRI analysis using bootstrap resampling in comparison to spatial smoothing

Functional magnetic resonance imaging (fMRI) is an effective tool for the measurement of brain neuronal activities. To date, several statistical methods have been proposed for analyzing fMRI datasets to select true active voxels among all the voxels appear to be positively activated. Finding a reliable and valid activation map is very important and becomes more crucial in clinical and neurosurgical investigations of single fMRI data, especially when pre-surgical planning requires accurate lateralization index as well as a precise localization of activation map. Defining a proper threshold to determine true activated regions, using common statistical processes, is a challenging task. This is due to a number of variation sources such as noise, artifacts, and physiological fluctuations in time series of fMRI data which affect spatial distribution of noise in an expected uniform activated region. Spatial smoothing methods are frequently used as a preprocessing step to reduce the effect of noise and artifacts. The smoothing may lead to a shift and enlargement of activation regions, and in some extend, unification of distinct regions. In this article, we propose a bootstrap resampling technique for analyzing single fMRI dataset with the aim of finding more accurate and reliable activated regions. This method can remove false positive voxels and present high localization accuracy in activation map without any spatial smoothing and statistical threshold setting.