Steve Pieper

3D Slicer as an image computing platform for the Quantitative Imaging Network

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.

The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community

Medical image computing researchers often face the problem of moving promising new algorithms from the proof of concept stage into a form compatible with clinical use. Algorithm developers lack the time and resources to engineer their code for robustness and compatibility, while end-users are anxious to try new techniques but require well designed and tested user interfaces to make practical use of them. The NA-MIC Kit is a collection of software and methodology specifically designed to address these problems and facilitate the rapid advancement of the field

Test-retest and between-site reliability in a multicenter fMRI study.

In the present report, estimates of test–retest and between‐site reliability of fMRI assessments were produced in the context of a multicenter fMRI reliability study (FBIRN Phase 1, www.nbirn.net). Five subjects were scanned on 10 MRI scanners on two occasions. The fMRI task was a simple block design sensorimotor task. The impulse response functions to the stimulation block were derived using an FIR‐deconvolution analysis with FMRISTAT. Six functionally‐derived ROIs covering the visual, auditory and motor cortices, created from a prior analysis, were used. Two dependent variables were compared: percent signal change and contrast‐to‐noise‐ratio. Reliability was assessed with intraclass correlation coefficients derived from a variance components analysis. Test–retest reliability was high, but initially, between‐site reliability was low, indicating a strong contribution from site and site‐by‐subject variance. However, a number of factors that can markedly improve between‐site reliability were uncovered, including increasing the size of the ROIs, adjusting for smoothness differences, and inclusion of additional runs. By employing multiple steps, between‐site reliability for 3T scanners was increased by 123%. Dropping one site at a time and assessing reliability can be a useful method of assessing the sensitivity of the results to particular sites. These findings should provide guidance toothers on the best practices for future multicenter studies. Hum Brain Mapp, 2008.

OpenIGTLink: an open network protocol for image-guided therapy environment

With increasing research on system integration for image‐guided therapy (IGT), there has been a strong demand for standardized communication among devices and software to share data such as target positions, images and device status.

GBM Volumetry using the 3D Slicer Medical Image Computing Platform

Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. (3D)Slicer – a free platform for biomedical research – provides an alternative to this manual slice-by-slice segmentation process, which is significantly faster and requires less user interaction. In this study, 4 physicians segmented GBMs in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. The time required for GrowCut segmentation was on an average 61% of the time required for a pure manual segmentation. A comparison of Slicer-based segmentation with manual slice-by-slice segmentation resulted in a Dice Similarity Coefficient of 88.43 ± 5.23% and a Hausdorff Distance of 2.32 ± 5.23 mm.

A computer modeling tool for comparing novel ICD electrode orientations in children and adults

BACKGROUND Use of implantable cardiac defibrillators (ICDs) in children and patients with congenital heart disease is complicated by body size and anatomy. A variety of creative implantation techniques has been used empirically in these groups on an ad hoc basis. OBJECTIVE To rationalize ICD placement in special populations, we used subject-specific, image-based finite element models (FEMs) to compare electric fields and expected defibrillation thresholds (DFTs) using standard and novel electrode configurations. METHODS FEMs were created by segmenting normal torso computed tomography scans of subjects ages 2, 10, and 29 years and 1 adult with congenital heart disease into tissue compartments, meshing, and assigning tissue conductivities. The FEMs were modified by interactive placement of ICD electrode models in clinically relevant electrode configurations, and metrics of relative defibrillation safety and efficacy were calculated. RESULTS Predicted DFTs for standard transvenous configurations were comparable with published results. Although transvenous systems generally predicted lower DFTs, a variety of extracardiac orientations were also predicted to be comparably effective in children and adults. Significant trend effects on DFTs were associated with body size and electrode length. In many situations, small alterations in electrode placement and patient anatomy resulted in significant variation of predicted DFT. We also show patient-specific use of this technique for optimization of electrode placement. CONCLUSION Image-based FEMs allow predictive modeling of defibrillation scenarios and predict large changes in DFTs with clinically relevant variations of electrode placement. Extracardiac ICDs are predicted to be effective in both children and adults. This approach may aid both ICD development and patient-specific optimization of electrode placement. Further development and validation are needed for clinical or industrial utilization.

Summarizing and visualizing uncertainty in non-rigid registration

Registration uncertainty may be important information to convey to a surgeon when surgical decisions are taken based on registered image data. However, conventional non-rigid registration methods only provide the most likely deformation. In this paper we show how to determine the registration uncertainty, as well as the most likely deformation, by using an elastic Bayesian registration framework that generates a dense posterior distribution on deformations. We model both the likelihood and the elastic prior on deformations with Boltzmann distributions and characterize the posterior with a Markov Chain Monte Carlo algorithm. We introduce methods that summarize the high-dimensional uncertainty information and show how these summaries can be visualized in a meaningful way. Based on a clinical neurosurgical dataset, we demonstrate the importance that uncertainty information could have on neurosurgical decision making.

Finite element modeling of subcutaneous implantable defibrillator electrodes in an adult torso

BACKGROUND Total subcutaneous implantable subcutaneous defibrillators are in development, but optimal electrode configurations are not known. OBJECTIVE We used image-based finite element models (FEM) to predict the myocardial electric field generated during defibrillation shocks (pseudo-DFT) in a wide variety of reported and innovative subcutaneous electrode positions to determine factors affecting optimal lead positions for subcutaneous implantable cardioverter-defibrillators (S-ICD). METHODS An image-based FEM of an adult man was used to predict pseudo-DFTs across a wide range of technically feasible S-ICD electrode placements. Generator location, lead location, length, geometry and orientation, and spatial relation of electrodes to ventricular mass were systematically varied. Best electrode configurations were determined, and spatial factors contributing to low pseudo-DFTs were identified using regression and general linear models. RESULTS A total of 122 single-electrode/array configurations and 28 dual-electrode configurations were simulated. Pseudo-DFTs for single-electrode orientations ranged from 0.60 to 16.0 (mean 2.65 +/- 2.48) times that predicted for the base case, an anterior-posterior configuration recently tested clinically. A total of 32 of 150 tested configurations (21%) had pseudo-DFT ratios </=1, indicating the possibility of multiple novel, efficient, and clinically relevant orientations. Favorable alignment of lead-generator vector with ventricular myocardium and increased lead length were the most important factors correlated with pseudo-DFT, accounting for 70% of the predicted variation (R(2) = 0.70, each factor P < .05) in a combined general linear model in which parameter estimates were calculated for each factor. CONCLUSION Further exploration of novel and efficient electrode configurations may be of value in the development of the S-ICD technologies and implant procedure. FEM modeling suggests that the choice of configurations that maximize shock vector alignment with the center of myocardial mass and use of longer leads is more likely to result in lower DFT.

Computational Radiomics System to Decode the Radiographic Phenotype

Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www.radiomics.io With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. Cancer Res; 77(21); e104-7. ©2017 AACR.

Extended Broca's area in the functional connectome of language in adults: combined cortical and subcortical single-subject analysis using fMRI and DTI tractography.

Traditional models of the human language circuitry encompass three cortical areas, Broca’s, Geschwind’s and Wernicke’s, and their connectivity through white matter fascicles. The neural connectivity deep to these cortical areas remains poorly understood, as does the macroscopic functional organization of the cortico-subcortical language circuitry. In an effort to expand current knowledge, we combined functional MRI (fMRI) and diffusion tensor imaging to explore subject-specific structural and functional macroscopic connectivity, focusing on Broca’s area. Fascicles were studied using diffusion tensor imaging fiber tracking seeded from volumes placed manually within the white matter. White matter fascicles and fMRI-derived clusters (antonym-generation task) of positive and negative blood-oxygen-level-dependent (BOLD) signal were co-registered with 3-D renderings of the brain in 12 healthy subjects. Fascicles connecting BOLD-derived clusters were analyzed within specific cortical areas: Broca’s, with the pars triangularis, the pars opercularis, and the pars orbitaris; Geschwind’s and Wernicke’s; the premotor cortex, the dorsal supplementary motor area, the middle temporal gyrus, the dorsal prefrontal cortex and the frontopolar region. We found a functional connectome divisible into three systems—anterior, superior and inferior—around the insula, more complex than previously thought, particularly with respect to a new extended Broca’s area. The extended Broca’s area involves two new fascicles: the operculo-premotor fascicle comprised of well-organized U-shaped fibers that connect the pars opercularis with the premotor region; and (2) the triangulo-orbitaris system comprised of intermingled U-shaped fibers that connect the pars triangularis with the pars orbitaris. The findings enhance our understanding of language function.