INTRODUCTION: Neuronavigation training is currently restricted by the high cost and logistical barriers of using cadavers or commercial high-fidelity phantoms. We present an open-source, low-cost workflow that deformably warps clinical head MRIs to conform to an inexpensive (similar to$30) cosmetology manikin, enabling the creation of patient-specific training phantoms with anatomically plausible internal structures. METHODS: A standard plastic manikin was digitized using smartphone photogrammetry (RealityScan and Polycam), with geometric accuracy validated against a ground-truth CT scan. In 3D Slicer, a cohort of 12 clinical T1-weighted MRIs (IXI dataset) were registered to the manikin surface using a spacing-aware Signed Distance Map objective. To prevent non-physical distortion of facial features, the registration was constrained by a scalp mask that explicitly excluded the face and neck. We compared three registration stages: (1) fiducial initialization, (2) ANTs Rigid+Affine, and (3) deformable registration using ANTs SyN (diffeomorphic) versus Elastix B-Spline. The final volumes were generated using a boolean fusion logic that seamlessly integrates the patient's brain anatomy inside the mask with the solid manikin geometry on the outside. RESULTS: Smartphone photogrammetry yielded a surface mesh with sub-millimetric concordance (< 1.0 mm mean error) to the CT ground truth. For MRI-to-manikin registration, ANTs SyN achieved the highest surface accuracy on the scalp ROI (ASD 0.2 +/- 0.0 mm, HD95 0.8 +/- 0.3 mm) but required longer computation time (similar to 285 s), whereas Elastix B-Spline provided a rapid alternative (ASD 0.6 +/- 0.1 mm, similar to 38 s). Jacobian analysis confirmed anatomical plausibility for both deformable methods (Mean J approximate to 1.4), with negligible topological folding (< 0.01% voxels with J <= 0). Functional verification in the open-source NousNav platform demonstrated a final RMS surface registration error of approximate to 0.5 mm. CONCLUSION: We validated an end-to-end pipeline that converts routine clinical MRIs into physical training phantoms without the need for a phantom CT scan. This workflow lowers the cost and resource barriers to accessing neuronavigation training, offering a scalable, low-cost alternative to expensive commercial simulators.

Finite element (FE) simulations emulating transcatheter pulmonary valve (TPV) system deployment in patient-specific right ventricular outflow tracts (RVOT) assume material properties for the RVOT and adjacent tissues. Sensitivity of the deployment to variation in RVOT material properties is unknown. Moreover, the effect of a transannular patch stiffness and location on simulated TPV deployment has not been explored. A sensitivity analysis on the material properties of a patient-specific RVOT during TPV deployment, modeled as an uncoupled HGO material, was conducted using FEBioUncertainSCI. Further, the effects of a transannular patch during TPV deployment were analyzed by considering two patch locations and four patch stiffnesses. Visualization of results and quantification were performed using custom metrics implemented in SlicerHeart and FEBio. Sensitivity analysis revealed that the shear modulus of the ground matrix ( c ) , fiber modulus k 1 , and fiber mean orientation angle ( γ ) had the greatest effect on 95th %ile stress, whereas only c had the greatest effect on 95th %ile Lagrangian strain. First-order sensitivity indices contributed the greatest to the total-order sensitivity indices. Simulations using a transannular patch revealed that peak stress and strain were dependent on patch location. As stiffness of the patch increased, greater stress was observed at the interface connecting the patch to the RVOT, and stress in the patch itself increased while strain decreased. The total enclosed volume by the TPV device remained unchanged across all simulated patch cases. This study highlights that while uncertainties in tissue material properties and patch locations may influence functional outcomes, FE simulations provide a reliable framework for evaluating these outcomes in TPVR.

Purpose To develop a volume rendering method to visualize myocardial structures and blood flow using three-dimensional (3D), four-dimensional (4D) cine, and 4D flow cardiac MRI. Materials and Methods In this retrospective study, custom transfer functions for volume rendering and novel dense streamline-based 4D flow visualizations were developed and implemented in 3D Slicer, an open-source image computing platform. These techniques were applied to the cardiac MRI studies (April 2023 to January 2025) of four pediatric patients with congenital heart disease to inform comprehension of complex anatomy and to guide surgical repair. Results Applications are demonstrated in four patients (mean age, 4.5 years ± 1.1 [SD]; three female, one male), including integration into interventional planning workflows. Cardiac MRI enabled rapid and dynamic 3D and 4D visualization of the myocardium and valves in less than 1 second, with image refinement completed in less than 3 minutes. The integration of tissue rendering with 4D flow visualization allowed the simultaneous depiction of valve dynamics, regurgitation, and stenosis. Conclusion Volume rendering of the myocardium and cardiac valves in MRI studies enables rapid, dynamic visualization and can be feasibly applied in clinical settings. Integration with 4D flow visualization may improve the understanding of valve dysfunction and support procedural planning. Keywords: Pediatrics, MR Imaging, Heart, Valves Supplemental material is available for this article. © RSNA, 2026.

Stenting is among the most common transcatheter interventions for congenital heart disease (CHD). Patient-specific computational fluid dynamics (CFD) simulations can predict hemodynamic outcomes of intervention scenarios but require post-operative vascular geometries that reflect stent-induced shape changes, which existing tools either model inadequately or require extensive time or manual effort to generate. We present SDFStent, a signed distance function (SDF) based mesh deformation method for virtual stenting that operates in real time, maintains mesh integrity, and preserves junction geometry. The stent is modeled as a pipe surface composed of piecewise-capsule SDFs joined by a smooth-minimum operator. Mesh vertices near the expanding SDF surface are displaced along the SDF gradient with a compactly supported fall-off function and an alpha blending mask. SDFStent was benchmarked against three existing approaches and validated on three tetralogy of Fallot (ToF) patients and three coarctation of the aorta (CoA) patients using rigid-wall steady-state CFD simulations against clinical catheterization measurements. Against a prescribed diameter of 6.0 mm, the method produced a mean stented diameter of 5.92 $\pm$ 0.08 mm in 1.5 s, over 100$\times$ faster than the best stenting-specific comparator. All output meshes were watertight and self-intersection-free. CFD-simulated post-operative pressure drops agreed with clinical measurements within 4 mmHg (mean error 2 mmHg). SDFStent produces simulation-ready post-stent models that match prescribed stent dimensions at interactive speeds, from pre-operative anatomy and catheterization data alone. The implementation is open-source and available in 3D Slicer. Its scriptable architecture enables automated generation of large synthetic cohorts for data-driven surrogate modeling.

Many approaches have been used to model chordae tendineae geometries in finite element simulations of atrioventricular heart valves. Unfortunately, current "functional" chordae tendineae geometries lack fidelity that would be helpful when informing clinical decisions. The objectives of this work are (i) to improve synthetic chordae tendineae geometry fidelity to consider branching and (ii) to define how the chordae tendineae geometry affects finite element simulations of valve closure. In this work, we develop an open-source method to construct synthetic chordae tendineae geometries in the SlicerHeart Extension of 3D Slicer. The generated geometries are then used in FEBio finite element simulations of atrioventricular valve function to evaluate how variations in chordae tendineae geometry influence valve behavior. Effects are evaluated using functional and mechanical metrics. Our findings demonstrated that altering the chordae tendineae geometry of a stereotypical mitral valve led to changes in clinically relevant valve metrics and valve mechanics. Specifically, cross sectional area had the most influence over valve closure metrics, followed by chordae tendineae density, length, radius and branches. We then used this information to showcase the flexibility of our new workflow by altering the chordae tendineae geometry of two additional geometries (mitral valve with annular dilation and tricuspid valve) to improve finite element predictions. This study presents a flexible, open-source method for generating synthetic chordae tendineae with realistic branching structures. Further, we establish relationships between the chordae tendineae geometry and valve functional/mechanical metrics. This research contribution helps enrich our open-source workflow and brings the finite element simulations closer to use in a patient-specific clinical setting.

3D Slicer, an open-source software platform for the analysis and 3D visualization of medical imaging data, aims to make cutting-edge research tools available to clinical researchers and scientists worldwide. Until recently, the platform was only available in English. This study describes the development of an ad hoc methodology that addresses key linguistic challenges, including domain-specific vocabulary, acronyms, word order, passive voice, syntagms, and adaptation of technical terms. The translation process focuses on ensuring textual uniformity, cohesion, and accuracy while minimizing errors in biomedical computational contexts. The methodology presented may serve as a framework for similar translation efforts in diverse non-English speaking countries. Paulo Eduardo de Barros Veiga, born in Ribeirão Preto, São Paulo (Brazil), holds degrees in Music, Language, and Literature, focusing on Criticism and Translation. He completed a postdoctoral fellowship (Process No. 2018/01418-2, São Paulo Research Foundation – FAPESP). He served as a collaborator and temporary professor in the Department of Music at FFCLRP, University of São Paulo, where he worked on the History and Philosophy of Art. Currently, he is involved in a research project within the Department of Computing and Mathematics at the same university, focusing on developing a biomedical imaging data platform (3D Slicer Software). Under the coordination of Prof. Sonia Pujol (Harvard University) and Prof. Luiz Murta (USP), he leads the translation of the 3D Slicer software into Brazilian Portuguese and proposes solutions and methods in Portuguese. He also engages in music, translation, research, and education projects freelance. Additionally, he is a member of the Póıesis Crítica research group under NAPI-CIPEM. Paulo holds a bachelor’s, master’s, and doctoral degree in Literary Studies from the FCLAr at UNESP (São Paulo State University). He received a CAPES scholarship during his Master’s and was awarded the Emerging Leaders in the Americas Program by the Canadian government, having studied at the University of Winnipeg. He has also lived in London.

Background - Pulmonary insufficiency is a consequence of transannular patch repair in Tetralogy of Fallot (ToF), leading to late morbidity and mortality. Transcatheter native outflow tract pulmonary valve replacement (TPVR) has become common, but assessment of patient candidacy and selection of the optimal device remains challenging. We demonstrate an integrated open-source workflow for simulation of TPVR in image-derived models to inform device selection. Methods - Machine learning-based segmentation of CT scans was implemented to define the right ventricular outflow tract (RVOT). A custom workflow for device positioning and pre-compression was implemented in SlicerHeart. Resulting geometries were exported to FEBio for simulation. Visualization of results and quantification were performed using custom metrics implemented in SlicerHeart and FEBio. Results - RVOT model creation and device placement could be completed in under 1 minute. Virtual device placement using FE simulations visually mimicked actual device placement and allowed quantification of vessel strain, stress, and contact area. Regions of higher strain and stress were observed at the proximal and distal end locations of the TPVs where the devices impinge the RVOT wall. No other consistent trends were observed across simulations. The observed variability in mechanical metrics across RVOTS, stents, and locations in the RVOT highlights that no single device performs optimally in all anatomies, thereby reinforcing the need for simulation-based patient-specific assessment. Conclusions - This study demonstrates the feasibility of a novel open-source workflow for the rapid simulation of TPVR which with further refinement may inform assessment of patient candidacy and optimal device selection.

BACKGROUND:Ductus arteriosus stenting (DAS) is used to palliate infants with ductal-dependent pulmonary blood flow (DD-PBF), however patent ductus arteriosus (PDA) anatomy can be complex and heterogenous. AIMS:We developed custom, open-source software to model and quantify PDA anatomy. METHODS:We retrospectively identified 33 neonates with DD-PBF with a CTA before DAS. A novel custom workflow was implemented in 3D Slicer and SlicerHeart to semi-automatically extract centerlines of the course of the PDA and surrounding vessels. 3D ductal length, diameter, curvature and tortuosity were automatically calculated (3D automatic) and compared to manually adjusted 3D measurements (3D semi-automatic), and manual measurements of PDA dimensions in 2D projectional angiograms before and after stent angioplasty. RESULTS:Ductal anatomy was successfully modeled and quantified in all subjects. 3D automatic and semi-automatic measurements of straight-line aortic to pulmonary artery length were not significantly different than 2D measurements. Semi-automatic 3D measurements were similar to 2D measurements of the total length. Minimum and maximum ductal diameters were not significantly different by 3D automatic and 2D measurements, however semi-automatic 3D diameters were significantly larger. Inter-reader reliability of ductal length and diameter was higher with manual adjustment of 3D centerlines compared to standard measurement of 2D angiograms. These differences were consistent across PGE doses between CTA and DAS. CONCLUSIONS:Automatic PDA modeling is feasible and efficient, enabling reproducible quantification of ductal anatomy for procedural planning of DAS in patients with DD-PBF. Further development is needed as well as investigation of whether 3D modeling-derived measurements influence procedural duration or outcome.

Background Transcatheter cardiac interventions in congenital heart disease require a precise understanding of 3-dimensional (3D) anatomical structures represented through projectional angiograms. However, intraprocedural optimization of angiograms is limited by the need to reduce exposure to radiation and nephrogenic contrast. Preprocedural optimization using 3D images has the potential to improve patient outcomes and trainee education. We sought to simulate fluoroscopic projections from 3D computed tomography images within an integrated procedural planning framework with the goal of informing training and the planning of complex interventions. Methods We developed the Virtual Cath Lab simulator in SlicerHeart to generate fluoroscopic projections from cross-sectional 3D images contextualized in a realistic biplane C-arm model. Segmented images were used to simulate angiograms. Simulated projections were compared to actual angiograms obtained in the catheterization laboratory to assess realism and accuracy. Results The Virtual Cath Lab allowed realistic movement of a C-arm model in synchrony with the generation of realistic fluoroscopic projections. Seventeen subjects were modeled (10 ductus arteriosus stents, 4 transcatheter pulmonary valve replacement, 1 tetralogy of Fallot with major aortopulmonary collateral arteries, 1 aortopulmonary fistula, and 1 reverse Potts shunt). The simulator successfully generated fluoroscopic projections of each subject, rapidly producing clear and anatomically accurate images, suitable for procedural planning in all cases. Conclusions We report the development and application of an open-source, freely available, biplane fluoroscopy simulator based on computed tomography images. Integrated visualization of complex vascular anatomy prior to catheterization may facilitate optimization of fluoroscopic angles and procedural decision-making while also supporting education. Further studies are needed to demonstrate the clinical and educational benefits.

The anatomy of structurally complex ventricular septal defects (SC-VSD) can be difficult to assess using 2-dimensional (2D) images and traditional 2D multiplanar views of 3D images. Direct identification and visualization are not always possible via the standard tricuspid approach in surgical repair. Volume rendering is a near instant method for 3D visualization of computed tomography angiography images, but application of this method to the planning of SC-VSD closure has not been described. We describe the integration of virtual patch and device placement within volume-rendered computed tomography angiography images within SlicerHeart to inform surgical and transcatheter closure of SC-VSDs in 3 patients. Virtual heart models were created and examined by a multidisciplinary team. Virtual device placement and surgical patch design was applied to better inform patient candidacy and procedural planning. Volume rendering-based visualization of SC-VSDs is feasible and may inform understanding of anatomy and conceptualization of the optimal repair. Further study is needed to demonstrate improvement in outcomes.

BACKGROUND:The potential for coronary artery compression (CC) during transcatheter pulmonary valve replacement (TPVR) using self-expanding valves (SEV) is not fully understood, yet anecdotal reports suggest that this risk exists. AIMS AND METHODS:We performed a retrospective cohort study of patients evaluated for SEV-TPVR to evaluate the relationship between the right ventricular outflow tract (RVOT) and coronary arteries (CA). CT-derived segmentations of the RVOT and CA were created using machine learning. A 2D map of the distance between the RVOT surface and CA, in systole and diastole, was created. In the subset of patients with post-procedural CTA, the distance before and after TPVR was measured. RESULTS:Forty-two individuals underwent screening for SEV-TPVR, of which 83% (n = 35) had SEV implanted (Harmony = 24; Alterra = 11). Median age was 22.9 years (range 12-60) and 76% had tetralogy of Fallot (TOF). There was no significant change in the distance between the RVOT and LCA between diastole and systole (p = 0.31), yet the RVOT area nearest to the LCA displaced proximally by 11 mm (IQR: 5.6-19.9) in systole. In 8 patients with pre- and post-TPVR CTA, no statistically significant differences were observed in the RVOT-to-LCA relation after intervention. The distance to the LCA was smaller in pulmonary stenosis/atresia patients than those with TOF (median distance 1.2 and 2.1 mm, respectively; p = 0.185). CONCLUSION:The RVOT area in closest proximity to LCA is dynamic and should be considered when planning TPVR. Special attention should be given to patients with a diagnosis of pulmonary stenosis/atresia.

Purpose Interventional radiology procedures typically utilize multiple imaging modalities for navigation in real time, and augmented reality (AR) has emerging potential to improve this. This case is a first in human novel single session workflow that incorporated an AR-headset for navigational guidance during an intercostal cryoneurolysis procedure. Methods A quadragenarian woman in a tertiary care center with chronic neuropathic pain was treated with cryoneurolysis. An augmented reality headset was incorporated using cone-beam CT in a novel workflow in this IDEAL stage-I study. Outcomes measured included technical success, length of time in procedure, and subjective clinician reactions. Results The procedure with AR-integration was completed in a single session, including cone-beam computed tomography (CBCT) imaging, automatic segmentation, segmentation review, registration of the 3D-imaging dataset to the AR system fiducials, and ultrasound-guided cryoablation of the affected nerve. The AR-system allowed for simultaneous viewing of segmented CT-based anatomy and projected real-time ultrasound images, which improved visualization and procedural ergonomics. A web-based system (ImagineHive) which utilizes customized versions of 3D-Slicer and TotalSegmentator was used for segmentation and image analysis. The workflow from CBCT to registration was completed in 32 min, and showed the potential for further efficiency with greater experience. The procedure was successful, and the patient experienced symptom improvement without adverse events at the six-week follow-up. Conclusion This case highlights the potential of AR-technology integrated with intraoperative CBCT, and a streamlined segmentation workflow, to optimize patient care outcomes in the interventional radiology setting. Future research should focus on assessing the accuracy, cost-effectiveness, and usability of these integrated technologies across various procedures.

This video guides the viewer over the process of how a segmentation created using ITK-Snap can be converted into a standard DICOM representation using dcmqi.

BACKGROUND: Maldistribution of pulmonary blood flow in patients with congenital heart disease impacts exertional performance and pulmonary artery growth. Currently, measurement of relative pulmonary perfusion can only be performed outside the catheterization laboratory. We sought to develop a tool for measuring relative lung perfusion using readily available fluoroscopy sequences. METHODS: A retrospective cohort study was conducted on patients with conotruncal anomalies who underwent lung perfusion scans and subsequent cardiac catheterizations between 2011 and 2022. Inclusion criteria were nonselective angiogram of pulmonary vasculature, oblique angulation ≤20°, and an adequate view of both lung fields. A method was developed and implemented in 3D Slicer’s SlicerHeart extension to calculate the amount of contrast that entered each lung field from the start of contrast injection and until the onset of levophase. The predicted perfusion distribution was compared with the measured distribution of pulmonary blood flow and evaluated for correlation, accuracy, and bias. RESULTS: In total, 32% (79/249) of screened studies met the inclusion criteria. A strong correlation between the predicted flow split and the measured flow split was found ( R 2 =0.83; P <0.001). The median absolute error was 6%, and 72% of predictions were within 10% of the true value. Bias was not systematically worse at either extreme of the flow distribution. The prediction was found to be more accurate for either smaller and younger patients (age 0–2 years), for right ventricle injections, or when less cranial angulations were used (≤20°). In these cases (n=40), the prediction achieved R 2 =0.87, median absolute error of 5.5%, and 78% of predictions were within 10% of the true flow. CONCLUSIONS: The current study demonstrates the feasibility of a novel method for measuring relative lung perfusion using conventional angiograms. Real-time measurement of lung perfusion at the catheterization laboratory has the potential to reduce unnecessary testing, associated costs, and radiation exposure. Further optimization and validation is warranted.

3D medical image segmentation is a key step in numerous clinical applications. Even though many automatic segmentation solutions have been proposed, it is arguably that medical image segmentation is more of a preference than a reference as inter- and intra-variability are widely observed in final segmentation output. Therefore, designing a user oriented and open-source solution for interactive annotation is of great value for the community. In this paper, we present an effective interactive segmentation method that employs an adaptive dynamic programming approach to incorporates users' interactions efficiently. The method first initializes an segmentation through a feature-based geodesic computation. Then, the segmentation is further refined by using an efficient updating scheme requiring only local computations when new user inputs are available, making it applicable to high resolution images and very complex structures. The proposed method is implemented as a user-oriented software module in 3D Slicer. Our approach demonstrates several strengths and contributions. First, we proposed an efficient and effective 3D interactive algorithm with the adaptive dynamic programming method. Second, this is not just a presented algorithm, but also a software with well-designed GUI for users. Third, its open-source nature allows users to make customized modifications according to their specific requirements.

PURPOSE: Accurate preoperative planning is crucial for liver resection surgery due to the complex anatomical structures and variations among patients. The need of virtual resections utilizing deformable surfaces presents a promising approach for effective liver surgery planning. However, the range of available surface definitions poses the question of which definition is most appropriate. METHODS: The study compares the use of NURBS and Bezier surfaces for the definition of virtual resections through a usability study, where 25 participants (19 biomedical researchers and 6 liver surgeons) completed tasks using varying numbers of control points driving surface deformations and different surface types. Specifically, participants aim to perform virtual liver resections using 16 and 9 control points for NURBS and Bezier surfaces. The goal is to assess whether they can attain an optimal resection plan, effectively balancing complete tumor removal with the preservation of enough healthy liver tissue and function to prevent postoperative liver dysfunction, despite working with fewer control points and different surface properties. Accuracy was assessed using Hausdorff distance and average surface distance. A survey based on the NASA Task Load Index measured user performance and preferences. RESULTS: NURBS surfaces exhibit improved accuracy and consistency over Bezier surfaces, with lower average surface distance and variability of results. The 95th percentile Hausdorff Distance indicates the robustness of NURBS surfaces for the task. Task completion time was influenced by control point dimensions, favoring NURBS 3x3 (vs. 4x4) surfaces for a balanced accuracy-efficiency trade-off. Finally, the survey results indicated participants preferred NURBS surfaces over Bezier, emphasizing the improved performance, surface manipulation, and reduced effort. CONCLUSION: The integration of NURBS surfaces into liver resection planning offers a promising advancement. This study demonstrates their superiority in accuracy, efficiency, and user preference compared to Bezier surfaces. The findings underscore the potential of NURBS-based preoperative planning tools to enhance surgical outcomes in liver resection procedures.

This video explains how to set up the environment, install related tools, and install dcmqi from Docker Hub.

Background: The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists' assessments. Methods: A total of 81 patients from an open -source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using CT scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts. Results: The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P<0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (P<0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs . the anterior halves and the lower vs . the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and the rating by radiological experts. Conclusions: The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.

This video guides the user over the steps how to visualize the DICOM segmentation objects and structured measurements using 3D Slicer platform.