scholarly journals SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization

2020 ◽  
pp. 299-309 ◽  
Author(s):  
Fan Zhang ◽  
Thomas Noh ◽  
Parikshit Juvekar ◽  
Sarah F. Frisken ◽  
Laura Rigolo ◽  
...  
Keyword(s):  
Open Source ◽  
Diffusion Tensor ◽  
Intraoperative Imaging ◽  
Time Integration ◽  
Three Dimensional ◽  
High Grade Glioma ◽  
Patient Specific ◽  
Imaging Data ◽  
3D Slicer ◽  
Research Software

PURPOSE We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health–supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

Open Source Applications for Image Visualization and Processing in Neuroimaging Training

2015 ◽  
pp. 1319-1332
Author(s):  
Juan A. Juanes ◽  
Pablo Ruisoto ◽  
Alberto Prats-Galino ◽  
Andrés Framiñán
Keyword(s):  
Magnetic Resonance ◽  
Medical Imaging ◽  
Open Source ◽  
Medical Images ◽  
Three Dimensional ◽  
Mr Images ◽  
Image Visualization ◽  
3D Slicer ◽  
Volumetric Reconstruction ◽  
Key Features

The aim of this paper is to demonstrate the major role and potential of three of the most powerful open source computerized tools for the advanced processing of medical images, in the study of neuroanatomy. DICOM images were acquired with radiodiagnostic equipment using 1.5 Tesla Magnetic Resonance (MR) images. Images were further processed using the following applications: first, OsiriXTM version 4.0 32 bits for OS; Second, 3D Slicer version 4.3; and finally, MRIcron, version 6. Advanced neuroimaging processing requires two key features: segmentation and three-dimensional or volumetric reconstruction. Examples of identification and reconstruction of some of the most complex neuroimaging elements such vascular ones and tractographies are included in this paper. The three selected applications represent some of the most versatile technologies within the field of medical imaging.

scholarly journals Main Clinical Use of Additive Manufacturing (Three-Dimensional Printing) in Finland Restricted to the Head and Neck Area in 2016–2017

2019 ◽  
Vol 109 (2) ◽  
pp. 166-173 ◽  
Author(s):  
A.B.V. Pettersson ◽  
M. Salmi ◽  
P. Vallittu ◽  
W. Serlo ◽  
J. Tuomi ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Computer Aided Design ◽  
Three Dimensional ◽  
Patient Specific ◽  
Future Research ◽  
Imaging Data ◽  
University Hospitals ◽  
Three Dimensional Printing ◽  
Dimensional Printing ◽  
Head Area

Background and Aims: Additive manufacturing or three-dimensional printing is a novel production methodology for producing patient-specific models, medical aids, tools, and implants. However, the clinical impact of this technology is unknown. In this study, we sought to characterize the clinical adoption of medical additive manufacturing in Finland in 2016–2017. We focused on non-dental usage at university hospitals. Materials and Methods: A questionnaire containing five questions was sent by email to all operative, radiologic, and oncologic departments of all university hospitals in Finland. Respondents who reported extensive use of medical additive manufacturing were contacted with additional, personalized questions. Results: Of the 115 questionnaires sent, 58 received answers. Of the responders, 41% identified as non-users, including all general/gastrointestinal (GI) and vascular surgeons, urologists, and gynecologists; 23% identified as experimenters or previous users; and 36% identified as heavy users. Usage was concentrated around the head area by various specialties (neurosurgical, craniomaxillofacial, ear, nose and throat diseases (ENT), plastic surgery). Applications included repair of cranial vault defects and malformations, surgical oncology, trauma, and cleft palate reconstruction. Some routine usage was also reported in orthopedics. In addition to these patient-specific uses, we identified several off-the-shelf medical components that were produced by additive manufacturing, while some important patient-specific components were produced by traditional methodologies such as milling. Conclusion: During 2016–2017, medical additive manufacturing in Finland was routinely used at university hospitals for several applications in the head area. Outside of this area, usage was much less common. Future research should include all patient-specific products created by a computer-aided design/manufacture workflow from imaging data, instead of concentrating on the production methodology.

scholarly journals Method for Calibration of Left Ventricle Material Properties Using Three-Dimensional Echocardiography Endocardial Strains

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Yaghoub Dabiri ◽  
Kevin L. Sack ◽  
Nuno Rebelo ◽  
Peter Wang ◽  
Yunjie Wang ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Left Ventricle ◽  
Diffusion Tensor ◽  
Diastolic Pressure ◽  
Three Dimensional ◽  
Heart Association ◽  
Mean Difference ◽  
Imaging Data ◽  
The Mean

We sought to calibrate mechanical properties of left ventricle (LV) based on three-dimensional (3D) speckle tracking echocardiographic imaging data recorded from 16 segments defined by American Heart Association (AHA). The in vivo data were used to create finite element (FE) LV and biventricular (BV) models. The orientation of the fibers in the LV model was rule based, but diffusion tensor magnetic resonance imaging (MRI) data were used for the fiber directions in the BV model. A nonlinear fiber-reinforced constitutive equation was used to describe the passive behavior of the myocardium, whereas the active tension was described by a model based on tissue contraction (Tmax). isight was used for optimization, which used abaqus as the forward solver (Simulia, Providence, RI). The calibration of passive properties based on the end diastolic pressure volume relation (EDPVR) curve resulted in relatively good agreement (mean error = −0.04 ml). The difference between the experimental and computational strains decreased after segmental strain metrics, rather than global metrics, were used for calibration: for the LV model, the mean difference reduced from 0.129 to 0.046 (circumferential) and from 0.076 to 0.059 (longitudinal); for the BV model, the mean difference nearly did not change in the circumferential direction (0.061) but reduced in the longitudinal direction from 0.076 to 0.055. The calibration of mechanical properties for myocardium can be improved using segmental strain metrics. The importance of realistic fiber orientation and geometry for modeling of the LV was shown.

Custom Patient-Specific Three-Dimensional Printed Mitral Valve Models for Pre-Operative Patient Education Enhance Patient Satisfaction and Understanding

2019 ◽  
Vol 13 (3) ◽  
Author(s):  
Kay S. Hung ◽  
Michael J. Paulsen ◽  
Hanjay Wang ◽  
Camille Hironaka ◽  
Y. Joseph Woo
Keyword(s):  
Patient Education ◽  
Mitral Valve ◽  
Three Dimensional ◽  
3D Models ◽  
Patient Specific ◽  
Imaging Data ◽  
Anatomical Models ◽  
Mitral Valves ◽  
Flexible Polymer ◽  
3D Printed

In recent years, advances in medical imaging and three-dimensional (3D) additive manufacturing techniques have increased the use of 3D-printed anatomical models for surgical planning, device design and testing, customization of prostheses, and medical education. Using 3D-printing technology, we generated patient-specific models of mitral valves from their pre-operative cardiac imaging data and utilized these custom models to educate patients about their anatomy, disease, and treatment. Clinical 3D transthoracic and transesophageal echocardiography images were acquired from patients referred for mitral valve repair surgery and segmented using 3D modeling software. Patient-specific mitral valves were 3D-printed using a flexible polymer material to mimic the precise geometry and tissue texture of the relevant anatomy. 3D models were presented to patients at their pre-operative clinic visit and patient education was performed using either the 3D model or the standard anatomic illustrations. Afterward, patients completed questionnaires assessing knowledge and satisfaction. Responses were calculated based on a 1–5 Likert scale and analyzed using a nonparametric Mann–Whitney test. Twelve patients were presented with a patient-specific 3D-printed mitral valve model in addition to standard education materials and twelve patients were presented with only standard educational materials. The mean survey scores were 64.2 (±1.7) and 60.1 (±5.9), respectively (p = 0.008). The use of patient-specific anatomical models positively impacts patient education and satisfaction, and is a feasible method to open new opportunities in precision medicine.

scholarly journals Personalized Three-Dimensional Printed Models in Congenital Heart Disease

2019 ◽  
Vol 8 (4) ◽  
pp. 522 ◽  
Author(s):  
Sun ◽  
Lau ◽  
Wong ◽  
Yeong
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Congenital Heart ◽  
Three Dimensional ◽  
Patient Specific ◽  
Future Research ◽  
Imaging Data ◽  
Doctor Patient Communication ◽  
Medical Education And Training ◽  
3D Printed

Patient-specific three-dimensional (3D) printed models have been increasingly used in cardiology and cardiac surgery, in particular, showing great value in the domain of congenital heart disease (CHD). CHD is characterized by complex cardiac anomalies with disease variations between individuals; thus, it is difficult to obtain comprehensive spatial conceptualization of the cardiac structures based on the current imaging visualizations. 3D printed models derived from patient’s cardiac imaging data overcome this limitation by creating personalized 3D heart models, which not only improve spatial visualization, but also assist preoperative planning and simulation of cardiac procedures, serve as a useful tool in medical education and training, and improve doctor–patient communication. This review article provides an overall view of the clinical applications and usefulness of 3D printed models in CHD. Current limitations and future research directions of 3D printed heart models are highlighted.

scholarly journals Three-Dimensional Printed Molds for Image-Guided Surgical Biopsies: An Open Source Computational Platform

2020 ◽  
pp. 736-748
Author(s):  
Mireia Crispin-Ortuzar ◽  
Marcel Gehrung ◽  
Stephan Ursprung ◽  
Andrew B. Gill ◽  
Anne Y. Warren ◽  
...  
Keyword(s):  
Open Source ◽  
Tumor Heterogeneity ◽  
Spatial Scales ◽  
Three Dimensional ◽  
Patient Specific ◽  
Sampling Location ◽  
Precision Oncology ◽  
Similarity Coefficients ◽  
Tissue Samples ◽  
Manual Intervention

PURPOSE Spatial heterogeneity of tumors is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood because it relies on the accurate coregistration of medical images and tissue biopsies. Tumor molds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and difficult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumor heterogeneity data. METHODS We have developed an open-source computational framework to automatically produce patient-specific 3-dimensional–printed molds that can be used in the clinical setting. Our approach achieves accurate coregistration of sampling location between tissue and imaging, and integrates seamlessly with clinical, imaging, and pathology workflows. RESULTS We applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalized molds for 6 patients, obtaining Dice similarity coefficients between imaging and tissue sections ranging from 0.86 to 0.96 for tumor regions and between 0.70 and 0.76 for healthy kidneys. The framework required minimal manual intervention, producing the final mold design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes. CONCLUSION Our work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumor heterogeneity on multiple spatial scales.

Does the DSA reconstruction kernel affect hemodynamic predictions in intracranial aneurysms? An analysis of geometry and blood flow variations

2017 ◽  
Vol 10 (3) ◽  
pp. 290-296 ◽  
Author(s):  
P Berg ◽  
S Saalfeld ◽  
S Voß ◽  
T Redel ◽  
B Preim ◽  
...  
Keyword(s):  
Blood Flow ◽  
Intracranial Aneurysms ◽  
Three Dimensional ◽  
Patient Specific ◽  
Parent Artery ◽  
Imaging Data ◽  
Specific Flow ◽  
Aneurysm Neck ◽  
Velocity Magnitude ◽  
Reconstruction Kernel

BackgroundComputational fluid dynamics (CFD) blood flow predictions in intracranial aneurysms promise great potential to reveal patient-specific flow structures. Since the workflow from image acquisition to the final result includes various processing steps, quantifications of the individual introduced potential error sources are required.MethodsThree-dimensional (3D) reconstruction of the acquired imaging data as input to 3D model generation was evaluated. Six different reconstruction modes for 3D digital subtraction angiography (DSA) acquisitions were applied to eight patient-specific aneurysms. Segmentations were extracted to compare the 3D luminal surfaces. Time-dependent CFD simulations were carried out in all 48 configurations to assess the velocity and wall shear stress (WSS) variability due to the choice of reconstruction kernel.ResultsAll kernels yielded good segmentation agreement in the parent artery; deviations of the luminal surface were present at the aneurysm neck (up to 34.18%) and in distal or perforating arteries. Observations included pseudostenoses as well as noisy surfaces, depending on the selected reconstruction kernel. Consequently, the hemodynamic predictions show a mean SD of 11.09% for the aneurysm neck inflow rate, 5.07% for the centerline-based velocity magnitude, and 17.83%/9.53% for the mean/max aneurysmal WSS, respectively. In particular, vessel sections distal to the aneurysms yielded stronger variations of the CFD values.ConclusionsThe choice of reconstruction kernel for DSA data influences the segmentation result, especially for small arteries. Therefore, if precise morphology measurements or blood flow descriptions are desired, a specific reconstruction setting is required. Furthermore, research groups should be encouraged to denominate the kernel types used in future hemodynamic studies.

scholarly journals Boundary conditions of patient-specific fluid dynamics modelling of cavopulmonary connections: possible adaptation of pulmonary resistances results in a critical issue for a virtual surgical planning

2011 ◽  
Vol 1 (3) ◽  
pp. 297-307 ◽  
Author(s):  
Giancarlo Pennati ◽  
Chiara Corsini ◽  
Daria Cosentino ◽  
Tain-Yen Hsia ◽  
Vincenzo S. Luisi ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Boundary Conditions ◽  
Three Dimensional ◽  
Critical Issue ◽  
Patient Specific ◽  
Imaging Data ◽  
Venae Cavae ◽  
Cavopulmonary Anastomosis ◽  
Operative Planning ◽  
The Right

Cavopulmonary connections are surgical procedures used to treat a variety of complex congenital cardiac defects. Virtual pre-operative planning based on in silico patient-specific modelling might become a powerful tool in the surgical decision-making process. For this purpose, three-dimensional models can be easily developed from medical imaging data to investigate individual haemodynamics. However, the definition of patient-specific boundary conditions is still a crucial issue. The present study describes an approach to evaluate the vascular impedance of the right and left lungs on the basis of pre-operative clinical data and numerical simulations. Computational fluid dynamics techniques are applied to a patient with a bidirectional cavopulmonary anastomosis, who later underwent a total cavopulmonary connection (TCPC). Multi-scale models describing the surgical region and the lungs are adopted, while the flow rates measured in the venae cavae are used at the model inlets. Pre-operative and post-operative conditions are investigated; namely, TCPC haemodynamics, which are predicted using patient-specific pre-operative boundary conditions, indicates that the pre-operative balanced lung resistances are not compatible with the TCPC measured flows, suggesting that the pulmonary vascular impedances changed individually after the surgery. These modifications might be the consequence of adaptation to the altered pulmonary blood flows.

scholarly journals Vascular flow simulations using SimVascular and OpenFOAM

2021 ◽  
Author(s):  
Swetha Yogeswaran ◽  
Fei Liu
Keyword(s):  
Fluid Dynamics ◽  
Blood Flow ◽  
Open Source ◽  
Open Source Software ◽  
Flow Analysis ◽  
Coarctation Of The Aorta ◽  
Patient Specific ◽  
Specific Information ◽  
Imaging Data ◽  
Set Up

AbstractApplications of computational fluid dynamics (CFD) techniques to aid in the diagnosis and treatment of cardiovascular disease have entered the research domain in recent years, due to their ability to provide valuable patient-specific information without risks associated with highly invasive procedures. SimVascular [1] [2] is an open-source software which allows streamlined processing and CFD blood flow analysis of medical imaging data. OpenFOAM [3] is a proven open-source software which allows for versatile modeling of various fluid dynamics phenomena. In this study, both SimVascular and OpenFOAM simulations are set up with identical computational mesh, similar numerical schemes, boundary conditions, and material properties, to model blood flow in the coronary artery of a 10 year old patient with Coarctation of the Aorta (CoA) who underwent end-to-side anastomosis. Difference in the flow fields such as flow rate, pressure, vorticity, and wall shear stress between SimVascular and OpenFOAM are analyzed. Similar results are obtained in both simulations up to a certain model time, before the results become drastically different. Both the similarities and differences are documented and discussed.

scholarly journals TeraVR Empowers Precise Reconstruction of Complete 3-D Neuronal Morphology in the Whole Brain

2019 ◽  
Author(s):  
Yimin Wang ◽  
Qi Li ◽  
Lijuan Liu ◽  
Zhi Zhou ◽  
Yun Wang ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Open Source ◽  
Three Dimensional ◽  
Neuronal Morphology ◽  
Imaging Data ◽  
Cell Type ◽  
Neuron Morphology ◽  
Whole Brain ◽  
Annotation System ◽  
Quantitative Profiling

AbstractNeuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection. Whole-brain reconstruction of neuron morphology is even more challenging as it involves processing tens of teravoxels of imaging data. Validating such reconstructions is extremely laborious. We developed TeraVR, an open-source virtual reality annotation system, to address these challenges. TeraVR integrates immersive and collaborative 3-D visualization, interaction, and hierarchical streaming of teravoxel-scale images. Using TeraVR, we produced precise 3-D full morphology of long-projecting neurons in whole mouse brains and developed a collaborative workflow for highly accurate neuronal reconstruction.

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