Patient-specific Imaging Research Articles

Im2mesh: A Python Library to Reconstruct 3D Meshes from Scattered Data and 2D Segmentations, Application to Patient-Specific Neuroblastoma Tumour Image Sequences

The future of personalised medicine lies in the development of increasingly sophisticated digital twins, where the patient-specific data is fed into predictive computational models that support the decisions of clinicians on the best therapies or course actions to treat the patient’s afflictions. The development of these personalised models from image data requires a segmentation of the geometry of interest, an estimation of intermediate or missing slices, a reconstruction of the surface and generation of a volumetric mesh and the mapping of the relevant data into the reconstructed three-dimensional volume. There exist a wide number of tools, including both classical and artificial intelligence methodologies, that help to overcome the difficulties in each stage, usually relying on the combination of different software in a multistep process. In this work, we develop an all-in-one approach wrapped in a Python library called im2mesh that automatizes the whole workflow, which starts reading a clinical image and ends generating a 3D finite element mesh with the interpolated patient data. In this work, we apply this workflow to an example of a patient-specific neuroblastoma tumour. The main advantages of our tool are its straightforward use and its easy integration into broader pipelines.

Patient-Specific Computational Modeling of Different Cannulation Strategies for Extracorporeal Membrane Oxygenation.

Institution of extracorporeal membrane oxygenation (ECMO) results in unique blood flow characteristics to the end-organ vascular beds. We studied the interplay between cardiac-driven and extracorporeal membrane oxygenation (ECMO)-driven flow to vascular beds in different ECMO configurations using a patient-specific computational fluid dynamics (CFD) analysis. A computational ECMO model (femoral artery cannulation [FAC]) was constructed using patient-specific imaging and hemodynamic data. Following model calibration, we augmented the 3D geometrical model to represent alternative ECMO configurations (ascending aorta cannulation [AAC] and subclavian artery cannulation [SAC]). We performed CFD analyses, including a novel virtual color-dye analysis to compare global and regional blood flow and pressure characteristics as well as contributions of cardiac and ECMO-derived flow to the various vascular beds. Flow waveforms at all the aortic branch vessels were pulsatile, despite low cardiac output and predominant nonpulsatile ECMO-driven hemodynamics. Virtual color-dye analysis revealed differential contribution of cardiac and ECMO-derived flow to the end-organ vascular beds in the FAC model, while this was more evenly distributed in the AAC and SAC models. While global hemodynamics were relatively similar between various ECMO configurations, several distinct hemodynamic indices, in particular wall shear stress and oscillatory shear patterns, as well as differential contribution of ECMO-derived flow to various vascular beds, showed remarkable differences. The clinical impact of this study highlighting the relevance of CFD modeling in assessment of complex hemodynamics in ECMO warrants further evaluation.

A fully-decoupled discontinuous Galerkin approximation of the Cahn–Hilliard–Brinkman–Ohta–Kawasaki tumor growth model

In this article, we consider the Cahn–Hilliard–Brinkman–Ohta–Kawasaki tumor growth system, which couples the Brinkman flow equations in the porous medium and the Cahn–Hilliard type equation with the nonlocal Ohta–Kawasaki term. We first construct a fully-decoupled discontinuous Galerkin method based on a decoupled, stabilized energy factorization approach and implicit-explicit Euler method in the time discretization, and strictly prove its unconditional energy stability. The optimal error estimate for the tumor interstitial fluid pressure is further obtained. Numerical results are also carried out to demonstrate the effectiveness of the proposed numerical scheme and verify the theoretical results. Finally, we apply the scheme to simulate the evolution of brain tumors based on patient-specific magnetic resonance imaging, and the obtained computational results show that the proposed numerical model and scheme can provide realistic calculations and predictions, thus providing an in-depth understanding of the mechanism of brain tumor growth.

Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics.

This study establishes a fluid dynamics model personalized with patient-specific imaging data to optimize neoadjuvant therapy (i.e., doxorubicin) protocols for breast cancers. Ten patients recruited at the University of Chicago were included in this study. Quantitative dynamic contrast-enhanced and diffusion weighted magnetic resonance imaging data are leveraged to estimate patient-specific hemodynamic properties, which are then used to constrain the mechanism-based drug delivery model. Then, computer simulations of this model yield the subsequent drug distribution throughout the breast. By systematically varying the dosing schedule, we identify an optimized regimen for each patient using the maximum safe therapeutic duration (MSTD), which is a metric balancing treatment efficacy and toxicity. With an individually optimized dose (range = 12.11-15.11 mg/m2 per injection), a 3-week regimen consisting of a uniform daily injection significantly outperforms all other scheduling strategies (P < 0.001). In particular, the optimal protocol is predicted to significantly outperform the standard protocol (P < 0.001), improving the MSTD by an average factor of 9.93 (range = 6.63 to 14.17). A clinical-mathematical framework was developed by integrating quantitative MRI data, advanced image processing, and computational fluid dynamics to predict the efficacy and toxicity of neoadjuvant therapy protocols, thus enabling the rational identification of an optimal therapeutic regimen on a patient-specific basis. Our clinical-computational approach has the potential to enable optimization of therapeutic regimens on a patient-specific basis and provide guidance for prospective clinical trials aimed at refining neoadjuvant therapy protocols for breast cancers.

Evaluating the Quality of Patient-Specific Deformable Image Registration in Adaptive Radiotherapy Using a Digitally Enhanced Head and Neck Phantom

Despite the availability of national and international guidelines, an accurate and efficient, patient-specific, deformable image registration (DIR) validation methodology is not yet established, and several groups have found an incompatibility of the various digital phantoms with the commercial systems. To evaluate the quality of the computed tomography (CT) and on-board cone-beam CT (CBCT) DIRs, a novel methodology was developed and tested on 10 head and neck (HN) patients, using CT and CBCT anthropomorphic HN phantom images, digitally reprocessed to include the common organs at risk. Reference DVFs (refDVFs) were generated from the clinical patient CT-CBCT fused images using an independent registration software. The phantom CT images were artificially deformed, using the refDVFs, and registered with the phantom CBCT images, using the clinical registration software, generating a test DVF (testDVF) dataset. The clinical plans were recalculated on the daily patient ‘deformed’ CTs, and the dose maps transferred to the patient-planning CT, using both the refDVF and testDVF. The spatial and dosimetric errors were quantified and the DIR performance evaluated using an established operative tolerance level. The method showed the ability to quantify the DIR spatial errors and assess their dose impact at the voxel level and could be applied to patient-specific DIR evaluation during adaptive HN radiotherapy in routine practice.

AR-Supported Supervision of Conditional Autonomous Robots: Considerations for Pedicle Screw Placement in the Future.

Robotic assistance is applied in orthopedic interventions for pedicle screw placement (PSP). While current robots do not act autonomously, they are expected to have higher autonomy under surgeon supervision in the mid-term. Augmented reality (AR) is promising to support this supervision and to enable human–robot interaction (HRI). To outline a futuristic scenario for robotic PSP, the current workflow was analyzed through literature review and expert discussion. Based on this, a hypothetical workflow of the intervention was developed, which additionally contains the analysis of the necessary information exchange between human and robot. A video see-through AR prototype was designed and implemented. A robotic arm with an orthopedic drill mock-up simulated the robotic assistance. The AR prototype included a user interface to enable HRI. The interface provides data to facilitate understanding of the robot’s ”intentions”, e.g., patient-specific CT images, the current workflow phase, or the next planned robot motion. Two-dimensional and three-dimensional visualization illustrated patient-specific medical data and the drilling process. The findings of this work contribute a valuable approach in terms of addressing future clinical needs and highlighting the importance of AR support for HRI.

Overview of cone beam CT patient radiation dose levels in a dental institution regarding patient age, clinical indications, and CBCT scanners

<h3>Objective</h3> To present an overview of typical cone beam CT patient radiation dose levels in a dental institution for different age groups, clinical indications, and CBCT scanners. <h3>Study Design</h3> The patient dose database for CBCT exams performed in 2019, using the 3D Accuitomo 170 (Morita, Kyoto, Japan) and NewTom VGi evo (QR, Verona, Italy) CBCT systems in a dental education institution was assessed and extracted through an automated dose quality management system DOSE (Qaelum NV, Leuven, Belgium). Data regarding age, gender, and clinical indications related to patients, and the type of CBCT scanner, FOV size (small, medium, and large), and dose-area product (DAP) value related to imaging were recorded for each CBCT imaging procedure. A descriptive data analysis was performed, and the average DAP, an average of effective dose, and frequency of examinations were calculated. <h3>Results</h3> A total of 5,166 CBCT examinations were screened. Surgical planning and follow-up were the most frequent clinical indication being the majority indication in medium (35.7%) and large (27.6%) FOVs. In general, DAP and effective dose increased with FOV size. The effective dose ranged from 42.9 to 188.1 µSv for Accuitomo 170 to 46.7 to 140.1 µSv for NewTom VGi evo. <h3>Conclusion</h3> DAP values and effective doses were heterogeneously distributed amongst age groups and mostly influenced by clinical indications of the related FOV size selection. While patient-specific imaging remains advocated, the fact that two-thirds of the indications are surgically oriented makes the medium and large field of views predominant. Developers of CBCT systems could be advised to move towards patient-specific collimation and dynamic field-of-view selection, preferably via an automated algorithm to facilitate clinical implementation of the optimal patient-specific and indication-oriented dose (ALADAIP). <b>Statement of Ethical Review</b> Ethical review was sought and study was exempted from ethical review

AOSLO-net: A Deep Learning-Based Method for Automatic Segmentation of Retinal Microaneurysms From Adaptive Optics Scanning Laser Ophthalmoscopy Images.

PurposeAccurate segmentation of microaneurysms (MAs) from adaptive optics scanning laser ophthalmoscopy (AOSLO) images is crucial for identifying MA morphologies and assessing the hemodynamics inside the MAs. Herein, we introduce AOSLO-net to perform automatic MA segmentation from AOSLO images of diabetic retinas.MethodAOSLO-net is composed of a deep neural network based on UNet with a pretrained EfficientNet as the encoder. We have designed customized preprocessing and postprocessing policies for AOSLO images, including generation of multichannel images, de-noising, contrast enhancement, ensemble and union of model predictions, to optimize the MA segmentation. AOSLO-net is trained and tested using 87 MAs imaged from 28 eyes of 20 subjects with varying severity of diabetic retinopathy (DR), which is the largest available AOSLO dataset for MA detection. To avoid the overfitting in the model training process, we augment the training data by flipping, rotating, scaling the original image to increase the diversity of data available for model training.ResultsThe validity of the model is demonstrated by the good agreement between the predictions of AOSLO-net and the MA masks generated by ophthalmologists and skillful trainees on 87 patient-specific MA images. Our results show that AOSLO-net outperforms the state-of-the-art segmentation model (nnUNet) both in accuracy (e.g., intersection over union and Dice scores), as well as computational cost.ConclusionsWe demonstrate that AOSLO-net provides high-quality of MA segmentation from AOSLO images that enables correct MA morphological classification.Translational RelevanceAs the first attempt to automatically segment retinal MAs from AOSLO images, AOSLO-net could facilitate the pathological study of DR and help ophthalmologists make disease prognoses.

Non-contrast enhanced silent MR angiography to evaluate hemodynamics and morphology of unruptured intracranial aneurysms: a comparative computational fluid dynamics study

BackgroundSilent MR angiography (silent MRA) is a new generation of non-contrast enhanced angiography with outstanding advantages in visualizing cerebrovascular lesions and the follow-up after endovascular treatment for intracranial aneurysms (IAs)....

Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study.

While the recent advancements of computed tomography (CT) technology have contributed in reducing radiation dose and image noise, an objective evaluation of image quality in patient scans has not yet been established. In this study, we present a patient-specific CT image quality evaluation method that includes fully automated measurements of noise level, structure sharpness, and alteration of structure. This study used the CT images of 120 patients from four different CT scanners reconstructed with three types of algorithm: filtered back projection (FBP), vendor-specific iterative reconstruction (IR), and a vendor-agnostic deep learning model (DLM, ClariCT.AI, ClariPi Inc.). The structure coherence feature (SCF) was used to divide an image into the homogeneous (RH) and structure edge (RS) regions, which in turn were used to localize the regions of interests (ROIs) for subsequent analysis of image quality indices. The noise level was calculated by averaging the standard deviations from five randomly selected ROIs on RH, and the mean SCFs on RS was used to estimate the structure sharpness. The structure alteration was defined by the standard deviation ratio between RS and RH on the subtraction image between FBP and IR or DLM, in which lower structure alterations indicate successful noise reduction without degradation of structure details. The estimated structure sharpness showed a high correlation of 0.793 with manually measured edge slopes. Compared to FBP, IR and DLM showed 34.38% and 51.30% noise reduction, 2.87% and 0.59% lower structure sharpness, and 2.20% and -12.03% structure alteration, respectively, on an average. DLM showed statistically superior performance to IR in all three image quality metrics. This study is expected to contribute to enhance the CT protocol optimization process by allowing a high throughput and quantitative image quality evaluation during the introduction or adjustment of lower-dose CT protocol into routine practice.

Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets

Objective. Therapeutic efficacy of deep brain stimulation (DBS) in both established and emerging indications, is highly dependent on accurate lead placement and optimized clinical programming. The latter relies on clinicians’ experience to search among available sets of stimulation parameters and can be limited by the time constraints of clinical practice. Recent innovations in device technology have expanded the number of possible electrode configurations and parameter sets available to clinicians, amplifying the challenge of time constraints. We hypothesize that patient specific neuroimaging data can effectively assist the clinical programming using automated algorithms. Approach. This paper introduces the DBS Illumina 3D algorithm as a tool which uses patient-specific imaging to find stimulation settings that optimizes activating a target area while minimizing the stimulation of areas outside the target that could result in unknown or undesired side effects. This approach utilizes preoperative neuroimaging data paired with the postoperative reconstruction of the lead trajectory to search the available stimulation space and identify optimized stimulation parameters. We describe the application of this algorithm in three patients with treatment-resistant depression who underwent bilateral implantation of DBS in subcallosal cingulate cortex and ventral capsule/ventral striatum using tractography optimized targeting with an imaging defined target previously described. Main results. Compared to the stimulation settings selected by the clinicians (informed by anatomy), stimulation settings produced by the algorithm that achieved similar or greater target coverage, produced a significantly smaller stimulation area that spilled outside the target (P = 0.002). Significance. The DBS Illumina 3D algorithm is seamlessly integrated with the clinician programmer software and effectively and rapidly assists clinicians with the analysis of image based anatomy, and provides a starting point to search the highly complex stimulation parameter space and arrive at the stimulation settings that optimize activating a target area.

Personalizing repetitive transcranial magnetic stimulation for precision depression treatment based on functional brain network controllability and optimal control analysis

Brain neuromodulation effectively treats neurological diseases and psychiatric disorders such as Depression. However, due to patient heterogeneity, neuromodulation treatment outcomes are often highly variable, requiring patient-specific stimulation protocols throughout the recovery stages to optimize treatment outcomes. Therefore, it is critical to personalize neuromodulation protocol to optimize the patient-specific stimulation targets and parameters by accommodating inherent interpatient variability and intersession alteration during treatments. The study aims to develop a personalized repetitive transcranial magnetic stimulation (rTMS) protocol and evaluate its feasibility in optimizing the treatment efficiency using an existing dataset from an antidepressant experimental imaging study in depression. The personalization of the rTMS treatment protocol was achieved by personalizing both stimulation targets and parameters via a novel approach integrating the functional brain network controllability analysis and optimal control analysis. First, the functional brain network controllability analysis was performed to identify the optimal rTMS stimulation target from the effective connectivity network constructed from patient-specific resting-state functional magnetic resonance imaging data. The optimal control algorithm was then applied to optimize the rTMS stimulation parameters based on the optimized target. The performance of the proposed personalized rTMS technique was evaluated using datasets collected from a longitudinal antidepressant experimental imaging study in depression (n = 20). Simulation models demonstrated that the proposed personalized rTMS protocol outperformed the standard rTMS treatment by efficiently steering a depressive resting brain state to a healthy resting brain state, indicated by the significantly less control energy needed and higher model fitting accuracy achieved. The node with the maximum average controllability of each patient was designated as the optimal target region for the personalized rTMS protocol. Our results also demonstrated the theoretical feasibility of achieving comparable neuromodulation efficacy by stimulating a single node compared to stimulating multiple driver nodes. The findings support the feasibility of developing personalized neuromodulation protocols to more efficiently treat depression and other neurological diseases.

Extended Reality International Grand Rounds: An Innovative Approach to Medical Education in the Pandemic Era.

While bedside training has always presented its own unique challenges, the COVID-19 pandemic era has intensified barriers to suitable provider and trainee experiences for both patient care and medical education. This project introduced an innovative solution with the Extended Reality International Grand Rounds, a collaboration between the University of Michigan Center for Medical and Surgical Extended Reality and Imperial College London. Three complex cases were presented to trainees through a wireless, extended reality (XR) headset and augmented by holographic visual aids and expert commentary. This pilot rounding experience was performed through the first-person view of one clinician at the bedside. In 2020, 140 attendees participated in XR International Grand Rounds, and 82 (59%) and 61 (44%) completed pre- and postsurveys, respectively. Survey analysis showed that the majority of respondents (65, 79.3%) had very little to no baseline experience with XR technologies and nearly all (75, 91.5%) agreed that the development and implementation of XR curricula are important in medical training, indicating an unmet need. Nearly all respondents (59, 96.7%) found value in the ability to visualize patients' clinical findings in the XR rounding experience and 60 (98.4%) found value in the ability to visualize patient-specific imaging and test findings in an XR format. Limiting exposure to high-risk patients and care team members with this innovative format was believed to be important to 79 (96.3%) respondents at baseline and that perception was unchanged following the event. This solution to a long-standing dilemma, newly stressed by a unique era in medicine, was a successful collaboration using state-of-the-art XR technology. Next steps will include introducing more advanced physical exam visualization and detection and comprehensive evaluation of the patient experience, as well as expanding the international experience in a format that is scalable to other interested institutions.

Abstract 474: Integrating imaging and multi-omics data to elucidate racial differences in breast tumor biology to optimize precision oncology approaches and patient outcome

Abstract Background: Racial disparities in breast cancer incidence and clinical outcome have been well characterized. In particular, African American (AA) patients have higher mortality rates than other racial groups. Research into these racial disparities has primarily focused on socioeconomic determinants, with some studies exploring potential molecular determinants and limited biological differences found between AA and Caucasian patient tumors. Tumor intrinsic and extrinsic factors that promote racial differences in breast cancer development and patient outcome are not fully understood and strategies to address this are scarce. As a result, we have developed a platform that combines patient-specific imaging and multi-omics data to elucidate how biological differences integrate to promote racial disparities in tumor behavior and patient outcome. Methods: Imaging and transcriptomic data from over 500 patients were integrated within the SimBioSys TumoScope biophysical modeling software to understand racial differences in tumor biology and coordinated tumor behavior. Results: AA patients have tumors that display higher growth rates, characteristically have higher adipose tissue around the tumor, higher spatial variation in drug secretion, lower spatial variation in blood flow/and increased tumor stiffness. Conclusion: Using a transdisciplinary approach, our results show that metabolic and structural tumor features are associated with the disproportionately poor survival rates observed in AA breast cancer patients. By characterizing racial-specific tumor intrinsic and extrinsic biological factors and their potential interactions, strategies may emerge to personalize clinical management based on racial differences. In this way, precision oncology approaches can be applied to increase life-expectancy for racial minorities with breast cancer. Citation Format: Nicole Liadis, Tyler Earnest, Eduardo Braun, Anu Antony, Lance Munn, Michael Hallock, Daniel Cook, The SimBioSys Team. Integrating imaging and multi-omics data to elucidate racial differences in breast tumor biology to optimize precision oncology approaches and patient outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 474.

Patient-specific structural connectivity informs outcomes of responsive neurostimulation for temporal lobe epilepsy.

Responsive neurostimulation is an effective therapy for patients with refractory mesial temporal lobe epilepsy. However, clinical outcomes are variable, few patients become seizure-free, and the optimal stimulation location is currently undefined. The aim of this study was to quantify responsive neurostimulation in the mesial temporal lobe, identify stimulation-dependent networks associated with seizure reduction, and determine if stimulation location or stimulation-dependent networks inform outcomes. We modeled patient-specific volumes of tissue activated and created probabilistic stimulation maps of local regions of stimulation across a retrospective cohort of 22 patients with mesial temporal lobe epilepsy. We then mapped the network stimulation effects by seeding tractography from the volume of tissue activated with both patient-specific and normative diffusion-weighted imaging. We identified networks associated with seizure reduction across patients using the patient-specific tractography maps and then predicted seizure reduction across the cohort. Patient-specific stimulation-dependent connectivity was correlated with responsive neurostimulation effectiveness after cross-validation (p=.03); however, normative connectivity derived from healthy subjects was not (p=.44). Increased connectivity from the volume of tissue activated to the medial prefrontal cortex, cingulate cortex, and precuneus was associated with greater seizure reduction. Overall, our results suggest that the therapeutic effect of responsive neurostimulation may be mediated by specific networks connected to the volume of tissue activated. In addition, patient-specific tractography was required to identify structural networks correlated with outcomes. It is therefore likely that altered connectivity in patients with epilepsy may be associated with the therapeutic effect and that utilizing patient-specific imaging could be important for future studies. The structural networks identified here may be utilized to target stimulation in the mesial temporal lobe and to improve seizure reduction for patients treated with responsive neurostimulation.

Patient-specific synthetic magnetic resonance imaging generation from cone beam computed tomography for image guidance in liver stereotactic body radiation therapy.

Despite its prevalence, cone beam computed tomography (CBCT) has poor soft-tissue contrast, making it challenging to localize liver tumors. We propose a patient-specific deep learning model to generate synthetic magnetic resonance imaging (MRI) from CBCT to improve tumor localization. A key innovation is using patient-specific CBCT-MRI image pairs to train a deep learning model to generate synthetic MRI from CBCT. Specifically, patient planning CT was deformably registered to prior MRI, and then used to simulate CBCT with simulated projections and Feldkamp, Davis, and Kress reconstruction. These CBCT-MRI images were augmented using translations and rotations to generate enough patient-specific training data. A U-Net-based deep learning model was developed and trained to generate synthetic MRI from CBCT in the liver, and then tested on a different CBCT dataset. Synthetic MRIs were quantitatively evaluated against ground-truth MRI. The synthetic MRI demonstrated superb soft-tissue contrast with clear tumor visualization. On average, the synthetic MRI achieved 28.01, 0.025, and 0.929 for peak signal-to-noise ratio, mean square error, and structural similarity index, respectively, outperforming CBCT images. The model performance was consistent across all three patients tested. Our study demonstrated the feasibility of a patient-specific model to generate synthetic MRI from CBCT for liver tumor localization, opening up a potential to democratize MRI guidance in clinics with conventional LINACs.

&lt;em&gt;In Silico&lt;/em&gt; Clinical Trials for Cardiovascular Disease

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses.

In Silico Clinical Trials for Cardiovascular Disease.

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses.

OC-0619 Flat panel proton radiography with a patient specific imaging field for accurate WEPL assessment

OC-0619 Flat panel proton radiography with a patient specific imaging field for accurate WEPL assessment

Image Registration-Based Method for Reconstructing Transcatheter Heart Valve Geometry from Patient-Specific CT Scans.

Accurate reconstruction of transcatheter aortic valve (TAV) geometries and other stented cardiac devices from computed tomography (CT) images is challenging, mainly associated with blooming artifacts caused by the metallic stents. In addition, bioprosthetic leaflets of TAVs are difficult to segment due to the low signal strengths of the tissues. This paper describes a method that exploits the known device geometry and uses an image registration-based reconstruction method to accurately recover the in vivo stent and leaflet geometries from patient-specific CT images. Error analyses have shown that the geometric error of the stent reconstruction is around 0.1mm, lower than 1/3 of the stent width or most of the CT scan resolutions. Moreover, the method only requires a few human inputs and is robust to input biases. The geometry and the residual stress of the leaflets can be subsequently computed using finite element analysis (FEA) with displacement boundary conditions derived from the registration. Finally, the stress distribution in self-expandable stents can be reasonably estimated by an FEA-based simulation. This method can be used in pre-surgical planning for TAV-in-TAV procedures or for in vivo assessment of surgical outcomes from post-procedural CT scans. It can also be used to reconstruct other medical devices such as coronary stents.