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Cardiac Stress Test. PET Cardiac Indications. Multimodality image fusion for intraoperative guidance in transcatheter structural heart disease procedures.

Author s : Sihong He, Siu-Chun M. Ho, McGovern Medical School, The Univ. of Texas Health Science Ctr. at Houston United States ; Andrew Kuhls-Gilcrist, Todd Erpelding, Canon Medical Systems USA, Inc.

United States ; Richard Smalling, Memorial Hermann Heart and Vascular Institute United States , McGovern Medical School, The Univ. at Houston United States. Mentally integrating the information from these images can be challenging during procedures and can take up time and increase radiation exposure.

This study used the free Unity graphics engine and tailored LabVIEW and Python algorithms, along with deep learning, to merge echocardiography, CT-derived 3D heart models, and fiber optic shape sensing data with fluoroscopic imaging. Tests were performed on a patient specific ballistic gel heart model.

This is the first attempt at fusing the above four imaging modalities together and can pave the way for more advanced guidance techniques in the future.

Risk prediction of stereotactic-body-radiotherapy-induced vertebral compression fracture using multi-modal deep learning network. Author s : Seoyoung Lee, Hyoyi Kim, KAIST Korea, Republic of ; Haeyoung Kim, SAMSUNG Medical Ctr.

Korea, Republic of ; Seungryong Cho, KAIST Korea, Republic of. In this study, we propose a multi-modal deep network for risk prediction of VCF after SBRT that uses clinical records, CT images, and radiotherapy factors altogether without explicit feature extraction.

The retrospective study was conducted on a cohort of patients who received SBRT for spinal bone metastasis. A 1-D feature vector was generated from clinical information. We cropped a 3-D patch of the lesion area from pretreatment CT images and planning dose images.

We designed a three-branch multi-modal deep learning network. From the k-fold validation and ablation study, our proposed multi-modal network showed the best performance with an area under the curve AUC of 0.

Smart line detection and histogram-based approach to robust freehand ultrasound calibration. Author s : William R. Warner, Xiaoyao Fan, Ryan B. Duke, Kristen L. Chen, Chengpei Li, Haley E. Stoner, Thayer School of Engineering at Dartmouth United States ; Kirthi S.

Bellamkonda, Linton T. Evans, Richard J. Powell, Dartmouth-Hitchcock Medical Ctr. United States ; Sohail K. Paulsen, Thayer School of Engineering at Dartmouth United States. Accurate spatial calibration is essential to enable iUS navigation. Utilizing sterilizable ultrasound probes introduces new calibration challenges that can be solved by a robust, efficient and user independent calibration technique to be performed sterilely at the time of surgery in the sterile field.

This study investigates the effect of pose variation on the accuracy of a plane-based ultrasound calibration technique through analysis of a comprehensive dataset. The location of the tracked tool attached to the probe is decoupled into 6 degrees of freedom and monitored during data acquisition.

An intelligent line detection algorithm is deployed to US video feed during acquisition. The range of the degrees of freedom of the data set are iteratively reduced to study its effect on the spatial calibration accuracy. Analysis of reducing the translation and rotation range increased both TRE and standard deviation emphasizing the need for increasing pose variation during calibration data acquisition to ensure high calibration accuracy This work facilitates a larger development toward sterile time-of-surgery calibration.

Adaptive octree cube refinement depending on grasping position for deformable organ models. Author s : Rintaro Miyazaki, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori, Nagoya Univ. Surgical simulation is one of the most promising ways for surgical training. Laparoscopic surgery simulators are already in practical use and have been evaluated for their effectiveness.

To realize a high-quality simulator, it is important to efficiently process organ deformation models. In this study, we extend adaptive mesh refinement and apply it to an octree cube structure. Refinement of the structure is performed based on the grasping position. This approach improves the resolution of the octree around the grasping position.

In addition, it makes it easier to detect interference between the grasp model and the high-resolution grid of the octree. Simulation results showed there were cubes before and cubes after refinement, and the FPS decreased from Exploring optical flow inclusion into nnU-Net framework for surgical instrument segmentation.

Author s : Marcos Fernández-Rodríguez, Life and Health Sciences Research Institute, Univ. do Minho Portugal , School of Medicine, Univ. do Minho Portugal ; Bruno Silva, Sandro Queirós, Life and Health Sciences Research Institute, Univ.

do Minho Portugal ; Helena R. Torres, Applied Artificial Intelligence Laboratory Portugal ; Bruno Oliveira, Life and Health Sciences Research Institute, Univ. do Minho Portugal ; Pedro Morais, Applied Artificial Intelligence Laboratory, Instituto Politécnico do Cávado e do Ave Portugal ; Lukas R.

KG Germany ; Jorge Correia-Pinto, Life and Health Sciences Research Institute Portugal , School of Medicine Portugal ; Estevão Lima, Life and Health Sciences Research Institute, Univ.

do Minho Portugal ; João L. Vilaça, Applied Artificial Intelligence Laboratory, Instituto Politécnico do Cávado e do Ave Portugal. The dynamic setting of laparoscopic surgery still makes it hard to obtain a precise segmentation. The nnU-Net framework, excelled in semantic segmentation analyzing single frames without temporal information.

Optical flow OF estimates motion and represent it in a single frame, containing temporal information. Meanwhile, in surgeries, instruments often show the most movement.

Novel method to improve feature extraction in MR for model-based image updating in image-guided neurosurgery. Author s : Kristen L. Chen, Chengpei Li, Xiaoyao Fan, Scott Davis, Thayer School of Engineering at Dartmouth United States ; Linton T. United States ; Keith D. United States , Norris Cotton Cancer Ctr.

Image-guided systems incorporate this spatial information to provide real-time information on where surgical instruments are located with respect to preoperative imaging. The accuracy of these systems become degraded due to intraoperative brain shift.

To account for brain shift, we previously developed an image-guidance updating framework that incorporates brain shift information acquired from registering intraoperative stereovision iSV surface with the pMR surface to create an updated magnetic resonance image uMR.

To register the iSV surface and the pMR surface, the two surfaces must have some matching features that can be used for registration. To capture features falling outside of the brain volume, we have developed a method to improve feature extraction, which involves performing a selective dilation in the region of the stereovision surface.

The goal of this method is to capture useful features that can be use to improve image registration. In-silico CT lung phantom generated from finite-element mesh. United States ; Bradford J. Smith, Univ. United States ; Rahim R. Rizi, Univ. Image registration through the use of dynamic imaging has emerged as a powerful tool to assess the kinematic and deformation behavior of lung parenchyma during respiration.

However, the difficulty in validating the results provided by image registration has limited its use in clinical settings.

To overcome this barrier, we developed a method to convert an FE mesh of the lung to a phantom CT image. Through the generation of the phantom image, we were able to isolate the geometry of the lung and large airways.

A series of high-quality phantom images generated from the FE mesh deformed through in-silico experiments simulating the respiratory cycle will allow for the validation and evaluation of image-registration algorithms.

The method presented in this study will serve as an essential step towards the implementation of dynamic imaging and image registration in clinical settings to assess regional deformation in patients as a diagnostic and risk-stratification tool.

Comprehensive examination of personalized microwave ablation: exploring the effects of blood perfusion rate and metabolic heat on treatment responses. Author s : Amirreza Heshmat, Caleb S. O'Connor, Jun Hong, Jessica Albuquerque Marques Silva, Iwan Paolucci, Aaron K.

Jones, Bruno C. Odisio, Kristy K. Brock, The Univ. The Penne bioheat equation explains heat distribution in tissues, including factors like blood perfusion rate BPR and metabolic heat MH.

We employed 3D patient-specific models and sensitivity analysis to examine how BPR and MH affect MWA results.

Numerical simulations using a triaxial antenna and 65 Watts power on tumors demonstrated that lower BPR led to less damage and complete tumor destruction. Models without MH had less liver damage.

The study highlights the importance of tailored ablation parameters for personalized treatments, revealing the impact of BPR and MH on MWA outcomes.

Comparative analysis of non-rigid registration techniques for liver surface registration. Author s : Bipasha Kundu, Zixin Yang, Richard Simon, Cristian A.

Linte, Rochester Institute of Technology United States. To address limited access to liver registration methods, we compare the robustness of three open-source optimization-based nonrigid registration methods and one data-driven method to a reduced visibility ratio reduced partial views of the surface and an increasing deformation level mean displacement , reported as the root mean square error RMSE between the pre- and intra-operative liver surface meshed following registration.

The Gaussian Mixture Model-Finite Element Model GMM-FEM method consistently yields a lower post-registration error than the other three tested methods in the presence of both reduced visibility ratio and increased intra-operative surface displacement, therefore offering a potentially promising solution for pre- to intra-operative nonrigid liver surface registration.

Auditory nerve fiber localization using a weakly supervised non-rigid registration U-Net. Author s : Hannah G. Mason, Ziteng Liu, Jack H. CIs induce hearing sensation by stimulating auditory nerve fibers ANFs using an electrode array that is surgically implanted into the cochlea.

After the device is implanted, an audiologist programs the CI processor to optimize hearing performance. Without knowing which ANFs are being stimulated by each electrode, audiologists must rely solely on patient performance to inform programming adjustments.

Patient-specific neural stimulation modeling has been proposed to assist audiologists, but requires accurate localization of ANFs. In this paper, we propose an automatic neural-network-based method for atlas-based localization of the ANFs.

Our results show that our method is able to produce smooth ANF predictions that are more realistic than those produced by a previously proposed semi-manual localization method. Accurate and realistic ANF localizations are critical for constructing patient-specific ANF stimulation models for model guided CI programming.

A comparison of onboard and offboard user interfaces for handheld robots. Author s : Ethan Wilke, Jesse F. d'Almeida, Jason Shrand, Tayfun Ertop, Nicholas L.

Kavoussi, Amy Reed, Duke Herrell, Robert J. Webster, Vanderbilt Univ. Several research groups have shown that robots can be made so small and light that they can become hand-held tools. This hand-held paradigm enables robots to fit much more seamlessly into existing clinical workflows.

In this paper, we compare an onboard user interface approach against the traditional offboard approach. In the latter, the surgeon positions the robot, and a support arm holds it in place while the surgeon operates the manipulators using the offboard surgeon console.

The surgeon can move back and forth between the robot and the console as often as desired. Three experiments were conducted, and results show that the onboard interface enables statistically significantly faster performance in a point-touching task performed in a virtual reality environment.

Author s : Connor Mitchell, Robarts Research Institute Canada ; Shuwei Xing, Robarts Research Institute Canada , Western Univ. Canada ; Derek W. Cool, London Health Sciences Ctr.

Canada , Robarts Research Institute Canada ; David Tessier, Robarts Research Institute Canada ; Aaron Fenster, Robarts Research Institute Canada , Western Univ.

For this procedure, the radiologist must compare the pre-operative with the post-operative CT to determine the presence of residual tumors. Distinguishing between malignant and benign kidney tumors poses a significant challenge.

To automate this tumor coverage evaluation step and assist the radiologist in identifying kidney tumors, we proposed a coarse-to-fine U-Net-based model to segment kidneys and masses. We used the TotalSegmentator tool to obtain an approximate segmentation and region of interest of the kidneys, which was inputted into our 3D segmentation network trained using the nnUNet library to fully segment the kidneys and masses within them.

Our model achieved an aggregated DICE score of 0. Our results indicate the model will be useful for tumour identification and evaluation. Automating creation of high-fidelity holographic hand animations for surgical skills training using mixed reality headsets.

Author s : Regina W. Leung, Ge Shi, Western Univ. Canada ; Christina A. Using this methodology, we successfully developed a 3D holographic animation of one-handed knot ties used in surgery.

With regards to the quality of the produced animation, our qualitative pilot study demonstrated comparable successful learning of knot-ties from the holographic animation to in-person demonstration. Furthermore, participants found learning knot-ties from the holographic animation to be easier, more effective, were more confident in mastery of the skill in comparison to in-person demonstration, and also found the animation comparable to real hands showing promise for application in surgical skills training applications.

A robust system for capture and archival of high-definition stereoendoscopic video. Author s : Michael A. Kokko, Ryan J. Capturing stereo video for the purpose of offline reconstruction requires dedicated hardware, a mechanism for temporal synchronization, and video processing tools that perform accurate clip extraction, frame extraction, and lossless compression for archival.

This work describes a minimal hardware setup comprising entirely off-the-shelf components for capturing video from the da Vinci and similar 3D-enabled surgical systems.

Software utilities are also provided for synchronizing data collection and accurately handling captured video files. End-to-end testing demonstrates that all processing functions clipping, frame cropping, compression, un-compression, and frame extraction operate losslessly, and can be combined to generate reconstruction-ready stereo pairs from raw surgical video.

Author s : Soyoung Park, Sahaja Acharya, Matthew Ladra, The Johns Hopkins Univ. School of Medicine United States ; Junghoon Lee, Johns Hopkins Univ. For pediatric cancer patients, reducing ionizing radiation from CT scans is preferred for which MRI-based RT planning and assessment is truly beneficial.

For accurate pediatric CT image synthesis, we investigated a 3D conditional generative adversarial network cGAN -based transfer learning approach due to the lack of sufficient pediatric data compared to adult data.

Our model was first trained using adult data with downscaling to simulate pediatric data, followed by fine-tuning on a smaller number of pediatric data. The proposed 3D cGAN-based transfer learning was able to accurately synthesize pediatric CT images from MRI, allowing us to realize pediatric MR-only RT planning, QA, and treatment assessment.

Monocular microscope to CT registration using pose estimation of the incus for augmented reality cochlear implant surgery. Author s : Yike Zhang, Eduardo Davalos Anaya, Ange Lou, Dingjie Su, Jack H.

Augmented reality AR surgery may improve CI procedures and hearing outcomes. Typically, AR solutions for image-guided surgery rely on optical tracking systems to register pre-op planning information to the display so that hidden anatomy or other information can be overlayed co-registered with the view of the surgical scene.

In this work, our goal is to develop a method that permits direct 2D-to-3D registration of the microscope video to the pre-operative CT scan without the need for external tracking equipment.

Our proposed solution involves surface-mapping a portion of the incus in the video and determining the pose of this structure relative to the surgical microscope by solving the perspective-n-point pose computation to achieve 2D-to-3D registration.

This registration can then be applied to pre-operative segmentation of other hidden anatomy as well as the planned electrode insertion trajectory to co-register this information for AR display. Initial implementation of robot-integrated specimen imaging in transoral robotic surgery.

Kokko, Thayer School of Engineering at Dartmouth United States ; Andrew Y. Lee, Geisel School of Medicine, Dartmouth College United States ; Joseph A.

When margins are positive, it is critical that resection specimens be accurately oriented in anatomical context for gross and microscopic evaluation, and also that surgeons, pathologists, and other care team members share an accurate spatial awareness of margin locations.

With clinical interest in digital pathology on the rise, this work outlines a proposed framework for generating 3D specimen models intraoperatively via robot-integrated stereovision, and using these models to visualize involved margins in both ex vivo flattened and in situ conformed configurations.

Preliminary pilot study results suggest that stereo specimen imaging can be easily integrated into the transoral robotic surgery workflow, and that the expected accuracy of raw reconstructions is around 1. Ongoing data collection and technical development will support a full system evaluation.

Using artificial intelligence to classify point-of-care ultrasound images. Author s : Owen Anderson, Biomedical Imaging Resource Core, Mayo Clinic United States ; Garrett Regan, Songnan Wen, Deepa Mandale, Tasneem Naqvi, David R. Holmes, Mayo Clinic United States.

Such a device can now perform an echocardiogram while connected to a smartphone. While the accessibility of performing a test has been greatly improved, expertise is still required to provide usable results and diagnoses. The goal of this study is to improve the clinical utility of mobile ultrasound echocardiograms with AI machine learning.

By integrating artificial intelligence into this workflow, feedback can be given to the provider during its operation to maximize the usability of the ultrasound data and allow more tests to be performed properly.

The Intel GETi framework was used to create computer vision models that could quantify the readability of frames taken from an echocardiogram. These models determine the quality and the orientation of each frame. Feedback from these models can alert the user to proper positioning and technique to gather good ultrasound data.

Testing accuracy can also be improved with. Tuesday Morning Keynotes. Unlocking the value of 3D printing medical devices in hospitals and universities Keynote Presentation. Author s : Frank J. Rybicki, The Univ. of Arizona College of Medicine United States. This talk describes those patients, how their medical images undergo Computer Aided Design CAD , and how that data reaches a Final Anatomic Realization, one of which is 3D printing.

The talk includes medical oversight, data generation, and a specific, durable definition of value for medical devices that are 3D printed in hospitals. The talk also includes clinical appropriateness, and how it folds into accreditation for 3D printing in hospitals and universities.

Up to the minute information on reimbursement for medical devices that are 3D printed in hospitals and universities will be presented. Clinical AI model translation and deployment: creating a scalable, standardized, and responsible AI lifecycle framework in healthcare Keynote Presentation.

Author s : David S. McClintock, Mayo Clinic United States. However, amongst that excitement, one topic that has lacked direction is how healthcare institutions, from small clinical practices to large health systems, should approach AI model deployment.

Unlike typical healthcare IT implementations, AI models have special considerations that must be addressed prior to moving them into clinical practice. This talk will review the major issues surrounding clinical AI implementations and present a scalable, standardized, and responsible framework for AI deployment that can be adopted by many different healthcare organizations, departments, and functional areas.

Session Chairs: Cristian A. Linte , Rochester Institute of Technology United States , William E. Higgins , The Pennsylvania State Univ. Democratizing surgical skills via surgical data science Invited Paper. Author s : Stefanie Speidel, Nationales Centrum für Tumorerkrankungen Dresden Germany.

Although a lot of data is available, the human ability to use these possibilities especially in a complex and time-critical situation such as surgery is limited and is extremely dependent on the experience of the surgical staff.

This talks focuses on AI-assisted surgery with a specific focus on analysis of intraoperative video data. The goal is to democratize surgical skills and enhance the collaboration between surgeons and cyber-physical systems by quantifying surgical experience and make it accessible to machines.

Several examples to optimize the therapy of the individual patient along the surgical treatment path are given. Finally, remaining challenges and strategies to overcome them are discussed. Dual-camera laparoscopic imaging with super-resolution reconstruction for intraoperative hyperspectral image guidance.

Author s : Ling Ma, Kelden T. Pruitt, Baowei Fei, The Univ. of Texas at Dallas United States. Hyperspectral imaging HSI is an emerging medical imaging modality, which has proved useful for intraoperative image guidance. Snapshot hyperspectral cameras are ideal for intraoperative laparoscopic imaging because of their compact size and light weight, but low spatial resolution can be a limitation.

In this work, we developed a dual-camera laparoscopic imaging system that comprises of a high-resolution color camera and a snapshot hyperspectral camera, and we employ super-resolution reconstruction to fuse the images from both cameras to generate high-resolution hyperspectral images.

The experiment results show that our method can significantly improve the resolution of hyperspectral images without compromising the image quality or spectral signatures.

The proposed super-resolution reconstruction method is promising to promote the employment of high-speed hyperspectral imaging in laparoscopic surgery. Dense surface reconstruction using a learning-based vSLAM model for laparoscopic surgery. Author s : James Yu, The Univ. of Texas at Dallas United States ; Kelden T.

Pruitt, Nati Nawawithan, The Univ. at Dallas United States ; Baowei Fei, The Univ. While previous works have utilized pre-operative imaging such as computed tomography or magnetic resonance images, registration methods still lack the ability to accurately register deformable anatomical structures across modalities and dimensionalities.

This is especially true of minimally invasive abdominal surgeries due to limitations of the monocular laparoscope. Surgical scene reconstruction is a critical component towards AR-guided surgical interventions and other AR applications such as remote assistance or surgical simulation.

In this work, we show how to generate a dense 3D reconstruction with camera pose estimations and depth maps from video obtained with a monocular laparoscope utilizing a state-of-the-art deep-learning-based visual simultaneous localization and mapping vSLAM model.

The proposed method can robustly reconstruct surgical scenes using real-time data and provide camera pose estimations without stereo or other sensors, which increases its usability and is less intrusive.

AVA: automated viewability analysis for ureteroscopic intrarenal surgery. Author s : Daiwei Lu, Yifan Wu, Xing Yao, Vanderbilt Univ. United States ; Nicholas L. Kavoussi, Vanderbilt Univ. United States ; Ipek Oguz, Vanderbilt Univ. This contributes to a high recurrence rate for both kidney stone and UTUC patients.

We introduce an automated patient-specific analysis for determining viewability in the renal collecting system using pre-operative CT scans. WS-SfMLearner: self-supervised monocular depth and ego-motion estimation on surgical videos with unknown camera parameters.

Author s : Ange Lou, Jack H. However, it is difficult and time consuming to create depth map ground truth datasets in surgical videos due in part to inconsistent brightness and noise in the surgical scene. Therefore, building an accurate and robust self-supervised depth and camera ego-motion estimation system is gaining more attention from the computer vision community.

Although several self-supervision methods alleviate the need for ground truth depth maps and poses, they still need known camera intrinsic parameters, which are often missing or not recorded. Moreover, the camera intrinsic prediction methods in existing works depend heavily on the quality of datasets.

In this work, we aim to build a self-supervised depth and ego-motion estimation system which can predict not only accurate depth maps and camera pose, but also camera intrinsic parameters. We propose a cost-volume-based supervision approach to give the system auxiliary supervision for camera parameters prediction.

Session Chairs: Pierre Jannin , Lab. Traitement du Signal et de l'Image France , Junghoon Lee , Johns Hopkins Univ. End-to-End 3D neuroendoscopic video reconstruction for robot-assisted ventriculostomy.

Author s : Prasad Vagdargi, Ali Uneri, Stephen Z. Liu, Craig K. Jones, Alejandro Sisniega, Johns Hopkins Univ. United States ; Junghoon Lee, The Johns Hopkins Univ. School of Medicine United States ; Patrick A.

United States ; William S. Anderson, Mark Luciano, The Johns Hopkins Univ. School of Medicine United States ; Gregory D. Hager, Johns Hopkins Univ.

We introduce a vision-based navigation solution using NeRFs for 3D neuroendoscopic reconstruction on the Robot-Assisted Ventriculoscopy RAV platform. An end-to-end 3D reconstruction method using posed images was developed and integrated with RAV.

System performance was evaluated in terms of geometric accuracy, precision and runtime across multiple clinically feasible trajectories, achieving accurate sub-mm projected error. Clinical neuroendoscopic video reconstruction and registration was successfully achieved with sub-mm geometric accuracy and high precision.

Intraoperative stereovision cortical surface segmentation using fast segment anything model. Author s : Chengpei Li, Dartmouth College United States ; Xiaoyao Fan, Kristen L. Chen, Ryan B. Duke, Thayer School of Engineering at Dartmouth United States ; Linton T.

A biomechanical model updates pre-op MR images using intraoperative stereovision iSV for accuracy. Traditional methods require manual cortical surface segmentation from iSV, demanding expertise and time.

This study introduces the Fast Segment Anything Model FastSAM , a deep learning approach, for automatic segmentation from iSV. FastSAM's performance was compared with manual segmentation and a U-Net model in a patient case, focusing on segmentation accuracy Dice coefficient and image updating accuracy target registration errors; TRE.

FastSAM and manual segmentation had similar TREs 2. FastSAM's performance aligns with manual segmentation in accuracy, suggesting its potential to replace manual methods for efficiency and reduced user dependency.

Joint MR to CT synthesis and segmentation for MR-only pediatric brain radiation therapy planning. Author s : Lina Mekki, Sahaja Acharya, Matthew Ladra, Junghoon Lee, Johns Hopkins Univ. RT plans are typically optimized using CT, thus exposing patients to ionizing radiation.

Manual contouring of organs-at-risk OARs is time-consuming, particularly difficult due to the small size of brain structures, and suffers from inter-observer variability. While numerous methods have been proposed to solve MR to CT image synthesis or OAR segmentation separately, there exist only a handful of methods tackling both problems jointly, even less specifically developed for pediatric brain cancer RT.

We propose a multi-task convolutional neural network to jointly synthesize CT from MRI and segment OARs eyes, optic nerves, optic chiasm, brainstem, temporal lobes, and hippocampi for pediatric brain RT planning. Effect of the prior distribution on a Bayesian model or errors of type for transcranial magnetic stimulation.

Author s : John S. Baxter, Pierre Jannin, Univ. de Rennes 1 France. If the target may be highly ambiguous, different experts may fundamentally select different targets, believing them to refer to the same region, a phenomenon called an error of type.

This paper investigates the effects of changing the prior distribution on a Bayesian model for errors of type specific to transcranial magnetic stimulation TMS planning.

Our results show that a particular prior can be chosen which is analytically solvable, removes spurious modes, and returns estimates that are coherent with the TMS literature. This is a step towards a fully rigorous model that can be used in system evaluation and machine learning.

An adaptable model for estimating patient-specific electrical properties of the implanted cochlea. Author s : Erin L. Bratu, Vanderbilt Univ. United States ; Katelyn A. Berg, Andrea J. DeFreese, Rene H. Gifford, Vanderbilt Univ. United States ; Jack H. One limitation of these models is the large amount of data required to create them, with the resulting model being highly optimized to these single sets of measurements.

Thus, it is desirable to create a new model of equal or better quality that does not require this data to create the model and that is adaptable to new sets of clinical data. In this work, we present a methodology for one component of such a model, which uses simulations of voltage spread in the cochlea to estimate patient-specific electric potentials.

Session 6: Joint Session with Conferences and Session Chairs: Purang Abolmaesumi , The Univ. of British Columbia Canada , Josquin Foiret , Stanford Univ. Real-time vasculature segmentation during laparoscopic liver resection using attention-enriched U-Net model in intraoperative ultrasound videos.

Author s : Muhammad Awais, Mais Altaie, Caleb S. O'Connor, Austin H. Castelo, Hop S. Tran Cao, Kristy K. We propose an AI-driven solution to enhance real-time vessel identification inferior vena cava IVC , right hepatic vein RHV , left hepatic vein LHV , and middle hepatic vein MHV using a visual saliency approach that integrates attention blocks into a novel U-Net model with integrated attention blocks.

The study encompasses a dataset of IOUS video recordings from 12 patients, acquired during liver surgeries. Employing leave-one-out cross-validation, the model achieves mean dice scores of 0. This innovative approach holds the potential to revolutionize liver surgery by enabling precise vessel segmentation, with future prospects including broader vasculature segmentation and real-time application in the operating room.

An automated system for registration and fusion of 3D ultrasound images during cervical brachytherapy procedures. Author s : Tiana Trumpour, Robarts Research Institute Canada ; Jamiel Nasser, Univ. of Waterloo Canada ; Jessica R. Rodgers, Univ. of Manitoba Canada ; Jeffrey Bax, Lori Gardi, Robarts Research Institute Canada ; Lucas C.

Mendez, Kathleen Surry, London Regional Cancer Program Canada ; Aaron Fenster, Robarts Research Institute Canada. Radiation is delivered using specialized applicators or needles that are inserted within the patient using medical imaging guidance.

However, advanced imaging modalities may be unavailable in underfunded healthcare centers, suggesting a need for accessible imaging techniques during brachytherapy procedures.

This work focuses on the development and validation of a spatially tracked mechatronic arm for 3D trans-abdominal and trans-rectal ultrasound imaging. The arm will allow automated acquisition and inherent registration of two 3D ultrasound images, resulting in a fused image volume of the whole female pelvic region.

The results of our preliminary testing demonstrate this technique as a suitable alternative to advanced imaging for providing visual information to clinicians during brachytherapy applicator insertions, potentially aiding in improved patient outcomes.

Percutaneous nephrostomy needle guidance using real-time 3D anatomical visualization with live ultrasound segmentation. Author s : Andrew S. Kim, Chris Yeung, Queen's Univ. Canada ; Robert Szabo, Óbuda Univ. Hungary ; Kyle Sunderland, Rebecca Hisey, David Morton, Queen's Univ.

Canada ; Ron Kikinis, Brigham and Women's Hospital United States ; Babacar Diao, Univ. Cheikh Anta Diop Senegal ; Parvin Mousavi, Tamas Ungi, Gabor Fichtinger, Queen's Univ.

Current percutaneous nephrostomy needle guidance methods can be difficult, expensive, or not portable. We propose an open-source based real-time 3D anatomical visualization aid for needle guidance with live ultrasound segmentation and 3D volume reconstruction using deep learning and free, open-source software.

Participants performed needle insertions with visualization aid and conventional ultrasound needle guidance. Visualization aid guidance showed a significantly higher accuracy, while needle insertion time and success rate were not statistically significant at our sample size.

We found that real-time 3D anatomical visualization aid for needle guidance produced increased accuracy and an overall mostly positive experience. Mirror-based ultrasound system for exploring hand gesture classification through convolutional neural network and vision transformer.

Author s : Keshav Bimbraw, Haichong K. Zhang, Worcester Polytechnic Institute United States. Hand gesture recognition with ultrasound has gained interest in prosthetic control and human-computer interaction.

Traditional methods used for hand gesture estimation involve placing an ultrasound probe perpendicular to the forearm causing discomfort and interference with arm movement.

To address this, a novel approach utilizing acoustic reflection is proposed wherein a convex ultrasound probe is strategically positioned in parallel alignment with the forearm, and a mirror is placed at a degree angle for transmission and reception of ultrasound waves.

This positioning enhances stability and reduces arm strain. CNNs and ViT are employed for feature extraction and classification. The system's performance is compared to the traditional perpendicular method, demonstrating comparable results.

The experimental outcomes showcase the potential of the system for efficient hand gesture recognition. Design and evaluation of an educational system for ultrasound-guided interventional procedures.

Author s : Purnima Rajan, Martin Hossbach, Pezhman Foroughi, Alican Demir, Christopher Schlichter, Clear Guide Medical United States ; Karina Gattamorta, Shayne Hauglum, School of Nursing and Health Studies, Univ. of Miami United States. The system consists of an ultrasound needle guidance system which overlays virtual needle trajectories on the ultrasound screen, and custom anatomical phantoms tailored to specific anesthesiology procedures.

The system utilizes artificial intelligence-based optical needle tracking. It serves two main functions: skill evaluation, providing feedback to students and instructors, and as a learning tool, guiding students in achieving correct needle trajectories.

The system was evaluated in a study with nursing students, showing significant improvements in guided procedures compared to non-guided ones.

Live Demonstrations Workshop. Session Chairs: Karen Drukker , The Univ. of Chicago United States , Lubomir M. Hadjiiski , Michigan Medicine United States , Horst Karl Hahn , Fraunhofer-Institut für Digitale Medizin MEVIS Germany.

Publicly Available Data and Tools to Promote Machine Learning: an interactive workshop exploring MIDRC. Session Chairs: Weijie Chen , U. Food and Drug Administration United States , Heather M. Whitney , The Univ.

of Chicago United States. Chair welcome and introduction. Additive manufacturing: the promise and the challenge. Author s : David W.

Holdsworth, Western Univ. The regulatory environment for medical devices is geared towards conventional manufacturing techniques, making it challenging to certify 3D-printed devices. Additive manufacturing may still not be competitive when scaled up for industrial production, and the need for post-processing may negate some of the benefits.

The promises and the challenges of additive manufacturing will be explored in the context of medical imaging device design. Point-of-care manufacturing at Mayo Clinic.

Author s : Jonathan M. Morris, Mayo Clinic United States. Using additive manufacturing we focus on five distinct areas. First to create diagnostic anatomic models for each surgical subspecialty from diagnostic imaging.

Second to manufacture custom patient-specific sterilizable osteotomy cutting guides for ENT, OMFS, Orthopedics, and Orthopedic Oncology. Third to build simulators and phantoms using a combination of special effects and 3Dprinting. Fourth using 3D printers to create custom phantoms, phantom holders, and other custom medical devices such as pediatric airway devices, proton beam appliances, and custom jigs and fixtures for the department and hospital.

Finally to transfer the digital twins into virtual and augmented reality environments for preoperative surgical planning and immersive educational tools.

Mayo Clinic has scaled this endeavor to all three of its main campuses including Jacksonville Fl and Scottsdale AZ to complete the enterprise approach. In doing so we have been able to advance patient care locally as well as assist in building the national IT, regulatory, billing, RSAN 3D SIG, and quality control infrastructure needed to assure scaling across this and other countries.

Author s : Alex Grenning, The Jacobs Institute, Inc. Test fixtures define the goal posts for device evaluation. It is important for test fixtures to accurately represent the critical conditions of operation and be supported with justification for regulatory review.

This presentation explores the role of 3D printing and model design workflows in producing anatomically relevant text fixtures which can be used to guide, and more importantly accelerate the device development process.

The Jacobs Institute is a one-of-a-kind, not-for-profit vascular medical technology innovation center. Author s : Devarsh Vyas, Benjamin Johnson, 3D Systems Corp.

Common uses for AM include the printing of patient-specific surgical implants and instruments derived from imaging data and the manufacturing of metal implants and instruments with features that are impossible to fabricate using traditional subtractive manufacturing.

In addition to reducing costs, patient-specific solutions—such as customized surgical plans and personalized implants—aim to improve surgical outcomes for patients and give surgeons more options and more flexibility in the OR.

With advancement in technology, implants are 3D printed in various materials and at various manufacturing sites including at the point-of-care. Panel Discussion. Establishing Ground Truth in Radiology and Pathology.

Wednesday Morning Keynotes. The journey to better breast cancer detection: a trilogy Keynote Presentation. Author s : Robert M. Nishikawa, Univ. of Pittsburgh United States.

Technology assessment metrics were used to develop mammography systems, first with screen-film mammography and then to digital mammography and digital breast tomosynthesis. To optimize these systems clinically, it became necessary to determine what type of information a radiologist needed to make a correct diagnosis.

Image perception studies helped define what spatial frequencies were necessary to detect breast cancers and how different sources of noise affected detectability.

Finally, observer performance studies were used to show that advances in the imaging system led to better detection and diagnoses by radiologists. In parallel to these developments, these three concepts were used to develop computer-aided diagnosis systems.

In this talk, I will highlight how image perception, observer performance, and technology assessment were leveraged to produce technologies that allow radiologists to be highly effective in detecting breast cancer. A tale of two imaging informatics translational licensing models: commercial and open source Keynote Presentation.

Author s : Gordon J. Harris, Massachusetts General Hospital United States. Session Chairs: John S. Baxter , Univ. de Rennes 1 France , Satish E. Viswanath , Case Western Reserve Univ. Advances in model-guided interventions Invited Paper. Author s : Michael I. This reality motivates many questions, both exhilarating and provocative.

The assertion in this talk is that treatment platform technologies of the future will need to be intentionally designed for the dual purpose of treatment and discovery.

Exemplar surgical and interventional technologies will be discussed that involve complex biophysical models, methods of automation and procedural field surveillance, efforts toward data-driven procedures and therapy forecasting, and approaches integrating disease phenotypic biomarkers.

The common thread to the work is the use of computational models driven by sparse procedural data as a constraining environment to enable guidance and therapy delivery. Optimal hyperparameter selection in deformable image registration using information criterion and band-limited modal reconstruction.

Author s : Jon S. United States ; Morgan J. Ringel, Vanderbilt Univ. United States ; Jayasree Chakraborty, William R. Jarnagin, Memorial Sloan-Kettering Cancer Ctr. However, these procedures may not produce results that optimally generalize to inter- and intra-dataset variabilities.

We present a parameter estimation framework based on the Akaike Information Criterion AIC that permits dynamic runtime adaptation of model parameters by maximizing the informativeness of the registration model against the specific data constraints available to the registration.

This parameter adaptation framework is implemented in a frequency band-limited reconstruction approach to efficiently resolve modal harmonics of soft tissue deformation in image registration. Our approach automatically selects optimal model complexity via AIC to match informational constraints via a parallel-computed ensemble model that achieves excellent TRE without the need for any hyperparameter tuning.

GhostMorph: a computationally efficient model for deformable inter-subject registration. Author s : Mingzhe Hu, Xiaofeng Yang, Shaoyan Pan, Emory Univ.

GhostMorph addresses the computational challenges inherent in medical image registration, particularly in deformable registration where complex local and global deformations are prevalent. By integrating Ghost modules and 3D depth-wise separable convolutions into its architecture, GhostMorph significantly reduces computational demands while maintaining high performance.

The study benchmarks GhostMorph against state-of-the-art registration methods using the Liver Tumor Segmentation Benchmark LiTS dataset, demonstrating its comparable accuracy and improved computational efficiency.

GhostMorph emerges as a viable, scalable solution for real-time and resource-constrained clinical scenarios, marking a notable advancement in medical image registration technology.

Joint synthesis and registration of MRI and cone-beam CT Images using deep evidential uncertainty estimation of deformation fields. Author s : Murong Yi, Ruxiao Duan, Zhikai Li, Jeffrey H. Siewerdsen, Ali Uneri, Junghoon Lee, Craig K. Jones, Johns Hopkins Univ. By framing warping field prediction as a pixel-wise regression problem, we employ pixel-wise evidential deep learning to predict uncertainties.

Visualized uncertainty maps revealed a strong correlation between high warping magnitude and uncertainty. Numeric outcomes on segmentation maps substantiated the benefit of uncertainty integration, yielding improvements significantly better than the training without uncertainty, which shows that introducing uncertainty to the registration network holds great promise.

Automatic auditory nerve fiber localization using geodesic paths. Author s : Ziteng Liu, Erin L. Bratu, Jack H. Thus, many programming sessions are needed and usually lead to suboptimal results.

Previously, our group has developed an ANF localization approach in order to simulate the neural response triggered by CIs. That method relies heavily on manual adjustment and is error prone. In this work, we introduce a fully automatic and accurate ANF localization method, where the peripheral and central axon of an ANF can be estimated individually based on five sets of automatically generated landmarks; the fast marching method can be used to find geodesic paths between landmarks; and cylindrical coordinate systems can be constructed based on the landmarks in order to smoothly interpolate trajectories between landmarks.

Experiments show that our proposed method outperforms the original method and achieves impressive performance qualitatively and quantitatively. Session Chairs: Jeffrey H. Siewerdsen , The Univ. United States , Stefanie Speidel , Nationales Centrum für Tumorerkrankungen Dresden Germany. Augmented reality guidance for rib fracture surgery: a feasibility study.

Author s : Abdullah Thabit, Maartje Eijssen, Mohamed Benmahdjoub, Bart Cornelissen, Mark G. van Vledder, Theo van Walsum, Erasmus MC Netherlands. Rib fractures can be observed in X-ray and CT scans allowing for better surgical planning.

However, translating the surgical plan to the operating table through mental mapping remains a challenging task. Using augmented reality AR , a preoperative plan can be intraoperatively visualized in the field of view of the surgeon, allowing for a more accurate determination of the size and location of the incision for optimum access to the fractured ribs.

This study aims to evaluate the use of AR for guidance in rib fracture procedures. To that end, an AR system using the HoloLens 2 was developed to visualize surgical incisions directly overlayed on the patient. To evaluate the feasibility of the system, a torso phantom was scanned for preoperative planning of the incision lines.

A user study with 13 participants was conducted to align the preoperative model and delineate the visualized incisions.

For a total of 39 delineated incisions, a mean distance error of 3. The study shows the potential of using AR as an alternative to the traditional palpation approach for locating rib fractures, which has an error of up to 5 cm.

Intraoperative tracked ultrasound imaging for resolving deformations during spine surgery. Author s : Jinchi Wei, Debarghya China, Kai Ding, Johns Hopkins Univ. United States ; Neil Crawford, Norbert Johnson, Globus Medical Inc.

United States ; Nicholas Theodore, The Johns Hopkins Univ. This study presents an ultrasound-guided approach and an integrated real-time system for verifying and recovering tracking accuracy following spinal deformations. The approach combines deep-learning segmentation of the posterior vertebral cortices with a multi-step point-to-surface registration to map reconstructed US features to the 3D CT image.

The solution was evaluated in cadaver specimens with induced deformation and demonstrated 1. Author s : Lucas Hintz, Sarah C. United States ; Sarah Dance, Kochai Jawed, Matthew Oetgen, Children's National Medical Ctr.

United States ; Tamas Ungi, Gabor Fichtinger, Queen's Univ. Canada ; Christopher Schlenger, Verdure Imaging Inc. United States ; Kevin Cleary, Children's National Medical Ctr. Adaptive loss engine for x-ray segmentation ALEXS for scoliosis intervention: assess digital segmentation and angle approximation.

Author s : Yunbo Shao, Skyline High School United States ; Shuo Li, Case Western Reserve Univ. This paper aims to contribute to developing a fully automated method of estimating Cobb angles—a measurement commonly used for scoliosis—through the use of a specialized image segmentation model trained specifically on x-rays to automatically identify vertebrae within x-ray images.

This model is named the Adaptive Loss Engine for X-ray Segmentation ALEXS. Besides training, another method of improving the performance of the ALEXS is altering the original x-ray image without changing the locations of the vertebrae.

Sharpening the image and increasing its contrast allowed the ALEXS to identify many more vertebrae than before. Based on the results that were obtained, using the ALEXS combined with altered images produces superior results compared to some previous attempts.

These improved methods allow for a more accurate end-to-end process for automatically diagnosing scoliosis. Surgical process modeling of image-guided spine surgery. Author s : Anshuj Deva, Tatiana A. Rypinski, Bhavin Soni, Parvathy Pillai, Laurence D.

Rhines, Claudio E. Tatsui, Christopher Alvarez-Breckenridge, Robert Y. North, Justin E. Bird, Jeffrey H. Session 9: Deep Image Analysis for Image-Guided Interventions. Session Chairs: Shuo Li , Case Western Reserve Univ.

United States , Matthieu Chabanas , Univ. Grenoble Alpes France. Optimal strategies for modeling anatomy in a hybrid intelligence framework for auto-segmentation of organs. Author s : You K.

Hao, Jayaram K. Udupa, Yubing Tong, Univ. of Pennsylvania United States ; Tiange Liu, Yanshan Univ. China ; Caiyun Wu, Dewey Odhner, Drew A. Torigian, Univ. In this paper, we introduce several advances related to modeling of the NI component, so the HI-system becomes substantially more efficient.

We demonstrate a fold computational improvement in the auto-segmentation task for RT planning via clinical studies obtained from 4 different RT centers, while retaining state-of-the-art accuracy of the previous system in segmenting 28 objects in Thorax and Neck.

Benchmarking image transformers for prostate cancer detection from ultrasound data. Author s : Mohamed Harmanani, Paul F. Wilson, Queen's Univ. Canada ; Fahimeh Fooladgar, The Univ.

of British Columbia Canada ; Amoon Jamzad, Mahdi Gilany, Queen's Univ. Canada ; Minh Nguyen Nhat To, The Univ. of British Columbia Canada ; Brian Wodlinger, Exact Imaging Inc.

Canada ; Purang Abolmaesumi, The Univ. of British Columbia Canada ; Parvin Mousavi, Queen's Univ. It seeks to establish a baseline for the performance of these architectures on cancer detection, both in specific regions of interest ROI-scale methods and across the entire biopsy core using multiple ROIs multi-scale methods.

This work also introduces a novel framework for multi-objective learning with transformers by combining the loss for individual ROI predictions with the loss for the core prediction, thereby improving performance over baseline methods.

Self-supervised monocular pose and depth estimation for wireless capsule endoscopy using transformers. Author s : Nahid Nazifi, Institut National des Sciences Appliquées Centre Val de Loire France , Institute de Sistemas e Robotica Portugal ; Helder Araujo, Univ.

de Coimbra Portugal ; Gopi Krishna Erabati, Institute de Sistemas e Robotica Portugal ; Omar Tahri, VIBOT-ImViA, Univ.