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Ringel MJ, Heiselman JS, Richey WL, Meszoely IM, Jarnagin WR, Miga MI. Comparing regularized Kelvinlet functions and the finite element method for registration of medical images to sparse organ data. Med Image Anal 2024; 96:103221. [PMID: 38824864 DOI: 10.1016/j.media.2024.103221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 05/06/2024] [Accepted: 05/25/2024] [Indexed: 06/04/2024]
Abstract
Image-guided surgery collocates patient-specific data with the physical environment to facilitate surgical decision making. Unfortunately, these guidance systems commonly become compromised by intraoperative soft-tissue deformations. Nonrigid image-to-physical registration methods have been proposed to compensate for deformations, but clinical utility requires compatibility of these techniques with data sparsity and temporal constraints in the operating room. While finite element models can be effective in sparse data scenarios, computation time remains a limitation to widespread deployment. This paper proposes a registration algorithm that uses regularized Kelvinlets, which are analytical solutions to linear elasticity in an infinite domain, to overcome these barriers. This algorithm is demonstrated and compared to finite element-based registration on two datasets: a phantom liver deformation dataset and an in vivo breast deformation dataset. The regularized Kelvinlets algorithm resulted in a significant reduction in computation time compared to the finite element method. Accuracy as evaluated by target registration error was comparable between methods. Average target registration errors were 4.6 ± 1.0 and 3.2 ± 0.8 mm on the liver dataset and 5.4 ± 1.4 and 6.4 ± 1.5 mm on the breast dataset for the regularized Kelvinlets and finite element method, respectively. Limitations of regularized Kelvinlets include the lack of organ-specific geometry and the assumptions of linear elasticity and infinitesimal strain. Despite limitations, this work demonstrates the generalizability of regularized Kelvinlets registration on two soft-tissue elastic organs. This method may improve and accelerate registration for image-guided surgery, and it shows the potential of using regularized Kelvinlets on medical imaging data.
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Affiliation(s)
- Morgan J Ringel
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA.
| | - Jon S Heiselman
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA; Memorial Sloan-Kettering Cancer Center, Department of Surgery, New York, NY, USA
| | - Winona L Richey
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA
| | - Ingrid M Meszoely
- Vanderbilt University Medical Center, Division of Surgical Oncology, Nashville, TN, USA
| | - William R Jarnagin
- Memorial Sloan-Kettering Cancer Center, Department of Surgery, New York, NY, USA
| | - Michael I Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA
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Mazier A, Bordas SPA. Breast simulation pipeline: From medical imaging to patient-specific simulations. Clin Biomech (Bristol, Avon) 2024; 111:106153. [PMID: 38061204 DOI: 10.1016/j.clinbiomech.2023.106153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 11/17/2023] [Accepted: 11/20/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND Breast-conserving surgery is the most acceptable operation for breast cancer removal from an invasive and psychological point of view. Before the surgical procedure, a preoperative MRI is performed in the prone configuration, while the surgery is achieved in the supine position. This leads to a considerable movement of the breast, including the tumor, between the two poses, complicating the surgeon's task. METHODS In this work, a simulation pipeline allowing the computation of patient-specific geometry and the prediction of personalized breast material properties was put forward. Through image segmentation, a finite element model including the subject-specific geometry is established. By first computing an undeformed state of the breast, the geometrico-material model is calibrated by surface acquisition in the intra-operative stance. FINDINGS Using an elastic corotational formulation, the patient-specific mechanical properties of the breast and skin were identified to obtain the best estimates of the supine configuration. The final results are a shape-fitting closest point residual of 4.00 mm for the mechanical parameters Ebreast=0.32 kPa and Eskin=22.72 kPa, congruent with the current state-of-the-art. The Covariance Matrix Adaptation Evolution Strategy optimizer converges on average between 5 to 30 min depending on the initial parameters, reaching a simulation speed of 20 s. To our knowledge, our model offers one of the best compromises between accuracy and speed. INTERPRETATION Satisfactory results were obtained for the estimation of breast deformation from preoperative to intra-operative configuration. Furthermore, we have demonstrated the clinical feasibility of such applications using a simulation framework that aims at the smallest disturbance of the actual surgical pipeline.
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Affiliation(s)
- Arnaud Mazier
- Institute of Computational Engineering, Department of Engineering, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Stéphane P A Bordas
- Institute of Computational Engineering, Department of Engineering, Université du Luxembourg, Esch-sur-Alzette, Luxembourg.
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Ringel MJ, Richey WL, Heiselman JS, Meszoely IM, Miga MI. Incorporating heterogeneity and anisotropy for surgical applications in breast deformation modeling. Clin Biomech (Bristol, Avon) 2023; 104:105927. [PMID: 36890069 PMCID: PMC10122703 DOI: 10.1016/j.clinbiomech.2023.105927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/10/2023]
Abstract
BACKGROUND Simulating soft-tissue breast deformations is of interest for many applications including image fusion, longitudinal registration, and image-guided surgery. For the surgical use case, positional changes cause breast deformations that compromise the use of preoperative imaging to inform tumor excision. Even when acquiring imaging in the supine position, which better reflects surgical presentation, deformations still occur due to arm motion and orientation changes. A biomechanical modeling approach to simulate supine breast deformations for surgical applications must be both accurate and compatible with the clinical workflow. METHODS A supine MR breast imaging dataset from n = 11 healthy volunteers was used to simulate surgical deformations by acquiring images in arm-down and arm-up positions. Three linear-elastic modeling approaches with varying levels of complexity were used to predict deformations caused by this arm motion: a homogeneous isotropic model, a heterogeneous isotropic model, and a heterogeneous anisotropic model using a transverse-isotropic constitutive model. FINDINGS The average target registration errors for subsurface anatomical features were 5.4 ± 1.5 mm for the homogeneous isotropic model, 5.3 ± 1.5 mm for the heterogeneous isotropic model, and 4.7 ± 1.4 mm for the heterogeneous anisotropic model. A statistically significant improvement in target registration error was observed between the heterogeneous anisotropic model and both the homogeneous and the heterogeneous isotropic models (P < 0.01). INTERPRETATION While a model that fully incorporates all constitutive complexities of anatomical structure likely achieves the best accuracy, a computationally tractable heterogeneous anisotropic model provided significant improvement and may be applicable for image-guided breast surgeries.
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Affiliation(s)
- Morgan J Ringel
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA.
| | - Winona L Richey
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA
| | - Jon S Heiselman
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA; Memorial Sloan-Kettering Cancer Center, Department of Surgery, NY, New York, USA
| | - Ingrid M Meszoely
- Vanderbilt University Medical Center, Division of Surgical Oncology, Nashville, TN, USA
| | - Michael I Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA; Vanderbilt University, Department of Radiology and Radiological Sciences, Nashville, TN, USA; Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, TN, USA; Vanderbilt University Medical Center, Department of Otolaryngology-Head and Neck Surgery, Nashville, TN, USA
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Richey WL, Heiselman JS, Ringel MJ, Meszoely IM, Miga MI. Computational Imaging to Compensate for Soft-Tissue Deformations in Image-Guided Breast Conserving Surgery. IEEE Trans Biomed Eng 2022; 69:3760-3771. [PMID: 35604993 PMCID: PMC9811993 DOI: 10.1109/tbme.2022.3177044] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
OBJECTIVE During breast conserving surgery (BCS), magnetic resonance (MR) images aligned to accurately display intraoperative lesion locations can offer improved understanding of tumor extent and position relative to breast anatomy. Unfortunately, even under consistent supine conditions, soft tissue deformation compromises image-to-physical alignment and results in positional errors. METHODS A finite element inverse modeling technique has been developed to nonrigidly register preoperative supine MR imaging data to the surgical scene for improved localization accuracy during surgery. Registration is driven using sparse data compatible with acquisition during BCS, including corresponding surface fiducials, sparse chest wall contours, and the intra-fiducial skin surface. Deformation predictions were evaluated at surface fiducial locations and subsurface tissue features that were expertly identified and tracked. Among n = 7 different human subjects, an average of 22 ± 3 distributed subsurface targets were analyzed in each breast volume. RESULTS The average target registration error (TRE) decreased significantly when comparing rigid registration to this nonrigid approach (10.4 ± 2.3 mm vs 6.3 ± 1.4 mm TRE, respectively). When including a single subsurface feature as additional input data, the TRE significantly improved further (4.2 ± 1.0 mm TRE), and in a region of interest within 15 mm of a mock biopsy clip TRE was 3.9 ± 0.9 mm. CONCLUSION These results demonstrate accurate breast deformation estimates based on sparse-data-driven model predictions. SIGNIFICANCE The data suggest that a computational imaging approach can account for image-to-surgery shape changes to enhance surgical guidance during BCS.
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Ringel MJ, Richey WL, Heiselman JS, Luo M, Meszoely IM, Miga MI. Supine magnetic resonance image registration for breast surgery: insights on material mechanics. J Med Imaging (Bellingham) 2022; 9:065001. [PMID: 36388143 PMCID: PMC9659944 DOI: 10.1117/1.jmi.9.6.065001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 10/26/2022] [Indexed: 11/15/2022] Open
Abstract
Purpose Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, is being explored since it more closely represents the surgical presentation compared to conventional diagnostic imaging positions. Despite this preoperative imaging position, deformation is still present between the supine imaging and surgical state. As a result, a fast and accurate image-to-physical registration approach is needed to realize image-guided breast surgery. Approach In this study, three registration methods were investigated on healthy volunteers' breasts ( n = 11 ) with the supine arm-down position simulating preoperative imaging and supine arm-up position simulating intraoperative presentation. The registration methods included (1) point-based rigid registration using synthetic fiducials, (2) nonrigid biomechanical model-based registration using sparse data, and (3) a data-dense three-dimensional diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. Additionally, deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field to better understand breast material mechanics. Results The average target registration errors (TRE) were 10.4 ± 2.3 , 6.4 ± 1.5 , and 2.8 ± 1.3 mm (mean ± standard deviation) and the average fiducial registration errors (FRE) were 7.8 ± 1.7 , 2.5 ± 1.1 , and 3.1 ± 1.1 mm for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. The mechanics-based deformation metrics revealed an overall anisotropic tissue behavior and a statistically significant difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy. Conclusions Overall, registration accuracy significantly improved with increasingly flexible and data-dense registration methods. Analysis of these outcomes may inform the future development of image guidance systems for lumpectomy procedures.
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Affiliation(s)
- Morgan J. Ringel
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, United States
| | - Winona L. Richey
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, United States
| | - Jon S. Heiselman
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, United States
- Memorial Sloan-Kettering Cancer Center, Department of Surgery, New York, New York, United States
| | - Ma Luo
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, United States
| | - Ingrid M. Meszoely
- Vanderbilt University Medical Center, Division of Surgical Oncology, Nashville, Tennessee, United States
| | - Michael I. Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Otolaryngology-Head and Neck Surgery, Nashville, Tennessee, United States
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Ringel MJ, Richey WL, Heiselman J, Luo M, Meszoely IM, Miga MI. Breast image registration for surgery: Insights on material mechanics modeling. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12034:1203411. [PMID: 35607388 PMCID: PMC9124453 DOI: 10.1117/12.2611787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, more closely represents the surgical presentation compared to conventional diagnostic pendant positioning. Optimal utilization for surgical guidance, however, requires a fast and accurate image-to-physical registration from preoperative imaging to intraoperative surgical presentation. In this study, three registration methods were investigated on healthy volunteers' breasts (n=11) with the arm-down position simulating preoperative imaging and arm-up position simulating intraoperative data. The registration methods included: (1) point-based rigid registration using synthetic fiducials, (2) non-rigid biomechanical model-based registration using sparse data, and (3) a data-dense 3D diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. The average target registration errors (TRE) were 10.4 ± 2.3, 6.4 ± 1.5, and 2.8 ± 1.3 mm (mean ± standard deviation) and the average fiducial registration errors (FRE) were 7.8 ± 1.7, 2.5 ± 1.1, and 3.1 ± 1.1 mm (mean ± standard deviation) for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. Additionally, common mechanics-based deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field. The average metrics revealed anisotropic tissue behavior and a statistical difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy. Overall, registration accuracy significantly improved with increasingly flexible registration methods, which may inform future development of image guidance systems for lumpectomy procedures.
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Affiliation(s)
- Morgan J Ringel
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, TN USA
| | - Winona L Richey
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, TN USA
| | - Jon Heiselman
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, TN USA
| | - Ma Luo
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, TN USA
| | - Ingrid M Meszoely
- Vanderbilt University Medical Center, Division of Surgical Oncology, Nashville, TN USA
| | - Michael I Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN USA
- Vanderbilt University Department of Radiology and Radiological Sciences, Nashville, TN USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, TN USA
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, TN USA
- Vanderbilt University Medical Center, Department of Otolaryngology-Head and Neck Surgery, Nashville, TN USA
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Richey WL, Heiselman J, Ringel M, Meszoely IM, Miga MI. Tumor deformation correction for an image guidance system in breast conserving surgery. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12034:120340K. [PMID: 35611302 PMCID: PMC9126640 DOI: 10.1117/12.2611570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Breast cancer is the most common cancer in women, and surgical resection is standard of care for the majority of breast cancer patients. Unfortunately, current reoperation rates are 10-29%. Uncertainty in lesion localization is one of the main factors contributing to these high reoperation rates. This work uses the linearized iterative boundary reconstruction approach to model patient breast deformation due to abduction of the ipsilateral arm. A preoperative supine magnetic resonance (MR) image was obtained with the patient's arms down near the torso. A mock intraoperative breast shape was measured from a supine MR image obtained with the patient's arm up near the head. Sparse data was subsampled from the full volumetric image to represent realistic intraoperative data collection: surface fiducial points, the intra-fiducial skin surface, and the chest wall as measured with 7 tracked ultrasound images. The deformed preoperative arm-down data was compared to the ground truth arm-up data. From rigid registration to model correction the tumor centroid distance improves from 7.3 mm to 3.3 mm, average surface fiducial error across 9 synthetic fiducials and the nipple improves from 7.4 ± 2.2 to 1.3 ± 0.7, and average subsurface error across 14 corresponding features improves from 6.2 ± 1.4 mm to 3.5 ± 1.1 mm. Using preoperative supine MR imaging and sparse data in the deformed position, this modeling framework can correct for breast shape changes between imaging and surgery to more accurately predict intraoperative position of the tumor as well as 10 surface fiducials and 14 subsurface features.
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Affiliation(s)
- Winona L Richey
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN USA
| | - Jon Heiselman
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN USA
| | - Morgan Ringel
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN USA
| | - Ingrid M Meszoely
- Vanderbilt University Medical Center, Division of Surgical Oncology, Nashville, TN USA
| | - Michael I Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN USA
- Vanderbilt University Department of Radiology and Radiological Sciences, Nashville, TN USA
- Vanderbilt Institute for Surgery and Engineering, Nashville, TN USA
- Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, TN USA
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Richey WL, Heiselman JS, Luo M, Meszoely IM, Miga MI. Impact of deformation on a supine-positioned image-guided breast surgery approach. Int J Comput Assist Radiol Surg 2021; 16:2055-2066. [PMID: 34382176 DOI: 10.1007/s11548-021-02452-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 07/06/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE To reduce reoperation rates for image-guided breast-conserving surgery, the enhanced sensitivity of magnetic resonance (MR) supine imaging may be leveraged. However, accurate tissue correspondence between images and their physical counterpart in the surgical presentation is challenging due to breast deformations (e.g., from patient/arm position changes, and operating room table rotation differences). In this study, standard rigid registration methods are employed and tissue deformation is characterized. METHODS On n = 10 healthy breasts, surface displacements were measured by comparing intraoperative fiducial locations as the arm was moved from conventional MR scanning positions (arm-down and arm-up) to the laterally extended surgical configuration. Supine MR images in the arm-down and arm-up positions were registered to mock intraoperative presentations. RESULTS Breast displacements from a supine MR imaging configuration to a mock surgical presentation were 28.9 ± 9.2 mm with shifts occurring primarily in the inferior/superior direction. With respect to supine MR to surgical alignment, the average fiducial, target, and maximum target registration errors were 9.0 ± 1.7 mm, 9.3 ± 1.7 mm, and 20.0 ± 7.6 mm, respectively. Even when maintaining similar arm positions in the MR image and mock surgery, the respective averages were 6.0 ± 1.0 mm, 6.5 ± 1.1 mm, and 12.5 ± 2.8 mm. CONCLUSION From supine MR positioning to surgical presentation, the breast undergoes large displacements (9.9-70.1 mm). The data also suggest that significant nonrigid deformations (9.3 ± 1.7 mm with 20.0 mm average maximum) exist that need to be considered in image guidance and modeling applications.
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Affiliation(s)
- Winona L Richey
- Department of Biomedical Engineering, Vanderbilt University, 1225 Stevenson Center Ln, Nashville, 37235, USA.
- Vanderbilt Institute for Surgery and Engineering, 1161 21st Ave. S, Nashville, 37204, USA.
- Vanderbilt University, 1225 Stevenson Center Ln, Stevenson Center 5824, Nashville, TN, 37240, USA.
| | - Jon S Heiselman
- Department of Biomedical Engineering, Vanderbilt University, 1225 Stevenson Center Ln, Nashville, 37235, USA
- Vanderbilt Institute for Surgery and Engineering, 1161 21st Ave. S, Nashville, 37204, USA
| | - Ma Luo
- Department of Biomedical Engineering, Vanderbilt University, 1225 Stevenson Center Ln, Nashville, 37235, USA
- Vanderbilt Institute for Surgery and Engineering, 1161 21st Ave. S, Nashville, 37204, USA
| | - Ingrid M Meszoely
- Vanderbilt Institute for Surgery and Engineering, 1161 21st Ave. S, Nashville, 37204, USA
- Division of Surgical Oncology, Vanderbilt University Medical Center, 719 Thompson Ln Suite 22100, Nashville, 37232, USA
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, 1225 Stevenson Center Ln, Nashville, 37235, USA
- Vanderbilt Institute for Surgery and Engineering, 1161 21st Ave. S, Nashville, 37204, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. S, Nashville, 37232, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN, 37232, USA
- Department of Otolaryngology - Head and Neck Surgery, Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, TN, 37232, USA
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Diab M, Kumaraswamy N, Reece GP, Hanson SE, Fingeret MC, Markey MK, Ravi-Chandar K. Characterization of human female breast and abdominal skin elasticity using a bulge test. J Mech Behav Biomed Mater 2020; 103:103604. [PMID: 32090931 DOI: 10.1016/j.jmbbm.2019.103604] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 11/15/2019] [Accepted: 12/20/2019] [Indexed: 11/19/2022]
Abstract
Characterization of material properties of human skin is required to develop a physics-based biomechanical model that can predict deformation of female breast after cosmetic and reconstructive surgery. In this paper, we have adopted an experimental approach to characterize the biaxial response of human skin using bulge tests. Skin specimens were harvested from breast and abdominal skin of female subjects who underwent mastectomy and/or reconstruction at The University of Texas MD Anderson Cancer Center and who provided informed consent. The specimens were tested within 2 h of harvest, and after freezing for different time periods but not exceeding 6 months. Our experimental results show that storage in a freezer at -20 °C for up to about 40 days does not lead to changes in the mechanical response of the skin beyond statistical variation. Moreover, displacement at the apex of the bulged specimen versus applied pressure varies significantly between different specimens from the same subject and from different subjects. The bulge test results were used in an inverse optimization procedure in order to calibrate two different constitutive material models - the angular integration model proposed by Lanir (1983) and the generalized structure tensor formulation of Gasser et al. (2006). The material parameters were estimated through a cost function that penalized deviations of the displacement and principal curvatures at the apex. Generally, acceptable fits were obtained with both models, although the angular integration model was able to fit the curvatures slightly better than the Gasser et al. model. The range of the model parameters has been extracted for use in physics-based biomechanical models of the breast.
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Affiliation(s)
- Mazen Diab
- Department of Aerospace Engineering & Engineering Mechanics, The University of Texas at Austin, Austin, TX, USA; Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
| | - Nishamathi Kumaraswamy
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Gregory P Reece
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Summer E Hanson
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michelle C Fingeret
- Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mia K Markey
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Krishnaswamy Ravi-Chandar
- Department of Aerospace Engineering & Engineering Mechanics, The University of Texas at Austin, Austin, TX, USA
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Chaplin V, Phipps MA, Jonathan SV, Grissom WA, Yang PF, Chen LM, Caskey CF. On the accuracy of optically tracked transducers for image-guided transcranial ultrasound. Int J Comput Assist Radiol Surg 2019; 14:1317-1327. [PMID: 31069643 DOI: 10.1007/s11548-019-01988-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 04/24/2019] [Indexed: 01/12/2023]
Abstract
PURPOSE Transcranial focused ultrasound (FUS) is increasingly being explored to modulate neuronal activity. To target neuromodulation, researchers often localize the FUS beam onto the brain region(s) of interest using spatially tracked tools overlaid on pre-acquired images. Here, we quantify the accuracy of optically tracked image-guided FUS with magnetic resonance imaging (MRI) thermometry, evaluate sources of error and demonstrate feasibility of these procedures to target the macaque somatosensory region. METHODS We developed an optically tracked FUS system capable of projecting the transducer focus onto a pre-acquired MRI volume. To measure the target registration error (TRE), we aimed the transducer focus at a desired target in a phantom under image guidance, heated the target while imaging with MR thermometry and then calculated the TRE as the difference between the targeted and heated locations. Multiple targets were measured using either an unbiased or bias-corrected calibration. We then targeted the macaque S1 brain region, where displacement induced by the acoustic radiation force was measured using MR acoustic radiation force imaging (MR-ARFI). RESULTS All calibration methods enabled registration with TRE on the order of 3 mm. Unbiased calibration resulted in an average TRE of 3.26 mm (min-max: 2.80-4.53 mm), which was not significantly changed by prospective bias correction (TRE of 3.05 mm; 2.06-3.81 mm, p = 0.55). Restricting motion between the transducer and target and increasing the distance between tracked markers reduced the TRE to 2.43 mm (min-max: 0.79-3.88 mm). MR-ARFI images showed qualitatively similar shape and extent as projected beam profiles in a living non-human primate. CONCLUSIONS Our study describes methods for image guidance of FUS neuromodulation and quantifies errors associated with this method in a large animal. The workflow is efficient enough for in vivo use, and we demonstrate transcranial MR-ARFI in vivo in macaques for the first time.
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Affiliation(s)
- V Chaplin
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, AA 1105 MCN, 1161 21st Ave. S, Nashville, TN, TN 37232, USA
| | - M A Phipps
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, AA 1105 MCN, 1161 21st Ave. S, Nashville, TN, TN 37232, USA
| | - S V Jonathan
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - W A Grissom
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, AA 1105 MCN, 1161 21st Ave. S, Nashville, TN, TN 37232, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - P F Yang
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, AA 1105 MCN, 1161 21st Ave. S, Nashville, TN, TN 37232, USA
| | - L M Chen
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, AA 1105 MCN, 1161 21st Ave. S, Nashville, TN, TN 37232, USA
| | - C F Caskey
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University Medical Center, AA 1105 MCN, 1161 21st Ave. S, Nashville, TN, TN 37232, USA.
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
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11
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Iterative simulations to estimate the elastic properties from a series of MRI images followed by MRI-US validation. Med Biol Eng Comput 2018; 57:913-924. [PMID: 30483912 DOI: 10.1007/s11517-018-1931-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 11/17/2018] [Indexed: 10/27/2022]
Abstract
The modeling of breast deformations is of interest in medical applications such as image-guided biopsy, or image registration for diagnostic purposes. In order to have such information, it is needed to extract the mechanical properties of the tissues. In this work, we propose an iterative technique based on finite element analysis that estimates the elastic modulus of realistic breast phantoms, starting from MRI images acquired in different positions (prone and supine), when deformed only by the gravity force. We validated the method using both a single-modality evaluation in which we simulated the effect of the gravity force to generate four different configurations (prone, supine, lateral, and vertical) and a multi-modality evaluation in which we simulated a series of changes in orientation (prone to supine). Validation is performed, respectively, on surface points and lesions using as ground-truth data from MRI images, and on target lesions inside the breast phantom compared with the actual target segmented from the US image. The use of pre-operative images is limited at the moment to diagnostic purposes. By using our method we can compute patient-specific mechanical properties that allow compensating deformations. Graphical Abstract Workflow of the proposed method and comparative results of the prone-to-supine simulation (red volumes) validated using MRI data (blue volumes).
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12
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Joukainen S, Masarwah A, Könönen M, Husso M, Sutela A, Kärjä V, Vanninen R, Sudah M. Feasibility of mapping breast cancer with supine breast MRI in patients scheduled for oncoplastic surgery. Eur Radiol 2018; 29:1435-1443. [PMID: 30120494 DOI: 10.1007/s00330-018-5681-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 07/09/2018] [Accepted: 07/24/2018] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To prospectively determine the feasibility of preoperative supine breast MRI in breast cancer patients scheduled for oncoplastic breast-conserving surgery. METHODS In addition to a diagnostic prone breast MRI, a supplementary supine MRI was performed with the patient in the surgical position including skin markers. Tumours' locations were ink-marked on the skin according to findings obtained from supine MRI. Changes in tumours' largest diameter and locations between prone and supine MRI were measured and compared to histology. Nipple-to-tumour and tumour-to-chest wall distances were also measured. Tumours and suspicious areas were surgically removed according to skin ink-markings. The differences between MRI measurements with reference to histopathology were evaluated with the paired-sample t test. RESULTS Fourteen consecutive patients, 15 breasts and 27 lesions were analysed. Compared to histology, prone MRI overestimated tumour size by 47.1% (p = 0.01) and supine MRI by 14.5% (p = 0.259). In supine MRI, lesions' mean diameters and areas were smaller compared to prone MRI (- 20.9%, p = 0.009 and - 38.3%, p = 0.016, respectively). This difference in diameter was more pronounced in non-mass lesions (- 31.2%, p = 0.031) compared to mass lesions (- 9.2%, p = 0.009). Tumours' mean distance from chest wall diminished by 69.4% (p < 0.001) and from nipple by 18.2% (p < 0.001). Free microscopic margins were achieved in first operation in all patients. CONCLUSIONS Supine MRI in the surgical position is feasible and useful in the precise localisation of prone MRI-detected lesions and provides a helpful tool to implement in surgery. Supine MRI more accurately determines tumours' size and location and might have an important role to diminish overestimations. KEY POINTS • Breath-hold supine breast MRI is feasible using commercially available coils and sequences. • Size and area of lesions on MRI were consistently smaller when measured from the supine position as compared to the prone position. • Supine breast MRI is useful in the precise preoperative localisation of prone MRI-detected lesions. •.
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Affiliation(s)
- S Joukainen
- Department of Surgery, Division of Plastic Surgery, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland.
| | - A Masarwah
- Department of Clinical Radiology, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland
| | - M Könönen
- Department of Clinical Radiology, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland
| | - M Husso
- Department of Clinical Radiology, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland
| | - A Sutela
- Department of Clinical Radiology, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland
| | - V Kärjä
- Department of Pathology, Diagnostic Imaging Centre, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland
| | - R Vanninen
- Department of Clinical Radiology, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Clinical Radiology, University of Eastern Finland, Yliopistonranta 1, 70210, Kuopio, Finland
- Cancer Center of Eastern Finland, University of Eastern Finland, Yliopistonranta 1, 70210, Kuopio, Finland
| | - M Sudah
- Department of Clinical Radiology, Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland
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13
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Miura S, Shintaku Y, Ishiuchi H, Parque V, Miyashita T. Enhanced Frequency Difference of Tumor inside Vibrated Tissue by a Compression Cylinder. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:380-383. [PMID: 30440415 DOI: 10.1109/embc.2018.8512437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Breast cancer diagnosis has been mostly accomplished by imaging technologies. These methods have the great advantages of detecting the presence and location of breast cancer. However, it's difficult to distinguish between a benign and malignant tumor in a deep position because both tumor types look similar. In this paper, we vibrated the tissue including tumor from skin with a compression cylinder to analyze the frequency difference for distinguishing the tissue type. Before distinguishing a benign and malignant tumor, it's necessary to validate to distinguish between normal tissue and tumor. The objective is to validate the feasibility of using a compression cylinder that emphasizes the differences in frequency between normal tissue and tumor. In two experiments, we measured the displacement on the surface of a breast phantom vibrated by an impulse hammer. We compared the frequency difference with and without a cylinder. We also studied the frequency changes in the relationship between tumor and cylinder position. We found a 5.0 Hz difference in compliance between normal tissue and the simulated tumor using a compression cylinder. The difference in frequency correlated negatively with distance from the simulated tumor to a compression cylinder. We concluded that a compression cylinder would enhance the frequency difference between normal tissue and a simulated tumor with appropriate configuration.
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14
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Analytical derivation of elasticity in breast phantoms for deformation tracking. Int J Comput Assist Radiol Surg 2018; 13:1641-1650. [PMID: 29869320 PMCID: PMC6153655 DOI: 10.1007/s11548-018-1803-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 05/25/2018] [Indexed: 11/03/2022]
Abstract
PURPOSE Patient-specific biomedical modeling of the breast is of interest for medical applications such as image registration, image guided procedures and the alignment for biopsy or surgery purposes. The computation of elastic properties is essential to simulate deformations in a realistic way. This study presents an innovative analytical method to compute the elastic modulus and evaluate the elasticity of a breast using magnetic resonance (MRI) images of breast phantoms. METHODS An analytical method for elasticity computation was developed and subsequently validated on a series of geometric shapes, and on four physical breast phantoms that are supported by a planar frame. This method can compute the elasticity of a shape directly from a set of MRI scans. For comparison, elasticity values were also computed numerically using two different simulation software packages. RESULTS Application of the different methods on the geometric shapes shows that the analytically derived elongation differs from simulated elongation by less than 9% for cylindrical shapes, and up to 18% for other shapes that are also substantially vertically supported by a planar base. For the four physical breast phantoms, the analytically derived elasticity differs from numeric elasticity by 18% on average, which is in accordance with the difference in elongation estimation for the geometric shapes. The analytic method has shown to be multiple orders of magnitude faster than the numerical methods. CONCLUSION It can be concluded that the analytical elasticity computation method has good potential to supplement or replace numerical elasticity simulations in gravity-induced deformations, for shapes that are substantially supported by a planar base perpendicular to the gravitational field. The error is manageable, while the calculation procedure takes less than one second as opposed to multiple minutes with numerical methods. The results will be used in the MRI and Ultrasound Robotic Assisted Biopsy (MURAB) project.
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15
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Griesenauer RH, Weis JA, Arlinghaus LR, Meszoely IM, Miga MI. Toward quantitative quasistatic elastography with a gravity-induced deformation source for image-guided breast surgery. J Med Imaging (Bellingham) 2018; 5:015003. [PMID: 29430479 DOI: 10.1117/1.jmi.5.1.015003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 01/15/2018] [Indexed: 11/14/2022] Open
Abstract
Biomechanical breast models have been employed for applications in image registration and diagnostic analysis, breast augmentation simulation, and for surgical and biopsy guidance. Accurate applications of stress-strain relationships of tissue within the breast can improve the accuracy of biomechanical models that attempt to simulate breast deformations. Reported stiffness values for adipose, glandular, and cancerous tissue types vary greatly. Variations in reported stiffness properties have been attributed to differences in testing methodologies and assumptions, measurement errors, and natural interpatient differences in tissue elasticity. Therefore, the ability to determine patient-specific in vivo breast tissue properties would be advantageous for these procedural applications. While some in vivo elastography methods are not quantitative and others do not measure material properties under deformation conditions that are appropriate to the application of concern, in this study, we developed an elasticity estimation method that is performed using deformations representative of supine therapeutic procedures. More specifically, reconstruction of mechanical properties appropriate for the standard-of-care supine lumpectomy was performed by iteratively fitting two anatomical images before and after deformations taking place in the supine breast configuration. The method proposed is workflow-friendly, quantitative, and uses a noncontact, gravity-induced deformation source.
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Affiliation(s)
- Rebekah H Griesenauer
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Jared A Weis
- Wake Forest School of Medicine, Department of Biomedical Engineering, Winston Salem, North Carolina, United States
| | - Lori R Arlinghaus
- Vanderbilt University Medical Center, Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, United States
| | - Ingrid M Meszoely
- Vanderbilt University Medical Center, Department of Surgery, Nashville, Tennessee, United States
| | - Michael I Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States.,Vanderbilt University Medical Center, Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, United States.,Vanderbilt University, Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, United States.,Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States.,Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States
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16
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Griesenauer RH, Weis JA, Arlinghaus LR, Meszoely IM, Miga MI. Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation. Phys Med Biol 2017; 62:4756-4776. [PMID: 28520556 DOI: 10.1088/1361-6560/aa700a] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Tissue stiffness interrogation is fundamental in breast cancer diagnosis and treatment. Furthermore, biomechanical models for predicting breast deformations have been created for several breast cancer applications. Within these applications, constitutive mechanical properties must be defined and the accuracy of this estimation directly impacts the overall performance of the model. In this study, we present an image-derived computational framework to obtain quantitative, patient specific stiffness properties for application in image-guided breast cancer surgery and interventions. The method uses two MR acquisitions of the breast in different supine gravity-loaded configurations to fit mechanical properties to a biomechanical breast model. A reproducibility assessment of the method was performed in a test-retest study using healthy volunteers and was further characterized in simulation. In five human data sets, the within subject coefficient of variation ranged from 10.7% to 27% and the intraclass correlation coefficient ranged from 0.91-0.944 for assessment of fibroglandular and adipose tissue stiffness. In simulation, fibroglandular content and deformation magnitude were shown to have significant effects on the shape and convexity of the objective function defined by image similarity. These observations provide an important step forward in characterizing the use of nonrigid image registration methodologies in conjunction with biomechanical models to estimate tissue stiffness. In addition, the results suggest that stiffness estimation methods using gravity-induced excitation can reliably and feasibly be implemented in breast cancer surgery/intervention workflows.
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Affiliation(s)
- Rebekah H Griesenauer
- Department of Biomedical Engineering, Vanderbilt University, 5824 Stevenson Center, Nashville, TN 37235, United States of America. Vanderbilt Institute in Surgery and Engineering (VISE), Nashville, TN, United States of America
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Kassab GS, An G, Sander EA, Miga MI, Guccione JM, Ji S, Vodovotz Y. Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective. Ann Biomed Eng 2016; 44:2611-25. [PMID: 27015816 DOI: 10.1007/s10439-016-1596-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Accepted: 03/18/2016] [Indexed: 12/18/2022]
Abstract
In this era of tremendous technological capabilities and increased focus on improving clinical outcomes, decreasing costs, and increasing precision, there is a need for a more quantitative approach to the field of surgery. Multiscale computational modeling has the potential to bridge the gap to the emerging paradigms of Precision Medicine and Translational Systems Biology, in which quantitative metrics and data guide patient care through improved stratification, diagnosis, and therapy. Achievements by multiple groups have demonstrated the potential for (1) multiscale computational modeling, at a biological level, of diseases treated with surgery and the surgical procedure process at the level of the individual and the population; along with (2) patient-specific, computationally-enabled surgical planning, delivery, and guidance and robotically-augmented manipulation. In this perspective article, we discuss these concepts, and cite emerging examples from the fields of trauma, wound healing, and cardiac surgery.
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Affiliation(s)
- Ghassan S Kassab
- California Medical Innovations Institute, San Diego, CA, 92121, USA
| | - Gary An
- Department of Surgery, University of Chicago, Chicago, IL, 60637, USA
| | - Edward A Sander
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, 52242, USA
| | - Michael I Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37235, USA
| | - Julius M Guccione
- Department of Surgery, University of California, San Francisco, CA, 94143, USA
| | - Songbai Ji
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA.,Department of Surgery and of Orthopaedic Surgery, Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
| | - Yoram Vodovotz
- Department of Surgery, University of Pittsburgh, W944 Starzl Biomedical Sciences Tower, 200 Lothrop St., Pittsburgh, PA, 15213, USA. .,Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, 15219, USA.
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18
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Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery. Ann Biomed Eng 2015; 44:128-38. [PMID: 26354118 DOI: 10.1007/s10439-015-1433-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Accepted: 08/18/2015] [Indexed: 01/14/2023]
Abstract
With the recent advances in computing, the opportunities to translate computational models to more integrated roles in patient treatment are expanding at an exciting rate. One area of considerable development has been directed towards correcting soft tissue deformation within image guided neurosurgery applications. This review captures the efforts that have been undertaken towards enhancing neuronavigation by the integration of soft tissue biomechanical models, imaging and sensing technologies, and algorithmic developments. In addition, the review speaks to the evolving role of modeling frameworks within surgery and concludes with some future directions beyond neurosurgical applications.
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