The success of deep brain stimulation (DBS) is dependent on the accurate placement of electrodes in the operating room (OR). However, due to intraoperative brain shift, the accuracy of pre-operative scans and pre-surgical planning are often degraded. To compensate for brain shift, we created a finite element bio-mechanical brain model that updates preoperative images by assimilating intraoperative sparse data from the brain surface or deep brain targets. Additionally, we constructed an artificial neural network (ANN) that leveraged a large number of ventricle nodal displacements to estimate brain shift. The machine learning method showed potential in incorporating ventricle sparse data to accurately compute shift at the brain surface. Thus, in this paper, we propose using this machine learning model to estimate brain atrophy at deep brain targets such as the anterior commissure (AC) and the posterior commissure (PC). The ANN consists of an input layer with nine hand-engineered features, such as the distance between the deep brain target and the ventricle node, two hidden layers and an output layer. This model was trained using eight patient cases and tested on two patient cases.
Ventricular tachycardia (VT) can be treated with catheter ablation therapy, a technique in which catheters are guided into the ventricle and radiofrequency energy is delivered into the myocardial tissue to stop arrhythmic electrical pathways. These procedures are invasive and come with associated risks; therefore, recent efforts have investigated the use of noninvasive proton beam therapy for treatment of VT. In this approach, target regions are identified in a pre-treatment computed tomography scan of the left ventricle followed by proton beam ablation therapy. The effects of beam ablation therapy in myocardial tissue can be characterized using imaging, electroanatomic mapping, and histology. These data are also important for determining the appropriate dose for effective treatment of VT while minimizing collateral damage to surrounding healthy tissue. Studies conducted to date demonstrate that proton beam ablation is a promising new approach for treatment of VT.
Breast conserving surgery is a common treatment option for early-stage breast cancer, and it relies on complete tumor excision such that no residual cancer is left in the resection cavity. However, the use of preoperative imaging to inform excision is compromised by intraoperative deformations that change the location, volume, and shape of the tumor compared to the imaging configuration. For intra-procedural guidance specifically, incision and retraction alter the tumor presentation and geometry. Being able to compensate for retraction deformations intraoperatively may increase the utility of image guidance technologies. In this work, a breast retraction phantom and deformation modeling approach are developed to explore the potential of modeling retraction for image guidance during BCS. Surface and subsurface beads were embedded in a realistic silicone breast phantom, and CT images were acquired in undeformed and retracted states. A reconstructive, sparse-data registration method was used to model retraction. Modeling accuracy was evaluated by comparing model-predicted and ground-truth bead displacements. The average surface bead registration error after retraction modeling in a region of interest was 0.5 ± 0.1 mm (maximum 0.5 mm). The average subsurface bead registration error in a region of interest was 1.2 ± 0.6 mm (maximum 2.6 mm). A biomechanical modeling method that includes retraction may improve the accuracy of image guidance for breast conserving surgery, but more work is needed to evaluate its utility.
Efficient and accurate segmentation of the rectum in images acquired with a low-field (58-74mT), prostate Magnetic Resonance Imaging (MRI) scanner may be advantageous for MRI-guided prostate biopsy and focal treatment guidance. However, automated rectum segmentation on low-field MRI images is challenging due to spatial resolution and signal-to-Noise Ratio (SNR) constraints. This study aims to develop a deep learning model to automatically segment the rectum in a low-field MRI prostate image. 132, 3D images from ten patients were assembled. A 3D, U-Net model with the input matrix size 120×120×40 voxels was trained to detect and segment the rectum. The 3D U-Net can learn and integrate the relative information between adjacent MRI slices, which can enforce 3D patterns such as rectal wall smoothness and thus compensate for slice-to-slice variability in SNR and rectal boundary fuzziness. Contrast stretching, histogram equalization, and brightness enhancement were also investigated and applied to normalize intra- and inter- image intensity heterogeneity. Data augmentation methods such as elastic deformation, flipping, rotation, and scaling were also applied to reduce the risk of overfitting in model training. The model was trained and tested using a 4-fold cross-validation method with 3:1:2 split for training, validation, and testing. Study results show that the mean intersection over union score (IOUs) is 0.63 for the rectum on the testing dataset. Additionally, visual examination suggests that the displacement between the centroids of the ground truth and inferred volumetric segmentations is less than 3mm. Thus, this study demonstrates that (1) a 3D U-Net model can effectively segment the rectum on low-field MRI scans and (2) applying image processing and data augmentation can boost model performance.
Mass Spectrometry Imaging (MSI) is a powerful tool capable of visualizing molecular patterns to identify disease markers in tissue analysis. However, data analysis is computationally heavy and currently time-consuming as there is no single platform capable of performing the entire preprocessing, visualization, and analysis pipeline end-to-end. Using different software tools and file formats required for such tools also makes the process prone to error. The purpose of this work is to develop a free, open-source software implementation called “Visualization, Preprocessing, and Registration Environment” (ViPRE), capable of end-to-end analysis of MSI data. ViPRE was developed to provide various functionalities required for MSI analysis including data import, data visualization, data registration, Region of Interest (ROI) selection, spectral data alignment and data analysis. The software implementation is offered as an open-source module in 3D Slicer, a medical imaging platform. It is also designed for flexibility and usability throughout the user experience. ViPRE was tested using sample MSI data to evaluate the computational pipeline, with the results showing successful implementation of its functionalities and end-to-end usage. A preliminary usability test was also performed to assess user experience, with findings showing positive results. ViPRE aspires to satisfy the need for a single-stop comprehensive interface for MSI data analysis. The source code and documentation will be made publicly available.
