X-ray image decomposition for improved magnetic navigation.

PURPOSE: Existing field generators (FGs) for magnetic tracking cause severe image artifacts in X-ray images. While FG with radio-lucent components significantly reduces these imaging artifacts, traces of coils and electronic components may still be visible to trained professionals. In the context of X-ray-guided interventions using magnetic tracking, we introduce a learning-based approach to further reduce traces of field-generator components from X-ray images to improve visualization and image guidance. METHODS: An adversarial decomposition network was trained to separate the residual FG components (including fiducial points introduced for pose estimation), from the X-ray images. The main novelty of our approach lies in the proposed data synthesis method, which combines existing 2D patient chest X-ray and FG X-ray images to generate 20,000 synthetic images, along with ground truth (images without the FG) to effectively train the network. RESULTS: For 30 real images of a torso phantom, our enhanced X-ray image after image decomposition obtained an average local PSNR of 35.04 and local SSIM of 0.97, whereas the unenhanced X-ray images averaged a local PSNR of 31.16 and local SSIM of 0.96. CONCLUSION: In this study, we proposed an X-ray image decomposition method to enhance X-ray image for magnetic navigation by removing FG-induced artifacts, using a generative adversarial network. Experiments on both synthetic and real phantom data demonstrated the efficacy of our method.

Automatic Plane of Minimal Hiatal Dimensions Extraction From 3D Female Pelvic Floor Ultrasound.

There is an increasing interest in the applications of 3D ultrasound imaging of the pelvic floor to improve the diagnosis, treatment, and surgical planning of female pelvic floor dysfunction (PFD). Pelvic floor biometrics are obtained on an oblique image plane known as the plane of minimal hiatal dimensions (PMHD). Identifying this plane requires the detection of two anatomical landmarks, the pubic symphysis and anorectal angle. The manual detection of the anatomical landmarks and the PMHD in 3D pelvic ultrasound requires expert knowledge of the pelvic floor anatomy, and is challenging, time-consuming, and subject to human error. These challenges have hindered the adoption of such quantitative analysis in the clinic. This work presents an automatic approach to identify the anatomical landmarks and extract the PMHD from 3D pelvic ultrasound volumes. To demonstrate clinical utility and a complete automated clinical task, an automatic segmentation of the levator-ani muscle on the extracted PMHD images was also performed. Experiments using 73 test images of patients during a pelvic muscle resting state showed that this algorithm has the capability to accurately identify the PMHD with an average Dice of 0.89 and an average mean boundary distance of 2.25mm. Further evaluation of the PMHD detection algorithm using 35 images of patients performing pelvic muscle contraction resulted in an average Dice of 0.88 and an average mean boundary distance of 2.75mm. This work had the potential to pave the way towards the adoption of ultrasound in the clinic and development of personalized treatment for PFD.

A Robust Edge-Preserving Stereo Matching Method for Laparoscopic Images.

Stereo matching has become an active area of research in the field of computer vision. In minimally invasive surgery, stereo matching provides depth information to surgeons, with the potential to increase the safety of surgical procedures, particularly those performed laparoscopically. Many stereo matching methods have been reported to perform well for natural images, but for images acquired during a laparoscopic procedure, they are limited by image characteristics including illumination differences, weak texture content, specular highlights, and occlusions. To overcome these limitations, we propose a robust edge-preserving stereo matching method for laparoscopic images, comprising an efficient sparse-dense feature matching step, left and right image illumination equalization, and refined disparity optimization. We validated the proposed method using both benchmark biological phantoms and surgical stereoscopic data. Experimental results illustrated that, in the presence of heavy illumination differences between image pairs, texture and textureless surfaces, specular highlights and occlusions, our proposed approach consistently obtains a more accurate estimate of the disparity map than state-of-the-art stereo matching methods in terms of robustness and boundary preservation.

Longitudinal Visualization of Viable Cancer Cell Intratumoral Distribution in Mouse Models Using Oatp1a1-Enhanced Magnetic Resonance Imaging.

OBJECTIVES: Multimodality reporter gene imaging provides valuable, noninvasive information on the fate of engineered cell populations. To complement magnetic resonance imaging (MRI) measures of tumor volume and 2-dimensional reporter-based optical measures of cell viability, reporter-based MRI may offer 3-dimensional information on the distribution of viable cancer cells in deep tissues. MATERIALS AND METHODS: Here, we engineered human and murine triple-negative breast cancer cells with lentivirus encoding tdTomato and firefly luciferase for fluorescence imaging and bioluminescence imaging (BLI). A subset of these cells was additionally engineered with lentivirus encoding organic anion transporting polypeptide 1a1 (Oatp1a1) for MRI. Oatp1a1 operates by transporting gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) into cells, and it concomitantly improves BLI substrate uptake. After orthotopic implantation of engineered cells expressing or not expressing Oatp1a1, longitudinal fluorescence imaging, BLI, and 3-Tesla MRI were performed. RESULTS: Oatp1a1-expressing tumors displayed significantly increased BLI signals relative to control tumors at all time points (P < 0.05). On MRI, post-Gd-EOB-DTPA T1-weighted images of Oatp1a1-expressing tumors exhibited significantly increased contrast-to-noise ratios compared with control tumors and precontrast images (P < 0.05). At endpoint, tumors expressing Oatp1a1 displayed intratumoral MR signal heterogeneity not present at earlier time points. Pixel-based analysis of matched in vivo MR and ex vivo fluorescence microscopy images revealed a strong, positive correlation between MR intensity and tdTomato intensity for Oatp1a1-expressing tumors (P < 0.05), but not control tumors. CONCLUSIONS: These results characterize Oatp1a1 as a sensitive, quantitative, positive contrast MRI reporter gene for 3-dimensional assessment of viable cancer cell intratumoral distribution and concomitant BLI enhancement. This multimodality reporter gene system can provide new insights into the influence of viable cancer cell intratumoral distribution on tumor progression and metastasis, as well as improved assessments of anticancer therapies.

Endoscopic image enhancement with noise suppression.

Stereoscopic endoscopes have been used increasingly in minimally invasive surgery to visualise the organ surface and manipulate various surgical tools. However, insufficient and irregular light sources become major challenges for endoscopic surgery. Not only do these conditions hinder image processing algorithms, sometimes surgical tools are barely visible when operating within low-light regions. In addition, low-light regions have low signal-to-noise ratio and metrication artefacts due to quantisation errors. As a result, present image enhancement methods usually suffer from heavy noise amplification in low-light regions. In this Letter, the authors propose an effective method for endoscopic image enhancement by identifying different illumination regions and designing the enhancement design criteria for desired image quality. Compared with existing image enhancement methods, the proposed method is able to enhance the low-light region while preventing noise amplification during image enhancement process. The proposed method is tested with 200 images acquired by endoscopic surgeries. Computed results show that the proposed algorithm can outperform state-of-the-art algorithms for image enhancement, in terms of naturalness image quality evaluator and illumination index.

Image registration assessment in radiotherapy image guidance based on control chart monitoring.

Image guidance with cone beam computed tomography in radiotherapy can guarantee the precision and accuracy of patient positioning prior to treatment delivery. During the image guidance process, operators need to take great effort to evaluate the image guidance quality before correcting a patient's position. This work proposes an image registration assessment method based on control chart monitoring to reduce the effort taken by the operator. According to the control chart plotted by daily registration scores of each patient, the proposed method can quickly detect both alignment errors and image quality inconsistency. Therefore, the proposed method can provide a clear guideline for the operators to identify unacceptable image quality and unacceptable image registration with minimal effort. Experimental results demonstrate that by using control charts from a clinical database of 10 patients undergoing prostate radiotherapy, the proposed method can quickly identify out-of-control signals and find special cause of out-of-control registration events.

Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling.

Three-dimensional ultrasound segmentation of mitral valve (MV) at diastole is helpful for duplicating geometry and pathology in a patient-specific dynamic phantom. The major challenge is the signal dropout at leaflet regions in transesophageal echocardiography image data. Conventional segmentation approaches suffer from missing sonographic data leading to inaccurate MV modeling at leaflet regions. This paper proposes a signal dropout correction-based ultrasound segmentation method for diastolic MV modeling. The proposed method combines signal dropout correction, image fusion, continuous max-flow segmentation, and active contour segmentation techniques. The signal dropout correction approach is developed to recover the missing segmentation information. Once the signal dropout regions of TEE image data are recovered, the MV model can be accurately duplicated. Compared with other methods in current literature, the proposed algorithm exhibits lower computational cost. The experimental results show that the proposed algorithm gives competitive results for diastolic MV modeling compared with conventional segmentation algorithms, evaluated in terms of accuracy and efficiency.

Dynamic Patient-Specific Three-Dimensional Simulation of Mitral Repair: Can We Practice Mitral Repair Preoperatively?

OBJECTIVE: Planned mitral repair strategies are generally established from preoperative echocardiography; however, specific details of the repair are often determined intraoperatively. We propose that three-dimensional printed, patient-specific, dynamic mitral valve models may help surgeons plan and trial all the details of a specific patient's mitral repair preoperatively. METHODS: Using preoperative echocardiography, segmentation, modeling software, and three-dimensional printing, we created dynamic, high-fidelity, patient-specific mitral valve models including the subvalvular apparatus. We assessed the accuracy of 10 patient mitral valve models anatomically and functionally in a heart phantom simulator, both objectively by blinded echocardiographic assessment, and subjectively by two mitral repair experts. After this, we attempted model mitral repair and compared the outcomes with postoperative echocardiography. RESULTS: Model measurements were accurate when compared with patients on anterior-posterior diameter, circumference, and anterior leaflet length; however, less accurate on posterior leaflet length. On subjective assessment, Likert scores were high at 3.8 ± 0.4 and 3.4 ± 0.7, suggesting good fidelity of the dynamic model echocardiogram and functional model in the phantom to the preoperative three-dimensional echocardiogram, respectively. Mitral repair was successful in all 10 models with significant reduction in mitral insufficiency. In two models, mitral repair was performed twice, using two different surgical techniques to assess which provided a better outcome. When compared with the actual patient mitral repair outcome, the repaired models compared favorably. CONCLUSIONS: Complex mitral valve modeling seems to predict an individual patient's mitral anatomy well, before surgery. Further investigation is required to determine whether deliberate preoperative practice can improve mitral repair outcomes.