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.
KEYWORDS: Image segmentation, Image processing algorithms and systems, 3D modeling, Electronic filtering, Ultrasonography, Data modeling, Image fusion, 3D image processing, 3D acquisition, Echocardiography
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.
KEYWORDS: 3D modeling, Data modeling, 3D printing, Surgery, Diagnostics, 3D image processing, Modeling, Heart, Silicon, Ultrasonography, Doppler effect, Process modeling, Motion models, Image segmentation
Significant mitral valve regurgitation affects over 2% of the population. Over the past few decades, mitral valve (MV) repair has become the preferred treatment option, producing better patient outcomes than MV replacement, but requiring more expertise. Recently, 3D printing has been used to assist surgeons in planning optimal treatments for complex surgery, thus increasing the experience of surgeons and the success of MV repairs. However, while commercially available 3D printers are capable of printing soft, tissue-like material, they cannot replicate the demanding combination of echogenicity, physical flexibility and strength of the mitral valve. In this work, we propose the use of trans-esophageal echocardiography (TEE) 3D image data and inexpensive 3D printing technology to create patient specific mitral valve models. Patient specific 3D TEE images were segmented and used to generate a profile of the mitral valve leaflets. This profile was 3D printed and integrated into a mold to generate a silicone valve model that was placed in a dynamic heart phantom. Our primary goal is to use silicone models to assess different repair options prior to surgery, in the hope of optimizing patient outcomes. As a corollary, a database of patient specific models can then be used as a trainer for new surgeons, using a beating heart simulator to assess success. The current work reports preliminary results, quantifying basic morphological properties. The models were assessed using 3D TEE images, as well as 2D and 3D Doppler images for comparison to the original patient TEE data.
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