9,185 research outputs found

    Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation.

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    PurposeWith the advent of MR guided radiotherapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods.Methods and materialT2 weighted HASTE and T1 weighted VIBE images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging (MSM), distance regularized level set (DRLS), graph cuts (GC) and the segmentation results were compared to manual contours using Dice's index (DI), Hausdorff distance and shift of the-center-of-the-organ (SHIFT).ResultsAll VIBE images were successfully segmented by at least one of the auto-segmentation method with DI >0.83 and SHIFT ≤2 mm using the best automated segmentation method. The automated segmentation error of HASTE images was significantly greater. DL is statistically superior to the other methods in Dice's overlapping index. For the Hausdorff distance and SHIFT measurement, DRLS and DL performed slightly superior to the GC method, and substantially superior to MSM. DL required least human supervision and was faster to compute.ConclusionOur study demonstrated potential feasibility of automated segmentation of the pancreas on MRI images with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization

    From 4D medical images (CT, MRI, and Ultrasound) to 4D structured mesh models of the left ventricular endocardium for patient-specific simulations

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    With cardiovascular disease (CVD) remaining the primary cause of death worldwide, early detection of CVDs becomes essential. The intracardiac flow is an important component of ventricular function, motion kinetics, wash-out of ventricular chambers, and ventricular energetics. Coupling between Computational Fluid Dynamics (CFD) simulations and medical images can play a fundamental role in terms of patient-specific diagnostic tools. From a technical perspective, CFD simulations with moving boundaries could easily lead to negative volumes errors and the sudden failure of the simulation. The generation of high-quality 4D meshes (3D in space + time) with 1-to-l vertex becomes essential to perform a CFD simulation with moving boundaries. In this context, we developed a semiautomatic morphing tool able to create 4D high-quality structured meshes starting from a segmented 4D dataset. To prove the versatility and efficiency, the method was tested on three different 4D datasets (Ultrasound, MRI, and CT) by evaluating the quality and accuracy of the resulting 4D meshes. Furthermore, an estimation of some physiological quantities is accomplished for the 4D CT reconstruction. Future research will aim at extending the region of interest, further automation of the meshing algorithm, and generating structured hexahedral mesh models both for the blood and myocardial volume

    Log File-Based Dose Reconstruction to Moving Targets during Lung Stereotactic Body Radiation Therapy

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    Purpose: To perform film-based verification of 4D dose reconstruction to moving targets during lung stereotactic body radiation therapy (SBRT). Introduction: Current patient-specific quality assurance measures to test deliverability of plans with dynamic intensity modulation involve delivering beams to static measurement device and comparing the planned dose to measurement. However, motion-induced dose errors are not detected with static measurement. Previous studies have investigated combining machine log data with respiratory tracking to determine moving-target dose. By combining machine log data with anatomic and density information at each breathing phase from 4D-CT, intrafraction anatomical deformation due to respiration may be accounted for. However, to our knowledge, a film-based verification of dose reconstruction using machine log data, intrafraction respiratory tracking, and 4D-CT has yet to be performed. Methods: Lung SBRT plans were anonymized for 12 patients treated at our institution. Treatment plans were copied onto known geometry (programmable respiratory phantom) and dose was computed. Each SBRT plan was delivered to the phantom twice; first using 3 sec/breath (SPB), again at 6 SPB. Respiratory traces were acquired during treatment. Logfiles were acquired after treatment and partitioned according to breathing amplitude. Next, in-house code was used to import logfile beams into the treatment planning system. Dose was computed on each 4D-CT image using the imported beams and deformably accumulated. The accumulated, planned, and measured doses for each plan and breathing rate were compared using gamma analysis. Results: Gamma passing rates (GPR) (3%, 2mm, 10% threshold) of 4D dose reconstruction vs. planned dose were \u3e94% (mean 98.9% range 94.1%-100%) for all plans at each breathing rate. No significant difference was found between the 3 and 6 SPB GPRs (p=0.310). Overall, the 4D dose reconstructions were found to better agree with film measurement, within the tumor motion extent, than the treatment plan for both breathing rates (3 SPB: p=0.013, 6 SPB: p=0.017). Conclusions: Log file-based dose reconstruction was verified using film measurement for 12 lung SBRT plans delivered to a respiratory motion phantom. We showed that, given predictable phantom motion, 4D dose reconstruction resulted in significantly higher GPR compared with film than treatment plan to static geometry

    A Novel Imaging System for Automatic Real-Time 3D Patient-Specific Knee Model Reconstruction Using Ultrasound RF Data

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    This dissertation introduces a novel imaging method and system for automatic real-time 3D patient-specific knee model reconstruction using ultrasound RF data. The developed method uses ultrasound to transcutaneously digitize a point cloud representing the bone’s surface. This point cloud is then used to reconstruct 3D bone model using deformable models method. In this work, three systems were developed for 3D knee bone model reconstruction using ultrasound RF data. The first system uses tracked single-element ultrasound transducer, and was experimented on 12 knee phantoms. An average reconstruction accuracy of 0.98 mm was obtained. The second system was developed using an ultrasound machine which provide real-time access to the ultrasound RF data, and was experimented on 2 cadaveric distal femurs, and proximal tibia. An average reconstruction accuracy of 0.976 mm was achieved. The third system was developed as an extension of the second system, and was used for clinical study of the developed system further assess its accuracy and repeatability. A knee scanning protocol was developed to scan the different articular surfaces of the knee bones to reconstruct 3D model of the bone without the need for bone-implanted motion tracking reference probes. The clinical study was performed on 6 volunteers’ knees. Average reconstruction accuracy of 0.88 mm was achieved with 93.5% repeatability. Three extensions to the developed system were investigated for future work. The first extension is 3D knee injection guidance system. A prototype for the 3D injection guidance system was developed to demonstrate the feasibility of the idea. The second extension in a knee kinematics tracking system using A-mode ultrasound. A simulation framework was developed to study the feasibility of the idea, and to find the best number of single-element ultrasound transducers and their spatial distribution that yield the highest kinematics tracking accuracy. The third extension is 3D cartilage model reconstruction. A preliminary method for cartilage echo detection from ultrasound RF data was developed, and experimented on the distal femur scans of one of the clinical study’s volunteers to reconstruct a 3D point cloud for the cartilage

    Fluoroscopy-based tracking of femoral kinematics with statistical shape models

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    Precise knee kinematics assessment helps to diagnose knee pathologies and to improve the design of customized prosthetic components. The first step in identifying knee kinematics is to assess the femoral motion in the anatomical frame. However, no work has been done on pathological femurs, whose shape can be highly different from healthy ones

    Segmenting root systems in X-ray computed tomography images using level sets

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    The segmentation of plant roots from soil and other growing media in X-ray computed tomography images is needed to effectively study the root system architecture without excavation. However, segmentation is a challenging problem in this context because the root and non-root regions share similar features. In this paper, we describe a method based on level sets and specifically adapted for this segmentation problem. In particular, we deal with the issues of using a level sets approach on large image volumes for root segmentation, and track active regions of the front using an occupancy grid. This method allows for straightforward modifications to a narrow-band algorithm such that excessive forward and backward movements of the front can be avoided, distance map computations in a narrow band context can be done in linear time through modification of Meijster et al.'s distance transform algorithm, and regions of the image volume are iteratively used to estimate distributions for root versus non-root classes. Results are shown of three plant species of different maturity levels, grown in three different media. Our method compares favorably to a state-of-the-art method for root segmentation in X-ray CT image volumes.Comment: 11 page

    Augmented Image-Guidance for Transcatheter Aortic Valve Implantation

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    The introduction of transcatheter aortic valve implantation (TAVI), an innovative stent-based technique for delivery of a bioprosthetic valve, has resulted in a paradigm shift in treatment options for elderly patients with aortic stenosis. While there have been major advancements in valve design and access routes, TAVI still relies largely on single-plane fluoroscopy for intraoperative navigation and guidance, which provides only gross imaging of anatomical structures. Inadequate imaging leading to suboptimal valve positioning contributes to many of the early complications experienced by TAVI patients, including valve embolism, coronary ostia obstruction, paravalvular leak, heart block, and secondary nephrotoxicity from contrast use. A potential method of providing improved image-guidance for TAVI is to combine the information derived from intra-operative fluoroscopy and TEE with pre-operative CT data. This would allow the 3D anatomy of the aortic root to be visualized along with real-time information about valve and prosthesis motion. The combined information can be visualized as a `merged\u27 image where the different imaging modalities are overlaid upon each other, or as an `augmented\u27 image, where the location of key target features identified on one image are displayed on a different imaging modality. This research develops image registration techniques to bring fluoroscopy, TEE, and CT models into a common coordinate frame with an image processing workflow that is compatible with the TAVI procedure. The techniques are designed to be fast enough to allow for real-time image fusion and visualization during the procedure, with an intra-procedural set-up requiring only a few minutes. TEE to fluoroscopy registration was achieved using a single-perspective TEE probe pose estimation technique. The alignment of CT and TEE images was achieved using custom-designed algorithms to extract aortic root contours from XPlane TEE images, and matching the shape of these contours to a CT-derived surface model. Registration accuracy was assessed on porcine and human images by identifying targets (such as guidewires or coronary ostia) on the different imaging modalities and measuring the correspondence of these targets after registration. The merged images demonstrated good visual alignment of aortic root structures, and quantitative assessment measured an accuracy of less than 1.5mm error for TEE-fluoroscopy registration and less than 6mm error for CT-TEE registration. These results suggest that the image processing techniques presented have potential for development into a clinical tool to guide TAVI. Such a tool could potentially reduce TAVI complications, reducing morbidity and mortality and allowing for a safer procedure
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