Assessing and Improving 4D-CT Imaging for Radiotherapy Applications

Lung cancer has both a high incidence and death rate. A contributing factor to these high rates comes from the difficulty of treating lung cancers due to the inherent mobility of the lung tissue and the tumour. 4D-CT imaging has been developed to image lung tumours as they move during respiration. Most 4D-CT imaging methods rely on data from an external respiratory surrogate to sort the images according to respiratory phase. However, it has been shown that respiratory surrogate 4D-CT methods can suffer from imaging artifacts that degrade the image quality of the 4D-CT volumes that are used to plan a patient\u27s radiation therapy. In Chapter 2 of this thesis a method to investigate the correlation between an external respiratory surrogate and the internal anatomy was developed. The studies were performed on ventilated pigs with an induced inconsistent amplitude of breathing. The effect of inconsistent breathing on the correlation between the external marker and the internal anatomy was tested using a linear regression. It was found in 10 of the 12 studies performed that there were significant changes in the slope of the regression line as a result of inconsistent breathing. From this study we conclude that the relationship between an external marker and the internal anatomy is not stable and can be perturbed by inconsistent breathing amplitudes. Chapter 3 describes the development of a image based 4D-CT imaging algorithm based on the concept of normalized cross correlation (NCC) between images. The volumes produced by the image based algorithm were compared to volumes produced using a clinical external marker 4D-CT algorithm. The image based method produced 4D-CT volumes that had a reduced number of imaging artifacts when compared to the external marker produced volumes. It was shown that an image based 4D-CT method could be developed and perform as well or better than external marker methods that are currently in clinical use. In Chapter 4 a method was developed to assess the uncertainties of the locations of anatomical structures in the volumes produced by the image based 4D-CT algorithm developed in Chapter 3. The uncertainties introduced by using NCC to match a pair of images according to respiratory phase were modeled and experimentally determined. Additionally, the assumption that two subvolumes could be matched in respiratory phase using a single pair of 2D overlapping images was experimentally validated. It was shown that when the image based 4D-CT algorithm developed in Chapter 3 was applied to data acquired from a ventilated pig with induced inconsistent breathing the displacement uncertainties were on the order of 1.0 millimeter. The results of this thesis show that there exists the possibility of a miscorrelation between the motion of a respiratory surrogate (marker) and the internal anatomy under inconsistent breathing amplitude. Additionally, it was shown that an image based 4D-CT method that operates without the need of one or more external respiratory surrogate(s) could produce artifact free volumes synchronous with respiratory phase. The spatial uncertainties of the volumes produced by the image based 4D-CT method were quantified and shown to be small (~ 1mm) which is an acceptable accuracy for radiation treatment planning. The elimination of the external respiratory surrogates simplifies the implementation and increases the throughput of the image based 4D-CT method as well

Treatment Planning Automation for Rectal Cancer Radiotherapy

Background Rectal cancer is a common type of cancer. There is an acute health disparity across the globe where a significant population of the world lack adequate access to radiotherapy treatments which is a part of the standard of care for rectal cancers. Safe radiotherapy treatments require specialized planning expertise and are time-consuming and labor-intensive to produce. Purpose: To alleviate the health disparity and promote the safe and quality use of radiotherapy in treating rectal cancers, the entire treatment planning process needs to be automated. The purpose of this project is to develop automated solutions for the treatment planning process of rectal cancers that would produce clinically acceptable and high-quality plans. To achieve this goal, we first automated two common existing treatment techniques, 3DCRT and VMAT, for rectal cancers, and then explored an alternative method for creating a treatment plan using deep learning. Methods: To automate the 3DCRT treatment technique, we used deep learning to predict the shapes of field apertures for primary and boost fields based on CT and location and the shapes of GTV and involved lymph nodes. The results of the predicted apertures were evaluated by a GI radiation oncologist. We then designed an algorithm to automate the forward-planning process with the capacity of adding fields to homogenize the dose at the target volumes using the field-in-field technique. The algorithm was validated on the clinical apertures and the plans produced were scored by a radiation oncologist. The field aperture prediction and the algorithm were combined into an end-to-end process and were tested on a separate set of patients. The resulting final plans were scored by a GI radiation oncologist for their clinical acceptability. To automate of VMAT treatment technique, we used deep learning models to segment CTV and OARs and automated the inverse planning process, based on a RapidPlan model. The end-to-end process requires only the GTV contour and a CT scan as inputs. Specifically, the segmentation models could auto-segment CTV, bowel bag, large bowel, small bowel, total bowel, femurs, bladder, bone marrow, and female and male genitalia. All the OARs were contoured under the guidance of and reviewed by a GI radiation oncologist. For auto-planning, the RapidPlan model was designed for VMAT delivery with 3 arcs and validated separately by two GI radiation oncologists. Finally, the end-to-end pipeline was evaluated on a separate set of testing patients, and the resulting plans were scored by two GI radiation oncologists. Existing inverse planning methods rely on 1D information from DVH values,2D information from DVH lines,or 3D dose distributions using machine learning for plan optimizations. The project explored the possibility of using deep learning to create 3D dose distributions directly for VMAT treatment plans. The training data consisted of patients treated by the VMAT treatment technique in the short-course fractionation scheme that uses 5 Gy per fraction for 5 fractions. Two deep learning architectures were investigated for their ability to emulate clinical dose distributions: 3D DDUNet and 2D cGAN. The top-performing model for each architecture was identified based on the difference in DVH values, DVH lines, and dose distribution between the predicted dose and the corresponding clinical plans. Results: For 3DCRT automation, the predicted apertures were 100%, 95%, and 87.5% clinically acceptable for the posterior-anterior, laterals, and boost apertures, respectively. The forward planning algorithm created wedged plans that were 85% clinically acceptable with clinical apertures. The end-to-end workflow generated 97% clinically acceptable plans for the separate test patients. For the VMAT automation, CTV contours were 89% clinically acceptable without necessary modifications and all the OAR contours were clinically acceptable without edits except for large and small bowels. The RaidPlan model was evaluated to produce 100% and 91% of clinically acceptable plans per two GI radiation oncologists. For the testing of end-to-end workflow, 88% and 62% of the final plans were accepted by two GI radiation oncologists. For the evaluation of deep learning architectures, the top-performing model of the DDUNet architecture used the medium patch size and inputs of CT, PTV times prescription dose mask, CTV, PTV 10 mm expansion, and the external body structure. The model with inputs CT, PTV, and CTV masks performed the best for the cGAN architecture. Both the DDUNet and cGAN architectures could predict 3D dose distributions that had DVH values that were statistically the same as the clinical plans. Conclusions: We have successfully automated the clinical workflow for generating either 3DCRT or VMAT radiotherapy plans for rectal cancer for our institution. This project showed that the existing treatment planning techniques for rectal cancer can be automated to generate clinically acceptable and safe plans with minimal inputs and no human intervention for most patients. The project also showed that deep learning architectures can be used for predicting dose distributions

Automatic Segmentation of the Mandible for Three-Dimensional Virtual Surgical Planning

Three-dimensional (3D) medical imaging techniques have a fundamental role in the field of oral and maxillofacial surgery (OMFS). 3D images are used to guide diagnosis, assess the severity of disease, for pre-operative planning, per-operative guidance and virtual surgical planning (VSP). In the field of oral cancer, where surgical resection requiring the partial removal of the mandible is a common treatment, resection surgery is often based on 3D VSP to accurately design a resection plan around tumor margins. In orthognathic surgery and dental implant surgery, 3D VSP is also extensively used to precisely guide mandibular surgery. Image segmentation from the radiography images of the head and neck, which is a process to create a 3D volume of the target tissue, is a useful tool to visualize the mandible and quantify geometric parameters. Studies have shown that 3D VSP requires accurate segmentation of the mandible, which is currently performed by medical technicians. Mandible segmentation was usually done manually, which is a time-consuming and poorly reproducible process. This thesis presents four algorithms for mandible segmentation from CT and CBCT and contributes to some novel ideas for the development of automatic mandible segmentation for 3D VSP. We implement the segmentation approaches on head and neck CT/CBCT datasets and then evaluate the performance. Experimental results show that our proposed approaches for mandible segmentation in CT/CBCT datasets exhibit high accuracy

Sub-pixel Registration In Computational Imaging And Applications To Enhancement Of Maxillofacial Ct Data

In computational imaging, data acquired by sampling the same scene or object at different times or from different orientations result in images in different coordinate systems. Registration is a crucial step in order to be able to compare, integrate and fuse the data obtained from different measurements. Tomography is the method of imaging a single plane or slice of an object. A Computed Tomography (CT) scan, also known as a CAT scan (Computed Axial Tomography scan), is a Helical Tomography, which traditionally produces a 2D image of the structures in a thin section of the body. It uses X-ray, which is ionizing radiation. Although the actual dose is typically low, repeated scans should be limited. In dentistry, implant dentistry in specific, there is a need for 3D visualization of internal anatomy. The internal visualization is mainly based on CT scanning technologies. The most important technological advancement which dramatically enhanced the clinician\u27s ability to diagnose, treat, and plan dental implants has been the CT scan. Advanced 3D modeling and visualization techniques permit highly refined and accurate assessment of the CT scan data. However, in addition to imperfections of the instrument and the imaging process, it is not uncommon to encounter other unwanted artifacts in the form of bright regions, flares and erroneous pixels due to dental bridges, metal braces, etc. Currently, removing and cleaning up the data from acquisition backscattering imperfections and unwanted artifacts is performed manually, which is as good as the experience level of the technician. On the other hand the process is error prone, since the editing process needs to be performed image by image. We address some of these issues by proposing novel registration methods and using stonecast models of patient\u27s dental imprint as reference ground truth data. Stone-cast models were originally used by dentists to make complete or partial dentures. The CT scan of such stone-cast models can be used to automatically guide the cleaning of patients\u27 CT scans from defects or unwanted artifacts, and also as an automatic segmentation system for the outliers of the CT scan data without use of stone-cast models. Segmented data is subsequently used to clean the data from artifacts using a new proposed 3D inpainting approach

Segmentation, tracking, and kinematics of lung parenchyma and lung tumors from 4D CT with application to radiation treatment planning.

This thesis is concerned with development of techniques for efficient computerized analysis of 4-D CT data. The goal is to have a highly automated approach to segmentation of the lung boundary and lung nodules inside the lung. The determination of exact lung tumor location over space and time by image segmentation is an essential step to track thoracic malignancies. Accurate image segmentation helps clinical experts examine the anatomy and structure and determine the disease progress. Since 4-D CT provides structural and anatomical information during tidal breathing, we use the same data to also measure mechanical properties related to deformation of the lung tissue including Jacobian and strain at high resolutions and as a function of time. Radiation Treatment of patients with lung cancer can benefit from knowledge of these measures of regional ventilation. Graph-cuts techniques have been popular for image segmentation since they are able to treat highly textured data via robust global optimization, avoiding local minima in graph based optimization. The graph-cuts methods have been used to extract globally optimal boundaries from images by s/t cut, with energy function based on model-specific visual cues, and useful topological constraints. The method makes N-dimensional globally optimal segmentation possible with good computational efficiency. Even though the graph-cuts method can extract objects where there is a clear intensity difference, segmentation of organs or tumors pose a challenge. For organ segmentation, many segmentation methods using a shape prior have been proposed. However, in the case of lung tumors, the shape varies from patient to patient, and with location. In this thesis, we use a shape prior for tumors through a training step and PCA analysis based on the Active Shape Model (ASM). The method has been tested on real patient data from the Brown Cancer Center at the University of Louisville. We performed temporal B-spline deformable registration of the 4-D CT data - this yielded 3-D deformation fields between successive respiratory phases from which measures of regional lung function were determined. During the respiratory cycle, the lung volume changes and five different lobes of the lung (two in the left and three in the right lung) show different deformation yielding different strain and Jacobian maps. In this thesis, we determine the regional lung mechanics in the Lagrangian frame of reference through different respiratory phases, for example, Phase10 to 20, Phase10 to 30, Phase10 to 40, and Phase10 to 50. Single photon emission computed tomography (SPECT) lung imaging using radioactive tracers with SPECT ventilation and SPECT perfusion imaging also provides functional information. As part of an IRB-approved study therefore, we registered the max-inhale CT volume to both VSPECT and QSPECT data sets using the Demon\u27s non-rigid registration algorithm in patient subjects. Subsequently, statistical correlation between CT ventilation images (Jacobian and strain values), with both VSPECT and QSPECT was undertaken. Through statistical analysis with the Spearman\u27s rank correlation coefficient, we found that Jacobian values have the highest correlation with both VSPECT and QSPECT

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society