Organ Dose Estimates in Thorax CT: Voxel Phantom Organ Matching With Individual Patient Anatomy

Given the continuous usage and spread of computed tomography (CT), the potential harmful e↵ects and the radiation dose to the patient have become high interest topics among the scientific community. The main objective of this investigation was to modify existing three-dimensional (3D) voxel phantom models to resemble real patients as much as possible, trying to progress the concept of a more personalized patient dosimetry. This work focused essentially in one of the biggest and most radiosensitive organs in the thorax, the lungs. Additionally, the variations of organ doses when a standard phantom is used instead were studied. During the course of this work a FORTRAN-based program was developed, which is able to semi-automatically modify the volumetric information of organs of interest in a standard voxel phantom (Female ICRP Adult Reference). The voxel resolution was also altered so the phantom’s diameters match the patient’s ones. Monte Carlo (MC) PENELOPE simulation code was used to mimic CT scan conditions and, therefore, generate 2D projections, used for visual organ matching with clinical patient CT images, and access organ dose in both phantoms (ICRP standard and ICRP modified). The main results reported that matching the voxel phantom’s size and lungs provides organ dose values significantly di↵erent from the ones measured in the ICRP reference phantom. Voxel models matched to patients’ size and overall anatomy allow increased accuracy in organ dose estimation, which, as reported by this study, can su↵er from up to 20% underestimation and 40% overestimation. This study demonstrates that voxel phantoms developed using single patient data provide a better and more precise organ dose assessment by MC methods than a standard phantom. The presented methodology should be of interest for dose optimization studies and quick enough for routine clinical use

Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation

Automatic parsing of anatomical objects in X-ray images is critical to many clinical applications in particular towards image-guided invention and workflow automation. Existing deep network models require a large amount of labeled data. However, obtaining accurate pixel-wise labeling in X-ray images relies heavily on skilled clinicians due to the large overlaps of anatomy and the complex texture patterns. On the other hand, organs in 3D CT scans preserve clearer structures as well as sharper boundaries and thus can be easily delineated. In this paper, we propose a novel model framework for learning automatic X-ray image parsing from labeled CT scans. Specifically, a Dense Image-to-Image network (DI2I) for multi-organ segmentation is first trained on X-ray like Digitally Reconstructed Radiographs (DRRs) rendered from 3D CT volumes. Then we introduce a Task Driven Generative Adversarial Network (TD-GAN) architecture to achieve simultaneous style transfer and parsing for unseen real X-ray images. TD-GAN consists of a modified cycle-GAN substructure for pixel-to-pixel translation between DRRs and X-ray images and an added module leveraging the pre-trained DI2I to enforce segmentation consistency. The TD-GAN framework is general and can be easily adapted to other learning tasks. In the numerical experiments, we validate the proposed model on 815 DRRs and 153 topograms. While the vanilla DI2I without any adaptation fails completely on segmenting the topograms, the proposed model does not require any topogram labels and is able to provide a promising average dice of 85% which achieves the same level accuracy of supervised training (88%)

Patient Risk-Minimizing Tube Current Modulation in X-Ray Computed Tomography

This dissertation proposes a patient-specific tube current modulation for computed tomography (CT) that minimizes the individual patient risk (riskTCM). Modern CT scanners use automatic exposure control (AEC) techniques including tube current modulation (TCM) to reduce the radiation dose delivered to the patient while maintaining image quality. Today's TCM implementations aim at minimizing the tube current-time (mAs) product as a surrogate for patient dose, which is why they are referred to as mAsTCM hereafter. However, the actual patient risk, e.g., in the form of risk measures such as the effective dose Deff representing the sensitivity of individual organs with respect to ionizing radiation, is not taken into account. In order to be able to optimize the effective dose Deff or another biologically meaningful measure, organ doses must be estimated before the actual CT scan in order to compute an optimized riskTCM curve. This can be achieved using a machine learning approach and based on these information, the new patient risk-minimizing TCM curve can be obtained. The proposed riskTCM algorithm was evaluated in a simulation study for circular scans and compared against the current gold standard method mAsTCM and to a constant tube current as well as an organ-specific tube current modulation technique. The results illustrate that all anatomical regions can benefit from riskTCM and a reduction of effective dose of up to 30% can be expected compared to mAsTCM. Furthermore, riskTCM was extended to a spiral trajectory that is commonly used in clinical routine and initial measurements with phantoms have been performed. The introduction of riskTCM into clinical practice would only require a software update since almost all CT systems are already capable of modulating the tube current

Imaging procedures for stranded marine mammals

This section provides an introduction to biomedical imaging techniques and guidelines for diagnostic imaging of marine mammals to assist with both live examination and necropsy procedures. The procedures described are based on imaging equipment and techniques that are relatively common in human and veterinary facilities and to provide the majority of stranding response groups with the most likely options that will assist their efforts. The imaging techniques described include basic radiography, computed tomography (CT), and magnetic resonance imaging (MRI) and are applicable to both live and post-mortem cases. Special emphasis has been placed on whole body, airway, head and ear imaging procedures. Sub-sections cover basic information on the basic principles and appropriate applications for radiography vs. CT vs. MRI, handling and preparation of live and dead animals in clinical settings, and image and data formats that may be encountered. The protocols are also listed in outline form in order to provide a rapid overview. The introductory discussion of principles behind techniques is not required to employ the protocols but does provide additional information that can aid in deciding which techniques are most efficacious and what the limitations are for interpretation of imaging data. Examples of some pathology imaged with these procedures are also provided.Funding was provided by the Office of Naval Research through Contract No. N00244-071-0022

Validation of PET/CT dataset for radiation treatment planning

PET/CT scans are frequently used for radiation treatment planning (RTP). Our work demonstrates a practical approach for validating the PET/CT dataset for RTP. We tested this QA process on a Reveal HD PET/CT scanner. The phantom used is a TGM2 ISIS QA phantom, a 14 cm acrylic cube with a central bore for object inserts. It has four different built-in inserts for electron density verification. 22Na seeds are inserted into the pinholes at the side of the cube. PET/CT images of the phantom with 22Na seeds are acquired and fused in the scanner Syngo fusion software. Registration of the PET/CT dataset is visualized by raising the lower threshold of the PET images to reduce the 22Na point sources to a few pixels and comparing it with the CT images of 22Na seeds. Geometric scaling accuracy of the pixels is verified by measuring the dimension of the cube in x, y and z axes. The HU values of four electron density verification inserts are measured and compared with manufacturer specified HU values. These QA tests are repeated in the RTP software after importing the PET/CT dataset. A quantitative analysis of registration error and geometric scaling accuracy of pixels are verified independently using MATHEMATICA. The resolution of the PET scanner was determined by measuring the FWHM of capillary tube sources inserted in a Styrofoam block based on the NEMA-2 protocol. Minor misalignment of the fused images was detected in the scanner (~1 mm) while the imported dataset in the RTP system showed a major misalignment (~6 mm) when fused by auto fusion software. The maximum geometric scaling errors of object sizes were observed in the z direction (5.2% decrease) in the scanner and the scaling errors were less in the RTP software (2.9% decrease). The greatest HU errors in the CT image compared with expected HU values were observed in the bone density insert (28% increase) in the scanner and all HU values for different inserts were shifted up by a constant value in the RTP system. The resolution of the PET scanner was comparable to the manufacturer’s specification

Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening

One of the leading forms of cancer is colorectal cancer (CRC), which is responsible for increasing mortality in young people. The aim of this paper is to provide an experimental modification of deep learning of Xception with Swish and assess the possibility of developing a preliminary colorectal polyp screening system by training the proposed model with a colorectal topogram dataset in two and three classes. The results indicate that the proposed model can enhance the original convolutional neural network model with evaluation classification performance by achieving accuracy of up to 98.99% for classifying into two classes and 91.48% for three classes. For testing of the model with another external image, the proposed method can also improve the prediction compared to the traditional method, with 99.63% accuracy for true prediction of two classes and 80.95% accuracy for true prediction of three classes

Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening

One of the leading forms of cancer is colorectal cancer (CRC), which is responsible for increasing mortality in young people. The aim of this paper is to provide an experimental modification of deep learning of Xception with Swish and assess the possibility of developing a preliminary colorectal polyp screening system by training the proposed model with a colorectal topogram dataset in two and three classes. The results indicate that the proposed model can enhance the original convolutional neural network model with evaluation classification performance by achieving accuracy of up to 98.99% for classifying into two classes and 91.48% for three classes. For testing of the model with another external image, the proposed method can also improve the prediction compared to the traditional method, with 99.63% accuracy for true prediction of two classes and 80.95% accuracy for true prediction of three classes

The overactive bladder model anatomical and morphological correlates

After the overactive bladder modelling one could see the urinary ways in vivo changes registration using MRI with MR-urography before and after the diuretic load. The morphological investigations results proved the overactive bladder properties using MRI-registration. The internal organs and urinary ways showed no changes during the early terms. 30 days after the overactive bladder model induction one could register the morphological changes resulted in the ureterohydronephrosis development. The overactive bladder model stability and its adequate type to clinical condition was proved on the basis of the performed functional and morphological studies together with pathological changes dynamics characteristic for ureterohydronephrosis were studied