Breast CT is a promising modality whose inherent scatter could be used to reconstruct electron density (rho_e) images. This has led us to investigate the benefits of reconstructing linear attenuation coefficient (mu) and (rho_e) images of the breast. First generation CT provides a cost-effective and simple approach to reconstruct (rho_e) images in a laboratory but is limited by the anisotropic probability of scatter, attenuation, noise and contaminating scatter (coherent and multiple scatter). These issues were investigated using Monte Carlo (MC) simulations of a first generation breast scatter enhanced CT (B-SECT) system. A reconstruction algorithm was developed for the B-SECT system and is based on a ring of detectors which eliminates the scatter dependence on the relative position of the scattering centre. The algorithm incorporates an attenuation correction based on the (mu) image and was tested against analytical and MC simulations. MC simulations were also used to quantify the dose per scan. The ring measures a fraction of the total single incoherent scatter which is proportional to ray integrals of (rho_e) and can be quantified even when electron binding is non negligible. The algorithm typically reconstructs accurate (rho_e) images using a single correction for attenuation but has the capability for multiple iterations if required. MC simulations show that the dose coefficients are similar to those of cone beam breast CT. Coherent and multiple scatter can not be directly related to (rho_e) and lead to capping artifacts and overestimated (rho_e) by a factor greater than 2. This issue can be addressed using empirical corrections based on the radiological path of the incident beam and result in (rho_e) images of breast soft tissue with 1% accuracy, 3% precision and a mean glandular dose of 4 mGy for a 3D scan. The reconstructed (rho_e) image was more accurate than the (rho_e) estimate derived from the (mu) image. An alternative correction based on the thickness of breast traversed by the beam provides an enhanced contrast image reflecting the breast scatter properties. These results demonstrate the feasibility of detecting small (rho_e) changes in the intact breast and shows that further experimental evaluation of this technique is warranted.

Computed Tomography is one of the most important image modalities in medical imaging nowadays. Recent developments have led to a new acquisition technique called 'dual-energy', where images are taken with different x-ray spectra. This enables for the first time spectral information in the CT dataset. Our approach was to use an energy resolving detector (Medipix) and investigate its potential in the medical imaging domain. Images are taken in different energy bins. For acquisition of the data, a CT scanner called 'Medipix All Resolution System' (MARS) scanner was constructed. It was upgraded to achieve better image quality as well as faster scan time and a stable operation. In medical imaging, it is important to achieve a high contrast and a good detail recognition at a low dose. Therefore, it is common practice to use contrast agents to highlight certain regions of the body like e.g. the vascular system. But with a broad spectrum acquisition, it is often impossible to distinguish highly absorbing body elements like bones from the contrast agent. We target this problem by a contrast agent study using different energy bins. This so called spectral contrast agent study has been conducted with small animals using the MARS scanner. The data has been processed to create an optimal CT reconstruction. The image enhancement techniques consist of corrections for noisy pixels, intensity fluctuations and eliminating streaks in the sinograms to reduce ring artifacts. In order to evaluate the data, we used two methods of material identification. The material reconstruction method works on projection data and uses a maximum-likelihood estimation to reconstruct images of base materials. The second method, the principal component analysis (PCA), identifies the relevant information from the spectral dataset in a few derived variables that account for most of the variance in the dataset. This resulted in images with enhanced contrast and removed redundancies. It is possible to combine these images in one colour image where anatomical structures are shown in good detail and certain materials show up in different colors. Based on this new information from spectral data, we could show that it is possible to distinguish the spinal bone from contrast agent.

Oral bisphosphonate drug therapy is a common and effective treatment for osteoporosis. Little is known about the long-term effects of bisphosphonates on bone quality. This study examined the structural and mechanical properties of trabecular bone following 0-16 years of bisphosphonate treatment. Fifty-three iliac crest bone samples of Caucasian women diagnosed with low turnover osteoporosis were identified from the Kentucky Bone Registry. Forty-five were treated with oral bisphosphonates for 1 to 16 years while eight were treatment naive. A section of trabecular bone was chosen from a micro-computed tomography (Scanco µCT 40) scan of each sample for a uniaxial linearly elastic compression simulation using finite element analysis (ANSYS 14.0). Morphometric parameters (BV/TV, SMI, Tb.Sp., etc.) were computed using µCT. Apparent modulus, effective modulus and estimated failure stress were calculated. Biomechanical and morphometric parameters improved with treatment duration, peaked around 7 years, and then declined independently of age. The findings suggest a limit to the benefits associated with bisphosphonate treatment and that extended continuous bisphosphonate treatment does not continue to improve bone quality. Bone quality, and subsequently bone strength, may eventually regress to a state poorer than at the onset of treatment. Treatment duration limited to less than 7 years is recommended.

Genitourinary cancer refers to the presence of tumours in the genital or urinary organs such as bladder, kidney and prostate. In 2008 the worldwide incidence of bladder cancer was 382,600 with a mortality of 150,282. Radiotherapy is one of the main treatment choices for genitourinary cancer where accurate delineation of the gross tumour volume (GTV) on computed tomography (CT) images is crucial for the success of this treatment. Limited CT resolution and contrast in soft tissue organs make this difficult and has led to significant inter- and intra- clinical variability in defining the extent of the GTV, especially at the junctions of different organs. In addition the introduction of new imaging techniques and modalities has significantly increased the number of the medical images that require contouring. More advanced image processing is required to help reduce contouring variability and assist in handling the increased volume of data. In this thesis image analysis methodologies were used to extract low-level features such as entropy, moment and correlation from radiotherapy planning CT images. These distinctive features were identified and used for defining the GTV and to implement a fully-automatic contouring system. The first key contribution is to demonstrate that second-order statistics from co-occurrence matrices (GTSDM) give higher accuracy in classifying soft tissue regions of interest (ROIs) into GTV and non-GTV. Loadings of the principal components (PCs) of the GTSDM features were found to be consistent over different patients. Exhaustive feature selection suggested that entropies and correlations produced consistently larger areas under receiver operating characteristic (AUROC) curves than first-order features. The second significant contribution is to demonstrate that in the bladder-prostate junction, where the largest inter-clinical variability is observed, the second-order principal entropy from stationery wavelet denoised CT images (DPE) increased the saliency of the bladder prostate junction. As a result thresholding of the DPE produced good agreement between gold standard clinical contours and those produced by this approach with Dice coefficients. The third contribution is to implement a fully automatic and reproducible system for bladder cancer GTV auto-contouring based on classifying second-order statistics. The Dice similarity coefficients (DSCs) were employed to evaluate the automatic contours. It was found that in the mid-range of the bladder the automatic contours are accurate, but in the inferior and superior ends of bladder automatic contours were more likely to have small DSCs with clinical contours, which reconcile with the fact of clinical variability in defining GTVs. A novel male bladder probability atlas was constructed based on the clinical contours and volume estimation from the classification results. Registration of the classification results with this probabilistic atlas consistently increases the DSCs of the inferior slices.

<p>The cross-sectional images of computed tomography (CT) are calculated from a series of projections using reconstruction methods. Recently introduced on clinical CT scanners, iterative reconstruction (IR) method enables potential patient dose reduction with significantly reduced image noise, but is limited by its "waxy" texture and nonlinear nature. To balance the advantages and disadvantages of IR, evaluations are needed with diagnostic accuracy as the endpoint. Moreover, evaluations need to take into consideration the type of the imaging task (detection and quantification), the properties of the task (lesion size, contrast, edge profile, etc.), and other acquisition and reconstruction parameters. </p><p>To evaluate detection tasks, the more acceptable method is observer studies, which involve image preparation, graphical user interface setup, manual detection and scoring, and statistical analyses. Because such evaluation can be time consuming, mathematical models have been proposed to efficiently predict observer performance in terms of a detectability index (d'). However, certain assumptions such as system linearity may need to be made, thus limiting the application of the models to potentially nonlinear IR. For evaluating quantification tasks, conventional method can also be time consuming as it usually involves experiments with anthropomorphic phantoms. A mathematical model similar to d' was therefore proposed for the prediction of volume quantification performance, named the estimability index (e'). However, this prior model was limited in its modeling of the task, modeling of the volume segmentation process, and assumption of system linearity.</p><p>To expand prior d' and e' models to the evaluations of IR performance, the first part of this dissertation developed an experimental methodology to characterize image noise and resolution in a manner that was relevant to nonlinear IR. Results showed that this method was efficient and meaningful in characterizing the system performance accounting for the non-linearity of IR at multiple contrast and noise levels. It was also shown that when certain criteria were met, the measurement error could be controlled to be less than 10% to allow challenging measuring conditions with low object contrast and high image noise.</p><p>The second part of this dissertation incorporated the noise and resolution characterizations developed in the first part into the d' calculations, and evaluated the performance of IR and conventional filtered backprojection (FBP) for detection tasks. Results showed that compared to FBP, IR required less dose to achieve a threshold performance accuracy level, therefore potentially reducing the required dose. The dose saving potential of IR was not constant, but dependent on the task properties, with subtle tasks (small size and low contrast) enabling more dose saving than conspicuous tasks. Results also showed that at a fixed dose level, IR allowed more subtle tasks to exceed a threshold performance level, demonstrating the overall superior performance of IR for detection tasks.</p><p>The third part of this dissertation evaluated IR performance in volume quantification tasks with conventional experimental method. The volume quantification performance of IR was measured using an anthropomorphic chest phantom and compared to FBP in terms of accuracy and precision. Results showed that across a wide range of dose and slice thickness, IR led to accuracy significantly different from that of FBP, highlighting the importance of calibrating or expanding current segmentation software to incorporate the image characteristics of IR. Results also showed that despite IR's great noise reduction in uniform regions, IR in general had quantification precision similar to that of FBP, possibly due to IR's diminished noise reduction at edges (such as nodule boundaries) and IR's loss of resolution at low dose levels. </p><p>The last part of this dissertation mathematically predicted IR performance in volume quantification tasks with an e' model that was extended in three respects, including the task modeling, the segmentation software modeling, and the characterizations of noise and resolution properties. Results showed that the extended e' model correlated with experimental precision across a range of image acquisition protocols, nodule sizes, and segmentation software. In addition, compared to experimental assessments of quantification performance, e' was significantly reduced in computational time, such that it can be easily employed in clinical studies to verify quantitative compliance and to optimize clinical protocols for CT volumetry.</p><p>The research in this dissertation has two important clinical implications. First, because d' values reflect the percent of detection accuracy and e' values reflect the quantification precision, this work provides a framework for evaluating IR with diagnostic accuracy as the endpoint. Second, because the calculations of d' and e' models are much more efficient compared to conventional observer studies, the clinical protocols with IR can be optimized in a timely fashion, and the compliance of clinical performance can be examined routinely.</p> / Dissertation

Radiotherapy dose calculations have evolved from simple factor based methods performed with pen and paper, into computationally intensive simulations based on Monte Carlo theory and energy deposition kernel convolution. Similarly, in the field of positron emission tomography (PET), attenuation correction, which was originally omitted entirely, is now a crucial component of any PET reconstruction algorithm. Today, both of these applications – radiotherapy and PET – derive their needed in-tissue radiation attenuation coefficients from images acquired with X-ray computed tomography (CT). Since X-ray images are themselves acquired using ionizing radiation, the intensity at a point in an image will reflect the radiation interaction properties of the tissue located at that point. Magnetic resonance imaging (MRI), on the other hand, does not use ionizing radiation. Instead MRI make use of the net transverse magnetization resulting from the spin polarization of hydrogen nuclei. MR image contrast can be varied to a greater extent than CT and the soft tissue contrast is, for most MR sequences, superior to that of CT. Therefore, for many cases, MR images provide a considerable advantage over CT when identifying or delineating tumors or other diseased tissues. For this reason, there is an interest to replace CT with MRI for a great number of diagnostic and therapeutic workflows. Also, replacing CT with MRI would reduce the exposure to ionizing radiation experienced by patients and, by extension, reduce the associated risk to induce cancer. In part MRI has already replaced CT, but for radiotherapy dose calculations and PET attenuation correction, CT examinations are still necessary in clinical practice. One of the reasons is that the net transverse magnetization imaged in MRI cannot be converted into attenuation coefficients for ionizing radiation in a straightforward way. More specifically, regions with similar appearance in magnetic resonance (MR) images, such as bone and air pockets, are found at different ends of the spectrum of attenuation coefficients present in the human body. In a CT image, bone will appear bright white and air as black corresponding to high and no attenuation, respectively. In an MR image, bone and air both appear dark due to the lack of net transverse magnetization. The weak net transverse magnetization of bone is a result of low hydrogen density and rapid transverse relaxation. A particular category of MRI sequences with so-called ultrashort echo time (UTE) can sample the MRI signal from bone before it is lost due to transverse relaxation. Thus, UTE sequences permit bone to be imaged with MRI albeit with weak intensity and poor resolution. Imaging with UTE in combination with careful image analysis can permit ionizing-radiation attenuation-maps to be derived from MR images. This dissertation and appended articles present a procedure for this very purpose. However, as attenuation coefficients are radiation-quality dependent the output of the method is a Hounsfield unit map, i.e. a substitute for a CT image. It can be converted into an attenuation map using conventional clinical procedure. Obviating the use of CT would reduce the number of examinations that patients have to endure during preparation for radiotherapy. It would also permit PET attenuation correction to be performed on images from the new imaging modality that combines PET and MRI in one scanner – PET/MR.

This project implements a parallel algorithm for Computed Tomography based on the Algebraic Reconstruction Technique (ART) algorithm. This technique for reconstructing pictures from projections is useful for applications such as Computed Tomography (CT or CAT). The algorithm requires fewer views, and hence less radiation, to produce an image of comparable or better quality. However, the approach is not widely used because of its computationally intensive nature in comparison with rival technologies. A faster ART algorithm could reduce the amount of radiation needed for CT imaging by producing a better image with fewer projections. A reconstruction from projections version of the ART algorithm for two dimensions was implemented in parallel using the Message Passing Interface (MPI) and OpenMP extensions for C. The message passing implementation did not result in faster reconstructions due to prohibitively long and variant communication latency. The shared memory implementation produced positive results, showing a clear computational advantage for multiple processors and measured efficiency ranging from 60-95%. Consistent with the literature, image quality proved to be significantly better compared to the industry standard Filtered Backprojection algorithm especially when reconstructing from fewer projection angles.

Background/Objectives: Individuals with a spinal cord injury (SCI) experience reductions in lower-extremity muscle mass and increased fatty-infiltration of skeletal muscle, predisposing them to an increased risk of specific secondary health conditions. To date, few investigations have prospectively examined changes in muscle in the chronic stage of SCI. Peripheral quantitative computed tomography (pQCT) is an imaging technique capable of measuring lower-extremity skeletal muscle cross-sectional area (CSA) and muscle density, the latter is a surrogate measure of muscle fatty infiltration. The purpose of this project was to a) determine the magnitude of muscle CSA and muscle density reduction in a chronic-SCI population with diverse impairments; b) identify demographic and injury characteristics associated with muscle CSA and density status; and c) determine if muscle CSA and muscle density change over a two-year period following chronic-paralysis and if so, what factors are associated with the changes. Materials and Methods: Seventy individuals [50/20 m/f, mean (± SD) age 48.9 ± 11.5 years; duration of injury 15.5 ± 10.0 years] with chronic (>2 years post-injury) SCI (C1-T12, AIS A-D) were enrolled in a two-year cohort study. Muscle CSA and muscle density values were calculated from pQCT scans of the 66%-site of the calf obtained at baseline and two follow-up visits separated by one year. Possible correlates of muscle CSA and density selected a priori included: gender, age, height, weight, waist circumference, age at injury, level of injury, injury duration, leg spasm frequency and severity scale score (SFSS), ISNCSCI calf-muscle lower-extremity motor score (cLEMS), wheelchair use, serum vitamin D level, and physical activity level. Dependent t-tests were used to compare muscle CSA and muscle density values of participants with complete and incomplete-SCI to age, gender, and height matched able-bodied controls. Multiple linear regression models were used to determine correlates of muscle CSA and muscle density. Repeated measures analysis of variance (rANOVA) were used to examine change in muscle CSA and density over the two-year study duration and multiple linear regression models were created to determine correlates of muscle CSA and density change from baseline. Results: Individuals with motor-complete SCI had a 45% reduction in muscle CSA and a 32% reduction in muscle density relative to controls. Participants with motor-incomplete SCI had a 17% reduction in muscle CSA and a 14% reduction in muscle density relative to controls. A reduced height, waist circumference, cLEMS, and wheelchair use were associated with a smaller muscle CSA in the best-fitting regression model (R2 = 0.66; p<0.0001). In the best-fitting regression model for muscle density, increased age, a lower cLEMS, reduced SFSS, fewer minutes of daily vigorous physical activity, and wheelchair use were associated with a lower muscle density (R2= 0.37; p<0.001). A high degree of individual variability in muscle CSA change (mean ± SD: -1.9 ± 6.2cm2; range: -22.6 to 8.5 cm2) and muscle density change (mean ± SD: -1.2 ± 3.28mg/cc; range: -8.6 to 6.4 mg/cc) was observed in those with both complete and incomplete SCI over the two-year study duration. rANOVA indicated a significant reduction in both muscle CSA and density after controlling for individual variability. A greater waist circumference at baseline was weakly associated with a reduction in muscle CSA (R2 = 0.14, p<0.05), and a lower weight and waist circumference at baseline were associated with a reduction in muscle density (R2 = 0.26, p < 0.001 and R2 = 0.20, p < 0.01, respectively). Conclusion: Age, completeness of injury, spasticity, physical activity participation, and ambulation ability were identified as potential clinical predictors of muscle status in individuals with chronic-SCI. Muscle CSA and density does not reach a “steady-state” after chronic-SCI. Further investigation is needed to determine the mechanisms responsible muscle CSA and density change in order to prevent continued reductions after chronic-SCI.

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

Breast CT is a promising modality whose inherent scatter could be used to reconstruct electron density (rho_e) images. This has led us to investigate the benefits of reconstructing linear attenuation coefficient (mu) and (rho_e) images of the breast. First generation CT provides a cost-effective and simple approach to reconstruct (rho_e) images in a laboratory but is limited by the anisotropic probability of scatter, attenuation, noise and contaminating scatter (coherent and multiple scatter). These issues were investigated using Monte Carlo (MC) simulations of a first generation breast scatter enhanced CT (B-SECT) system. A reconstruction algorithm was developed for the B-SECT system and is based on a ring of detectors which eliminates the scatter dependence on the relative position of the scattering centre. The algorithm incorporates an attenuation correction based on the (mu) image and was tested against analytical and MC simulations. MC simulations were also used to quantify the dose per scan. The ring measures a fraction of the total single incoherent scatter which is proportional to ray integrals of (rho_e) and can be quantified even when electron binding is non negligible. The algorithm typically reconstructs accurate (rho_e) images using a single correction for attenuation but has the capability for multiple iterations if required. MC simulations show that the dose coefficients are similar to those of cone beam breast CT. Coherent and multiple scatter can not be directly related to (rho_e) and lead to capping artifacts and overestimated (rho_e) by a factor greater than 2. This issue can be addressed using empirical corrections based on the radiological path of the incident beam and result in (rho_e) images of breast soft tissue with 1% accuracy, 3% precision and a mean glandular dose of 4 mGy for a 3D scan. The reconstructed (rho_e) image was more accurate than the (rho_e) estimate derived from the (mu) image. An alternative correction based on the thickness of breast traversed by the beam provides an enhanced contrast image reflecting the breast scatter properties. These results demonstrate the feasibility of detecting small (rho_e) changes in the intact breast and shows that further experimental evaluation of this technique is warranted.