An experimental and theoretical approach to correct for the scattered radiation in an X-ray computer tomography system for industrial applications

The main problem connected to cone beam computed tomography (CT) systems for industrial applications employing 450 kV X-ray tubes is the high amount of scattered radiation which is added to the primary radiation (signal). This stray radiation leads to a significant degradation of the image quality. A better understanding of the scattering and methods to reduce its effects are therefore necessary to improve the image quality. Several studies have been carried out in the medical field at lower energies, whereas studies in industrial CT, especially for energies up to 450 kV, are lacking. Moreover, the studies reported in literature do not consider the scattered radiation generated by the CT system structure and the walls of the X-ray room (environmental scatter). In order to investigate the scattering on CT projections a GEANT4-based Monte Carlo (MC) model was developed. The model, which has been validated against experimental data, has enabled the calculation of the scattering including the environmental scatter, the optimization of an anti-scatter grid suitable for the CT system, and the optimization of the hardware components of the CT system. The investigation of multiple scattering in the CT projections showed that its contribution is 2.3 times the one of primary radiation for certain objects. The results of the environmental scatter showed that it is the major component of the scattering for aluminum box objects of front size 70 x 70 mm2 and that it strongly depends on the thickness of the object and therefore on the projection. For that reason, its correction is one of the key factors for achieving high quality images. The anti-scatter grid optimized by means of the developed MC model was found to reduce the scatter-toprimary ratio in the reconstructed images by 20 %. The object and environmental scatter calculated by means of the simulation were used to improve the scatter correction algorithm which could be patented by Empa. The results showed that the cupping effect in the corrected image is strongly reduced. The developed CT simulation is a powerful tool to optimize the design of the CT system and to evaluate the contribution of the scattered radiation to the image. Besides, it has offered a basis for a new scatter correction approach by which it has been possible to achieve images with the same spatial resolution as state-of-the-art well collimated fan-beam CT with a gain in the reconstruction time of a factor 10. This result has a high economic impact in non-destructive testing and evaluation, and reverse engineering.

Performance evaluation of detectors for digital radiography

To date the hospital radiological workflow is completing a transition from analog to digital technology. Since the X-rays digital detection technologies have become mature, hospitals are trading on the natural devices turnover to replace the conventional screen film devices with digital ones. The transition process is complex and involves not just the equipment replacement but also new arrangements for image transmission, display (and reporting) and storage. This work is focused on 2D digital detector’s characterization with a concern to specific clinical application; the systems features linked to the image quality are analyzed to assess the clinical performances, the conversion efficiency, and the minimum dose necessary to get an acceptable image. The first section overviews the digital detector technologies focusing on the recent and promising technological developments. The second section contains a description of the characterization methods considered in this thesis categorized in physical, psychophysical and clinical; theory, models and procedures are described as well. The third section contains a set of characterizations performed on new equipments that appears to be some of the most advanced technologies available to date. The fourth section deals with some procedures and schemes employed for quality assurance programs.

Adaptive multiscale biological signal processing

Biological processes are very complex mechanisms, most of them being accompanied by or manifested as signals that reflect their essential characteristics and qualities. The development of diagnostic techniques based on signal and image acquisition from the human body is commonly retained as one of the propelling factors in the advancements in medicine and biosciences recorded in the recent past. It is a fact that the instruments used for biological signal and image recording, like any other acquisition system, are affected by non-idealities which, by different degrees, negatively impact on the accuracy of the recording. This work discusses how it is possible to attenuate, and ideally to remove, these effects, with a particular attention toward ultrasound imaging and extracellular recordings. Original algorithms developed during the Ph.D. research activity will be examined and compared to ones in literature tackling the same problems; results will be drawn on the base of comparative tests on both synthetic and in-vivo acquisitions, evaluating standard metrics in the respective field of application. All the developed algorithms share an adaptive approach to signal analysis, meaning that their behavior is not dependent only on designer choices, but driven by input signal characteristics too. Performance comparisons following the state of the art concerning image quality assessment, contrast gain estimation and resolution gain quantification as well as visual inspection highlighted very good results featured by the proposed ultrasound image deconvolution and restoring algorithms: axial resolution up to 5 times better than algorithms in literature are possible. Concerning extracellular recordings, the results of the proposed denoising technique compared to other signal processing algorithms pointed out an improvement of the state of the art of almost 4 dB.

Enhancing the efficiency of solar concentrators by controlled optical aberrations

The diameters of traditional dish concentrators can reach several tens of meters, the construction of monolithic mirrors being difficult at these scales: cheap flat reflecting facets mounted on a common frame generally reproduce a paraboloidal surface. When a standard imaging mirror is coupled with a PV dense array, problems arise since the solar image focused is intrinsically circular. Moreover, the corresponding irradiance distribution is bell-shaped in contrast with the requirement of having all the cells under the same illumination. Mismatch losses occur when interconnected cells experience different conditions, in particular in series connections. In this PhD Thesis, we aim at solving these issues by a multidisciplinary approach, exploiting optical concepts and applications developed specifically for astronomical use, where the improvement of the image quality is a very important issue. The strategy we propose is to boost the spot uniformity acting uniquely on the primary reflector and avoiding the big mirrors segmentation into numerous smaller elements that need to be accurately mounted and aligned. In the proposed method, the shape of the mirrors is analytically described by the Zernike polynomials and its optimization is numerically obtained to give a non-imaging optics able to produce a quasi-square spot, spatially uniform and with prescribed concentration level. The freeform primary optics leads to a substantial gain in efficiency without secondary optics. Simple electrical schemes for the receiver are also required. The concept has been investigated theoretically modeling an example of CPV dense array application, including the development of non-optical aspects as the design of the detector and of the supporting mechanics. For the method proposed and the specific CPV system described, a patent application has been filed in Italy with the number TO2014A000016. The patent has been developed thanks to the collaboration between the University of Bologna and INAF (National Institute for Astrophysics).

CO2-EVAR: An innovative approach to Automated Carbon Dioxide Angiography during Endovascular Abdominal Aortic Aneurysm repair

Objectives CO2-EVAR was proposed for treatment of AAA especially in patients with CKD. Issues regarding standardization, such as visualization of lowest renal artery (LoRA) and quality image in angiographies performed from pigtail or introducer-sheath, are still unsolved. Aim of the study was to analyze different steps of CO2-EVAR to create an operative protocol to standardize the procedure. Methods Patients undergoing CO2-EVAR were prospectively enrolled in 5 European centers (2018-2021). CO2-EVAR was performed using an automated injector. LoRA visualization and image quality (1-4) were analyzed and compared at different procedure steps: preoperative CO2-angiography from Pigtail/Introducer-sheath (1st Step), angiographies from Pigtail at 0%,50%,100% main body (MB) deployment (2nd Step), contralateral hypogastric artery (CHA) visualization with CO2 injection from femoral Introducer-sheath (3rd Step) and completion angiogram from Pigtail/Introducer-sheath (4th Step). Intra-/postoperative adverse events were evaluated. Results Sixty-five patients undergoing CO2-EVAR were enrolled, 55/65(84.5%) male, median age 75(11.5) years. Median ICM was 20(54)cc; 19/65(29.2%) procedures were performed with 0-iodine. 1st Step: median image quality was significantly higher with CO2 injected from femoral introducer [Pigtail2(3)vs.3(3)Introducer,p=.008]. 2nd Step: LoRA was more frequently detected at 50% (93%vs.73.2%, p=.002) and 100% (94.1%vs.78.4%, p=.01) of MB deployment compared with first angiography from Pigtail; image quality was significantly higher at 50% [3(3)vs.2(3),p=<.001] and 100% [4(3) vs.2(3),p=.001] of MB deployment. CHA was detected in 93% cases (3rd Step). Mean image quality was significantly higher when final angiogram (4th Step) was performed from introducer (Pigtail2.6±1.1vs.3.1±0.9Introducer,p=<.001). Rates of intra-/postoperative adverse events (pain,vomit,diarrhea) were 7.7% and 12.5%. Conclusions Preimplant CO2-angiography should be performed from Introducer-sheath. MB steric bulk during its deployment should be used to improve image quality and LoRA visualization with CO2. CHA can be satisfactorily visualized with CO2. Completion CO2-angiogram should be performed from femoral Introducer-sheath. This operative protocol allows to perform CO2-EVAR with minimal ICM and low rate of mild complications.

CT perfusion image processing: analysis of liver tumors

Perfusion CT imaging of the liver has potential to improve evaluation of tumour angiogenesis. Quantitative parameters can be obtained applying mathematical models to Time Attenuation Curve (TAC). However, there are still some difficulties for an accurate quantification of perfusion parameters due, for example, to algorithms employed, to mathematical model, to patient’s weight and cardiac output and to the acquisition system. In this thesis, new parameters and alternative methodologies about liver perfusion CT are presented in order to investigate the cause of variability of this technique. Firstly analysis were made to assess the variability related to the mathematical model used to compute arterial Blood Flow (BFa) values. Results were obtained implementing algorithms based on “ maximum slope method” and “Dual input one compartment model” . Statistical analysis on simulated data demonstrated that the two methods are not interchangeable. Anyway slope method is always applicable in clinical context. Then variability related to TAC processing in the application of slope method is analyzed. Results compared with manual selection allow to identify the best automatic algorithm to compute BFa. The consistency of a Standardized Perfusion Index (SPV) was evaluated and a simplified calibration procedure was proposed. At the end the quantitative value of perfusion map was analyzed. ROI approach and map approach provide related values of BFa and this means that pixel by pixel algorithm give reliable quantitative results. Also in pixel by pixel approach slope method give better results. In conclusion the development of new automatic algorithms for a consistent computation of BFa and the analysis and definition of simplified technique to compute SPV parameter, represent an improvement in the field of liver perfusion CT analysis.

Content-aware approach for improving biomedical image analysis: an interdisciplinary study series

Biomedicine is a highly interdisciplinary research area at the interface of sciences, anatomy, physiology, and medicine. In the last decade, biomedical studies have been greatly enhanced by the introduction of new technologies and techniques for automated quantitative imaging, thus considerably advancing the possibility to investigate biological phenomena through image analysis. However, the effectiveness of this interdisciplinary approach is bounded by the limited knowledge that a biologist and a computer scientist, by professional training, have of each other’s fields. The possible solution to make up for both these lacks lies in training biologists to make them interdisciplinary researchers able to develop dedicated image processing and analysis tools by exploiting a content-aware approach. The aim of this Thesis is to show the effectiveness of a content-aware approach to automated quantitative imaging, by its application to different biomedical studies, with the secondary desirable purpose of motivating researchers to invest in interdisciplinarity. Such content-aware approach has been applied firstly to the phenomization of tumour cell response to stress by confocal fluorescent imaging, and secondly, to the texture analysis of trabecular bone microarchitecture in micro-CT scans. Third, this approach served the characterization of new 3-D multicellular spheroids of human stem cells, and the investigation of the role of the Nogo-A protein in tooth innervation. Finally, the content-aware approach also prompted to the development of two novel methods for local image analysis and colocalization quantification. In conclusion, the content-aware approach has proved its benefit through building new approaches that have improved the quality of image analysis, strengthening the statistical significance to allow unveiling biological phenomena. Hopefully, this Thesis will contribute to inspire researchers to striving hard for pursuing interdisciplinarity.

Inside standard and hyperspectral low-dose CT variational imaging: parameter identification and material decomposition

The main contribution of this thesis is the proposal of novel strategies for the selection of parameters arising in variational models employed for the solution of inverse problems with data corrupted by Poisson noise. In light of the importance of using a significantly small dose of X-rays in Computed Tomography (CT), and its need of using advanced techniques to reconstruct the objects due to the high level of noise in the data, we will focus on parameter selection principles especially for low photon-counts, i.e. low dose Computed Tomography. For completeness, since such strategies can be adopted for various scenarios where the noise in the data typically follows a Poisson distribution, we will show their performance for other applications such as photography, astronomical and microscopy imaging. More specifically, in the first part of the thesis we will focus on low dose CT data corrupted only by Poisson noise by extending automatic selection strategies designed for Gaussian noise and improving the few existing ones for Poisson. The new approaches will show to outperform the state-of-the-art competitors especially in the low-counting regime. Moreover, we will propose to extend the best performing strategy to the hard task of multi-parameter selection showing promising results. Finally, in the last part of the thesis, we will introduce the problem of material decomposition for hyperspectral CT, which data encodes information of how different materials in the target attenuate X-rays in different ways according to the specific energy. We will conduct a preliminary comparative study to obtain accurate material decomposition starting from few noisy projection data.