Digital breast tomosynthesis (DBT) is a new technology for breast cancer screening that promises to complement mammography or supersede it to become the standard for breast imaging. DBT involves taking multiple images in order to synthesize a new image that represents a slice through the breast volume -- hence the term tomosynthesis. The primary advantage of this paradigm is that it can reduce the amount of overlapping anatomy in the data, leading to improved visualization of potentially-cancerous findings. The difficulty in DBT is quantifying the advantages of the technology and determining the optimal conditions for its clinical use. This dissertation describes a virtual trial framework for assessing and optimizing DBT technology for the specific task of detecting small, low-contrast masses in the breast. It addresses each component of the imaging chain to some degree, from the patients/phantoms to the imaging hardware to the model observers used to measure signal detectability. The main focus, however, is on quantifying tradeoffs between three key parameters that affect image quality: (1) scan angle, (2) number of projections, and (3) exposure. We show that in low-density breast phantoms, detectability generally increases with both scan angle and number of projections in the anatomical-variability-limited (high-exposure) regime. We also investigate how breast density affects the optimal DBT scan parameters. We show task-specific results that support using an adaptive paradigm in DBT, where the imaging system reconfigures itself in response to information about the patient's breast density. The virtual framework described in this dissertation provides a platform for further investigations of image quality in 3D breast imaging.

A tomossíntese digital mamária (Digital Breast Tomosynthesis - DBT) é um exame radiográfico utilizado para o rastreamento do câncer de mama, que busca superar a limitação da sobreposição de tecidos existente na mamografia digital 2D. Nessa técnica são adquiridas projeções radiográficas em diferentes ângulos, que são processadas para a reconstrução do volume da mama. Um grande desafio é a elaboração dos algoritmos para a reconstrução tomográfica, visto que há um número limitado de projeções adquiridas com baixas doses de radiação, abrangendo uma estreita faixa de ângulo. Outro fator importante é o ruído presente nas imagens, que pode impactar o diagnóstico do câncer pelos radiologistas. Esse trabalho tem como objetivo apresentar uma ferramenta de reconstrução de imagens para DBT e fazer um estudo do comportamento do sinal e do ruído nas imagens reconstruídas. Os métodos analíticos de retroprojeção simples e filtrada, bem como os interativos de máxima verossimilhança e algébricos foram avaliados. A validação dos algoritmos de reconstrução foi feita por meio de métricas objetivas e as imagens reconstruídas foram comparadas com imagens obtidas a partir de um software de reconstrução para DBT desenvolvido pelo Food and Drug Administration (FDA). A partir das análises objetivas e visuais, demonstrou-se o potencial da ferramenta desenvolvida nesse trabalho. O ruído pós-reconstrução foi investigado através da aquisição de imagens de phantoms homogêneos, utilizando dois sistemas comerciais de DBT. As curvas de valor médio de pixel, a variância do ruído e a relação sinal-ruído seguiram o mesmo padrão já demonstrado para as projeções. A análise do espectro de potência do ruído demonstrou que o processo de reconstrução correlaciona o ruído para ambos os equipamentos. / Digital Breast Tomosynthesis (DBT) is a radiographic examination used for breast cancer screening, which seeks to overcome the tissue superposition in 2D digital mammography. In this technique, radiographic projections are acquired at different angles, which are processed for the reconstruction of the breast volume. A major challenge is the elaboration of algorithms for tomographic reconstruction since there are a limited number of projections acquired with low doses of radiation, covering a narrow-angle range. Another important factor is the noise present in this modality that can impact the diagnosis of tumors by radiologists. This work aims to present an image reconstruction toolbox for DBT and study the signal and noise behavior in the reconstructed slices. The analytical methods of simple and filtered back-projection, as well as the maximum likelihood and algebraic iterative methods were evaluated. The validation of the reconstruction algorithms was done by objective metrics and the reconstructed images were compared with the images obtained from a reconstruction software for DBT developed by the Food and Drug Administration (FDA). Through the objective and visual analysis, the potential of the toolbox developed in this work was demonstrated. The noise after reconstruction was investigated by means of the acquisition of homogeneous phantom images, using two commercial DBT systems. The mean pixel value, the noise variance and the signal-to-noise ratio follow the same curve shape already shown for the projection domain. The analysis of noise power spectrum demonstrated that the process of reconstruction correlates the noise for both systems used.

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<p>Breast cancer screening is currently performed by mammography, which is limited by overlying anatomy and dense breast tissue. Computer aided detection (CADe) systems can serve as a double reader to improve radiologist performance. Tomosynthesis is a limited-angle cone-beam x-ray imaging modality that is currently being investigated to overcome mammography's limitations. CADe systems will play a crucial role to enhance workflow and performance for breast tomosynthesis.</p><p>The purpose of this work was to develop unique CADe algorithms for breast tomosynthesis reconstructed volumes. Unlike traditional CADe algorithms which rely on segmentation followed by feature extraction, selection and merging, this dissertation instead adopts information theory principles which are more robust. Information theory relies entirely on the statistical properties of an image and makes no assumptions about underlying distributions and is thus advantageous for smaller datasets such those currently used for all tomosynthesis CADe studies.</p><p>The proposed algorithm has two 2 stages (1) initial candidate generation of suspicious locations (2) false positive reduction. Images were accrued from 250 human subjects. In the first stage, initial suspicious locations were first isolated in the 25 projection images per subject acquired by the tomosynthesis system. Only these suspicious locations were reconstructed to yield 3D Volumes of Interest (VOI). For the second stage of the algorithm false positive reduction was then done in three ways: (1) using only the central slice of the VOI containing the largest cross-section of the mass, (2) using the entire volume, and (3) making decisions on a per slice basis and then combining those decisions using either a linear discriminant or decision fusion. A 92% sensitivity was achieved by all three approaches with 4.4 FPs / volume for approach 1, 3.9 for the second approach and 2.5 for the slice-by-slice based algorithm using decision fusion.</p><p>We have therefore developed a novel CADe algorithm for breast tomosynthesis. The techniques uses an information theory approach to achieve very high sensitivity for cancer detection while effectively minimizing false positives.</p> / Dissertation

<p>Imaging respiratory induced tumor motion in the radiation therapy treatment room could eliminate the necessity for large motion encompassing margins that result in excessive irradiation of healthy tissues. Currently available image guidance technologies are ill-suited for this task. Two-dimensional fluoroscopic images are acquired with sufficient speed to image respiratory motion. However, volume information is not present, and soft tissue structures are often not visible because a large volume is projected onto a single plane. Currently available volumetric imaging modalities are not acquired with sufficient speed to capture full motion trajectory information. Four-dimensional cone-beam computed tomography (4D CBCT) using a gantry mounted 2D flat panel imaging device has been proposed but has been limited by high doses, long scan times and severe under-sampling artifacts. The focus of the work completed in this thesis was to find ways to improve 4D imaging using a gantry mounted 2D kV imaging system. Specifically, the goals were to investigate methods for minimizing imaging dose and scan time while achieving consistent, controllable, high quality 4D images.</p><p>First, we introduced four-dimensional digital tomosynthesis (4D DTS) and characterized its potential for 3D motion analysis using a motion phantom. The motion phantom was programmed to exhibit motion profiles with various known amplitudes in all three dimensions and scanned using a 2D kV imaging system mounted on a linear accelerator. Two arcs of projection data centered about the anterior-posterior and lateral axes were used to reconstruct phase resolved DTS coronal and sagittal images. Respiratory signals were obtained by analyzing projection data, and these signals were used to derive phases for each of the projection images. Projection images were sorted according to phase, and DTS phase images were reconstructed for each phase bin. 4D DTS target location accuracies for peak inhalation and peak exhalation in all three dimensions were limited only by the 0.5 mm pixel resolution for all DTS scan angles. The average localization errors for all phases of an assymetric motion profile with a 2 cm peak-to-peak amplitude were 0.68, 0.67 and 1.85 mm for 60 <super> o <super/> 4D DTS, 360<super> o <super/> CBCT and 4DCT, respectively. Motion artifacts for 4D DTS were found to be substantially less than those seen in 4DCT, which is the current clinical standard in 4D imaging. </p><p>We then developed a comprehensive framework for relating patient respiratory parameters with acquisition and reconstruction parameters for slow gantry rotation 4D DTS and 4D CBCT imaging. This framework was validated and optimized with phantom and lung patient studies. The framework facilitates calculation of optimal frame rates and gantry rotation speeds based on patient specific respiratory parameters and required temporal resolution (task dependent). We also conducted lung patient studies to investigate required scan angles for 4D DTS and achievable dose and scan times for 4D DTS and 4D CBCT using the optimized framework. This explicit and comprehensive framework of relationships allowed us to demonstrate that under-sampling artifacts can be controlled, and 4D CBCT images can be acquired using lower doses than previously reported. We reconstructed 4D CBCT images of three patients with accumulated doses of 4.8 to 5.7 cGy. These doses are three times less than the doses used for the only previously reported 4D CBCT investigation that did not report images characterized by severe under-sampling artifacts. </p><p>We found that scan times for 200<super> o <super/> 4D CBCT imaging using acquisition sequences optimized for reduction of imaging dose and under-sampling artifacts were necessarily between 4 and 7 minutes (depending on patient respiration). The results from lung patient studies concluded that scan times could be reduced using 4D DTS. Patient 4D DTS studies demonstrated that tumor visibility for the lung patients we studied could be achieved using 30<super> o <super/> scan angles for coronal views. Scan times for those cases were between 41 and 50 seconds. Additional dose reductions were also demonstrated. Image doses were between 1.56 and 2.13 cGy. These doses are well below doses for standard CBCT scans. The techniques developed and reported in this thesis demonstrate how respiratory motion can be imaged in the radiotherapy treatment room using clinically feasible imaging doses and scan times.</p> / Dissertation

A novel imaging technology, digital breast tomosynthesis (DBT), is a technique that overcomes the tissue superposition limitation of conventional mammography by acquiring a limited number of X-ray projections from a narrow angular range, and combining these projections to reconstruct a pseudo-3D image. The emergence of DBT as a potential replacement or adjunct to mammographic screening mandates that solutions be found to two of its major limitations, namely X-ray scatter and mono-energetic reconstruction methods. A multi-faceted software-based approach to enhance the image quality of DBT imaging has the potential to increase the sensitivity and specificity of breast cancer detection and diagnosis. A scatter correction (SC) algorithm and a spectral reconstruction (SR) algorithm are both ready for implementation and clinical evaluation in a DBT system and exhibit the potential to improve image quality. A principal component analysis (PCA) based model of breast shape and a PCA model of X-ray scatter optimize the SC algorithm for the clinical realm. In addition, a comprehensive dosimetric characterization of a FDA approved DBT system has also been performed, and the feasibility of a new dual-spectrum, single-acquisition DBT imaging technique has also been evaluated.

Conventional 2D mammography was the most effective approach to detecting early stage breast cancer in the past decades of years. Tomosynthetic breast imaging is a potentially more valuable 3D technique for breast cancer detection. The limitations of current tomosynthesis systems include a longer scanning time than a conventional digital X-ray modality and a low spatial resolution due to the movement of the single X-ray source. Dr.Otto Zhou's group proposed the concept of stationary digital breast tomosynthesis (s-DBT) using a Carbon Nano-Tube (CNT) based X-ray source array. Instead of mechanically moving a single X-ray tube, s-DBT applies a stationary X-ray source array, which generates X-ray beams from different view angles by electronically activating the individual source prepositioned at the corresponding view angle, therefore eliminating the focal spot motion blurring from sources. The scanning speed is determined only by the detector readout time and the number of sources regardless of the angular coverage spans, such that the blur from patient's motion can be reduced due to the quick scan. S-DBT is potentially a promising modality to improve the early breast cancer detection by providing decent image quality with fast scan and low radiation dose. DBT system acquires a limited number of noisy 2D projections over a limited angular range and then mathematically reconstructs a 3D breast. 3D reconstruction is faced with the challenges of cone-beam and flat-panel geometry, highly incomplete sampling and huge reconstructed volume. In this research, we investigated several representative reconstruction methods such as Filtered backprojection method (FBP), Simultaneous algebraic reconstruction technique (SART) and Maximum likelihood (ML). We also compared our proposed statistical iterative reconstruction (IR) with particular prior and computational technique to these representative methods. Of all available reconstruction methods in this research, our proposed statistical IR appears particularly promising since it provides the flexibility of accurate physical noise modeling and geometric system description. In the following chapters, we present multiple key techniques of statistical IR to tomosynthesis imaging data to demonstrate significant image quality improvement over conventional techniques. These techniques include the physical modeling with a local voxel-pair based prior with the flexibility in its parameters to fine-tune image quality, the pre-computed parameter κ incorporated with the prior to remove the data dependence and to achieve a predictable resolution property, an effective ray-driven technique to compute the forward and backprojection and an over-sampled ray-driven method to perform high resolution reconstruction with a practical region of interest (ROI) technique. In addition, to solve the estimation problem with a fast computation, we also present a semi-quantitative method to optimize the relaxation parameter in a relaxed order-subsets framework and an optimization transfer based algorithm framework which potentially allows less iterations to achieve an acceptable convergence. The phantom data is acquired with the s-DBT prototype system to assess the performance of these particular techniques and compare our proposed method to those representatives. The value of IR is demonstrated in improving the detectability of low contrast and tiny micro-calcification, in reducing cross plane artifacts, in improving resolution and lowering noise in reconstructed images. In particular, noise power spectrum analysis (NPS) indicates a superior noise spectral property of our proposed statistical IR, especially in the high frequency range. With the decent noise property, statistical IR also provides a remarkable reconstruction MTF in general and in different areas within a focus plane. Although computational load remains a significant challenge for practical development, combined with the advancing computational techniques such as graphic computing, the superior image quality provided by statistical IR will be realized to benefit the diagnostics in real clinical applications.

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<p>The developments of highly conformal and precise radiation therapy techniques promote the necessity of more accurate treatment target localization and tracking. On-board imaging techniques, especially the x-ray based techniques, have found a great popularity nowadays for on-board target localization and tracking. With an objective to improve the accuracy of on-board imaging for lung cancer patients, the dissertation work focuses on the investigations of using limited-angle on-board x-ray projections for image guidance. The limited-angle acquisition enables scan time and imaging dose reduction and improves the mechanical clearance of imaging.</p><p>First of all, the dissertation developed a phase-matched digital tomosynthesis (DTS) technique using limited-angle (<=30 deg) projections for lung tumor localization. This technique acquires the same traditional motion-blurred on-board DTS image as the 3D-DTS technique, but uses the planning 4D computed tomography (CT) to synthesize a phase-matched reference DTS to register with the on-board DTS for tumor localization. Of the 324 different scenarios simulated using the extended cardiac torso (XCAT) digital phantom, the phase-matched DTS technique localizes the 3D target position with an localization error of 1.07 mm (± 0.57 mm) (average ± standard deviation (S.D.)). Similarly, for the total 60 scenarios evaluated using the computerized imaging reference system (CIRS) 008A physical phantom, the phase-matched DTS technique localizes the 3D target position with an average localization error of 1.24 mm (± 0.87 mm). In addition to the phantom studies, preliminary clinical cases were also studied using imaging data from three lung cancer patients. Using the localization results of 4D cone beam computed tomography (CBCT) as `gold-standard', the phase-matched DTS techniques localized the tumor to an average localization error of 1.5 mm (± 0.5 mm). </p><p>The phantom and patient study results show that the phase-matched DTS technique substantially improved the accuracy of moving lung target localization, as compared to the 3D-DTS technique. The phase-matched DTS technique can provide accurate lung target localizations like 4D-DTS, but with much reduced imaging dose and scan time. The phase-matched DTS technique is also found more robust, being minimally affected by variations of respiratory cycle lengths, fractions of respiration cycle contained within the DTS scan and the scan directions, which potentially enables quasi-instantaneous (within a sub-breathing cycle) moving target verification during radiation therapy, preferably arc therapy.</p><p>Though the phase-matched DTS technique can provide accurate target localization under normal scenarios, its accuracy is limited when the patient on-board breathing experiences large variations in motion amplitudes. In addition, the limited-angle based acquisition leads to severe structural distortions in DTS images reconstructed by the current clinical gold-standard Feldkamp-Davis-Kress (FDK) reconstruction algorithm, which prohibit accurate target deformation tracking, delineation and dose calculation. </p><p>To solve the above issues, the dissertation further developed a prior knowledge based image estimation technique to fundamentally change the landscape of limited-angle based imaging. The developed motion modeling and free-form deformation (MM-FD) method estimates high quality on-board 4D-CBCT images through applying deformation field maps to existing prior planning 4D-CT images. The deformation field maps are solved using two steps: first, a principal component analysis based motion model is built using the planning 4D-CT (motion modeling). The deformation field map is constructed as an optimized linear combination of the extracted motion modes. Second, with the coarse deformation field maps obtained from motion modeling, a further fine-tuning process called free-form deformation is applied to further correct the residual errors from motion modeling. Using the XCAT phantom, a lung patient with a 30 mm diameter tumor was simulated to have various anatomical and respirational variations from the planning 4D-CT to on-board 4D-CBCTs, including respiration amplitude variations, tumor size variations, tumor average position variations, and phase shift between tumor and body respiratory cycles. The tumors were contoured in both the estimated and the `ground-truth' on-board 4D-CBCTs for comparison. 3D volume percentage error (VPE) and center-of-mass error (COME) were calculated to evaluate the estimation accuracy of the MM-FD technique. For all simulated patient scenarios, the average (± S.D.) VPE / COME of the tumor in the prior image without image estimation was 136.11% (± 42.76%) / 15.5 mm (± 3.9 mm). Using orthogonal-view 30 deg scan angle, the average VPE/COME of the tumors in the MM-FD estimated on-board images was substantially reduced to 5.22% (± 2.12%) / 0.5 mm (± 0.4 mm). </p><p>In addition to XCAT simulation, CIRS phantom measurements and actual patient studies were also performed. For these clinical studies, we used the normalized cross-correlation (NCC) as a new similarity metric and developed an updated MMFD-NCC method, to improve the robustness of the image estimation technique to the intensity mismatches between CT and CBCT imaging systems. Using 4D-CBCT reconstructed from fully-sampled on-board projections as `gold-standard', for the CIRS phantom study, the average (± S.D.) VPE / COME of the tumor in the prior image and the tumors in the MMFD-NCC estimated images was 257.1% (± 60.2%) / 10.1 mm (± 4.5 mm) and 7.7% (± 1.2%) / 1.2 mm (± 0.2mm), respectively. For three patient cases, the average (± S.D.) VPE / COME of tumors in the prior images and tumors in the MMFD-NCC estimated images was 55.6% (± 45.9%) / 3.8 mm (± 1.9 mm) and 9.6% (± 6.1%) / 1.1 mm (± 0.5 mm), respectively. With the combined benefits of motion modeling and free-form deformation, the MMFD-NCC method has achieved highly accurate image estimation under different scenarios. </p><p>Another potential benefit of on-board 4D-CBCT imaging is the on-board dose calculation and verification. Since the MMFD-NCC estimates the on-board 4D-CBCT through deforming prior 4D-CT images, the 4D-CBCT inherently has the same image quality and Hounsfield unit (HU) accuracy as 4D-CT and therefore can potentially improve the accuracy of on-board dose verification. Both XCAT and CIRS phantom studies were performed for the dosimetric study. Various inter-fractional variations featuring patient motion pattern change, tumor size change and tumor average position change were simulated from planning CT to on-board images. The doses calculated on the on-board CBCTs estimated by MMFD-NCC (MMFD-NCC doses) were compared to the doses calculated on the `gold-standard' on-board images (gold-standard doses). The absolute deviations of minimum dose (DDmin), maximum dose (DDmax), mean dose (DDmean) and prescription dose coverage (DV100%) of the planning target volume (PTV) were evaluated. In addition, 4D on-board treatment dose accumulations were performed using 4D-CBCT images estimated by MMFD-NCC in the CIRS phantom study. The accumulated doses were compared to those measured using optically stimulated luminescence (OSL) detectors and radiochromic films. </p><p>The MMFD-NCC doses matched very well with the gold-standard doses. For the XCAT phantom study, the average (± S.D.) DDmin, DDmax, DDmean and DV100% (values normalized by the prescription dose or the total PTV volume) between the MMFD-NCC PTV doses and the gold-standard PTV doses were 0.3% (± 0.2%), 0.9% (± 0.6%), 0.6% (± 0.4%) and 1.0% (± 0.8%), respectively. Similarly, for the CIRS phantom study, the corresponding values between the MMFD-NCC PTV doses and the gold-standard PTV doses were 0.4% (± 0.8%), 0.8% (± 1.0%), 0.5% (± 0.4%) and 0.8% (± 0.8%), respectively. For the 4D dose accumulation study, the average (± S.D.) absolute dose deviation (normalized by local doses) between the accumulated doses and the OSL measured doses was 3.0% (± 2.4%). The average gamma index (3%/3mm) between the accumulated doses and the radiochromic film measured doses was 96.1%. The MMFD-NCC estimated 4D-CBCT enables accurate on-board dose calculation and accumulation for lung radiation therapy under different scenarios. It can potentially be valuable for treatment quality assessment and adaptive radiation therapy.</p><p>However, a major limitation of the estimated 4D-CBCTs above is that they can only capture inter-fractional patient variations as they were acquired prior to each treatment. The intra-treatment patient variations cannot be captured, which can also affect the treatment accuracy. In light of this issue, an aggregated kilo-voltage (kV) and mega-voltage (MV) imaging scheme was developed to enable intra-treatment imaging. Through using the simultaneously acquired kV and MV projections during the treatment, the MMFD-NCC method enabled 4D-CBCT estimation using combined kV and MV projections. </p><p>For all XCAT-simulated patient scenarios, the average (± S.D.) VPE / COME of the tumor in the prior image and tumors in the MMFD-NCC estimated images (using kV + open field MV) was 136.11% (± 42.76%) / 15.5 mm (± 3.9 mm) and 4.5% (± 1.9%) / 0.3 mm (± 0.4 mm), respectively. In contrast, the MMFD-NCC estimation using kV + beam's eye view (BEV) MV projections yielded results of 4.3% (± 1.5%) / 0.3 mm (± 0.3 mm). The kV + BEV MV aggregation can estimate the target as accurately as the kV + open field MV aggregation. The impact of this study is threefold: 1. the kV and MV projections can be acquired at the same time. The imaging time will be cut to half as compared to the cases which use kV projections only. 2. The kV and MV aggregation enables intra-treatment imaging and target tracking, since the MV projections can be the side products of the treatment beams (BEV MV). 3. As the BEV MV projections originate from the treatment beams, there will be no extra MV imaging dose to the patient.</p><p>The above introduced 4D-CBCT estimation techniques were all based on limited-angle acquisition. Though limited-angle acquisition enables substantial scan time and dose reduction as compared to the full-angle scan, it is still not real-time and cannot provide `cine' imaging, which refers to the instantaneous imaging with negligible scan time and imaging dose. Cine imaging is important in image guided radiation therapy practice, considering the respirational variations may occur quickly and frequently during the treatment. For instance, the patient may experience a breathing baseline shift after every respiratory cycle. The limited-angle 4D-CBCT approach still requires a scan time of multiple respiratory cycles, which will not be able to capture the baseline shift in a timely manner. </p><p>In light of this issue, based on the previously developed MMFD-NCC method, an AI-FD-NCC method was further developed to enable quasi-cine CBCT imaging using extremely limited-angle (<=6 deg) projections. Using pre-treatment 4D-CBCTs acquired just before the treatment as prior information, AI-FD-NCC enforces an additional prior adaptive constraint to estimate high quality `quasi-cine' CBCT images. Two on-board patient scenarios: tumor baseline shift and continuous motion amplitude change were simulated through the XCAT phantom. Using orthogonal-view 6 deg projections, for the baseline shift scenario, the average (± S.D.) VPE / COME of the tumors in the AI-FD-NCC estimated images was 1.3% (± 0.5%) / 0.4 mm (± 0.1 mm). For the amplitude variation scenario, the average (± S.D.) VPE / COME of the tumors in the AI-FD-NCC estimated images was 1.9% (± 1.1%) / 0.5 mm (± 0.2 mm). The impact of this study is three-fold: first, the quasi-cine CBCT technique enables actual real-time volumetric tracking of tumor and normal tissues. Second, the method enables real-time tumor and normal tissues dose calculation and accumulation. Third, the high-quality volumetric images obtained can potentially be used for real-time adaptive radiation therapy.</p><p>In summary, the dissertation work uses limited-angle on-board x-ray projections to reconstruct/estimate volumetric images for lung tumor localization, delineation and dose calculation. Limited-angle acquisition reduces imaging dose, scan time and improves imaging mechanical clearance. Using limited-angle projections enables continuous, sub respiratory-cycle tumor localization, as validated in the phase-matched DTS study. The combination of prior information, motion modeling, free-form deformation and limited-angle on-board projections enables high-quality on-board 4D-CBCT estimation, as validated by the MM-FD / MMFD-NCC techniques. The high-quality 4D-CBCT not only can be applied for accurate target localization and delineation, but also can be used for accurate treatment dose verification, as validated in the dosimetric study. Through aggregating the kV and MV projections for image estimation, intra-treatment 4D-CBCT imaging was also proposed and validated for its feasibility. At last, the introduction of more accurate prior information and additional adaptive prior knowledge constraints also enables quasi-cine CBCT imaging using extremely-limited angle projections. The dissertation work contributes to lung on-board imaging in many aspects with various approaches, which can be beneficial to the future lung image guided radiation therapy practice.</p> / Dissertation

Bröst består av mjölkkörtlar, subkutant fett och bindväv. Det finns också kärl och lymfa i brösten. Både män och kvinnor har bröst. Olika sjukdomar kan drabba brösten av benigna och maligna slag. Den mest använda undersökningsmetoden för att upptäcka bröstcancer är mammografi. Vid ytterligare undersökning av brösten kan digital bröst-tomosyntes (DBT) förekomma. DBT är en sorts begränsad vinkel-tomografi som producerar bilder på brösten i sektioner. Åsikter om DBT är motstridiga, en del studier säger att tomosyntes är bättre än mammografi gällande sensitivitet och andra säger att det är sämre eller ekvivalent. För att få kunskap om tomosyntes, mammografi och vad som skiljer i sensitivitet krävs det en sammanfattning av olika studier. Syftet med studien är att jämföra sensitivitet vid bröstundersökningar inom mammografi och tomosyntes. Via en systematisk litteraturstudie sammanfattas ett resultat utifrån kvantitativa artiklar som kvalitetsgranskas och analyseras. Arbetet har genomgått en etisk egengranskning. Resultatet skapades via hypotesprövning och SPSS och de påvisar att det finns en signifikant skillnad i sensitivitet mellan DBT och mammografi vilket innebär att DBT har högre sensitivitet sett till medelvärde och median.