Quantitative Statistical Methods for Image Quality Assessment

Quantitative measures of image quality and reliability are critical for both qualitative interpretation and quantitative analysis of medical images. While, in theory, it is possible to analyze reconstructed images by means of Monte Carlo simulations using a large number of noise realizations, the associated computational burden makes this approach impractical. Additionally, this approach is less meaningful in clinical scenarios, where multiple noise realizations are generally unavailable. The practical alternative is to compute closed-form analytical expressions for image quality measures. The objective of this paper is to review statistical analysis techniques that enable us to compute two key metrics: resolution (determined from the local impulse response) and covariance. The underlying methods include fixed-point approaches, which compute these metrics at a fixed point (the unique and stable solution) independent of the iterative algorithm employed, and iteration-based approaches, which yield results that are dependent on the algorithm, initialization, and number of iterations. We also explore extensions of some of these methods to a range of special contexts, including dynamic and motion-compensated image reconstruction. While most of the discussed techniques were developed for emission tomography, the general methods are extensible to other imaging modalities as well. In addition to enabling image characterization, these analysis techniques allow us to control and enhance imaging system performance. We review practical applications where performance improvement is achieved by applying these ideas to the contexts of both hardware (optimizing scanner design) and image reconstruction (designing regularization functions that produce uniform resolution or maximize task-specific figures of merit)

Optimization-based reconstruction with an image-total-variation constraint in PET

In an emission imaging method, emission imaging data are acquired for a subject using an emission imaging scanner (10) including radiation detectors (12). The emission imaging data are reconstructed to generate a reconstructed image by executing a constrained optimization program including a measure of data fidelity between the acquired emission imaging data an a reconstruct-image transformed by a data model of the imaging scanner to emission imaging data. During the reconstructing, each iteration of the constrained optimization program is constrained by an image variability constraint. The reconstructed image is displayed the reconstructed image on a display device. The emission imaging may be positron emission tomography (PET) imaging data, optionally acquired using a sparse detector array. The image variability constraint may be a constraint that an image total variation (image TV) of a latent image defined using a Gaussian blurring matrix be less than a maximum value

Exploring novel ways to improve the MRI-based image segmentation in the head region

Accurate electron density information is extremely important in positron emission tomography (PET) attenuation correction (AC) and radiotherapy (RT) treatment planning (RTP), especially in the head region, as many interesting brain regions are located near the skull. Achieving good electron density information for bone is not trivial when magnetic resonance imaging (MRI) is used as a source for the anatomical structures of the head, since many MRI sequences show bone in a similar fashion as air. Various atlas-based, emission-based, and segmentation-based methods have been explored to address this problem. In this PhD project, a pipeline for MRI-based substitute CT (sCT) creation is developed and novel ways are developed to further improve the quality of bone delineation in the head region. First, a robust sCT pipeline is developed and validated. This allows modular improvements of the various aspects of head sCT in later publications. The MRI image is segmented into different tissue classes and the final sCT image is constructed from these. The sCT images had good image quality with small non-systematic error. The time-of-flight (TOF) information improves the accuracy of PET reconstruction. The effect of TOF with different AC maps is evaluated to substantiate the need for accurate AC maps for a TOF capable system. The evaluation is performed on both subject and brain region level. While TOF information is helpful, it cannot negate the effect of the AC map quality. The sinus region is problematic in MRI-based sCT creation, as it is easily segmented as bone. Two new methods for addressing AC in the sinus region are presented. One method tries to find the cuboid that covers the largest area of air tissue incorrectly assigned as bone and then correct the incorrect attenuation coefficient. Another method uses the sinus covering cuboid in the normalized space, from which it is converted back to each subject’s individual space, after which the attenuation coefficients are calculated. Both methods improve the alignment of sCT and CT images. Finally, the possibilities of improving the quality of the bone segmentation by utilizing a random forest (RF) machine learning process is explored. The RF model is used to estimate the bone likelihood. The likelihood is then used to enhance the bone segmentation and to model the attenuation coefficient. The machine learning model improves the bone segmentation and reduces the error between sCT and CT images.Tutkimus uusien pään alueen MRI-kuvantamiseen pohjautuvien kuvasegmentaatiomenetelmien kehittämisestä. Tarkka elektronitiheystieto on hyvin tärkeää PET-kuvantamisen vaimenemiskorjauksessa sekä sädehoidon suunnittelussa erityisesti pään alueella, sillä useat kiinnostavat aivoalueet ovat kallon lähellä. Hyvän elektronitiheystiedon laskeminen luulle ei ole yksinkertaista MRI-kuvantamisen pohjalta, sillä useat MRI-sekvenssit näyttävät luut samoin kuin ilman. Useita atlas-, emissio- ja segmentointipohjaisia metodeja on tutkittu tämän ongelman ratkaisemiseksi. Tässä työssä MRI-pohjainen luotiin menetelmä MRI-pohjaisten vaihtoehto-TT-kuvien (sCT) laskemiseksi, sekä kehitetään uusia tapoja parantaa luun MRI-pohjaista erottelukykyä pään alueella. Ensin kehitettiin ja validoitiin sCT-menetelmä. Tämä mahdollisti modulaaristen parannusten lisäämiseen sCT-menetelmään tutkimuksen myöhemmissä vaiheissa. MRI-kuva segmentoidaan eri kudosluokkiin, ja sCT-kuva lasketaan niiden pohjalta. Näin saaduissa sCT-kuvissa oli hyvä kuvanlaatu pienin epäsystemaattisin virhein. PET-kuvantamisessa fotonin lentoaikatieto (TOF) parantaa PET-rekonstruktion tark-kuutta. Tämän parantumisen määrää tutkittiin eri vaimenemiskartoilla fotonin lento-aikaa mittaavien PET-kameroiden vaimenemiskartan laatuvaatimuksien arvioimiseksi. TOF-tieto ei kokonaan pysty poistamaan vaimenemiskartan laadun vaikutusta. Sinusten alue on ongelmallinen MRI-pohjaisessa sCT-kuvan luomisessa, sillä segmentointimenetelmät määrittävät sen usein luuksi. Kaksi uutta menetelmää esiteltiin sinusten alueen PET-kuvantamisen vaimenemiskarttojen laskentaan. Ensimmäinen menetelmä yrittää löytää sellaisen suorakulmaisen särmiön, joka kattaisi suurimman mahdollisen alueen ilmaa, joka on väärin segmentoitu luuksi, ja sitten korjata tämän alueen vaimenemiskertoimen. Toinen menetelmä asettaa suorakulmaisen särmiön normalisoituun kuva-avaruuteen, josta se käännetään takaisin kunkin henkilön omaan yksilölliseen kuva-avaruuteen, minkä jälkeen vaimenemiskertoimet määritetään. Molemmat menetelmät parantavat TT-kuvien ja sCT-kuvien vastaavuutta. Lopuksi tarkasteltiin mahdollisuuksia käyttää koneoppimista ja satunnaismetsäalgoritmeja luun segmentoinnin parantamiseen. Satunnaismetsäalgoritmia käytetään laskemaan ennusteita kunkin kuvapisteen luutodennäköisyydelle. Luutodennäköisyyksiä käytetään luun segmentaation parantamiseen sekä luun tiheyden arviointiin. Kone-oppimispohjainen malli parantaa luun segmentoinnin laatua, sekä vähentää virheitä TT-kuvien ja sCT-kuvien välillä

Learning Algorithms for Fat Quantification and Tumor Characterization

Obesity is one of the most prevalent health conditions. About 30% of the world\u27s and over 70% of the United States\u27 adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice

Advanced Imaging Techniques for Cardiovascular Research

Objectives: In this thesis we addressed some of those difficulties by exploring new applications of a 68Galabeled radiotracer (68Ga-DOTA). 68Ga can be obtained from a 68Ge/68Ga generator and has a half-life of 68 minutes, which makes it a convenient candidate for its widespread clinical use. We proposed and validated the use of 68Ga-DOTA as a radiotracer for assessment of myocardial blood flow (MBF), myocardial viability and pulmonary blood flow (PBF). Additionally, we introduced a new methodology to perform a PET scan in which this tracer could be coinjected simultaneously with some other radiotracers such as 18FDG (multi-tracer PET). Lastly, we developed an automatic detector able to perform blood spectroscopy analysis, which offered the possibility to perform multi-tracer PET with minimal human intervention. Methods To test the capability of 68Ga-DOTA to measure MBF, viability and PBF, different groups of Large White pigs underwent PET/CT scans using 68Ga-DOTA as the injected radiotracer. For PBF studies, a group of healthy pigs (n = 4) were scanned under rest conditions. For MBF studies, a group of 8 pigs were scanned under rest and pharmacologically-induced stress in order to perform rest/stress tests, as it is done for humans in clinical routine. Additionally, a group of 5 pigs were scanned 7 days after the induction of a myocardial infarction (MI) to assess viability and MBF in a MI model. MBF, extracellular volume fraction (ECV, for viability assessment) and PBF maps were obtained after fitting the dynamic PET images to the corresponding pharmacokinetic model followed by 68Ga-DOTA in each tissue under study. Global and regional perfusion maps for the myocardial tissue (MBF) and lungs (PBF) were obtained. For validation purposes, the “goldstandard” technique used in tissue perfusion quantification (fluorescent-labeled microspheres (MS)) was simultaneosly performed along with the PET/CT scans. The blood sampling spectroscopic methodology was evaluated and calibrated in vitro using different 68Ga/18F mixtures. Then, it was tested in pigs (n = 3) injected with 68Ga-DOTA and 18FDG in the same acquisition. The activity concentration of each radiotracer in myocardial tissue was subsequently measured ex vivo. The automatic blood sampling detector was built from scratch and characterized using a catheter filled with different 68Ga/18F mixtures. Finally, it was additionally evaluated in vivo in n = 3 pigs under conditions resembling to those encountered in clinical routine. Results Regarding MBF quantification and validation with 68Ga-DOTA-PET, a strong correlation (r = 0.91) between MBF measured with PET and MS was obtained (slope = 0.96 ± 0.10, y-intercept = 0.11 ± 0.19 ml·min−1·g−1). For the myocardial infarction model, MBF values obtained with 68Ga-DOTA-PET in the infarcted area (LAD, left anterior descendant) were significantly reduced in comparison to remote ones LCX (left circumflex artery, p < 0.0001) and RCA (right coronary artery, p < 0.0001). In addition, 68Ga-DOTA-PET detected a significant ECV increase in the infarcted area (p < 0.0001). The correlation evaluation between 68Ga-DOTA-PET and MS as a PBF radiotracer also showed a good and significant correlation (r = 0.74, p < 0.0001). The gamma spectroscopic analysis on blood samples proposed for multi-tracer PET imaging was also succesfully validated, showing a correlation of r = 0.95 (p < 0.0001) for 18FDG concentration in myocardium measured with multi-tracer PET and by ex vivo validation. The blood sampling detector was able to measure the arterial input function in pigs in an experimental setup under realistic conditions. Discussion and conclusions 68Ga-DOTA-PET allowed accurate non-invasive assessment of MBF and ECV in pigs with myocardial infarction and under rest-stress conditions. This technique could provide wide access to quantitative measurement of both MBF and ECV with PET imaging. 68Ga-DOTA-PET was also demonstrated to be a potential inexpensive method for measuring PBF in clinical settings. As for multi-tracer PET imaging, the proposed methodology allowed explicit measurement of separate arterial input functions, offering very similar results to those obtained as a reference from the ex vivo analysis of the tissue under evaluation. Finally, a novel blood sampling device was developed and characterized, showing performance parameters similar to other devices in the literature. Noteworthy, this detector has the additional and unique feature of allowing us to perform multi-tracer PET by means of a gamma spectroscopic analysis of the blood flowing between its detection blocks. All the results summarized in this abstract may contribute to spread the use of PET in clinical routine, either by the clinical use of 68Ga-DOTA as an inexpensive but accurate radiotracer for MBF, PBF or viability assessment, or by the implementation of multi-tracer PET, which could lead to cost reduction of PET examinations by shortening the scanning time and eliminating misalignment inaccuracies. This multi-tracer PET methodology could also be safely implemented using our proposed automated device that permits to perform the gamma spectroscopic analysis on blood samples with minimal human intervention

Exercise training-induced effects on brown and white adipose tissue metabolism in humans : positron emission tomography studies in health and insulin resistance

White adipose tissue (WAT) not only serves as a passive energy storage but also has an endocrine role releasing hormones that play a major role in the regulation of whole-body glucose homeostasis and insulin sensitivity. Active brown adipose tissue (BAT) is able to consume lipids and glucose to generate heat. The dysregulation of BAT and WAT may predispose a person to become obese and insulin resistant. Exercise training is established to reduce fat mass and insulin resistance. Some of the exercise-induced benefits may be dose-specific. However, only a few studies exist examining the effects of training on BAT metabolism in humans that are cross-sectional in nature and the results are contradictory. There are no controlled prospective intervention studies that have investigated exercise-induced effects on BAT metabolism directly in humans. Furthermore, there is no clear evidence that exercise improves WAT metabolism. The aim of this thesis was to investigate the effects of short-term (2wks) exercise training with either, sprint interval training (SIT) or moderate intensity continuous training (MICT) on BAT and WAT metabolism in middle-aged sedentary healthy (BMI 26.1±2.4; age 48±5) and insulin resistant (IR) subjects (BMI 30.1±2.5; age 49±4). Further, the effects of longer term (6wks) progressive endurance and resistance exercise training on cold-induced BAT metabolism in healthy men (BMI 23±0.9; age31±7) were studied. BAT and WAT glucose and free fatty acid was determined using positron emission tomography (PET). The results show that modifications after exercise training are not only adipose tissue depot-specific but also the type of exercise (SIT vs MICT) induces different responses. Training decreased insulin stimulated BAT glucose uptake but had no effect on cold stimulated BAT glucose uptake in healthy subjects. At baseline IR had impaired WAT GU compared to healthy subjects which normalized after training. SIT improves WAT insulin resistance while MICT decreases WAT free fatty acid metabolism in IR. This suggests that different adipose tissue depots respond differently to the metabolic demands of exercise training. Moreover, intensity affects different substrate uptake from WAT. This data suggests that changes in adipose tissue metabolism may help whole body insulin action. Overall, exercise-induced BAT and WAT adaptations provide potential therapeutic targets for obesity and type 2 diabetes

Internal radionuclide dosimetry of model and patient based voxelised phantoms using the GATE toolkit

The desire for realistic patient specific dosimetry estimates of internally distributed radioactivity are realised by using Monte Carlo simulations of voxelised phantoms. The purpose of this thesis was to validate the GATE Monte Carlo package as a dosimetry tool and to investigate the accurate application of model and patient specific voxelised phantoms. Validation of the GATE Monte Carlo package was performed by simulating the absorbed fractions of simple geometric spheres of uniform radioactivity compared to accepted values. Voxelised spheres have also been simulated and it was found that the GATE Low Energy physics package was the most suitable for simulations of voxelised phantoms. The simulation of the scalable XCAT voxelised phantom has been performed to evaluate the effect of voxel size and patient organ mass on the calculation of dose factors. It was found that for organ self-irradiation significantly small voxels are required to ensure that insufficient voxel sampling does not effect the absorbed dose calculation. A retrospective absorbed dose calculation of true patient images was then performed with a correction for insufficient voxel sampling. In this work the scalable XCAT phantom has also been used to show that a voxel size of 2 mm or less is suitable for accurate calculations of organ cross dose. By comparing the scaled XCAT phantoms with patient and traditional phantoms it was concluded that considerable care is required when adapting model-based phantom results to individual patients. As differences in patient anatomy contribute significant variability to the dosimetry calculation, it is therefore recommended that where available individual patient specific dosimetry should be calculated using direct Monte Carlo simulation in favor of organ mass scaling