| 1. |
- Nordanskog, Pia, 1971-
(författare)
-
On electroconvulsive therapy in depression : Clinical, cognitive and neurobiological aspects
- 2015
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Electroconvulsive therapy (ECT) is used worldwide to treat severe mental disorders. The most common mental disorder, and the third leading cause of disease burden in the world is depression. The clinical efficacy of ECT for severe depression is well-established. However, both the pathophysiology of depression and the mechanism of action of ECT remain elusive.The main aims of this thesis are to address the following issues: 1) the use and practice of ECT in Sweden has not been systematically evaluated since 1975, 2) cognitive side-effects (memory disturbances) are a major concern with ECT and 3) the mechanism of action of ECT remain elusive. The neurobiological aspects of ECT focus on two hypotheses. First, the recent years´ preclinical studies that have provided evidence that ECT induces hippocampal cell proliferation, including neurogenesis. Second, that enhanced functional inhibition of neuronal activity is a key feature.Current use and practice of ECT in Sweden (paper I) is based on data from the national quality register for ECT, the mandatory patient register of the National Board of Health and Welfare and a survey. Treated person rate (TPR) in Sweden 2013 was found to be 41 individuals / 100 000, and thus unchanged since the latest systematic investigation in Sweden 1975. In more than 70% of treatment series the indication was a depressive episode. The selection of patients for ECT and treatment technique in Sweden was similar to that in other western countries, but the consent procedure and the involvement of nurses and nursing assistants in the delivery of ECT differ. Data also shows that there is room for improvement in both the specificity of use and availability of ECT.The second study in this thesis is a longitudinal observational trial where 12 (paper II and III) and 14 (paper IV) patients with depression referred for ECT were investigated. Patients underwent a 3 T MRI structural scanning and DSC-MRI perfusion, a neuropsychological test battery and clinical ratings before ECT, within one to two weeks after ECT and after 6 and 12 months. In line with preclinical findings and the plasticity hypothesis of mechanism of action of ECT, the hippocampal volume increased after ECT in patients with depression. However, this increase was transient and returned to baseline levels within 6 months. No correlation was found between volumetric changes and clinical effect or cognitive outcome. Instead our results suggested an association to the number of treatments, without relation to the side of stimulation. A right-sided decrease in frontal blood flow distinguished remission from non-remission after ECT. There were significant impairments in verbal episodic memory and verbal fluency within one week after ending the ECT course, but these impairments were transient and no persistent cognitive impairments were seen during the follow-up.In summary, this thesis present the first update on the use and practice of ECT in Sweden in the last 40 years as well as a pioneering MRI-study on the hippocampal volume increase in the treatment of depression with ECT. Supportive to earlier findings we also found the cognitive side-effects that are measurable after ECT to be transient. Furthermore, we found that a decreased frontal blood flow is of importance for the anti-depressive response to ECT.
|
|
| 2. |
- Ahle, Margareta, 1966-
(författare)
-
Necrotising Enterocolitis : epidemiology and imaging
- 2017
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Necrotising enterocolitis (NEC) is a potentially devastating intestinal inflammation of multifactorial aetiology in premature or otherwise vulnerable neonates. Because of the broad spectrum of presentations, diagnosis and timing of surgical intervention may be challenging, and imaging needs to be an integrated part of management.The first four studies included in this thesis used routinely collected, nationwide register data to describe the incidence of NEC in Sweden 1987‒2009, its variation with time, seasonality, space-time clustering, and associations with maternal, gestational, and perinatal factors, and the risk of intestinal failure in the aftermath of the disease.Early infant survival increased dramatically during the study period. The incidence rate of NEC was 0.34 per 1,000 live births, rising from 0.26 per 1,000 live births in the first six years of the study period to 0.57 in the last five. The incidence rates in the lowest birth weights were 100‒160 times those of the entire birth cohort. Seasonal variation was found, as well as space-time clustering in association with delivery hospitals but not with maternal residential municipalities.Comparing NEC cases with matched controls, some factors, positively associated with NEC, were isoimmunisation, fetal distress, caesarean section, persistent ductus arteriosus, cardiac and gastrointestinal malformations, and chromosomal abnormalities. Negative associations included maternal pre-eclampsia, maternal urinary infection, and premature rupture of the membranes. Intestinal failure occurred in 6% of NEC cases and 0.4% of controls, with the highest incidence towards the end of the study period.The last study investigated current practices and perceptions of imaging in the management of NEC, as reported by involved specialists. There was great consensus on most issues. Areas in need of further study seem mainly related to imaging routines, the use of ultrasound, and indications for surgery.Developing alongside the progress of neonatal care, NEC is a complex, multifactorial disease, with shifting patterns of predisposing and precipitating causes, and potentially serious long-term complications. The findings of seasonal variation, spacetime clustering, and negative associations with antenatal exposure to infectious agents, fit into the growing understanding of the central role of bacteria and immunological processes in normal maturation of the intestinal canal as well as in the pathogenesis of NEC. Imaging in the management of NEC may be developed through future studies combining multiple diagnostic parameters in relation to clinical outcome.
|
|
| 3. |
- Blystad, Ida, 1972-
(författare)
-
Clinical Applications of Synthetic MRI of the Brain
- 2017
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Magnetic Resonance Imaging (MRI) has a high soft-tissue contrast with a high sensitivity for detecting pathological changes in the brain. Conventional MRI is a time-consuming method with multiple scans that relies on the visual assessment of the neuroradiologist. Synthetic MRI uses one scan to produce conventional images, but also quantitative maps based on relaxometry, that can be used to quantitatively analyse tissue properties and pathological changes. The studies presented here apply the use of synthetic MRI of the brain in different clinical settings.In the first study, synthetic MR images were compared to conventional MR images in 22 patients. The contrast, the contrast-to-noise ratio, and the diagnostic quality were assessed. Image quality was perceived to be inferior in the synthetic images, but synthetic images agreed with the clinical diagnoses to the same extent as the conventional images.Patients with early multiple sclerosis were analysed in the second study. In patients with multiple sclerosis, contrast-enhancing white matter lesions are a sign of active disease and can indicate a need for a change in therapy. Gadolinium-based contrast agents are used to detect active lesions, but concern has been raised regarding the long-term effects of repeated use of gadolinium. In this study, relaxometry was used to evaluate whether pre-contrast injection tissue-relaxation rates and proton density can identify active lesions without gadolinium. The findings suggest that active lesions often have relaxation times and proton density that differ from non-enhancing lesions, but with some overlap. This makes it difficult to replace gadolinium-based contrast agent injection with synthetic MRI in the monitoring of MS patients.Malignant gliomas are primary brain tumours with contrast enhancement due to a defective blood-brain barrier. However, they also grow in an infiltrative, diffuse manner, making it difficult to clearly delineate them from surrounding normal brain tissue in the diagnostic workup, at surgery, and during follow-up. The contrast-enhancing part of the tumour is easily visualised, but not the diffuse infiltration. In studies three and four, synthetic MRI was used to analyse the peritumoral area of malignant gliomas, and revealed quantitative findings regarding peritumoral relaxation changes and non-visible contrast enhancement suggestive of non-visible infiltrative tumour growth.In conclusion, synthetic MRI provides quantitative information about the brain tissue and this could improve the diagnosis and treatment for patients.
|
|
| 4. |
- De Geer, Jakob, 1970-
(författare)
-
On the use of computed tomography in cardiac imaging
- 2016
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundCardiac Computed Tomography Angiography (CCTA) is becoming increasingly useful in the work‐up of coronary artery disease (CAD). Several potential methods for increasing the diagnostic yield of cardiac CT are available.Purpose Study I: To investigate whether the use of a 2‐D, non‐linear adaptive noise reduction filter can improve CCTA image quality.Study II: To evaluate the variation in adenosine stress dynamic CT perfusion (CTP) blood flow as compared to stress 99mTc SPECT. Secondly, to compare the perfusion results from manual and automatic myocardial CTP segmentation.Study III: To evaluate the accuracy of non‐invasive, CCTA‐derived Fractional Flow Reserve (cFFR).Study IV: To evaluate the prognostic value of CCTA in terms of major adverse cardiac events (MACE).Materials and methodsStudy I: Single images from 36 consecutive CCTA exams performed with two different dose levels were used. Image quality in full dose, low‐dose and noise‐reduced low‐dose images was graded using visual grading analysis. Image noise was measured.Study II: CTP and SPECT were performed in 17 patients, and the variation in per AHA‐segment blood flow was evaluated and compared. CTP results from manual and automated image segmentation were compared.Study III: CCTA datasets from 21 patients were processed using cFFR software and the results compared to the corresponding invasively measured FFR (invFFR).Study IV: 1205 consecutive patients with chest pain of unknown origin underwent CCTA. Baseline data and data on subsequent MACE were retrieved from relevant registries. Survival, hazard ratios and the three‐year incidence of cardiac events and readmissions were calculated.Results Study I: There was significant improvement in perceived image quality for all criteria when the filter was applied, and a significant decrease in image noise.Study II: The correlation coefficients for CTP vs. SPECT were 0.38 and 0.41 (p<0.001, for manual and automated segmentation respectively. Mean per patient CTP blood flow in normal segments varied between 94‐183 ml/100 ml tissue/min for manual segmentation, and 104‐196 ml/100 ml tissue/min for automated segmentation. The Spearman rank correlation coefficient for manual vs. automated segmentation CTP was ρ = 0.88 (p<0.001) and the Intraclass Correlation Coefficient (ICC) was 0.93 (p<0.001).Study III: The Spearman rank correlation coefficient for cFFR vs. invFFR was ρ = 0.77 (p<0.001) and the ICC was 0.73 (p<0.001). Sensitivity, specificity, positive predictive value and negative predictive value for significant stenosis (FFR<0.80, per vessel) were 0.83, 0.76, 0.56 and 0.93 respectively.Study IV: The hazard ratio for non‐obstructive CAD vs. normal coronary arteries was 5.13 (95% C.I 1.03‐25.43, p<0.05), and 151.40 (95% C.I 37.03‐619.08, p<0.001) for obstructive CAD vs. normal coronary arteries. The three‐year incidence of MACE was 1.1% for patients with normal vessels on CCTA, 2.5% for patients with non‐obstructive CAD and 42.7% for patients with obstructive CAD (p<0.001).Conclusions:Study I: Image quality and noise levels of low dose images were significantly improved with the filter, even though the improvement was small compared to the image quality of the corresponding diastolic full‐dose images.Study II: Correlation between dynamic CTP and SPECT was positive but weak. There were large variations in CTP blood flow in normal segments on SPECT, rendering the definition of an absolute cut‐off value for normal vs. ischemic myocardium difficult. Manual and automatic segmentation were equally useful.Study III: The correlation between cFFR and invFFR was good, indicating that noninvasively estimated cFFR performs on a similar level as invasively measure FFR. Study IV: The long‐term risk for MACE was very low in patients without obstructive CAD on CCTA, though there seemed to be a substantial increase in the risk for MACE even in patients with non‐obstructive CAD as compared to normal coronary arteries. In addition, even patients with normal coronary arteries or non‐obstructive CAD continued to have a substantial number of readmissions for chest pain or angina pectoris.
|
|
| 5. |
- Kataria, Bharti, 1955-
(författare)
-
Visual grading evaluation of reconstruction methods and dose optimisation in abdominal Computed Tomography
- 2019
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Since its introduction in the 1970’s CT has emerged as a modality of choice because of its high sensitivity in producing accurate diagnostic images. A third of all Computed Tomography (CT) examinations are abdominal CTs which deliver one of the highest doses among common examinations. An increase in the number of CT examinations has raised concerns about the negative effects of ionising radiation as the dose is cumulative over the life span of the individual. Image quality in CT is closely related to the radiation dose, so that a certain dose with an associated small, but not negligible, risk is a prerequisite for high image quality. Typically, dose reduction in CT results in higher noise and a decrease in low contrast resolution which can be detrimental to the image quality produced. New technology presents a wide range of dose reduction strategies, the latest being iterative reconstruction (IR).The aim of this thesis was to evaluate two different classes of iterative reconstruction algorithms: statistical (SAFIRE) and model-based (ADMIRE) as well as to explore the diagnostic value of a low-dose abdominal CT for optimisation purposes.This thesis included a total of 140 human subjects in four image quality evaluation studies, three of which were prospective studies (Papers I, II and IV) and one retrospective study (Paper III). Visual grading experiments to determine the potential dose reductions, were performed with pairwise comparison of image quality in the same patient at different tube loads (dose) and reconstructed with Filtered back projection (FBP) and SAFIRE strength 1 in a low-dose abdominal CT (Paper I) and FBP and ADMIRE strengths 3 and 5 in a standard dose abdominal CT (Paper II). Paper IV evaluated the impact of slice thicknesses in CT images reconstructed with ADMIRE strengths 3 and 5 when comparing multiplanar reconstruction (MPR) formatted images in a standard dose abdominal CT. Paper III, on the other hand, was an absolute assessment of image quality and pathology between the three phases of a CT Urography (CTU) protocol to explore the diagnostic value of low-dose abdominal CT. The anonymised images were displayed in random order and image quality was assessed by a group of radiologists using image quality criteria from the “European guidelines of quality criteria for CT”. The responses from the reviewer assessment were analysed statistically with ordinal logistic regression i.e. Visual Grading Regression (VGR).Results in Paper I show that a small dose reduction (5-9 %) was possible using SAFIRE strength 1and indicated the need for further research to evaluate the dose reduction potential of higher strengths of the algorithm. In Paper II a 30% dose reduction was possible without change in ADMIRE algorithm strength as no improvement in image quality was observed between tube loads 98- and 140 mAs. When comparing tube loads 42 and 98 mAs, further dose reduction was possible with ADMIRE strength 3 (22-47%). However, for images reconstructed with ADMIRE strength 5, a dose reduction of 34-74% was possible for some, but not all image criteria. Image quality in low-contrast objects such as the liver parenchyma, was affected and a decline in diagnostic confidence was observed. Paper IV showed potential dose reductions are possible with increasing slice thickness from 1 mm to 2 mm (24-35%) and 1 mm to 3mm (25-41%). ADMIRE strength 3 continued to provide diagnostically acceptable images with possible dose reductions for all image criteria assessed. Despite objective evaluations showing a decrease in noise and an increase in contrast to noise ratio, ADMIRE strength 5 had diverse effects on the five image criteria, depending on slice thickness and further dose reductions were limited to certain image criteria. The findings do not support a general recommendation to replace ADMIRE3 with ADMIRE5 in clinical abdominal CT protocols.Paper III studied another aspect of optimisation and results show that visualisation of renal anatomy was as expected in favour of the post-contrast phases when compared to the native phase. Assessment of pathology showed no significant differences between the three phases. Significantly higher diagnostic certainty for renal anatomy was observed for the post-contrast phases when compared to the native phase. Significantly high certainty scores were also seen for the nephrographic phase for incidental findings. The conclusion is that a low-dose series seems to be sufficient as a first-line modality in certain patient groups.This thesis clinically evaluated the effect of IR in abdominal CT imaging and estimated potential dose reductions. The important conclusion from papers I, II and IV is that IR improves image quality in abdominal CT allowing for some dose reductions. However, the clinical utility of the highest strength of the algorithm is limited to certain criteria. The results can be used to optimise the clinical abdominal CT protocol. The conclusion from paper III may increase clinical awareness of the value of the low-dose abdominal protocol when choosing an imaging method for certain patient groups who are more sensitive to radiation.
|
|
| 6. |
- Maria Marreiros, Filipe Miguel
(författare)
-
Guidance and Visualization for Brain Tumor Surgery
- 2016
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Image guidance and visualization play an important role in modern surgery to help surgeons perform their surgical procedures. Here, the focus is on neurosurgery applications, in particular brain tumor surgery where a craniotomy (opening of the skull) is performed to access directly the brain region to be treated. In this type of surgery, once the skull is opened the brain can change its shape, and this deformation is known as brain shift. Moreover, the boundaries of many types of tumors are difficult to identify by the naked eye from healthy tissue. The main goal of this work was to study and develop image guidance and visualization methods for tumor surgery in order to overcome the problems faced in this type of surgery.Due to brain shift the magnetic resonance dataset acquired before the operation (preoperatively) no longer corresponds to the anatomy of the patient during the operation (intraoperatively). For this reason, in this work methods were studied and developed to compensate for this deformation. To guide the deformation methods, information of the superficial vessel centerlines of the brain was used. A method for accurate (approximately 1 mm) reconstruction of the vessel centerlines using a multiview camera system was developed. It uses geometrical constraints, relaxation labeling, thin plate spline filtering and finally mean shift to find the correct correspondences between the camera images.A complete non-rigid deformation pipeline was initially proposed and evaluated with an animal model. From these experiments it was observed that although the traditional non-rigid registration methods (in our case coherent point drift) were able to produce satisfactory vessel correspondences between preoperative and intraoperative vessels, in some specific areas the results were suboptimal. For this reason a new method was proposed that combined the coherent point drift and thin plate spline semilandmarks. This combination resulted in an accurate (below 1 mm) non-rigid registration method, evaluated with simulated data where artificial deformations were performed.Besides the non-rigid registration methods, a new rigid registration method to obtain the rigid transformation between the magnetic resonance dataset and the neuronavigation coordinate systems was also developed.Once the rigid transformation and the vessel correspondences are known, the thin plate spline can be used to perform the brain shift deformation. To do so, we have used two approaches: a direct and an indirect. With the direct approach, an image is created that represents the deformed data, and with the indirect approach, a new volume is first constructed and only after that can the deformed image be created. A comparison of these two approaches, implemented for the graphics processing units, in terms of performance and image quality, was performed. The indirect method was superior in terms of performance if the sampling along the ray is high, in comparison to the voxel grid, while the direct was superior otherwise. The image quality analysis seemed to indicate that the direct method is superior.Furthermore, visualization studies were performed to understand how different rendering methods and parameters influence the perception of the spatial position of enclosed objects (typical situation of a tumor enclosed in the brain). To test these methods a new single-monitor-mirror stereoscopic display was constructed. Using this display, stereo images simulating a tumor inside the brain were presented to the users with two rendering methods (illustrative rendering and simple alpha blending) and different levels of opacity. For the simple alpha blending method an optimal opacity level was found, while for the illustrative rendering method all the opacity levels used seemed to perform similarly.In conclusion, this work developed and evaluated 3D reconstruction, registration (rigid and non-rigid) and deformation methods with the purpose of minimizing the brain shift problem. Stereoscopic perception of the spatial position of enclosed objects was also studied using different rendering methods and parameter values.
|
|
| 7. |
- Vu, Minh Hoang, 1988-
(författare)
-
Resource efficient automatic segmentation of medical images
- 2023
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cancer is one of the leading causes of death worldwide. In 2020, there were around 10 million cancer deaths and nearly 20 million new cancer cases in the world. Radiation therapy is essential in cancer treatments because half of the cancer patients receive radiation therapy at some point. During a radiotherapy treatment planning (RTP), an oncologist must manually outline two types of areas of the patient’s body: target, which will be treated, and organs-at-risks (OARs), which are essential to avoid. This step is called delineation. The purpose of the delineation is to generate a sufficient dose plan that can provide adequate radiation dose to a tumor and limit the radiation exposure to healthy tissue. Therefore, accurate delineations are essential to achieve this goal.Delineation is tedious and demanding for oncologists because it requires hours of concentrating work doing a repeated job. This is a RTP bottleneck which is often time- and resource-intensive. Current software, such as atlasbased techniques, can assist with this procedure by registering the patient’s anatomy to a predetermined anatomical map. However, the atlas-based methods are often slowed down and erroneous for patients with abnormal anatomies.In recent years, deep learning (DL) methods, particularly convolutional neural networks (CNNs), have led to breakthroughs in numerous medical imaging applications. The core benefits of CNNs are weight sharing and that they can automatically detect important visual features. A typical application of CNNs for medical images is to automatically segment tumors, organs, and structures, which is assumed to save radiation oncologists much time when delineating. This thesis contributes to resource efficient automatic segmentation and covers different aspects of resource efficiency.In Paper I, we proposed a novel end-to-end cascaded network for semantic segmentation in brain tumors in the multi-modal magnetic resonance imaging challenge in 2019. The proposed method used the hierarchical structure of the tumor sub-regions and was one of the top-ranking teams in the task of quantification of uncertainty in segmentation. A follow-up work to this paper was ranked second in the same task in the same challenge a year later.We systematically assessed the segmentation performance and computational costs of the technique called pseudo-3D as a function of the number of input slices in Paper II. We compared the results to typical two-dimensional (2D) and three-dimensional (3D) CNNs and a method called triplanar orthogonal 2D. The typical pseudo-3D approach considers adjacent slices to be several image input channels. We discovered that a substantial benefit from employing multiple input slices was apparent for a specific input size.We introduced a novel loss function in Paper III to address diverse issues, including imbalanced datasets, partially labeled data, and incremental learning. The proposed loss function adjusts to the given data to use all accessible data, even if some lack annotations. We show that the suggested loss function also performs well in an incremental learning context, where an existing model can be modified to incorporate the delineations of newly appearing organs semi-automatically.In Paper IV, we proposed a novel method for compressing high-dimensional activation maps, which are the primary source of memory use in modern systems. We examined three distinct compression methods for the activation maps to accomplishing this. We demonstrated that the proposed method induces a regularization effect that acts on the layer weight gradients. By employing the proposed technique, we reduced activation map memory usage by up to 95%.We investigated the use of generative adversarial networks (GANs) to enlarge a small dataset by generating synthetic images in Paper V. We use the real and generated data during training CNNs for the downstream segmentation tasks. Inspired by an existing GAN, we proposed a conditional version to generate high-dimensional and high-quality medical images of different modalities and their corresponding label maps. We evaluated the quality of the generated medical images and the effect of this augmentation on the performance of the segmentation task on six datasets.
|
|
| 8. |
- Astaraki, Mehdi, PhD Student, 1984-
(författare)
-
Advanced Machine Learning Methods for Oncological Image Analysis
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally-invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow.This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis.The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head-neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy.Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power.Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra-dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses.In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis.
|
|
| 9. |
- Brusini, Irene
(författare)
-
Methods for the analysis and characterization of brain morphology from MRI images
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Brain magnetic resonance imaging (MRI) is an imaging modality that produces detailed images of the brain without using any ionizing radiation. From a structural MRI scan, it is possible to extract morphological properties of different brain regions, such as their volume and shape. These measures can both allow a better understanding of how the brain changes due to multiple factors (e.g., environmental and pathological) and contribute to the identification of new imaging biomarkers of neurological and psychiatric diseases. The overall goal of the present thesis is to advance the knowledge on how brain MRI image processing can be effectively used to analyze and characterize brain structure.The first two works presented in this thesis are animal studies that primarily aim to use MRI data for analyzing differences between groups of interest. In Paper I, MRI scans from wild and domestic rabbits were processed to identify structural brain differences between these two groups. Domestication was found to significantly reshape brain structure in terms of both regional gray matter volume and white matter integrity. In Paper II, rat brain MRI scans were used to train a brain age prediction model. This model was then tested on both controls and a group of rats that underwent long-term environmental enrichment and dietary restriction. This healthy lifestyle intervention was shown to significantly affect the predicted brain age trajectories by slowing the rats' aging process compared to controls. Furthermore, brain age predicted on young adult rats was found to have a significant effect on survival.Papers III to V are human studies that propose deep learning-based methods for segmenting brain structures that can be severely affected by neurodegeneration. In particular, Papers III and IV focus on U-Net-based 2D segmentation of the corpus callosum (CC) in multiple sclerosis (MS) patients. In both studies, good segmentation accuracy was obtained and a significant correlation was found between CC area and the patient's level of cognitive and physical disability. Additionally, in Paper IV, shape analysis of the segmented CC revealed a significant association between disability and both CC thickness and bending angle. Conversely, in Paper V, a novel method for automatic segmentation of the hippocampus is proposed, which consists of embedding a statistical shape prior as context information into a U-Net-based framework. The inclusion of shape information was shown to significantly improve segmentation accuracy when testing the method on a new unseen cohort (i.e., different from the one used for training). Furthermore, good performance was observed across three different diagnostic groups (healthy controls, subjects with mild cognitive impairment and Alzheimer's patients) that were characterized by different levels of hippocampal atrophy.In summary, the studies presented in this thesis support the great value of MRI image analysis for the advancement of neuroscientific knowledge, and their contribution is mostly two-fold. First, by applying well-established processing methods on datasets that had not yet been explored in the literature, it was possible to characterize specific brain changes and disentangle relevant problems of a clinical or biological nature. Second, a technical contribution is provided by modifying and extending already-existing brain image processing methods to achieve good performance on new datasets.
|
|
| 10. |
- Dahlström, Nils, 1969-
(författare)
-
Magnetic Resonance Imaging of the Hepatobiliary System Using Hepatocyte-Specific Contrast Media
- 2009
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- There are two Gadolinium-based liver-specific contrast media for Magnetic Resonance Imaging on the market, Gd-BOPTA (MultiHance®, Bracco Imaging, Milan, Italy) and Gd-EOB-DTPA (Primovist®, Bayer Schering Pharma, Berlin, Germany). The aim of this study in two parts was to evaluate the dynamics of biliary, parenchymal and vascular enhancement using these contrast media in healthy subjects. Ten healthy volunteers were examined in a 1.5 T magnetic resonance system using three-dimensional Volumetric Interpolated Breath-Hold (VIBE) sequences for dynamic imaging with both contrast media – at two different occasions – until five hours after injection. The doses given were 0.025 mmol/kg for Gd-EOB-DTPA and 0.1 mmol/kg for Gd-BOPTA. The enhancement over time of the common biliary duct in contrast to the liver parenchyma was analyzed in the first study. This was followed by a study of the image contrasts of the hepatic artery, portal vein and middle hepatic vein versus the liver parenchyma.While Gd-EOB-DTPA gave an earlier and more prolonged enhancement of the biliary duct, Gd-BOPTA achieved higher image contrast for all vessels studied, during the arterial and portal venous phases. There was no significant difference in the maximal enhancement obtained in the liver parenchyma.At the obtained time-points and at the dosage used, the high contrast between the common biliary duct and liver parenchyma had an earlier onset and longer duration for Gd-EOB-DTPA, while Gd-BOPTA achieved higher maximal enhancement of the hepatic artery, portal vein and middle hepatic vein than Gd-EOB-DTPA. Diseases of the liver and biliary system may affect the vasculature, parenchyma, biliary excretion or a combination of these. The clinical context regarding the relative importance of vascular, hepatic parenchymal and biliary processes should determine the choice of contrast media for each patient and examination.
|
|
