No description available.

A vital part of the human anatomy is the circulatory system, which branches out in a vast network of vessels delivering oxygen and other nutrients to all parts of the body. In a human adult, there are about 40 billion capillaries with a diameter of about 10 µm. The behavior of the blood flow in the capillaries can be used to identify, for example, diabetes or cancer. The current method for analyzing capillaries involves removing a section of the tissue and looking at it through a microscope. To avoid having to remove tissue from the patient, a method for imaging the capillaries inside living tissue is desired. A possible candidate for the future of capillary imaging is ultrasound localization microscopy (ULM). ULM attempts to solve a well-known limitation in ultrasound imaging, the diffraction limit. The classical limit of diffraction sets a limit on the resolution achievable based on the wavelength of the transmitted soundwave. The best possible resolution would be roughly half of the transmitted wavelength, which means that objects smaller than that cannot be imaged accurately. A standard clinical ultrasound system uses wavelengths in the hundreds of micrometers when imaging deep organs. Capillaries, which are much smaller than that, can not accurately be imaged with standard ultrasound systems. ULM utilizes the detection of individual microbubbles injected into the bloodstream to pinpoint the microbubble location to a much higher precision than what the diffraction limit would allow. By combining the localization of hundreds of microbubbles, an image of the capillaries is achieved. In this study, we investigate the performance of ULM for imaging the sub-wavelength structures of capillaries in skeletal muscle. A simulation model of capillaries in skeletal muscle was built to achieve the necessary images. The model was built in Vantage 4.2.0 (Verasonics Inc.), which runs in MATLAB. The simulation model was designed to simulate microbubbles moving in capillaries in the image plane. From the results in this study, we can conclude that ULM is a viable option for imaging capillaries in skeletal muscle and can achieve a resolution that far surpasses the diffraction limit. We show that the capillaries' shape and their proximity to each other can affect the final image. The intensity of background noise relative to the microbubble signal also substantially impacts the performance of ULM but might be avoided due to the high contrast between background noise and microbubble signal. Furthermore, we show that, if the background is stationary, the background tissue signal can easily be removed with singular value decomposition (SVD). Notice: The full text of this report has been censored due to confidentiality and will not be available to the public.

Alzheimer’s Disease (AD) is a widespread neurodegenerative disease. The disease causes brain atrophy, resulting in memory loss, decreased cognitive ability, and eventually death. There is currently no cure for the disease, but treatment may delay the onset. Therefore, it is crucial to detect the disease at an early stage. Medical imaging techniques, such as Positron Emission Tomography (PET), are heavily applied for this task. In recent years, machine learning approaches have shown success in identifying AD from such images. The thesis presents a pipeline approach to detect, extract and evaluate Region of Interest (ROI) for prioritization of informative regions in PET scans for classification of Alzheimer’s disease. The pipeline applies data acquired from Alzheimer’s Disease Neuroimaging Initiative (ADNI). An analysis of Weakly-Supervised Object Localization (WSOL) is discussed for detection of informative regions particularly indicative of AD. WSOL analyse the original full-volume 18F-fluorodeoxyglucose (18F-FDG)-PET scan to categorize the informative regions on subjects into Cognitively Normal (CN), Mild Cognitive Impairment (MCI), or AD. The detection of informative regions are processed to two approaches to extract ROI on the full-volume 18F-FDG-PET scan: Bounding-Box (BBox) Generatio nand Automated Anatomical Labeling (AAL) Generation. BBoxes Generation restricts the 18F-FDG-PET scans for Convolutional Neural Network (CNN) to BBox proposal swith particularly informative regions. The second approach ranks the anatomical regions of the brain through brain parcellation with the pre-defined atlas AAL3, and restricts a CNN to the highest-ranked regions. The results evaluate if ROIs increase the robustness for classification in relationto full-volume 18F-FDG-PET scan. The results suggest that full-volume 18F-FDG-PET with heavily restricted image size does not decrease classification performance. Instead, the BBox Generation results in a significant classification performance improvement on the test set from an Area under the ROC Curve (AuC) score of 70.08% to 97.73% and accuracy from 51.79% to 88.03%. AAL Generation suggests that the middle and inferior regions of the temporal lobe and the fusiform are essential to the classification. In addition, several regions of the frontal lobe were found to be highly important but could not alone discriminate between CN, MCI, and AD.

This master thesis is the study of multi-task learning to train a neural network to segment medical images and predict anatomical landmarks. The paper shows the results from experiments using medical landmarks in order to attempt to help the network learn the important organ structures quicker. The results found in this study are inconclusive and rather than showing the efficiency of the multi-task framework for learning, they tell a story of the importance of choosing the tasks and dataset wisely. The study also reflects and depicts the general difficulties and pitfalls of performing a project of this type.

In recent years, pictures from handheld devices such as smartphones have been increasingly utilized as a documentation tool by medical practitioners not trained to take professional photographs. Similarly to the other types of image modalities, the images should be taken in a way to capture the vital information in the region of interest. Nevertheless, image capturing cannot always be done as desired, so images may exhibit different blur types at the region of interest. Having blurry images does not serve medical purposes, therefore, the patients might have to schedule a second appointment several days later to retake the images. A solution to this problem is to create an algorithm which immediately after capturing an image determines if it is medically useful and notifies the user of the result. The algorithm needs to perform the analysis at a reasonable speed, and at best, with a limited number of operations to make the calculations directly in the smartphone device. A large number of medical images must be available to create such an algorithm. Medical images are difficult to acquire, and it is specifically difficult to acquire blurry images since they are usually deleted. The main objective of this thesis is to determine the medical usefulness of images taken with smartphone cameras, using both machine learning and handcrafted algorithms, with a low number of floating point operations and a high performance. Seven different algorithms (one hand-crafted and six machine learned) are created and compared regarding both number of floating point operations and performance. Fast Walsh-Hadamard transforms are the basis of the hand-crafted algorithm. The employed machine learning algorithms are both based on common convolutional neural networks (MobileNetV3 and ResNet50) and on our own designs. The issue with the low number of medical images acquired is solved by training the machine learning models on a synthetic dataset, where the non-medically useful images are generated by applying blur on the medically useful images. These models do, however, undergo evaluation using a real dataset, containing medically useful images as well as non-medically useful images. Our results implicate that a real-time determination of the medical usefulness of images is possible on handheld devices, since our machine learned model DeepLAD-Net reaches the highest accuracy with 42 · 106 floating point operations. In terms of accuracy, MobileNetV3-large is the second best model with31 times as many floating point operations as our best model.

Sammanfattning   Hjärt- och kärlsjukdommar är den ledande dödsorsaken i världen. En av det vanligaste hjärt- och kärlsjukdomarna är åderförkalkning. Sjukdomen kännetecknas av förhårdning samt plackansamling i kärl och bidrar till stroke och hjärtinfarkt. Information om kärlväggens styvhet kan spela en viktig roll vid diagnostiseringen av bland annat åderförkalkning. Skjuvvågselastografi (SWE) är en noninvasiv ultraljudsbaserad metod som idag används för att mäta elasticitet och styvhet av större mjuka vävnader som lever- och bröstvävnad. Dock används inte metoden inom kärlapplikationer, då få genomgående studier har utförts på SWE för kärl. Målet med projektet är att automatisera kvantifieringen av skjuvvågshastigheten för SWE och undersöka hur automatiseringens förmåga och begränsningar beror av automatiseringsinställningar. Med verktyg erhållna från CBH (skolan för kemi, bioteknologi och hälsa) skapades ett MATLAB-program med denna förmåga. Programmet applicerades på två fantommodeller. Automatiseringsinställningarna påverkade automatiseringen av dessa modeller olika, vilket innebar att generella optimala inställningar inte kunde finnas. Optimala inställningar beror på vad automatiseringen skall undersöka. / Medicinsk avbildning

Medicinsk bildanalys är både tidskonsumerade och kräver expertis. I den härrapporten vidareutvecklas en 2.5D version av faltningsnätverket U-Net anpassadför automatiserad njuresegmentering. Faltningsnätverk har tidigare visatliknande prestation som experter. Träningsdata för nätverket anpassades genomatt manuellt segmentera MR-bilder av njurar. 2.5D U-Net nätverket tränades med64 st njursegmenteringar från tidigare arbete. Volymanalys på nätverketssegmenterings förslag av 38.000 patienter visade den mängden segmenteradevoxlar som inte tillhörde njurarna var 0,35 %. Efter tillägg av 56 st av vårasegmenteringar minskade det till 0.11 %, en reduktion av cirka 68 %. Det är enstor förbättring av nätverket och ett viktigt steg mot tillämpning avautomatiserad segmentering. / Medical image analysis is both time consuming and requires expertise. In thisreport, a 2.5D version of the U-net convolution network adapted for automatedkidney segmentation is further developed. Convolution neural networks havepreviously shown expert level performance in image segmentation. Training datafor the network was created by manually segmenting MRI images of kidneys.The 2.5D U-Net network was trained with 64 kidney segmentations fromprevious work. Volume analysis on the network’s kidney segmentation proposalsof 38,000 patients showed that the ammount of segmented voxels that are notpart of the kidneys was 0.35%. After the addition of 56 of our segmentations, itdecreased to just 0.11%, indicating a reduction of about 68%. This is a majorimprovement of the network and an important step towards the development ofpractical applications of automated segmentation.

Nonalcoholic liver steatosis (NALS) is a condition where fat infiltrates the tissue of the liver and accumulates in droplets. While not a dangerous condition on its own, if left for long enough it can develop into conditions which could cause serious and potentially permanent damage to the liver. One of the primary approaches for preventing NALS from progressing is through changes in diet and lifestyle. However, explaining to a patient the impact of such a change can be difficult, which hampers motivation in many instances. Digital twin technology can provide simulations of what will happen to the body after a lifestyle change, but the output data is very abstract and can thus be a challenge to convey properly to a patient. In this project I investigate a digital data visualization approach where a photo of a liver sample is morphed to showcase liver fat droplets shrinking as a result of a changed lifestyle, as simulated by the digital twin. The approach uses a simple image morphing algorithm that pulls pixel intensity values from regions designated by a morph field and composites a newimage from the updated values. By selectively choosing regions of interest to pull pixels towards or away from, with a ramping cutoff in morph field strength, it is possible to designate certain regions in the image to be morphed. The program is capable of generating time series of increasingly morphed images in both greyscale and truecolour, and it can save the time series as an animated .GIF file, with linear interpolation between the morphed images in the time series.

Magnetic resonance imaging (MRI) is a medical imaging method that has become increasingly more important during the last 4 decades. This is partly because it allows us to acquire a 3D-representation of a part of the body without exposing patients to ionizing radiation. Furthermore, it also typically gives better contrast between soft tissues than x-ray based techniques such as CT. The image acquisition procedure of MRI is also much more flexible. One can vary the signal sequence, not only to change how different types of tissue map to different intensities, but also to measure flow, diffusion or even brain activity over time.  Machine learning has gained great impetus the last decade and a half. This is probably partly because of the work done on the mathematical foundations of machine learning done at the end of last century in conjunction with the availability of specialized massively parallel processors, originally developed as graphical processing units (GPUs), which are ideal for training or running machine learning models. The work presented in this thesis combines MRI and machine learning in order to leverage the large amounts of MRI-data available in open data sets, to address questions of clinical relevance about the brain.  The thesis comprises three studies. In the first one the subproblem which augmentation methods are useful in the larger context of classifying autism, was investigated. The second study is about predicting brain age. In particular it aims to construct light-weight models using the MRI volumes in a condensed form, so that the model can be trained in a short time and still reach good accuracy. The third study is a development of the previous that investigates other ways of condensing the brain volumes. / Magnetresonansavbildningar, ofta kallat MR eller MRI, är en bilddiagnostik-metod som har blivit allt viktigare under de senaste 40 åren. Detta på grund av att man kan erhålla 3D-bilder av kroppsdelar utan att utsätta patienter för joniserande strålning. Dessutom får man typiskt bättre kontraster mellan mjukdelar än man får med motsvarande genomlysningsmetod (CT, eller 3D röntgen). Själva bildinsamlingsförfarandet är också mera flexibelt med MR. Man kan genom att ändra program för utsända och registrerade signa-ler, inte bara ändra vad som framförallt framträder på bilden (t.ex. vatten, fett, H-densitet, o.s.v.) utan även mäta flöde och diffusion eller till och med hjärnaktivitet över tid. Maskininlärning har fått ett stort uppsving under 2010-talet, dels på grund av utveckling av teknologin för att träna och konstruera maskininlärningsmodeller dels på grund av tillgängligheten av massivt parallella specialprocessorer – initialt utvecklade för att generera datorgrafik. Detta arbete kombinerar MR med maskininlärning, för att dra nytta av de stora mängder MR data som finns samlad i öppna databaser, för att adressera frågor av kliniskt intresse angående hjärnan. Avhandlingen innehåller tre studier. I den första av dessa undersöks del-problemet vilken eller vilka metoder för att artificiellt utöka träningsdata som är bra vid klassificering om en person har autism. Det andra arbetet adresserar bedömning av så kallad "hjärn-ålder". Framför allt strävar arbetet efter att hitta lättviktsmodeller som använder en komprimerad form av varje hjärnvolym, och därmed snabbt kan tränas till att bedöma en persons ålder från en MR-volym av hjärnan. Det tredje arbetet utvecklar modellen från det föregående genom att undersöka andra typer av komprimering. / <p><strong>Funding:</strong> This research was supported by the Swedish research council (2017-04889), the ITEA/VINNOVA project ASSIST (Automation, Surgery Support and Intuitive 3D visualization to optimize workflow in IGT SysTems, 2021-01954), and the Åke Wiberg foundation (M20-0031, M21-0119, M22-0088).</p>

During resting state the brain exhibits synchronized activity within all major brain networks. Using blood oxygen level dependent (BOLD) resting state functional magnetic resonance imaging (fMRI) based detection it is possible to quantify the degree of correlation, connectivity, between regions of interest and assess information regarding the integrity of the inter-regional functional integration. A newly available multiband echo planar imaging (EPI) fMRI sequence allows for faster scan times which possibly allows us to better examine large-scale networks and increase the understanding of brain function/dysfunction. This thesis will assess how the newly developed sequence compares to a conventional EPI sequence for detecting resting state connectivity of canonical brain networks. The data acquisitions were made on a 3 Tesla scanner using a 32 channel head coil. The hypothesis was that the multiband sequence would produce a better result since it has faster sampling rate, thus more data points in its time-series to support the statistical analyses. Using Pearson’s linear correlation between the average time-series (approximately 12 minutes long) within a seed-region and all voxels contained in the image volume, correlation maps where created for each of the eight participants using data normalized to Montreal Neurological Institute (MNI) space. The resting state networks (RSN) were then found by performing a one sample T-test on group level. Six seed-coordinates, based on literature, where used revealing the the homotopic connections in anterior Hippocampus, Motor cortex, Dorsal attention, Visual and the Default mode network (DMN) as well for an anterior-posterior connection in the DMN. By comparing the maximum T-values within the regions for the RSN no systematic difference could be found between the multiband and conventional fMRI data. Further tests were conducted to evaluate if the sequences would differentiate in their results if the acquisition time was shortened, i.e shortening the time-series in the voxels. However no such difference could be established.Importantly, the results are specific to the 32 channel head coil used in the current study. Presumably recently available and improved coil designs could better exploit the multiband technique.