Doctor of Philosophy

dissertationMagnetic resonance guided high intensity focused ultrasound (MRgHIFU) is a promising minimal invasive thermal therapy for the treatment of breast cancer. This study develops techniques for determining the tissue parameters - tissue types and perfusion rate - that influence the local temperature during HIFU thermotherapy procedures. For optimal treatment planning for each individual patient, a 3D volumetric breast tissue segmentation scheme based on the hierarchical support vector machine (SVM) algorithm was developed to automatically segment breast tissues into fat, fibroglandular tissue, skin and lesions. Compared with fuzzy c-mean and conventional SVM algorithm, the presented technique offers tissue classification performance with the highest accuracy. The consistency of the segmentation results along both the sagittal and axial orientations indicates the stability of the proposed segmentation routine. Accurate knowledge of the internal anatomy of the breast can be utilized in the ultrasound beam simulation for the treatment planning of MRgHIFU therapy. Completely noninvasive MRI techniques were developed for visualizing blood vessels and determining perfusion rate to assist in the MRgHIFU therapy. Two-point Dixon fat-water separation was achieved using a 3D dual-echo SSFP sequence for breast vessel imaging. The performances of the fat-water separation with various readout gradient designs were evaluated on a water-oil phantom, ex vivo pork sample and in vivo breast imaging. Results suggested that using a dual-echo SSFP readout with bipolar readout gradient polarity, blood vasculature could be successfully visualized through the thin-slab maximum intensity projection SSFP water-only images. For determining the perfusion rate, we presented a novel imaging pulse sequence design consisting of a single arterial spin labeling (ASL) magnetization preparation followed by Look-Locker-like image readouts. This flow quantification technique was examined through simulation, in vitro and in vivo experiments. Experimental results from a hemodialyzer when fitted with a Bloch-equation-based model provide flow measurements that are consistent with ground truth velocities. With these tissue properties, it is possible to compensate for the dissipative effects of the flowing blood and ultimately improve the efficacy of the MRgHIFU therapies. Complete noninvasiveness of these techniques allows multiple measurements before, during and after the treatment, without the limitation of washout of the injected contrast agent

Computer-Assisted Characterization of Prostate Cancer on Magnetic Resonance Imaging

Prostate cancer (PCa) is one of the most prevalent cancers among men. Early diagnosis can improve survival and reduce treatment costs. Current inter-radiologist variability for detection of PCa is high. The use of multi-parametric magnetic resonance imaging (mpMRI) with machine learning algorithms has been investigated both for improving PCa detection and for PCa diagnosis. Widespread clinical implementation of computer-assisted PCa lesion characterization remains elusive; critically needed is a model that is validated against a histologic reference standard that is densely sampled in an unbiased fashion. We address this using our technique for highly accurate fusion of mpMRI with whole-mount digitized histology of the surgical specimen. In this thesis, we present models for characterization of malignant, benign and confounding tissue and aggressiveness of PCa. Further validation on a larger dataset could enable improved characterization performance, improving survival rates and enabling a more personalized treatment plan

Validation Strategies Supporting Clinical Integration of Prostate Segmentation Algorithms for Magnetic Resonance Imaging

Segmentation of the prostate in medical images is useful for prostate cancer diagnosis and therapy guidance. However, manual segmentation of the prostate is laborious and time-consuming, with inter-observer variability. The focus of this thesis was on accuracy, reproducibility and procedure time measurement for prostate segmentation on T2-weighted endorectal magnetic resonance imaging, and assessment of the potential of a computer-assisted segmentation technique to be translated to clinical practice for prostate cancer management. We collected an image data set from prostate cancer patients with manually-delineated prostate borders by one observer on all the images and by two other observers on a subset of images. We used a complementary set of error metrics to measure the different types of observed segmentation errors. We compared expert manual segmentation as well as semi-automatic and automatic segmentation approaches before and after manual editing by expert physicians. We recorded the time needed for user interaction to initialize the semi-automatic algorithm, algorithm execution, and manual editing as necessary. Comparing to manual segmentation, the measured errors for the algorithms compared favourably with observed differences between manual segmentations. The measured average editing times for the computer-assisted segmentation were lower than fully manual segmentation time, and the algorithms reduced the inter-observer variability as compared to manual segmentation. The accuracy of the computer-assisted approaches was near to or within the range of observed variability in manual segmentation. The recorded procedure time for prostate segmentation was reduced using computer-assisted segmentation followed by manual editing, compared to the time required for fully manual segmentation

Optimizing MRI-guided prostate ultrasound ablation therapy using retrospective analyses and artificial intelligence

Magnetic resonance imaging (MRI)-guided transurethral ultrasound ablation (TULSA) is an emerging therapy that has been used to treat prostate cancer (PCa). TULSA destroys prostate tissue with heat using therapeutic ultrasound. The heating is monitored in real-time using MRI thermometry. Despite TULSA’s promise, there are several challenges that have slowed its widespread adoption. Fortunately, MRI images and heating parameters from all TULSA treatments are stor ed. By conducting detailed retrospective analyses and applying deep learning on existing treatments, we can extract valuable information and then leverage this knowledge to optimize future TULSA treatments. One major challenge occurs for those patients who had PCa radiation therapy failure and are seeking salvage treatment with TULSA. Many of these patients have leftover metal markers in the prostate. These markers can hamper subsequent TULSA therapy because they introduce susceptibility artifacts in the MRI image and may also block the ultrasound, which may compromise treatment safety and efficacy. Through an extensive retrospective analysis, we have determined that gold markers tend not to affect the treatment outcome, except when located simultaneously close to the urethra and far from the target boundary, or when located directly on the target boundary itself. Clinically, gold markers had no apparent effect on treatment safety and efficacy compared to a control cohort without markers at the 12-month follow-up. Conversely, nitinol markers are generally problematic for TULSA. A second major challenge applies to all TULSA treatment indications. Immediately after TULSA therapy, MRI contrast agents are used to visualize the non-perfused volume, an objective measure of the ablation outcome. Unfortunately, even if undertreatment is observed, retreatment is not possible, forcing an additional treatment several months later, and with it the associated risks of a second intervention. By training a deep learning model with existing TULSA treatment-day, contrast-free MRI image sets, we have predicted the non-perfused volume with an accuracy comparable to modern-day deep learning prostate segmentation methods. Overall, this work will help daily clinical practice and increase the odds of a successful TULSA therapy.MRI-ohjatun eturauhasen ultraääniablaatiohoidon optimointi retrospektiivisten analyysien ja tekoälyn avulla Magneettikuvaus(MRI)-ohjattu virtsaputken kautta annettu ultraääniablaatio (TULSA) on uusi primaarin ja sädehoidon jälkeen paikallisesti uusiutuneen eturauhassyövän (PCa) hoitomuoto. Menetelmässä eturauhaskudosta koaguloidaan korkean intensiteetin ultraäänellä reaaliaikaisessa MRI-ohjauksessa, mikä parantaa hoidon tarkkuutta. Lupaavista kliinisistä tuloksista huolimatta MRI-ohjaus altistaa teknisille ja kliinisille haasteille, mitkä ovat hidastaneet TULSA-hoidon laajempaa käyttöönottoa. TULSA-hoidossa jokainen vaihe rekisteröidään MRI-kuvin. Koneoppimista hyödyntämällä voidaan retrospektiivisesti analysoida näitä MRI-kuvia TULSA-hoitotulosten optimoimiseksi. Sädehoidon ohjauksessa käytetyt eturauhaseen asetetut merkkijyvät saattavat vaikuttaa TULSA-hoidon tehoon ja turvallisuuteen uusiutuneessa PCa:ssä, koska ne voivat aiheuttaa artefaktoja MRI-kuvaan ja estää ultraäänen etenemisen. Laajassa retrospektiivisessa analyysissä todettiin, että kultamerkkijyvät eivät yleensä vaikuta hoitotulokseen, elleivät ne sijaitse samanaikaisesti lähellä virtsaputkea ja kaukana hoitokohteesta tai suoraan kohteen edessä. Kultamerkkijyvillä ei ollut ilmeistä vaikutusta hoidon turvallisuuteen ja tehokkuuteen verrattuna kontrolliryhmään ilman merkkijyviä 12 kuukauden seurannassa. Välittömästi TULSA-hoidon jälkeen hoitotulos varmistetaan merkkiainetehosteisilla MRI-kuvilla, joilla visualisoidaan verenkierroton alue, mikä korreloi akuuttiin kudosvaurioon eli onnistuneeseen hoitovasteeseen. Ongelmana on, että vaikka merkkiainetehosteisissa MRI-kuvissa todettaisiin riittämätön hoitovaste, uudelleenhoito ei ole samalla hoitokerralla mahdollista, koska eturauhaseen kerääntynyt merkkiaine estää hoidon. Tällöin tarvitaan uusi hoitokerta kuukausien kuluttua toimenpiteen sisältämineen riskeineen, mikä viivästyttää hoitoa ja kuormittaa potilasta. Tässä tutkimuksessa onnistuttiin tarkasti ennustamaan verenkierroton alue hoidonaikaisista merkkiainetehostamattomista MRI-kuvista hyödyntämällä syväoppimismallia. Näillä havainnoilla on tärkeä kliininen merkitys TULSA-hoitotulosten parantamisessa