- Title
- Improved prostate tumour identification and delineation using multiparametric magnetic resonance imaging
- Creator
- Gholizadeh, Neda
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2019
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- In the last few decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed to improve early-stage detection and diagnosis of prostate cancer. MRI plays an important role in improving the current strategies for detection, delineation and risk stratification for prostate cancer. T2-weighted imaging (T2WI) is the primary imaging technique to evaluate anatomy and pathology in the prostate. However MRI images are expressed in arbitrary units and the absolute values contained within those images can vary due to differences between: MRI scanners; scanning techniques or other technical influences resulting in variations between patients or for the same patient when rescanned at different time points. In this thesis, statistically-based scale standardisation methods for two different centres have been used to address these problems. The results demonstrated a robust and reliable standardisation method for quantitative image assessment. The combination of conventional anatomical and advanced MRI is known as multiparametric MRI (mp-MRI). Mp-MRI is considered to have great potential in accurate prostate cancer diagnosis and characterization. Advanced MRI techniques provide physiological information about tissue to improve prostate cancer diagnosis and characterization. A recent advanced MRI technique is diffusion tensor imaging (DTI). Quantitative analysis of DTI could improve the detection and characterization of prostate cancer by providing additional information. However, difficulties in the processing and interpretation of DTI has limited the role of this imaging in clinical mp-MRI procedures. To evaluate the diagnostic performance of DTI, quantitative DTI and DTI tractography parameters with a focus on their impact in diagnostic and managing prostate cancer patients were extracted and evaluated. A fast imaging technique was utilised to decrease motion artefacts throughout the acquisition of prostate cancer patient data in an ethics approved MRI imaging study. The results demonstrated that DTI and DTI tractography have the potential to provide imaging biomarkers in the detection and characterization of prostate cancer in the peripheral zone. In addition, the utility of DTI in addition to T2-weighted imaging (T2WI) and diffusion weighted imaging (DWI) was assessed for the voxel based detection and prediction of peripheral zone dominant prostate tumours using supervised machine learning technique. Machine learning is a method of data analysis that automates analytical model building. These results demonstrated that DTI in combination with T2WI and DWI can improve the accuracy of prostate cancer detection and delineation. However, T2WI, DWI and DTI have limitations for central gland prostate cancer detection. It is well established that magnetic resonance spectroscopic imaging (MRSI) can provide valuable metabolic information for the non-invasive assessment of central gland prostate cancer. However, MRSI has been excluded from routine clinical mp-MRI in the most recently updated Prostate Imaging Reporting and Data System PI-RADS V2 guideline, probably due to moderate metabolite signal-to-noise ratio (SNR), relatively long acquisition times, the need for a high level of operator expertise, low spectral resolution (especially at 1.5T) and non-standardised acquisition and postprocessing techniques. In the most recent years, MRSI have undergone several technical improvements and show renewed promise for prostate tumour detection and localization. The most recently developed MRSI acquisition technique is known as a gradient-modulated offset-independent adiabatic (GOIA) semi-localized adiabatic selective refocusing (sLASER) (GOIA-sLASER) pulse sequence, which enables acquisition of metabolic data with a high spectral and spatial resolution without an endorectal coil at 3T in a clinically feasible scanning time (8-10 minutes). This thesis investigated the efficacy of in vivo MRSI using the GOIA-sLASER pulse sequence without an endorectal coil for detection and characterization of central gland prostate cancer. This results showed that the GOIA-sLASER sequence with an external phased-array coil allows for an accurate assessment of central gland prostate cancer. In addition, the diagnostic performance of mp-MRI, including T2WI, DWI and dynamic contrast with and without MRSI was assessed. These results demonstrated that MRSI using GOIA-sLASER can be a promising technique for non-invasive and accurate diagnosis of prostate cancer in the central zone. The performance of DTI and MRSI shown in this thesis illustrates potential advantages of non-invasive mp-MRI imaging in the course of prostate cancer detection and diagnosis.
- Subject
- prostate cancer; multiparametric MRI; machine learning; thesis by publication
- Identifier
- http://hdl.handle.net/1959.13/1411984
- Identifier
- uon:36411
- Rights
- Copyright 2019 Neda Gholizadeh
- Language
- eng
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT01 | Thesis | 5 MB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Abstract | 529 KB | Adobe Acrobat PDF | View Details Download |