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- Adjeiwaah, Mary, 1980-
(author)
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Quality assurance for magnetic resonance imaging (MRI) in radiotherapy
- 2017
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Licentiate thesis (other academic/artistic)abstract
- Magnetic resonance imaging (MRI) utilizes the magnetic properties of tissues to generate image-forming signals. MRI has exquisite soft-tissue contrast and since tumors are mainly soft-tissues, it offers improved delineation of the target volume and nearby organs at risk. The proposed Magnetic Resonance-only Radiotherapy (MR-only RT) work flow allows for the use of MRI as the sole imaging modality in the radiotherapy (RT) treatment planning of cancer. There are, however, issues with geometric distortions inherent with MR image acquisition processes. These distortions result from imperfections in the main magnetic field, nonlinear gradients, as well as field disturbances introduced by the imaged object. In this thesis, we quantified the effect of system related and patient-induced susceptibility geometric distortions on dose distributions for prostate as well as head and neck cancers. Methods to mitigate these distortions were also studied.In Study I, mean worst system related residual distortions of 3.19, 2.52 and 2.08 mm at bandwidths (BW) of 122, 244 and 488 Hz/pixel up to a radial distance of 25 cm from a 3T PET/MR scanner was measured with a large field of view (FoV) phantom. Subsequently, we estimated maximum shifts of 5.8, 2.9 and 1.5 mm due to patient-induced susceptibility distortions. VMAT-optimized treatment plans initially performed on distorted CT (dCT) images and recalculated on real CT datasets resulted in a dose difference of less than 0.5%. The magnetic susceptibility differences at tissue-metallic,-air and -bone interfaces result in local B0 magnetic field inhomogeneities. The distortion shifts caused by these field inhomogeneities can be reduced by shimming. Study II aimed to investigate the use of shimming to improve the homogeneity of local B0 magnetic field which will be beneficial for radiotherapy applications. A shimming simulation based on spherical harmonics modeling was developed. The spinal cord, an organ at risk is surrounded by bone and in close proximity to the lungs may have high susceptibility differences. In this region, mean pixel shifts caused by local B0 field inhomogeneities were reduced from 3.47±1.22 mm to 1.35±0.44 mm and 0.99±0.30 mm using first and second order shimming respectively. This was for a bandwidth of 122 Hz/pixel and an in-plane voxel size of 1×1 mm2. Also examined in Study II as in Study I was the dosimetric effect of geometric distortions on 21 Head and Neck cancer treatment plans. The dose difference in D50 at the PTV between distorted CT and real CT plans was less than 1.0%.In conclusion, the effect of MR geometric distortions on dose plans was small. Generally, we found patient-induced susceptibility distortions were larger compared with residual system distortions at all delineated structures except the external contour. This information will be relevant when setting margins for treatment volumes and organs at risk. The current practice of characterizing MR geometric distortions utilizing spatial accuracy phantoms alone may not be enough for an MR-only radiotherapy workflow. Therefore, measures to mitigate patient-induced susceptibility effects in clinical practice such as patient-specific correction algorithms are needed to complement existing distortion reduction methods such as high acquisition bandwidth and shimming.
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- Adjeiwaah, Mary, 1980-
(author)
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Quality assurance for magnetic resonance imaging (MRI) in radiotherapy
- 2019
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Doctoral thesis (other academic/artistic)abstract
- The use of Magnetic Resonance Imaging (MRI) in the radiotherapy (RT) treatment planning workflow is increasing. MRI offers superior soft-tissue contrast compared to Computed Tomography (CT) and therefore improves the accuracy in target volume definitions. There are, however concerns with inherent geometric distortions from system- (gradient nonlinearities and main magnetic field inhomogeneities) and patient-related sources (magnetic susceptibility effect and chemical shift). The lack of clearly defined quality assurance (QA) procedures has also raised questions on the ability of current QA protocols to detect common image quality degradations under radiotherapy settings. To fully implement and take advantage of the benefits of MRI in radiotherapy, these concerns need to be addressed.In Papers I and II, the dosimetric impact of MR distortions was investigated. Patient CTs (CT) were deformed with MR distortion vector fields (from the residual system distortions after correcting for gradient nonlinearities and patient-induced susceptibility distortions) to create distorted CT (dCT) images. Field parameters from volumetric modulated arc therapy (VMAT) treatment plans initially optimized on dCT data sets were transferred to CT data to compute new treatment plans. Data from 19 prostate and 21 head and neck patients were used for the treatment planning. The dCT and CT treatment plans were compared to determine the impact of distortions on dose distributions. No clinically relevant dose differences between distorted CT and original CT treatment plans were found. Mean dose differences were < 1.0% and < 0.5% at the planning target volume (PTV) for the head and neck, and prostate treatment plans, respectively. Strategies to reduce geometric distortions were also evaluated in Papers I and II. Using the vendor-supplied gradient non-linearity correction algorithm reduced overall distortions to less than half of the original value. A high acquisition bandwidth of 488 Hz/pixel (Paper I) and 488 Hz/mm (Paper II) kept the mean geometric distortions at the delineated structures below 1 mm. Furthermore, a patient-specific active shimming method implemented in Paper II significantly reduced the number of voxels with distortion shifts > 2 mm from 15.4% to 2.0%.B0 maps from patient-induced magnetic field inhomogeneities obtained through direct measurements and by simulations that used MR-generated synthetic CT (sCT) data were compared in Paper III. The validation showed excellent agreement between the simulated and measured B0 maps.In Paper IV, the ability of current QA methods to detect common MR image quality degradations under radiotherapy settings were investigated. By evaluating key image quality parameters, the QA protocols were found to be sensitive to some of the introduced degradations. However, image quality issues such as those caused by RF coil failures could not be adequately detected.In conclusion, this work has shown the feasibility of using MRI data for radiotherapy treatment planning as distortions resulted in a dose difference of less than 1% between distorted and undistorted images. The simulation software can be used to produce accurate B0 maps, which could then be used as the basis for the effective correction of patient-induced field inhomogeneity distortions and for the QA verification of sCT data. Furthermore, the analysis of the strengths and weaknesses in current QA tools for MRI in RT contribute to finding better methods to efficiently identify image quality errors.
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- Björeland, Ulrika, 1974-
(author)
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MRI in prostate cancer : implications for target volume
- 2023
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Doctoral thesis (other academic/artistic)abstract
- Prostate cancer (PCa) is the most common cancer among men, with 10 000 new cases per year in Sweden [1]. To diagnose PCa, magnetic resonance imaging (MRI) is used to identify and classify the disease. The patient’s treatment strategy depends on PCa classification and clinical data, which are weighted together into a risk group classification from 1–5. For patients with higher risk classes (>3), radiotherapy together with hormone therapy is a common treatment option [2].In radiotherapy (RT), individual treatment plans are created based on the patient’s anatomy. These plans are based on computed tomography (CT), often supplemented with MRI images. MRI and CT complement each other, as MRI has better soft tissue contrast and CT has better bone contrast. Based on the images, the volumes to be treated (target) and the volumes to be avoided (risk organs) are defined. Prostate RT is complex, and there are uncertainties regarding the patient's internal movements and how the patient is positioned before each treatment. To account for these uncertainties, the radiation field is expanded (extended margins to target) to ensure that the treatment volume receives its radiotherapy. RT is most often given in fractions. Fractionation, dose, and treatment volume depend on the patient’s risk category. The treatment area can be, for example, only prostate, prostate with extra radiation dose (boost) to an intraprostatic tumour, or prostate with lymph node (LN) irradiation. LN irradiation is most often given for preventive purposes for PCa with a risk classification >4, which means no cancer has been identified, but any microscopic spread to the LNs is being treated profylactically.In RT, target identification is essential both in the treatment planning images (CT/MRI) and at treatment. Studies have shown that PCa often re-occurs in or near the volume of the dominant (often largest) intraprostatic tumour [3, 4], and this volume is relevant for boosting. For patients treated with hormone therapy before radiotherapy, tumour identification is complicated. Hormones change the tumour characteristics, affecting the image contrast and making the tumour difficult to identify. To study this, we investigated whether texture analysis could identify the tumour volume after hormone therapy (paper II). However, even with texture analysis, the tumour was difficult to identify. A follow-up study examined whether the image information in MRI images taken before hormone therapy could indicate how the treatment fell out (paper IV). However, no correlation was seen between image features and the progression of PCa.Identifying the target and correctly positioning the patient for each treatment fraction is the most important procedure in radiotherapy. The prostate is a mobile organ; therefore, intraprostatic fiducial markers are inserted before treatment planning to reduce positioning uncertainties. Each radiotherapy session begins with an X-ray image where the markers are visible, and the radiation can be delivered based on the markers' position. The markers are also used as guidance for large target volumes, such as for prostate with LN irradiation. With better knowledge of the prostate and LN movements, the margins can potentially be reduced, followed by reduced radiation dose to healthy tissue and therefore reduced side effects for patients. Movements in the radiotherapy volume were the focus of paper I. Using MRI images, the movements of the prostate and LNs were measured during the course of radiotherapy, and we found that LN movement is independent of the movement of the prostate and that the movement varies in the target volume.In addition to the recurrence of PCa in the tumour area, there is an increased risk of recurrence in the prostate periphery close to the rectum. Since the rectum and prostate are in contact for some patients, RT must be adapted to make rectum side effects tolerable. One way to increase the distance between the prostate and the rectum is to inject a gel between the two organs. The distance makes it easier to achieve a better dose distribution to the PCa. This idea resulted in paper III, where patients were given a gel between the prostate and rectum. MRI was used to check the stability of the gel during the course of RT and was evaluated together with long-term follow-up of the patient’s well-being and acceptance of the gel. We found that the radiation dose to the rectum was lower with a spacer, although the spacer was not completely stable during treatment.
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- Brynolfsson, Patrik, 1981-
(author)
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Applications of statistical methods in quantitative magnetic resonance imaging
- 2017
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Doctoral thesis (other academic/artistic)abstract
- Magnetic resonance imaging, MRI, offers a vast range of imaging methods that can be employed in the characterization of tumors. MRI is generally used in a qualitative way, where radiologists interpret the images for e.g. diagnosis, follow ups, or assessment of treatment response. In the past decade, there has been an increasing interest for quantitative imaging, which give repeatable measurements of the anatomy. Quantitative imaging allows for objective analysis of the images, which are grounded in physical properties of the underlying tissues. The aim of this thesis was to improve quantitative measurements of Dynamic contrast enhanced MRI (DCE-MRI), and the texture analysis of diffusion weighted MRI (DW-MRI).DCE-MRI measures perfusion, which is the delivery of blood, oxygen and nutrients to the tissues. The exam involves continuously imaging the region of interest, e.g. a tumor, while injecting a contrast agent (CA) in the blood stream. By analyzing how fast and how much CA leaks out into the tissues, the cell density and the permeability of the capillaries can be estimated. Tumors often have an irregular and broken vasculature, and DCE-MRI can aid in tumor grading or treatment assessment. One step is crucial when performing DCE-MRI analysis, the quantification of CA in the tissue. The CA concentration is difficult to measure accurately due to uncertainties in the imaging, properties of the CA, and physiology of the patient. Paper I, the possibility of using two aspects of the MRI data, phase and magnitude, for improved CA quantification, is explored. We found that the combination of phase and magnitude information improved the CA quantification in regions with high CA concentration, and was more advantageous for high field strength scanners.DW-MRI measures the diffusion of water in and between cells, which reflects the cell density and structure of the tissue. The structure of a tumor can give insights into the prognosis of the disease. Tumors are heterogeneous, both genetically and in the distribution of cells, and tumors with high intratumoral heterogeneity have poorer prognosis. This heterogeneity can be measured using texture analysis. In 1973, Haralick et al. presented a texture analysis method using a gray level co-occurrence matrix, GLCM, to gauge the spatial distribution of gray levels in the image. This method of assessing texture in images has been successfully applied in many areas of research, from satellite images to medical applications. Texture analysis in treatment outcome assessment is studied in Paper II, where we showed that texture can distinguish between groups of patients with different survival times, in images acquired prior to treatment start.However, this type of texture analysis is not inherently quantitative in the way it is calculated today. This was studied in Paper III, where we investigated how texture features were affected by five parameters related to image acquisition and pre-processing. We found that the texture feature values were dependent on the choice of these imaging and preprocessing parameters. In Paper IV, a novel method for calculating Haralick texture features was presented, which makes the texture features asymptotically invariant to the size of the GLCM. This method allows for comparison of textures between images that have been analyzed in different ways.In conclusion, the work in this thesis has been aimed at improving quantitative analysis of tumors using MRI and texture analysis.
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