Umeå universitets logga

Magnetic resonance imaging (MRI) offers high soft-tissue contrast compared to computed tomography (CT). This contrast is helpful in many cases, not least for delineating tumours for radiotherapy treatment, and has led to increasing use in radiotherapy treatment planning (RTP).

When RTP is based on CT images, the treatment planning system can get the approximate electron density of the tissues from the electron density equivalent information that constitutes the CT images. This information is needed to calculate the dose to the patient from radiotherapy. Therefore, for an MR-only workflow, the MRI image must be transformed into information that can yield electron density information. The predominant way is to convert the MRI images into CT-like images, also known as substitute CT (sCT).

Positron emission tomography (PET) imaging can benefit from being combined with anatomical imaging, and the PET/CT hybrid machine is well established. The soft tissue contrast properties of the MRI images are also valuable for a PET/MRI hybrid system. However, it also adds the option for simultaneous acquisition of PET and anatomical (MRI) images which is not feasible with CT images for a PET/CT system. However, the PET/MRI combination is more technically challenging. While most of the concerns have been solved or mitigated, and PET/MRI systems have been commercially available for some years, there are still outstanding issues. The attenuation maps used in the reconstruction of PET acquisitions are one of the concerns that have yet to be solved entirely. These attenuation maps in the PET/MRI systems are approximations where, e.g., bone is not fully accounted for in all parts of the body.

The MRI images suffer from geometric distortions dependent on the MRI scanner as well as the imaged patient itself. These distortions can affect the sCT conversion for RTP and the attenuation maps for PET reconstruction.

This thesis aimed to investigate the size of the geometric MRI distortions for different settings on the MRI scanner and their effect on the resulting RTP and reconstructed PET images. Such information can aid in optimising the MRI imaging for different purposes. It can also give some information needed to determine tests to run in a quality assurance (QA) regime.

In Paper I, we studied the machine-dependent MRI gradient-field nonlinearity distortions and their effect on PET reconstruction. We simulated different levels of incomplete corrections for gradient-field nonlinearities in CT images from PET/CT acquisitions. The resulting distorted images were then used for rerunning the reconstruction of PET data, and the effect on reconstructed standardised uptake value (SUV) was studied. We found that residual gradient-field nonlinearity dependent geometrical distortions of ±2.3 mm at 15 cm radius from the scanner isocenter lead to SUV quantification errors below 5%. This is also below the test-retest variability caused by instrumentation and intra-patient factors for PET/CT systems.

In Paper II, we developed a method for simulating the patient-induced susceptibility effect based on CT images. The method consisted of converting the CT images to magnetic susceptibility maps. These magnetic susceptibility maps were then used in the simulation by calculating the local shifts in the main magnetic field (B0). From these local shifts in B0, a displacement map was calculated, and this was, in turn, applied to the original CT images. The simulation was validated through comparisons between the simulation and analytical results for both a homogeneous sphere and a homogeneous cylinder. This method was tested on a set of eight prostate cancer patients. We found that setting the frequency encoding bandwidth to a minimum of twice the water-fat shift would keep the maximum distortion from the patient-induced susceptibility effect below 1 pixel. However, the required frequency encoding bandwidth was shown to be dependent on the imaged area, and lower bandwidth could, e.g., be used for the pelvic area.

The simulation method from Paper II was then used in Paper III, where we investigated the dosimetric impact of residual MRI system distortions, patient-induced susceptibility effects and patient-specific shimming. The latter was simulated using an in-house Matlab algorithm. The residual system distortions were determined using phantom measurements. These distortions were then combined with a simulated patient-induced susceptibility effect and patient-specific shimming. The combined distortions and patient-specific shimming were then applied to patient CT images. The distorted patient images were then used for RTP, and the resulting treatment plan was transferred to the original patient CT datasets and recalculated. This allowed for isolating the effect of the applied distortions on the dose distribution. We concluded that the dosimetric impact of MRI distortions within the target volume and nearby organs at risk is small for high bandwidth spin echo sequences. We also saw a worsening in field variations for the user-defined region ofinterest shimming.

Paper IV presented a proof of concept for patient-specific QA of sCTs and attenuation maps. In this study, we compared measured B0-maps with ones simulated using Paper II’s method. The simulations were based on sCT images from MRI acquisitions from the same imaging session as the measured B0-maps. This method shows potential for identifying errors or problematic areas of sCTs and attenuation maps. It should also be feasible to make the method fast enough to use while the patient is still in the scanner so that images could be retaken without having to recall thepatient if a problem is detected.

This work has contributed to the knowledge and methods needed for the necessary considerations for optimisation and setting up a QA protocol aimed at PET/MRI and MR-only radiotherapy.