A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images

Surrogate-driven respiratory motion models relate the motion of the internal anatomy to easily acquired respiratory surrogate signals, such as the motion of the skin surface. They are usually built by first using image registration to determine the motion from a number of dynamic images, and then fitting a correspondence model relating the motion to the surrogate signals. In this paper we present a generalized framework that unifies the image registration and correspondence model fitting into a single optimization. This allows the use of 'partial' imaging data, such as individual slices, projections, or k-space data, where it would not be possible to determine the motion from an individual frame of data. Motion compensated image reconstruction can also be incorporated using an iterative approach, so that both the motion and a motion-free image can be estimated from the partial image data. The framework has been applied to real 4DCT, Cine CT, multi-slice CT, and multi-slice MR data, as well as simulated datasets from a computer phantom. This includes the use of a super-resolution reconstruction method for the multi-slice MR data. Good results were obtained for all datasets, including quantitative results for the 4DCT and phantom datasets where the ground truth motion was known or could be estimated

A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images.

Surrogate-driven respiratory motion models relate the motion of the internal anatomy to easily acquired respiratory surrogate signals, such as the motion of the skin surface. They are usually built by first using image registration to determine the motion from a number of dynamic images, and then fitting a correspondence model relating the motion to the surrogate signals. In this paper we present a generalized framework that unifies the image registration and correspondence model fitting into a single optimization. This allows the use of 'partial' imaging data, such as individual slices, projections, or k-space data, where it would not be possible to determine the motion from an individual frame of data. Motion compensated image reconstruction can also be incorporated using an iterative approach, so that both the motion and a motion-free image can be estimated from the partial image data. The framework has been applied to real 4DCT, Cine CT, multi-slice CT, and multi-slice MR data, as well as simulated datasets from a computer phantom. This includes the use of a super-resolution reconstruction method for the multi-slice MR data. Good results were obtained for all datasets, including quantitative results for the 4DCT and phantom datasets where the ground truth motion was known or could be estimated

PET Respiratory Motion Correction in Simultaneous PET/MR

In Positron Emission Tomography (PET) imaging, patient motion due to respiration can lead to artefacts and blurring, in addition to quantification errors. The integration of PET imaging with Magnetic Resonance (MR) imaging in PET/MR scanners provides spatially aligned complementary clinical information, and allows the use of high spatial resolution and high contrast MR images to monitor and correct motion-corrupted PET data. In this thesis, we form a methodology for respiratory motion correction of PET data, and show it can improve PET image quality. The approach is practical, having minimal impact on clinical PET/MR protocols, with no need for external respiratory monitoring, using standard MR sequences and minimal extra acquisition time. First we validate the use of PET-derived respiratory signal to use for motion tracking, that uses raw PET data only, via Principal Component Analysis (PCA), then set up the tools to carry out PET Motion Compensated Image Reconstruction (MCIR). We introduce a joint PET-MR motion model, using one minute of PET and MR data to provide a motion model that captures inter-cycle and intra-cycle breathing variations. Different motion models (one/two surrogates, linear/polynomial) are evaluated on dynamic MR data sets. Finally we apply the methodology on 45 clinical PET-MR patient datasets. Qualitative PET reconstruction improvements and artefact reduction are assessed with visual analysis, and quantitative improvements are calculated using Standardised Uptake Value (SUV) changes in avid lesions. Lesion detectability changes are explored with a study where two radiologists identify lesions or ’hot spots’, with confidence levels, in uncorrected and motion-corrected images. In summary, we developed a methodology for motion correction in PET/MR by using a joint motion model and demonstrated the capability of a joint PET-MR motion model to predict respiratory motion by showing significantly improved image quality of PET data, with one minute of extra scan time, and no external hardware