Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation

Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics – particularly in routine clinical practice. A framework is developed to automate DCE-MRI tumour segmentation, by introducing: perfusion-supervoxels to over-segment and classify DCE-MRI volumes using the dynamic contrast enhancement characteristics; and the pieces-of-parts graphical model, which adds global (anatomic) constraints that further refine the supervoxel components that comprise the tumour. The framework was evaluated on 23 DCE-MRI scans of patients with rectal adenocarcinomas, and achieved a voxelwise area-under the receiver operating characteristic curve (AUC) of 0.97 compared to expert delineations. Creating a binary tumour segmentation, 21 of the 23 cases were segmented correctly with a median Dice similarity coefficient (DSC) of 0.63, which is close to the inter-rater variability of this challenging task. A second study is also included to demonstrate the method’s generalisability and achieved a DSC of 0.71. The framework achieves promising results for the underexplored area of rectal tumour segmentation in DCE-MRI, and the methods have potential to be applied to other DCE-MRI and supervoxel segmentation problems

Exploiting MRI information for improved kinetic modelling of dynamic PET data

Kinetic analysis of dynamic PET data requires an accurate estimation of the concen- tration of the available tracer in blood plasma, also known as the arterial input function (AIF). The gold standard method to determine the AIF involves serial blood sampling and is avoided in practice due to its invasiveness. An image derived input function (IDIF) can be a blood-free alternative but its accuracy is limited due to partial volume (PV) effects caused by the restricted spatial resolution of PET scanners. Furthermore, IDIFs are not accurate when metabolite products are present in the blood. Magnetic resonance imaging (MRI) can provide complementary information to PET with high spatial resolution and excellent soft tissue contrast. Furthermore, dynamic MRI techniques can be reliably used to measure the AIF, the concentration of contrast agent in plasma, due to their high temporal resolution. The underlying aim of this research is to improve IDIF estimation in PET, utilising spatial and temporal information from MRI. An IDIF measurement method was developed which involves segmentation of carotid arteries from MR angiography images and uses a practical PVC method to correct for PV effects. It was demonstrated that the IDIFs can be used to compute the cerebral metabolic rate of glucose in the brain with no significant difference compared to arterial sampling. The simultaneous estimation method (SIME) is an alternative technique used to estimate the AIF by fitting time activity curves derived from multiple regions. Due to its computational complexity, SIME is usually complemented with blood samples. In this work, we observed that the early part of an image derived blood curve or an MRI derived AIF could provide prior knowledge regarding the AIF. This was incorporated into SIME to make more accurate kinetic parameter estimations and to perform blood-free analysis of tracers with metabolites

Response evaluation during targeted therapy: early incorporation of imaging biomarkers

Smit, E.F. [Promotor]Hoekstra, O.S. [Promotor]Lubberink, M. [Copromotor

Personalised Estimation of the Arterial Input Function for Improved Pharmacokinetic Modelling of Colorectal Cancer Using dceMRI

dceMRI is becoming a key modality for tumour characterisation and monitoring of response to therapy, because of the ability to identify the underlying tumour physiology. Pharmacokinetic (PK) models relate the contrast enhancement seen in dceMRI to physiological parameters but require accurate measurement of the AIF, the time-dependant contrast concentration in blood plasma. In this study, a novel method is introduced that overcomes the challenges of direct AIF measurement, by automatically estimating the AIF from the tumour tissue. This approach was evaluated on synthetic data (10% noise) and achieved a relative error in Ktrans and kep of 11.8±3.5% and 25.7±4.7 %, respectively, compared to 41 ±15 % and 60 ±32 % using a population model. The method improved the fit of the PK model to clinical colorectal cancer cases, was stable for independent regions in the tumour, and showed improved localisation of the PK parameters. This demonstrates that personalised AIF estimation can lead to more accurate PK modelling. © 2013 Springer-Verlag