Mika Pollari

Correction of Movement Artifacts from 4-D Cardiac Short- and Long-Axis MR Data

Typically a cardiac MR cine series consists of images over several time points but only from one spatial location. The volumetric information is obtained by combining 2-D slices from different image series. If a patient moves during an MR imaging session, the slices from different image series shift relative to each other, and the 3-D volume reconstructed does not represent the real geometry. In this study, an algorithm was developed to correct movement artifacts simultaneously from short- and long-axis MR cine series. The performance of the algorithm was evaluated by calculating the accuracy of the method against simulated movements imposed on real data, and by visually inspecting the results with real patient images. In both cases, the algorithm reduced significantly movement artifacts.

Image-based multi-scale modelling and validation of radio-frequency ablation in liver tumours

The treatment of cancerous tumours in the liver remains clinically challenging, despite the wide range of treatment possibilities, including radio-frequency ablation (RFA), high-intensity focused ultrasound and resection, which are currently available. Each has its own advantages and disadvantages. For non- or minimally invasive modalities, such as RFA, considered here, it is difficult to monitor the treatment in vivo. This is particularly problematic in the liver, where large blood vessels act as heat sinks, dissipating delivered heat and shrinking the size of the lesion (the volume damaged by the heat treatment) locally; considerable experience is needed on the part of the clinician to optimize the heat treatment to prevent recurrence. In this paper, we outline our work towards developing a simulation tool kit that could be used both to optimize treatment protocols in advance and to train the less-experienced clinicians for RFA treatment of liver tumours. This tool is based on a comprehensive mathematical model of bio-heat transfer and cell death. We show how simulations of ablations in two pigs, based on individualized imaging data, compare directly with experimentally measured lesion sizes and discuss the likely sources of error and routes towards clinical implementation. This is the first time that such a ‘loop’ of mathematical modelling and experimental validation in vivo has been performed in this context, and such validation enables us to make quantitative estimates of error.

Probabilistic atlas can improve reconstruction from optical imaging of the neonatal brain

Diffuse optical imaging is an emerging medical imaging modality based on near-infrared and visible red light. The method can be used for imaging activations in the human brain. In this study, a deformable probabilistic atlas of the distribution of tissue types within the term neonatal head was created based on MR images. The use of anatomical prior information provided by such atlas in reconstructing brain activations from optical imaging measurements was studied using Monte Carlo simulations. The results suggest that use of generic anatomical information can greatly improve the spatial accuracy and robustness of the reconstruction when noise is present in the data.

Interactive Volumetry Of Liver Ablation Zones.

Percutaneous radiofrequency ablation (RFA) is a minimally invasive technique that destroys cancer cells by heat. The heat results from focusing energy in the radiofrequency spectrum through a needle. Amongst others, this can enable the treatment of patients who are not eligible for an open surgery. However, the possibility of recurrent liver cancer due to incomplete ablation of the tumor makes post-interventional monitoring via regular follow-up scans mandatory. These scans have to be carefully inspected for any conspicuousness. Within this study, the RF ablation zones from twelve post-interventional CT acquisitions have been segmented semi-automatically to support the visual inspection. An interactive, graph-based contouring approach, which prefers spherically shaped regions, has been applied. For the quantitative and qualitative analysis of the algorithm’s results, manual slice-by-slice segmentations produced by clinical experts have been used as the gold standard (which have also been compared among each other). As evaluation metric for the statistical validation, the Dice Similarity Coefficient (DSC) has been calculated. The results show that the proposed tool provides lesion segmentation with sufficient accuracy much faster than manual segmentation. The visual feedback and interactivity make the proposed tool well suitable for the clinical workflow.

Affine registration of diffusion tensor MR images

We present a new algorithm for affine registration of diffusion tensor magnetic resonance (DT-MR) images. The method is based on a new formulation of a point-wise tensor similarity measure, which weights directional and magnitude information differently depending on the type of diffusion. The method is compared to a reference method, which uses normalized mutual information (NMI), calculated either from a fractional anisotropy (FA) map or a T2-weighted MR image. The registration methods are applied to real and simulated DT-MR images. Visual assessment is done for real data and for simulated data, registration accuracy is defined. The results show that the proposed method outperforms the reference method.

Vessel segmentation for ablation treatment planning and simulation

In this paper, a novel segmentation method for liver vasculature is presented, intended for numerical simulation of radio frequency ablation (RFA). The developed method is a semiautomatic hybrid based on multi-scale vessel enhancement combined with ridge-oriented region growing and skeleton-based postprocessing. In addition, an interactive tool for segmentation refinement was developed. Four instances of three-phase contrast enhanced computed tomography (CT) images of porcine liver were used in the evaluation. The results showed improved accuracy over common approaches and illustrated the methods suitability for simulation purposes.

Segmentation of Cardiac Structures Simultaneously from Short- and Long-Axis MR Images

We introduce a framework for the automatic segmentation of the ventricles, atria, and epicardium simultaneously from cardiac magnetic resonance (MR) volumes. The basic idea is to utilize both short-axis (SA) and long-axis (LA) MR volumes. Consequently, anatomical information is available from the whole heart volume. In this paper, the framework is used with deformable model based registration and segmentation methods to segment the cardiac structures. A database consisting of the cardiac MR volumes of 25 healthy subjects is used to validate the methods.

Evaluation of cardiac PET-MRI registration methods using a numerical breathing phantom

The thorax structure movements (breathing, heart motion and patient motion) during the positron emission tomography (PET) scanning cause deformations and blurring to PET scans. These artifacts are important reasons, for the misregistration of PET volumes. In this paper, a breathing simulated PET phantom was constructed by deforming simulated (static) PET volume according to breathing model. The constructed volume was used as a ground truth in the assessment of a nonrigid registration method for cardiac MR and PET volumes. Results showed that the assessed nonrigid registration method was more accurate than a rigid registration method which was used as a reference.

RFA Guardian: Comprehensive Simulation of Radiofrequency Ablation Treatment of Liver Tumors

The RFA Guardian is a comprehensive application for high-performance patient-specific simulation of radiofrequency ablation of liver tumors. We address a wide range of usage scenarios. These include pre-interventional planning, sampling of the parameter space for uncertainty estimation, treatment evaluation and, in the worst case, failure analysis. The RFA Guardian is the first of its kind that exhibits sufficient performance for simulating treatment outcomes during the intervention. We achieve this by combining a large number of high-performance image processing, biomechanical simulation and visualization techniques into a generalized technical workflow. Further, we wrap the feature set into a single, integrated application, which exploits all available resources of standard consumer hardware, including massively parallel computing on graphics processing units. This allows us to predict or reproduce treatment outcomes on a single personal computer with high computational performance and high accuracy. The resulting low demand for infrastructure enables easy and cost-efficient integration into the clinical routine. We present a number of evaluation cases from the clinical practice where users performed the whole technical workflow from patient-specific modeling to final validation and highlight the opportunities arising from our fast, accurate prediction techniques.

RFA-Cut: Semi-automatic Segmentation of Radiofrequency Ablation Zones with and without Needles via Optimal s-t-Cuts

In this contribution, we present a semi-automatic segmentation algorithm for radiofrequency ablation (RFA) zones via optimal s-t-cuts. Our interactive graph-based approach builds upon a polyhedron to construct the graph and was specifically designed for computed tomography (CT) acquisitions from patients that had RFA treatments of Hepatocellular Carcinomas (HCC). For evaluation, we used twelve post-interventional CT datasets from the clinical routine and as evaluation metric we utilized the Dice Similarity Coefficient (DSC), which is commonly accepted for judging computer aided medical segmentation tasks. Compared with pure manual slice-by-slice expert segmentations from interventional radiologists, we were able to achieve a DSC of about eighty percent, which is sufficient for our clinical needs. Moreover, our approach was able to handle images containing (DSC=75.9%) and not containing (78.1%) the RFA needles still in place. Additionally, we found no statistically significant difference (p<;0.423) between the segmentation results of the subgroups for a Mann-Whitney test. Finally, to the best of our knowledge, this is the first time a segmentation approach for CT scans including the RFA needles is reported and we show why another state-of-the-art segmentation method fails for these cases. Intraoperative scans including an RFA probe are very critical in the clinical practice and need a very careful segmentation and inspection to avoid under-treatment, which may result in tumor recurrence (up to 40%). If the decision can be made during the intervention, an additional ablation can be performed without removing the entire needle. This decreases the patient stress and associated risks and costs of a separate intervention at a later date. Ultimately, the segmented ablation zone containing the RFA needle can be used for a precise ablation simulation as the real needle position is known.