A Chandler

Correction of misaligned slices in multi-slice cardiovascular magnetic resonance using slice-to-volume registration

A popular technique to reduce respiratory motion for cardiovascular magnetic resonance is to perform a multi-slice acquisition in which a patient holds their breath multiple times during the scan. The feasibility of rigid slice-to-volume registration to correct for misalignments of slice stacks in such images due to differing breath-hold positions is explored. Experimental results indicate that slice-to-volume registration can compensate for the typical misalignments expected. Correction of slice misalignment results in anatomically more correct images, as well as improved left ventricular volume measurements. The interstudy reproducibility has also been improved reducing the number of samples needed for cardiac MR studies.

Correction of misaligned slices in multi-slice MR cardiac examinations by using slice-to-volume registration

One of the main challenges with magnetic resonance (MR) cardiac image acquisition is to account for cardiac motion due to respiration. A popular technique to reduce respiratory motion is to perform a multi-slice acquisition in which a patient holds their breath multiple times during the scan. This paper explores the feasibility of using rigid slice-to-volume registration to correct for misalignments of slice stacks in such images due to differing breath-hold positions. The experimental results indicate that slice-to-volume registration is sufficiently accurate and robust to compensate for the typical misalignments expected. We show that correction of misalignments in such data results in anatomically more correct images, as well as improved left ventricular volume measurements. It also shows that by correcting for misalignments in short axis (SA) images, one can improve the interstudy reproducibility and hence reduce the number of samples needed for cardiac MR studies to show the same statistical significance

A comparison of internal target volume definition by limited four-dimensional computed tomography, the addition of patient-specific margins, or the addition of generic margins when planning radical radiotherapy for lymph node-positive non-small cell lung cancer

AIMS Radical radiotherapy for stage II/III non-small cell lung cancer (NSCLC) includes the primary tumour and positive mediastinal lymph nodes in the clinical target volume (CTV). These move independently of each other in magnitude and direction during respiration. To prevent a geographical miss, a generic margin is usually added to the CTV to create an internal target volume (ITV). Previous studies have investigated the use of additional breath-hold computed tomography to generate patient-specific ITVs for primary tumours alone. We used a similar technique to investigate the generation of patient-specific and generic ITVs for CTVs that include mediastinal lymph nodes. MATERIALS AND METHODS Thirteen patients with node-positive NSCLC had two limited end-tidal breath-hold computed tomography scans in addition to their planning computed tomography. The CTV was segmented in each scan and a rigid registration was carried out on the vertebral columns to align them. Different methods for generating an ITV were then analysed. RESULTS Generic margins provided >95% mean coverage of the reference ITV. However, with the exception of 1cm expansion margins, there were cases of inadequate coverage (<95%) for each ITV. With increasing ITV margins there was a small increase in reference ITV coverage, but at the expense of a large increase in the volume of normal tissue within the ITV. DISCUSSION For stage II/III NSCLC, ITV generation by the addition of a generic margin is not optimal. It can result in both geographical miss and excessive irradiation of normal tissue in the same treatment plan. A simple method for producing a patient-specific ITV is to co-register end-tidal breath-hold computed tomography scans to the planning scan. CONCLUSIONS Further work is required to determine whether end-tidal breath-hold scans are representative of the anatomy at the limits of tidal respiration. Planning strategies are also needed to account for breathing cycle variation during a course of radiotherapy.

Computational Models In Image Guided Interventions

In image-guided surgery and image-directed therapy a plan based on pre-procedure imaging is registered to the patient in the operating or treatment room using a 3D spatial localizer. The plan can be used as long as the transformation between plan and patient remains valid. Most systems use a rigid-body transformation restricting guidance to bony structures (e.g. orthopaedic surgery or maxillo-facial surgery) or structures that are rigidly related to bone (e.g. neurosurgery). Fully 3D intra-operative imaging such as interventional MR allows image guidance to be extended to structures that move or deform during an intervention. However, this technology is expensive, interferes significantly with standard surgical protocols and requires computationally expensive non-rigid registration of the plan to the current patient scan. This talk will describe four examples where computational models of motion and anatomy are combined with 2D intra-operative imaging to extend the scope of image directed methods. In the first, image guided neurosurgery, we show how intra-operative imaging may account for distortion caused by the intervention itself. In two further applications - percutaneous ablation of metastatic liver disease and external beam radiotherapy of the lung - we show how computational models of motion might be used in conjunction with a therapy plan to guide the intervention. In the final example, selected from orthopaedic surgery, we show recent advances that demonstrate how a statistical shape model generated from example 3D images, can be used to provide image guidance without any pre-operative 3D imaging

Slice-to-volume registration using mutual information between probabilistic image classifications

Intensity based registration algorithms have proved to be accurate and robust for 3D-3D registration tasks. However, these methods utilise the information content within an image, and therefore their performance is hindered for image data that is sparse. This is the case for the registration of a single image slice to a 3D image volume. There are some important applications that could benefit from improved slice-to-volume registration, for example, the planning of magnetic resonance (MR) scans or cardiac MR imaging, where images are acquired as stacks of single slices. We have developed and validated an information based slice-to-volume registration algorithm that uses vector valued probabilistic images of tissue classification that have been derived from the original intensity images. We believe that using such methods inherently incorporates into the registration framework more information about the images, especially in images containing severe partial volume artifacts. Initial experimental results indicate that the suggested method can achieve a more robust registration compared to standard intensity based methods for the rigid registration of a single thick brain MR slice, containing severe partial volume artifacts in the through-plane direction, to a complete 3D MR brain volume.