Postoperative evaluation of surgery for craniosynostosis based on image registration techniques

Craniosynostosis consists of a premature fusion of the sutures in an infant skull, which restricts the skull and brain growth. During the last decades there has been a rapid increase of fundamentally diverse surgical treatment methods. At present, the surgical outcome has been assessed using global variables such as cephalic index, head circumerence and intracranial volume. However, the variables have failed in describing the local deformations and morphological changes, which are proposed to more likely induce neurological disorders.

Geometry-Aware Multiscale Image Registration via OBBTree-Based Polyaffine Log-Demons

Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.

Feasibility of image-guided radiotherapy based on tomotherapy for the treatment of locally advanced anal carcinoma

The standard of care for locally advanced anal cancer has been concurrent chemoradiation. However, conventional treatment with 3-dimensional radiotherapy is associated with significant toxicity. The feasibility of new radiotherapy techniques such as image-guided radiotherapy (IGRT) in combination with chemotherapy for the treatment of this malignancy was assessed.

A survey of MRI-based medical image analysis for brain tumor studies

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

Development and implementation of a web-enabled 3D consultation tool for breast augmentation surgery based on 3D-image reconstruction of 2D pictures

Producing a rich, personalized Web-based consultation tool for plastic surgeons and patients is challenging.

Assessment of spline-based 2D-3D registration for image-guided spine surgery

2D-3D registration of pre-operative 3D volumetric data with a series of calibrated and undistorted intra-operative 2D projection images has shown great potential in CT-based surgical navigation because it obviates the invasive procedure of the conventional registration methods. In this study, a recently introduced spline-based multi-resolution 2D-3D image registration algorithm has been adapted together with a novel least-squares normalized pattern intensity (LSNPI) similarity measure for image guided minimally invasive spine surgery. A phantom and a cadaver together with their respective ground truths were specially designed to experimentally assess possible factors that may affect the robustness, accuracy, or efficiency of the registration. Our experiments have shown that it is feasible for the assessed 2D-3D registration algorithm to achieve sub-millimeter accuracy in a realistic setup in less than one minute.

Projector-based augmented reality for intuitive intraoperative guidance in image-guided 3D interstitial brachytherapy

PURPOSE: The aim of this study is to implement augmented reality in real-time image-guided interstitial brachytherapy to allow an intuitive real-time intraoperative orientation. METHODS AND MATERIALS: The developed system consists of a common video projector, two high-resolution charge coupled device cameras, and an off-the-shelf notebook. The projector was used as a scanning device by projecting coded-light patterns to register the patient and superimpose the operating field with planning data and additional information in arbitrary colors. Subsequent movements of the nonfixed patient were detected by means of stereoscopically tracking passive markers attached to the patient. RESULTS: In a first clinical study, we evaluated the whole process chain from image acquisition to data projection and determined overall accuracy with 10 patients undergoing implantation. The described method enabled the surgeon to visualize planning data on top of any preoperatively segmented and triangulated surface (skin) with direct line of sight during the operation. Furthermore, the tracking system allowed dynamic adjustment of the data to the patient's current position and therefore eliminated the need for rigid fixation. Because of soft-part displacement, we obtained an average deviation of 1.1 mm by moving the patient, whereas changing the projector's position resulted in an average deviation of 0.9 mm. Mean deviation of all needles of an implant was 1.4 mm (range, 0.3-2.7 mm). CONCLUSIONS: The developed low-cost augmented-reality system proved to be accurate and feasible in interstitial brachytherapy. The system meets clinical demands and enables intuitive real-time intraoperative orientation and monitoring of needle implantation.

ANIMATED TRANSITION IN SIMILARITY-BASED TILED IMAGE LAYOUT

Effective techniques for organizing and visualizing large image collections are in growing demand as visual search gets increasingly popular. iMap is a treemap representation for visualizing and navigating image search and clustering results based on the evaluation of image similarity using both visual and textual information. iMap not only makes effective use of available display area to arrange images but also maintains stable update when images are inserted or removed during the query. A key challenge of using iMap lies in the difficult to follow and track the changes when updating the image arrangement as the query image changes. For many information visualization applications, showing the transition when interacting with the data is critically important as it can help users better perceive the changes and understand the underlying data. This work investigates the effectiveness of animated transition in a tiled image layout where the spiral arrangement of the images is based on their similarity. Three aspects of animated transition are considered, including animation steps, animation actions, and flying paths. Exploring and weighting the advantages and disadvantages of different methods for each aspect and in conjunction with the characteristics of the spiral image layout, we present an integrated solution, called AniMap, for animating the transition from an old layout to a new layout when a different image is selected as the query image. To smooth the animation and reduce the overlap among images during the transition, we explore different factors that might have an impact on the animation and propose our solution accordingly. We show the effectiveness of our animated transition solution by demonstrating experimental results and conducting a comparative user study.

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

Adjuvant therapy after resection of brain metastases. Frameless image-guided LINAC-based radiosurgery and stereotactic hypofractionated radiotherapy

BACKGROUND: Tumor bed stereotactic radiosurgery (SRS) after resection of brain metastases is a new strategy to delay or avoid whole-brain irradiation (WBRT) and its associated toxicities. This retrospective study analyzes results of frameless image-guided linear accelerator (LINAC)-based SRS and stereotactic hypofractionated radiotherapy (SHRT) as adjuvant treatment without WBRT. MATERIALS AND METHODS: Between March 2009 and February 2012, 44 resection cavities in 42 patients were treated with SRS (23 cavities) or SHRT (21 cavities). All treatments were delivered using a stereotactic LINAC. All cavities were expanded by ≥ 2 mm in all directions to create the clinical target volume (CTV). RESULTS: The median planning target volume (PTV) for SRS was 11.1 cm(3). The median dose prescribed to the PTV margin for SRS was 17 Gy. Median PTV for SHRT was 22.3 cm(3). The fractionation schemes applied were: 4 fractions of 6 Gy (5 patients), 6 fractions of 4 Gy (6 patients) and 10 fractions of 4 Gy (10 patients). Median follow-up was 9.6 months. Local control (LC) rates after 6 and 12 months were 91 and 77 %, respectively. No statistically significant differences in LC rates between SRS and SHRT treatments were observed. Distant brain control (DBC) rates at 6 and 12 months were 61 and 33 %, respectively. Overall survival (OS) at 6 and 12 months was 87 and 63.5 %, respectively, with a median OS of 15.9 months. One patient treated by SRS showed symptoms of radionecrosis, which was confirmed histologically. CONCLUSION: Frameless image-guided LINAC-based adjuvant SRS and SHRT are effective and well tolerated local treatment strategies after resection of brain metastases in patients with oligometastatic disease.

Graphical Model-Based Vertebra Identification from X-Ray Image(s)

Automated identification of vertebrae from X-ray image(s) is an important step for various medical image computing tasks such as 2D/3D rigid and non-rigid registration. In this chapter we present a graphical model-based solution for automated vertebra identification from X-ray image(s). Our solution does not ask for a training process using training data and has the capability to automatically determine the number of vertebrae visible in the image(s). This is achieved by combining a graphical model-based maximum a posterior probability (MAP) estimate with a mean-shift based clustering. Experiments conducted on simulated X-ray images as well as on a low-dose low quality X-ray spinal image of a scoliotic patient verified its performance.

Ruler Based Automatic C-Arm Image Stitching Without Overlapping Constraint

In this paper, we propose a new method for stitching multiple fluoroscopic images taken by a C-arm instrument. We employ an X-ray radiolucent ruler with numbered graduations while acquiring the images, and the image stitching is based on detecting and matching ruler parts in the images to the corresponding parts of a virtual ruler. To achieve this goal, we first detect the regular spaced graduations on the ruler and the numbers. After graduation labeling, for each image, we have the location and the associated number for every graduation on the ruler. Then, we initialize the panoramic X-ray image with the virtual ruler, and we “paste” each image by aligning the detected ruler part on the original image, to the corresponding part of the virtual ruler on the panoramic image. Our method is based on ruler matching but without the requirement of matching similar feature points in pairwise images, and thus, we do not necessarily require overlap between the images. We tested our method on eight different datasets of X-ray images, including long bones and a complete spine. Qualitative and quantitative experiments show that our method achieves good results.