Simultaneous label fusion with vessel preserving for bone removal in CT angiography scans.

When visualizing vessels with CT angiography scans, the arteries are often obstructed by bones. Traditional methods require an additional non-enhanced scan to acquire a bone mask which is then subtracted from the original CTA scan. In this study, we present an automated bone removal method using only contrast enhanced scans based on simultaneous label fusion. We build an atlas database where each atlas is paired with a bone label and a vessel label. After the atlases are mapped to a subject, we propose a vessel preserving scheme to protect possible vessel areas from bone removal by simultaneous label fusion. Seven clinical data sets were used for validation and the results showed that this method can achieve consistent and thorough bone removal with maximal vessels preservation.

Atlas image labeling of subcortical structures and vascular territories in brain CT images.

We propose a multi-atlas labeling method for subcortical structures and cerebral vascular territories in brain CT images. Each atlas image is registered to the query image by a non-rigid registration and the deformation is then applied to the labeling of the atlas image to obtain the labeling of the query image. Four label fusion strategies (single atlas, most similar atlas, major voting, and STAPLE) were compared. Image similarity values in non-rigid registration were calculated and used to select and rank atlases. Major voting fusion strategy gave the best accuracy, with DSC (Dice similarity coefficient) around 0.85 ± 0.03 for caudate, putamen, and thalamus. The experimental results also show that fusing more atlases does not necessarily yield higher accuracy and we should be able to improve accuracy and decrease computation cost at the same time by selecting a preferred set with the minimum number of atlases.

Biomechanically driven registration of pre- to intra-operative 3D images for laparoscopic surgery.

Minimally invasive laparoscopic surgery is widely used for the treatment of cancer and other diseases. During the procedure, gas insufflation is used to create space for laparoscopic tools and operation. Insufflation causes the organs and abdominal wall to deform significantly. Due to this large deformation, the benefit of surgical plans, which are typically based on pre-operative images, is limited for real time navigation. In some recent work, intra-operative images, such as cone-beam CT or interventional CT, are introduced to provide updated volumetric information after insufflation. Other works in this area have focused on simulation of gas insufflation and exploited only the pre-operative images to estimate deformation. This paper proposes a novel registration method for pre- and intra-operative 3D image fusion for laparoscopic surgery. In this approach, the deformation of pre-operative images is driven by a biomechanical model of the insufflation process. The proposed method was validated by five synthetic data sets generated from clinical images and three pairs of in vivo CT scans acquired from two pigs, before and after insufflation. The results show the proposed method achieved high accuracy for both the synthetic and real insufflation data.

Simultaneous myocardial strain and dark-blood perfusion imaging using a displacement-encoded MRI pulse sequence.

The purpose of this study is to develop and evaluate a displacement-encoded pulse sequence for simultaneous perfusion and strain imaging. Displacement-encoded images in two to three myocardial slices were repeatedly acquired using a single-shot pulse sequence for 3 to 4 min, which covers a bolus infusion of Gadolinium contrast. The magnitudes of the images were T(1) weighted and provided quantitative measures of perfusion, while the phase maps yielded strain measurements. In an acute coronary occlusion swine protocol (n = 9), segmental perfusion measurements were validated against microsphere reference standard with a linear regression (slope 0.986, R(2) = 0.765, Bland-Altman standard deviation = 0.15 mL/min/g). In a group of ST-elevation myocardial infarction patients (n = 11), the scan success rate was 76%. Short-term contrast washout rate and perfusion are highly correlated (R(2) = 0.72), and the pixelwise relationship between circumferential strain and perfusion was better described with a sigmoidal Hill curve than linear functions. This study demonstrates the feasibility of measuring strain and perfusion from a single set of images.

High spatial and temporal resolution cardiac cine MRI from retrospective reconstruction of data acquired in real time using motion correction and resorting.

Cine MRI is used for assessing cardiac function and flow and is typically based on a breath-held, segmented data acquisition. Breath holding is particularly difficult for patients with congestive heart failure or in pediatric cases. Real-time imaging may be used without breath holding or ECG triggering. However, despite the use of rapid imaging sequences and accelerated parallel imaging, real-time imaging typically has compromised spatial and temporal resolution compared with gated, segmented breath-held studies. A new method is proposed that produces a cardiac cine across the full cycle, with both high spatial and temporal resolution from a retrospective reconstruction of data acquired over multiple heartbeats during free breathing. The proposed method was compared with conventional cine images in 10 subjects. The resultant image quality for the proposed method (4.2 +/- 0.4) without breath holding or gating was comparable to the conventional cine (4.4 +/- 0.5) on a five-point scale (P = n.s.). Motion-corrected averaging of real-time acquired cardiac images provides a means of attaining high-quality cine images with many of the benefits of real-time imaging, such as free-breathing acquisition and tolerance to arrhythmias.

Non-contrast-enhanced perfusion and ventilation assessment of the human lung by means of fourier decomposition in proton MRI.

Assessment of regional lung perfusion and ventilation has significant clinical value for the diagnosis and follow-up of pulmonary diseases. In this work a new method of non-contrast-enhanced functional lung MRI (not dependent on intravenous or inhalative contrast agents) is proposed. A two-dimensional (2D) true fast imaging with steady precession (TrueFISP) pulse sequence (TR/TE = 1.9 ms/0.8 ms, acquisition time [TA] = 112 ms/image) was implemented on a 1.5T whole-body MR scanner. The imaging protocol comprised sets of 198 lung images acquired with an imaging rate of 3.33 images/s in coronal and sagittal view. No electrocardiogram (ECG) or respiratory triggering was used. A nonrigid image registration algorithm was applied to compensate for respiratory motion. Rapid data acquisition allowed observing intensity changes in corresponding lung areas with respect to the cardiac and respiratory frequencies. After a Fourier analysis along the time domain, two spectral lines corresponding to both frequencies were used to calculate the perfusion- and ventilation-weighted images. The described method was applied in preliminary studies on volunteers and patients showing clinical relevance to obtain non-contrast-enhanced perfusion and ventilation data.

Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data.

UNLABELLED: Attenuation correction (AC) of whole-body PET data in combined PET/MRI tomographs is expected to be a technical challenge. In this study, a potential solution based on a segmented attenuation map is proposed and evaluated in clinical PET/CT cases. METHODS: Segmentation of the attenuation map into 4 classes (background, lungs, fat, and soft tissue) was hypothesized to be sufficient for AC purposes. The segmentation was applied to CT-based attenuation maps from (18)F-FDG PET/CT oncologic examinations of 35 patients with 52 (18)F-FDG-avid lesions in the lungs (n = 15), bones (n = 21), and neck (n = 16). The standardized uptake values (SUVs) of the lesions were determined from PET images reconstructed with nonsegmented and segmented attenuation maps, and an experienced observer interpreted both PET images with no knowledge of the attenuation map status. The feasibility of the method was also evaluated with 2 patients who underwent both PET/CT and MRI. RESULTS: The use of a segmented attenuation map resulted in average SUV changes of 8% +/- 3% (mean +/- SD) for bone lesions, 4% +/- 2% for neck lesions, and 2% +/- 3% for lung lesions. The largest SUV change was 13.1%, for a lesion in the pelvic bone. There were no differences in the clinical interpretations made by the experienced observer with both types of attenuation maps. CONCLUSION: A segmented attenuation map with 4 classes derived from CT data had only a small effect on the SUVs of (18)F-FDG-avid lesions and did not change the interpretation for any patient. This approach appears to be practical and valid for MRI-based AC.

Colon unfolding via skeletal subspace deformation.

We present an efficient method to digitally straighten a colon volume using mesh skinning, a technique well known in computer graphics to deform a polygonal mesh attached to a skeleton hierarchy. In our case, the colon centerline is used as the skeleton structure and the polyhedral model of the lumen as the skin that is to be deformed as the centerline is straightened. Once the colon has been straightened, we use standard rendering techniques to compute the virtual dissection. Our approach is significantly more efficient than previously proposed techniques.

Fully automatic, retrospective enhancement of real-time acquired cardiac cine MR images using image-based navigators and respiratory motion-corrected averaging.

Real-time imaging may be clinically important in patients with congestive heart failure, arrhythmias, or in pediatric cases. However, real-time imaging typically has compromised spatial and temporal resolution compared with gated, segmented studies. To combine the best features of both types of imaging, a new method is proposed that uses parallel imaging to improve temporal resolution of real-time acquired images at the expense of signal-to-noise ratio (SNR), but then produces an SNR-enhanced cine by means of respiratory motion-corrected averaging of images acquired in real-time over multiple heartbeats while free-breathing. The retrospective processing based on image-based navigators and nonrigid image registration is fully automated. The proposed method was compared with conventional cine images in 21 subjects. The resultant image quality for the proposed method (3.9+/-0.44) was comparable to the conventional cine (4.2+/-0.99) on a 5-point scale (P=not significant [n.s.]). The conventional method exhibited degraded image quality in cases of arrhythmias whereas the proposed method had uniformly good quality. Motion-corrected averaging of real-time acquired cardiac images provides a means of attaining high-quality cine images with many of the benefits of real-time imaging, such as free-breathing acquisition and tolerance to arrhythmias.

Magnetic resonance lung function--a breakthrough for lung imaging and functional assessment? A phantom study and clinical trial.

BACKGROUND: Chronic lung diseases are a major issue in public health. A serial pulmonary assessment using imaging techniques free of ionizing radiation and which provides early information on local function impairment would therefore be a considerably important development. Magnetic resonance imaging (MRI) is a powerful tool for the static and dynamic imaging of many organs. Its application in lung imaging however, has been limited due to the low water content of the lung and the artefacts evident at air-tissue interfaces. Many attempts have been made to visualize local ventilation using the inhalation of hyperpolarized gases or gadolinium aerosol responding to MRI. None of these methods are applicable for broad clinical use as they require specific equipment. METHODS: We have shown previously that low-field MRI can be used for static imaging of the lung. Here we show that mathematical processing of data derived from serial MRI scans during the respiratory cycle produces good quality images of local ventilation without any contrast agent. A phantom study and investigations in 85 patients were performed. RESULTS: The phantom study proved our theoretical considerations. In 99 patient investigations good correlation (r = 0.8; p < or = 0.001) was seen for pulmonary function tests and MR ventilation measurements. Small ventilation defects were visualized. CONCLUSION: With this method, ventilation defects can be diagnosed long before any imaging or pulmonary function test will indicate disease. This surprisingly simple approach could easily be incorporated in clinical routine and may be a breakthrough for lung imaging and functional assessment.

Panoramic views for virtual endoscopy.

This paper describes a panoramic projection designed to increase the surface visibility during virtual endoscopies. The proposed projection renders five faces of a cubic viewing space into the plane in a continuous fashion. Using this real-time and interactive visualization technique as a screening method for colon cancer could lead to significantly shorter evaluation time. It avoids having to fly through the colon in both directions and prevents the occlusion of potential polyps behind haustral folds.

Variational, geometric, and statistical methods for modeling brain anatomy and function.

We survey the recent activities of the Odyssée Laboratory in the area of the application of mathematics to the design of models for studying brain anatomy and function. We start with the problem of reconstructing sources in MEG and EEG, and discuss the variational approach we have developed for solving these inverse problems. This motivates the need for geometric models of the head. We present a method for automatically and accurately extracting surface meshes of several tissues of the head from anatomical magnetic resonance (MR) images. Anatomical connectivity can be extracted from diffusion tensor magnetic resonance images but, in the current state of the technology, it must be preceded by a robust estimation and regularization stage. We discuss our work based on variational principles and show how the results can be used to track fibers in the white matter (WM) as geodesics in some Riemannian space. We then go to the statistical modeling of functional magnetic resonance imaging (fMRI) signals from the viewpoint of their decomposition in a pseudo-deterministic and stochastic part that we then use to perform clustering of voxels in a way that is inspired by the theory of support vector machines and in a way that is grounded in information theory. Multimodal image matching is discussed next in the framework of image statistics and partial differential equations (PDEs) with an eye on registering fMRI to the anatomy. The paper ends with a discussion of a new theory of random shapes that may prove useful in building anatomical and functional atlases.