EpCAM as multi-tumour target for near-infrared fluorescence guided surgery.

BACKGROUND: Evaluation of resection margins during cancer surgery can be challenging, often resulting in incomplete tumour removal. Fluorescence-guided surgery (FGS) aims to aid the surgeon to visualize tumours and resection margins during surgery. FGS relies on a clinically applicable imaging system in combination with a specific tumour-targeting contrast agent. In this study EpCAM (epithelial cell adhesion molecule) is evaluated as target for FGS in combination with the novel Artemis imaging system. METHODS: The NIR fluorophore IRDye800CW was conjugated to the well-established EpCAM specific monoclonal antibody 323/A3 and an isotype IgG1 as control. The anti-EpCAM/800CW conjugate was stable in serum and showed preserved binding capacity as evaluated on EpCAM positive and negative cell lines, using flow cytometry and cell-based plate assays. Four clinically relevant orthotopic tumour models, i.e. colorectal cancer, breast cancer, head and neck cancer, and peritonitis carcinomatosa, were used to evaluate the performance of the anti-EpCAM agent with the clinically validated Artemis imaging system. The Pearl Impulse small animal imaging system was used as reference. The specificity of the NIRF signal was confirmed using bioluminescence imaging and green-fluorescent protein. RESULTS: All tumour types could clearly be delineated and resected 72 h after injection of the imaging agent. Using NIRF imaging millimetre sized tumour nodules were detected that were invisible for the naked eye. Fluorescence microscopy demonstrated the distribution and tumour specificity of the anti-EpCAM agent. CONCLUSIONS: This study shows the potential of an EpCAM specific NIR-fluorescent agent in combination with a clinically validated intraoperative imaging system to visualize various tumours during surgery.

Parametric optimization of a model-based segmentation algorithm for cardiac MR image analysis: a grid-computing approach.

In this work we present a Grid-based optimization approach performed on a set of parameters that affects both the geometric and grey-level appearance properties of a three-dimensional model-based algorithm for cardiac MRI segmentation. The search for optimal values was assessed by a Monte Carlo procedure using computational Grid technology. A series of segmentation runs were conducted on an evaluation database comprising 30 studies at two phases of the cardiac cycle (60 datasets), using three shape models constructed by different methods. For each of these model-patient combinations, six parameters were optimized in two steps: those which affect the grey-level properties of the algorithm first and those relating to the geometrical properties, secondly. Two post-processing tasks (one for each stage) collected and processed (in total) more than 70000 retrieved result files. Qualitative and quantitative validation of the fitting results indicates that the segmentation performance was greatly improved with the tuning. Based on the experienced benefits with the use of our middleware, and foreseeing the advent of large-scale tests and applications in cardiovascular imaging, we strongly believe that the use of Grid computing technology in medical image analysis constitutes a real necessity.

Respiratory motion estimation in x-ray angiography for improved guidance during coronary interventions.

During percutaneous coronary interventions (PCI) catheters and arteries are visualized by x-ray angiography (XA) sequences, using brief contrast injections to show the coronary arteries. If we could continue visualizing the coronary arteries after the contrast agent passed (thus in non-contrast XA frames), we could potentially lower contrast use, which is advantageous due to the toxicity of the contrast agent. This paper explores the possibility of such visualization in mono-plane XA acquisitions with a special focus on respiratory based coronary artery motion estimation. We use the patient specific coronary artery centerlines from pre-interventional 3D CTA images to project on the XA sequence for artery visualization. To achieve this, a framework for registering the 3D centerlines with the mono-plane 2D + time XA sequences is presented. During the registration the patient specific cardiac and respiratory motion is learned. We investigate several respiratory motion estimation strategies with respect to accuracy, plausibility and ease of use for motion prediction in XA frames with and without contrast. The investigated strategies include diaphragm motion based prediction, and respiratory motion extraction from the guiding catheter tip motion. We furthermore compare translational and rigid respiratory based heart motion. We validated the accuracy of the 2D/3D registration and the respiratory and cardiac motion estimations on XA sequences of 12 interventions. The diaphragm based motion model and the catheter tip derived motion achieved 1.58 mm and 1.83 mm median 2D accuracy, respectively. On a subset of four interventions we evaluated the artery visualization accuracy for non-contrast cases. Both diaphragm, and catheter tip based prediction performed similarly, with about half of the cases providing satisfactory accuracy (median error < 2 mm).

Characterization and evaluation of the artemis camera for fluorescence-guided cancer surgery.

PURPOSE: Near-infrared (NIR) fluorescence imaging can provide the surgeon with real-time visualization of, e.g., tumor margins and lymph nodes. We describe and evaluate the Artemis, a novel, handheld NIR fluorescence camera. PROCEDURES: We evaluated minimal detectable cell numbers (FaDu-luc2, 7D12-IRDye 800CW), preclinical intraoperative detection of sentinel lymph nodes (SLN) using indocyanine green (ICG), and of orthotopic tongue tumors using 7D12-800CW. Results were compared with the Pearl imager. Clinically, three patients with liver metastases were imaged using ICG. RESULTS: Minimum detectable cell counts for Artemis and Pearl were 2 × 10(5) and 4 × 10(4) cells, respectively. In vivo, seven SLNs were detected in four mice with both cameras. Orthotopic OSC-19-luc2-cGFP tongue tumors were clearly identifiable, and a minimum FaDu-luc2 tumor size of 1 mm(3) could be identified. Six human malignant lesions were identified during three liver surgery procedures. CONCLUSIONS: Based on this study, the Artemis system has demonstrated its utility in fluorescence-guided cancer surgery.

Automated registration of multispectral MR vessel wall images of the carotid artery.

PURPOSE: Atherosclerosis is the primary cause of heart disease and stroke. The detailed assessment of atherosclerosis of the carotid artery requires high resolution imaging of the vessel wall using multiple MR sequences with different contrast weightings. These images allow manual or automated classification of plaque components inside the vessel wall. Automated classification requires all sequences to be in alignment, which is hampered by patient motion. In clinical practice, correction of this motion is performed manually. Previous studies applied automated image registration to correct for motion using only nondeformable transformation models and did not perform a detailed quantitative validation. The purpose of this study is to develop an automated accurate 3D registration method, and to extensively validate this method on a large set of patient data. In addition, the authors quantified patient motion during scanning to investigate the need for correction. METHODS: MR imaging studies (1.5T, dedicated carotid surface coil, Philips) from 55 TIA∕stroke patients with ipsilateral <70% carotid artery stenosis were randomly selected from a larger cohort. Five MR pulse sequences were acquired around the carotid bifurcation, each containing nine transverse slices: T1-weighted turbo field echo, time of flight, T2-weighted turbo spin-echo, and pre- and postcontrast T1-weighted turbo spin-echo images (T1W TSE). The images were manually segmented by delineating the lumen contour in each vessel wall sequence and were manually aligned by applying throughplane and inplane translations to the images. To find the optimal automatic image registration method, different masks, choice of the fixed image, different types of the mutual information image similarity metric, and transformation models including 3D deformable transformation models, were evaluated. Evaluation of the automatic registration results was performed by comparing the lumen segmentations of the fixed image and moving image after registration. RESULTS: The average required manual translation per image slice was 1.33 mm. Translations were larger as the patient was longer inside the scanner. Manual alignment took 187.5 s per patient resulting in a mean surface distance of 0.271 ± 0.127 mm. After minimal user interaction to generate the mask in the fixed image, the remaining sequences are automatically registered with a computation time of 52.0 s per patient. The optimal registration strategy used a circular mask with a diameter of 10 mm, a 3D B-spline transformation model with a control point spacing of 15 mm, mutual information as image similarity metric, and the precontrast T1W TSE as fixed image. A mean surface distance of 0.288 ± 0.128 mm was obtained with these settings, which is very close to the accuracy of the manual alignment procedure. The exact registration parameters and software were made publicly available. CONCLUSIONS: An automated registration method was developed and optimized, only needing two mouse clicks to mark the start and end point of the artery. Validation on a large group of patients showed that automated image registration has similar accuracy as the manual alignment procedure, substantially reducing the amount of user interactions needed, and is multiple times faster. In conclusion, the authors believe that the proposed automated method can replace the current manual procedure, thereby reducing the time to analyze the images.

Statistical coronary motion models for 2D+t/3D registration of X-ray coronary angiography and CTA.

Accurate alignment of intra-operative X-ray coronary angiography (XA) and pre-operative cardiac CT angiography (CTA) may improve procedural success rates of minimally invasive coronary interventions for patients with chronic total occlusions. It was previously shown that incorporating patient specific coronary motion extracted from 4D CTA increases the robustness of the alignment. However, pre-operative CTA is often acquired with gating at end-diastole, in which case patient specific motion is not available. For such cases, we investigate the possibility of using population based coronary motion models to provide constraints for the 2D+t/3D registration. We propose a methodology for building statistical motion models of the coronary arteries from a training population of 4D CTA datasets. We compare the 2D+t/3D registration performance of the proposed statistical models with other motion estimates, including the patient specific motion extracted from 4D CTA, the mean motion of a population, the predicted motion based on the cardiac shape. The coronary motion models, constructed on a training set of 150 patients, had a generalization accuracy of 1mm root mean square point-to-point distance. Their 2D+t/3D registration accuracy on one cardiac cycle of 12 monoplane XA sequences was similar to, if not better than, the 4D CTA based motion, irrespective of which respiratory model and which feature based 2D/3D distance metric was used. The resulting model based coronary motion estimate showed good applicability for registration of a subsequent cardiac cycle.

Statistical shape model-based femur kinematics from biplane fluoroscopy.

Studying joint kinematics is of interest to improve prosthesis design and to characterize postoperative motion. State of the art techniques register bones segmented from prior computed tomography or magnetic resonance scans with X-ray fluoroscopic sequences. Elimination of the prior 3D acquisition could potentially lower costs and radiation dose. Therefore, we propose to substitute the segmented bone surface with a statistical shape model based estimate. A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame. The algorithm minimizes the difference between the projected bone contour and image edges. To increase robustness, we employ a dynamic prior, image features, and prior knowledge about bone edge appearances. This enables tracking and reconstruction from a single initial pose per sequence. We evaluated our method on the distal femur using eight biplane fluoroscopic drop-landing sequences. The proposed dynamic prior and features increased the convergence rate of the reconstruction from 71% to 91%, using a convergence limit of 3 mm. The achieved root mean square point-to-surface accuracy at the converged frames was 1.48 ± 0.41 mm. The resulting tracking precision was 1-1.5 mm, with the largest errors occurring in the rotation around the femoral shaft (about 2.5° precision).

Comprehensive visualization of multimodal cardiac imaging data for assessment of coronary artery disease: first clinical results of the SMARTVis tool.

PURPOSE: In clinical practice, both coronary anatomy and myocardial perfusion information are needed to assess coronary artery disease (CAD). The extent and severity of coronary stenoses can be determined using computed tomography coronary angiography (CTCA); the presence and amount of ischemia can be identified using myocardial perfusion imaging, such as perfusion magnetic resonance imaging (PMR). To determine which specific stenosis is associated with which ischemic region, experts use assumptions on coronary perfusion territories. Due to the high variability between patient's coronary artery anatomies, as well as the uncertain relation between perfusion territories and supplying coronary arteries, patient-specific systems are needed. MATERIAL AND METHODS: We present a patient-specific visualization system, called Synchronized Multimodal heART Visualization (SMARTVis), for relating coronary stenoses and perfusion deficits derived from CTCA and PMR, respectively. The system consists of the following comprehensive components: (1) two or three-dimensional fusion of anatomical and functional information, (2) automatic detection and ranking of coronary stenoses, (3) estimation of patient-specific coronary perfusion territories. RESULTS: The potential benefits of the SMARTVis tool in assessing CAD were investigated through a case-study evaluation (conventional vs. SMARTVis tool): two experts analyzed four cases of patients with suspected multivessel coronary artery disease. When using the SMARTVis tool, a more reliable estimation of the relation between perfusion deficits and stenoses led to a more accurate diagnosis, as well as a better interobserver diagnosis agreement. CONCLUSION: The SMARTVis comprehensive visualization system can be effectively used to assess disease status in multivessel CAD patients, offering valuable new options for the diagnosis and management of these patients.

2D-3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models.

Three-dimensional patient specific bone models are required in a range of medical applications, such as pre-operative surgery planning and improved guidance during surgery, modeling and simulation, and in vivo bone motion tracking. Shape reconstruction from a small number of X-ray images is desired as it lowers both the acquisition costs and the radiation dose compared to CT. We propose a method for pose estimation and shape reconstruction of 3D bone surfaces from two (or more) calibrated X-ray images using a statistical shape model (SSM). User interaction is limited to manual initialization of the mean shape. The proposed method combines a 3D distance based objective function with automatic edge selection on a Canny edge map. Landmark-edge correspondences are weighted based on the orientation difference of the projected silhouette and the corresponding image edge. The method was evaluated by rigid pose estimation of ground truth shapes as well as 3D shape estimation using a SSM of the whole femur, from stereo cadaver X-rays, in vivo biplane fluoroscopy image-pairs, and an in vivo biplane fluoroscopic sequence. Ground truth shapes for all experiments were available in the form of CT segmentations. Rigid registration of the ground truth shape to the biplane fluoroscopy achieved sub-millimeter accuracy (0.68mm) measured as root mean squared (RMS) point-to-surface (P2S) distance. The non-rigid reconstruction from the biplane fluoroscopy using the SSM also showed promising results (1.68mm RMS P2S). A feasibility study on one fluoroscopic time series illustrates the potential of the method for motion and shape estimation from fluoroscopic sequences with minimal user interaction.

Optical advances in skeletal imaging applied to bone metastases.

Optical Imaging has evolved into one of the standard molecular imaging modalities used in pre-clinical cancer research. Bone research however, strongly depends on other imaging modalities such as SPECT, PET, x-ray and µCT. Each imaging modality has its own specific strengths and weaknesses concerning spatial resolution, sensitivity and the possibility to quantify the signal. An increasing number of bone specific optical imaging models and probes have been developed over the past years. This review gives an overview of optical imaging modalities, models and probes that can be used to study skeletal complications of cancer in small laboratory animals.

Fully automated registration of first-pass myocardial perfusion MRI using independent component analysis.

This paper presents a novel method for registration of cardiac perfusion MRI. The presented method successfully corrects for breathing motion without any manual interaction using Independent Component Analysis to extract physiologically relevant features together with their time-intensity behavior. A time-varying reference image mimicking intensity changes in the data of interest is computed based on the results of ICA, and used to compute the displacement caused by breathing for each frame. Qualitative and quantitative validation of the method is carried out using 46 clinical quality, short-axis, perfusion MR datasets comprising 100 images each. Validation experiments showed a reduction of the average LV motion from 1.26+/-0.87 to 0.64+/-0.46 pixels. Time-intensity curves are also improved after registration with an average error reduced from 2.65+/-7.89% to 0.87+/-3.88% between registered data and manual gold standard. We conclude that this fully automatic ICA-based method shows an excellent accuracy, robustness and computation speed, adequate for use in a clinical environment.

Accuracy of short-axis cardiac MRI automatically derived from scout acquisitions in free-breathing and breath-holding modes.

To qualitatively assess the accuracy of automated cardiovascular magnetic resonance planning procedures devised from scout acquisitions in free-breathing and breath-holding modes, to quantitatively evaluate the accuracy of the derived left ventricular volumes, mass and function and compare these parameters with the ones obtained from the manually planned acquisitions. Ten healthy volunteers underwent cardiovascular MR (CMR) acquisitions for ventricular function assessment. Short-axis data sets of the left ventricle (LV) were manually planned and generated twice in an automatic fashion. Automated planning parameters were derived from gated scout acquisitions in free-breathing and breath-holding modes. End-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and left ventricular mass (LVM) were measured. The agreement between the manual and automatic planning methods, as well as the variability of the aforementioned measurements were assessed. The differences between two automated planning methods were also compared. The mean differences between the manual and automated CMR planning derived from gated scouts in free-breathing mode were 8.05 ml (EDV), 1.84 ml (ESV), 0.69% (EF), and 4.72 g (LVM). The comparison between manual and automated CMR planning derived from gated scouts in breath-holding mode yielded the following differences: 4.22 ml (EDV), 0.34 ml (ESV), 0.3% (EF), and -0.72 mg (LVM). The variability coefficients were 3.72 and 3.66 (EDV), 5.6 and 8.19 (ESV), 3.46 and 4.31 (EF), 6.49 and 5.20 (LVM) for the automated CMR planning methods derived from scouts in free-breathing and breath-holding modes, respectively. Automated CMR planning methods can provide accurate measurements of LV dimensions in normal subjects, and therefore may be utilized in the clinical environment to provide a cost-effective solution for functional assessment of the human cardiovascular system.

Automatic prediction of myocardial contractility improvement in stress MRI using shape morphometrics with independent component analysis.

An important assessment in patients with ischemic heart disease is whether myocardial contractility may improve after treatment. The prediction of myocardial contractility improvement is generally performed under physical or pharmalogical stress conditions. In this paper, we present a technique to build a statistical model of healthy myocardial contraction using independent component analysis. The model is used to detect regions with abnormal contraction in patients both during rest and stress.

Multi-View Active Appearance Models: application to X-ray LV angiography and cardiac MRI.

This paper describes a Multi-View Active Appearance Model (AAM) for coherent segmentation of multiple cardiac views. Cootes' AAM framework was adapted by considering shapes and intensities from multiple views, while eliminating trivial difference in object pose in different views. This way, the coherence in organ shape and intensities between different views is modeled, and utilized during image search. The method is validated in two substantially different and novel applications: segmentation of combined end-diastolic and end-systolic left ventricular X-ray angiograms, and simultaneous segmentation of a combination of four chamber, two chamber and short-axis cardiac MR views.

Automatic segmentation and plaque characterization in atherosclerotic carotid artery MR images.

In vivo MRI provides a means to non-invasively image and assess the morphological features of atherosclerotic carotid arteries. To assess quantitatively the degree of vulnerability and the type of plaque, the contours of the lumen, outer boundary of the vessel wall and plaque components, need to be traced. Currently this is done manually, which is time-consuming and sensitive to inter- and intra-observer variability. The goal of this work was to develop an automated contour detection technique for tracing the lumen, outer boundary and plaque contours in carotid MR short-axis black-blood images. Seventeen patients with carotid atherosclerosis were imaged using high-resolution in vivo MRI, generating a total of 50 PD- and T1-weighted MR images. These images were automatically segmented using the algorithm presented in this work, which combines model-based segmentation and fuzzy clustering to detect the vessel wall, lumen and lipid core boundaries. The results demonstrate excellent correspondence between automatic and manual area measurements for lumen (r = 0.92) and outer (r = 0.91), and acceptable correspondence for fibrous cap thickness (r = 0.71). Though further optimization is required, our algorithm is a powerful tool for automatic detection of lumen and outer boundaries, and characterization of plaque in atherosclerotic vessels.