waashk/dblp huggingface dataset:
3d surface matching and registration through shape images. in this paper, we present a novel and efficient surface matching framework through shape image representation. this representation allows us to simplify a 3d surface matching problem to a 2d shape image matching problem. furthermore, we present a shape image diffusion-based method to find the most robust features to construct the matching and registration of surfaces. this is particularly important for inter-subject surfaces from medical scans of different subjects since these surfaces exhibit the inherited physiological variances among subjects. we conducted extensive experiments on real 3d human neocortical surfaces, which demonstrate the excellent performance of our approach in terms of accuracy and robustness.
volumetric ultrasound panorama based on 3d sift. the reconstruction of three-dimensional (3d) ultrasound panorama from multiple ultrasound volumes can provide a wide field of view for better clinical diagnosis. registration of ultrasound volumes has been a key issue for the success of this panoramic process. in this paper, we propose a method to register and stitch ultrasound volumes, which are scanned by dedicated ultrasound probe, based on an improved 3d scale invariant feature transform (sift) algorithm. we propose methods to exclude artifacts from ultrasound images in order to improve the overall performance in 3d feature point extraction and matching. our method has been validated on both phantom and clinical data sets of human liver. experimental results show the effectiveness and stability of our approach, and the precision of our method is comparable to that of the position tracker based registration.
precision radiotherapy for small animal research. preclinical research using well characterized small animal models has provided tremendous benefits to medical research, enabling low cost, large scale trials with high statistical significance of observed effects. the goal of the small animal radiation research platform (sarrp) is to make those models available for the development and evaluation of novel radiation therapies. sarrp demonstrates the capabilities of delivering high resolution, sub-millimeter, optimally planned conformal radiation with on-board cone-beam ct (cbct) guidance. the system requires accurate calibration of the x-ray beam for both imaging and radiation treatment. in this paper, we present a novel technique using an x-ray camera for calibration of the treatment beam. this technique does not require precise positioning or calibration of the x-ray camera.
3d dynamic roadmapping for abdominal catheterizations. despite rapid advances in interventional imaging, the navigation of a guide wire through abdominal vasculature remains, not only for novice radiologists, a difficult task. since this navigation is mostly based on 2d fluoroscopic image sequences from one view, the process is slowed down significantly due to missing depth information and patient motion. we propose a novel approach for 3d dynamic roadmapping in deformable regions by predicting the location of the guide wire tip in a 3d vessel model from the tip's 2d location, respiratory motion analysis, and view geometry. in a first step, the method compensates for the apparent respiratory motion in 2d space before backprojecting the 2d guide wire tip into three dimensional space, using a given projection matrix. to countervail the error connected to the projection parameters and the motion compensation, as well as the ambiguity caused by vessel deformation, we establish a statistical framework, which computes a reliable estimate of the guide wire tip location within the 3d vessel model. with this 2d-to-3d transfer, the navigation can be performed from arbitrary viewing angles, disconnected from the static perspective view of the fluoroscopic sequence. tests on a realistic breathing phantom and on synthetic data with a known ground truth clearly reveal the superiority of our approach compared to naïve methods for 3d roadmapping.the concepts and information presented in this paper are based on research and are not commercially available.
a maximal mass confinement principle for rigid and locally rigid image registration. in this paper we propose (1) to set the problem of image registration as a contour/region-template-to-image matching problem using so-called confiners --- also called blobs or components --- as template regions, (2) to select the confiners of one of the images by passing through the hierarchical structure which they define and registering them successively rigidly form coarse-to-fine to the other image, the target image, and (3) we propose a maximum mass confinement (mmc) principle for contour-to-image registration. this principle allows us to derive a similarity measure assessing how well the confiner fits into the target image simply by calculating the gray value mass confined by its contour. by optimizing this measure for rigid transformations we obtain our mmc algorithm registering a contour locally rigid to the target image. we illustrate that by proceeding based on (1-3) problems can be avoided which were related to previous registration algorithms based on confiners. we compare our mmc algorithm with another template matching algorithm based on normalized mutual information. equally, we compare our hierarchical image registration strategy with b-spline based non-rigid registration using normalized mutual information. we performed our evaluation on real and simulated images in terms of robustness, accuracy and computation speed. we show that both, mmc template matching on its own and hierarchical image registration using mmc, in most cases outperform the respective alternative method.
prostate brachytherapy seed localization with gaussian blurring and camera self-calibration. a tomosynthesis-based prostate brachytherapy seed localization method is described. gaussian-blurred images are computed from a limited number of x-ray images, and a 3-d volume is reconstructed by backprojection. candidate seed locations are extracted from the reconstructed volume and false positive seeds are removed by optimizing a local cost function. in case where the estimated pose error is large, a self-calibration process corrects the estimation error of the intrinsic camera parameters and the translation of the pose in order to improve the reconstruction. simulation and phantom experiment results imply that the implanted seed locations can be estimated from four or five images depending on the number of seeds. the algorithm was also validated using patient data, successfully localizing the implanted seeds.
bayesian analysis of fmri data with ica based spatial prior. spatial modeling is essential for fmri analysis due to relatively high noise in the data. earlier approaches have been primarily concerned with the spatial coherence of the bold response in local neighborhoods. in addition to a smoothness constraint, we propose to incorporate prior knowledge of brain activation patterns learned from training samples. this spatially informed prior can significantly enhance the estimation process by inducing sensitivity to task related regions of the brain. as fmri data exhibits intersubject variability in functional anatomy, we design the prior using independent component analysis (ica). due to the non-gaussian assumption, ica does not regress to the mean activation pattern and thus avoids suppressing intersubject differences. results from a real fmri experiment indicate that our approach provides statistically significant improvement in estimating activation compared to the standard general linear model (glm) based methods.
intraoperative navigation of an optically tracked surgical robot. this paper presents an adaptive control scheme for improving the performance of a surgical robot when it executes tasks autonomously. a commercial tracking system is used to correlate the robot with the preoperative plan as well as to correct the position of the robot when errors between the real and planned positions are detected. due to the noisy signals provided by the tracking system, a kalman filter is proposed to smooth the variations and to increase the stability of the system. the efficiency of the approach has been validated using rigid and flexible endoscopic tools, obtaining in both cases that the target points can be reached with an error less than 1mm. these results make the approach suitable for a range of abdominal procedures, such as autonomous repositioning of endoscopic tools or probes for percutaneous procedures.
location registration and recognition (lrr) for longitudinal evaluation of corresponding regions in ct volumes. the algorithm described in this paper takes (a) two temporally-separated ct scans, i 1 and i 2, and (b) a series of locations in i 1, and it produces, for each location, an affine transformation mapping the locations and their immediate neighborhood from i 1 to i 2. it does this without deformable registration by using a combination of feature extraction, indexing, refinement and decision processes. together these essentially "recognize" the neighborhoods. we show on lung ct scans that this works at near interactive speeds, and is at least as accurate as the diffeomorphic demons algorithm [1]. the algorithm may be used both for diagnosis and treatment monitoring.
long bone x-ray image stitching using camera augmented mobile c-arm. x-ray images are widely used during surgery for long bone fracture fixation. mobile c-arms provide x-ray images which are used to determine the quality of trauma reduction, i.e. the extremity length and mechanical axis of long bones. standard x-ray images have a narrow field of view and can not visualize the entire long bone on a single image. in this paper, we propose a novel method to generate panoramic x-ray images in real time by using the previously introduced camera augmented mobile c-arm [1]. this advanced mobile c-arm system acquires registered x-ray and optical images by construction, which facilitates the generation of panoramic x-ray images based on first stitching the optical images and then embedding the x-ray images. we additionally introduce a method to reduce the parallax effect that leads to the blurring and measurement error on panoramic x-ray images. visual marker tracking is employed to automatically stitch the sequence of video images and to rectify images. our proposed method is suitable for intra-operative usage generating panoramic x-ray images, which enable metric measurements, with less radiation and without requirement of fronto-parallel setup and overlapping x-ray images. the results show that the panoramic x-ray images generated by our method are accurate enough (errors less than 1%) for metric measurements and suitable for many clinical applications in trauma reduction.
robust brain registration using adaptive probabilistic atlas. elastic image registration is widely used to adapt brain images to a common template space, and, in complementary fashion, to adapt an anatomical template to a subject's anatomy. although hammer is a very accurate image-registration algorithm, it requires a 3-class segmentation step prior to registration, and its performance is affected by segmentation quality. we here propose a new framework to improve this algorithm's robustness to poor initial segmentation. our new framework is based on adaptive generalized expectation maximization (agem) for unified segmentation and registration, in which we use an adaptive strategy to incorporate spatial information from a probabilistic atlas to improve segmentation and registration simultaneously. our experiments using real mr brain images indicate that our integrated approach improves registration accuracy; we have also found that our iterative approach renders hammer robust to low tissue contrast, which hinders 3-class segmentation.
detecting informative frames from wireless capsule endoscopic video using color and texture features. despite emerging technology, wireless capsule endoscopy needs high amount of diagnosis-time due to the presence of many useless frames, created by turbid fluids, foods, and faecal materials. these materials and fluids present a wide range of colors and/or bubble-like texture patterns. we, therefore, propose a cascade method for informative frame detection, which uses local color histogram to isolate highly contaminated non-bubbled (hcn) frames, and gauss laguerre transform (glt) based multiresolution norm-1 energy feature to isolate significantly bubbled (sb) frames. supervised support vector machine is used to classify hcn frames (stage-1), while automatic bubble segmentation followed by threshold operation(stage-2) is adopted to detect informative frames by isolating sb frames. an experiment with 20,558 frames from the three videos shows 97.48 % average detection accuracy by the proposed method, when compared with methods adopting gabor based-(75.52%) and discrete wavelet based features (63.15%) with the same color feature.
joint lmmse estimation of dwi data for dti processing. we propose a new methodology for linear minimum mean square error (lmmse) filtering of diffusion weighted imaging (dwi). we consider each voxel as an n-dimensional vector that comprises all the dwi volumes, and then compute the lmmse estimator for the whole dwi data set jointly, taking into account the underlying tensor model. our experiments, both with phantom and real data, show that this is a more convenient approach compared to the separate processing of each dwi, that translates to better removal of noise and preservation of structural information. besides, our model has a simple algebraic formulation which makes the overall computational complexity very close to that of the scalar case, and it does not need multiple samples per dwi.
human vocal tract analysis by in vivo 3d mri during phonation: a complete system for imaging, quantitative modeling, and speech synthesis. we present a complete system for image-based 3d vocal tract analysis ranging from mr image acquisition during phonation, semi-automatic image processing, quantitative modeling including model-based speech synthesis, to quantitative model evaluation by comparison between recorded and synthesized phoneme sounds. for this purpose, six professionally trained speakers, age 22-34y, were examined using a standardized mri protocol (1.5 t, t1w flash, st 4mm, 23 slices, acq. time 21s). the volunteers performed a prolonged (¿21s) emission of sounds of the german phonemic inventory. simultaneous audio tape recording was obtained to control correct utterance. scans were made in axial, coronal, and sagittal planes each. computer-aided quantitative 3d evaluation included (i) automated registration of the phoneme-specific data acquired in different slice orientations, (ii) semi-automated segmentation of oropharyngeal structures, (iii) computation of a curvilinear vocal tract midline in 3d by nonlinear pca, (iv) computation of cross-sectional areas of the vocal tract perpendicular to this midline. for the vowels /a/,/e/,/i/,/o/,/ø/,/u/,/y/, the extracted area functions were used to synthesize phoneme sounds based on an articulatory-acoustic model. for quantitative analysis, recorded and synthesized phonemes were compared, where area functions extracted from 2d midsagittal slices were used as a reference. all vowels could be identified correctly based on the synthesized phoneme sounds. the comparison between synthesized and recorded vowel phonemes revealed that the quality of phoneme sound synthesis was improved for phonemes /a/, /o/, and /y/, if 3d instead of 2d data were used, as measured by the average relative frequency shift between recorded and synthesized vowel formants (p<0.05, one-sided wilcoxon rank sum test). in summary, the combination of fast mri followed by subsequent 3d segmentation and analysis is a novel approach to examine human phonation in vivo. it unveils functional anatomical findings that may be essential for realistic modelling of the human vocal tract during speech production.
comparison of epi distortion correction methods in diffusion tensor mri using a novel framework. diffusion weighted images (dwis) are commonly acquired with echo-planar imaging (epi). b0 inhomogeneities affect epi by producing spatially nonlinear image distortions. several strategies have been proposed to correct epi distortions including b0 field mapping (b0m) and image registration. in this study, an experimental framework is proposed to evaluation the performance of different epi distortion correction methods in improving dt-derived quantities. a deformable registration based method with mutual information metric and cubic b-spline modeled constrained deformation field (bsp) is proposed as an alternative when b0 mapping data are not available. bsp method is qualitatively and quantitatively compared to b0m method using the framework. both methods can successful reduce epi distortions and significantly improve the quality of dt-derived quantities. overall, b0m was clearly superior in infratentorial regions including brainstem and cerebellum, as well as in the ventral areas of the temporal lobes while bsp was better in all rostral brain regions.
semi-automatic reference standard construction for quantitative evaluation of lung ct registration. an algorithm is presented for the efficient semi-automatic construction of a detailed reference standard for registration in thoracic ct. a well-distributed set of 100 landmarks is detected fully automatically in one scan of a pair to be registered. using a custom-designed interface, observers locate corresponding anatomic locations in the second scan. the manual annotations are used to learn the relationship between the scans and after approximately twenty manual marks the remaining points are matched automatically. inter-observer differences demonstrate the accuracy of the matching and the applicability of the reference standard is demonstrated on two different sets of registration results over 19 ct scan pairs.
estimation of ground-glass opacity measurement in ct lung images. we propose to measure quantitatively the opacity property of each pixel in a ground-glass opacity tumor from ct images. our method results in an opacity map in which each pixel takes opacity value of $[0\textrm{-}1]$. given a ct image, our method accomplishes the estimation by constructing a graph laplacian matrix and solving a linear equations system, with assistance from some manually drawn scribbles for which the opacity values are easy to determine manually. our method resists noise and is capable of eliminating the negative influence of vessels and other lung parenchyma. experiments on 40 selected ct slices of 11 patients demonstrate the effectiveness of this technique. the opacity map produced by our method is invaluable in practice. from this map, many features can be extracted to describe the spatial distribution pattern of opacity and used in a computer-aided diagnosis system.
optimal acquisition schemes in high angular resolution diffusion weighted imaging. the recent challenge in diffusion imaging is to find acquisition schemes and analysis approaches that can represent non-gaussian diffusion profiles in a clinically feasible measurement time. in this work we investigate the effect of b-value and the number of gradient vector directions on q-ball imaging and the diffusion orientation transform (dot) in a structured way using computational simulations, hardware crossing-fiber diffusion phantoms, and in-vivo brain scans. we observe that dot is more robust to noise and independent of the b-value and number of gradients, whereas q-ball dramatically improves the results for higher b-values and number of gradients and at recovering larger angles of crossing. we also show that laplace-beltrami regularization has wide applicability and generally improves the properties of dot. knowledge of optimal acquisition schemes for hardi can improve the utility of diffusion weighted mr imaging in the clinical setting for the diagnosis of white matter diseases and presurgical planning.
identification of atherosclerotic lesion-prone sites through patient-specific simulation of low-density lipoprotein accumulation. we present a patient-specific model of low-density lipoprotein (ldl) transport from blood into arterial walls. to this end, the arterial endothelium is represented by a shear-stress dependent three-pore model taking into account blood plasma and ldl passage through the vesicular pathway, normal junctions and leaky junctions. we virtually remove atherosclerotic plaque from an in-vivo left coronary artery computed tomography (ct) dataset to obtain an approximation of the artery anatomy in its healthy state. by applying our model, we show that the location of the plaque in the diseased state corresponds to one of the two sites with predicted high ldl concentration in the healthy state. we further show that in the diseased state, the site with high ldl concentration has shifted distally, which is in agreement with the clinical observation that plaques generally grow in downstream direction.
deformable ultrasound registration without reconstruction. ultrasound (us) imaging is often proposed as an interoperative imaging modality. this use nearly always requires that the collected data be registered to preoperative data of another modality. existing intensity-based registration approaches all begin by reconstructing a 3d us volume from the collected 2d slices. we propose to directly register the set of 2d slices to the preoperative images. we argue this has a number of advantages, including the omission of the potentially complex reconstruction step, greater adaptability of the similarity measures, and easier parallelization. we describe a system for performing this task and present results on phantom data that show that our slice based method consistently outperforms a reconstruction based method in both speed and accuracy.
anatomy-preserving nonlinear registration of deep brain rois using confidence-based block-matching. brain atlases are commonly used in a number of applications such as mri segmentation and surgery targetting. our goal is to register a basal ganglia atlas to a subject using mr image registration. existing registration methods are for the most part either too constrained (linear registration) or can deform deep brain rois into implausible anatomical shapes.we developed a block-matching registration method suitable for atlas registration, using a new confidence-based regularization of the vector field. the method was used to register a set of 17 manually segmented mri onto one subject. results show that basal ganglia structures were better registered than when using an affine registration method.
spatiotemporal decomposition in object-space along reconstruction in emission tomography. emission tomography has provided a new insight in brain mechanisms past years. although reconstructions are nowadays mostly static, trend is going toward dynamic acquisitions and reconstructions. this opens a new range of investigations, for instance for drugs discovery. indeed new drugs are studied through the dynamic ability of tissues to catch them. however, it is required to know radiotracer concentration of blood that irrigates tissues in order to draw conclusions on potentials of these drugs. this concentration is called 'input function' and this paper presents a new method for measuring it in a non-invasive way.our new method relies on simultaneous estimations of vessels kinetics and vessels spatial distribution. these estimations are performed during the reconstruction process and take into account the statistical nature of measured signals. indeed, this method is based on the maximisation of the likelihood of counts in detectors. it takes advantages of a non-negative matrix factorisation which separate spatial and temporal components. results are very promising, since it estimates arterial input function accurately although object emits just a limited amount of photons, especially within the first minutes.