Projection Matching Method Research Articles

Joint Iterative Fast Projection Matching for Fully Automatic Marker-free Alignment of Nano-tomography Reconstructions

Highly accurate, fully automatic marker-free image alignment plays an important role in nano-tomographic reconstruction, particularly in cases where the spatial resolution of the tomographic system is on the nanometer scale. However, highly accurate marker-free methods such as the projection matching method are computationally complex and time-consuming. Achieving alignment accuracy with reduced computational complexity remains a challenging problem. In this study, we propose an efficient method to achieve marker-free fully automatic alignment. Our method implements three main alignment procedures. First, the frequency-domain common line alignment method is used to correct the in-plane rotational errors of each projection. Second, real-space common line alignment method is used to correct the vertical errors of the projections. Finally, a single layer joint-iterative reconstruction and re-projection method is used to correct the horizontal projection errors. This combined alignment approach significantly reduces the computational complexity of the classical projection matching method, and increases the rate of convergence towards determining the accurate alignment. The total processing time can be reduced by up to 4 orders of magnitude as compared to the classical projection matching method. This suggests that the algorithm can be used to process image alignment of nano-tomographic reconstructions on a conventional personal computer in a reasonable time-frame.

Fast Projection Matching for X-ray Tomography

X-ray 3D tomographic techniques are powerful tools for investigating the morphology and internal structures of specimens. A common strategy for obtaining 3D tomography is to capture a series of 2D projections from different X-ray illumination angles of specimens mounted on a finely calibrated rotational stage. However, the reconstruction quality of 3D tomography relies on the precision and stability of the rotational stage, i.e. the accurate alignment of the 2D projections in the correct three-dimensional positions. This is a crucial problem for nano-tomographic techniques due to the non-negligible mechanical imperfection of the rotational stages at the nanometer level which significantly degrades the spatial resolution of reconstructed 3-D tomography. Even when using an X-ray micro-CT with a highly stabilized rotational stage, thermal effects caused by the CT system are not negligible and may cause sample drift. Here, we propose a markerless image auto-alignment algorithm based on an iterative method. This algorithm reduces the traditional projection matching method into two simplified matching problems and it is much faster and more reliable than traditional methods. This algorithm can greatly decrease hardware requirements for both nano-tomography and data processing and can be easily applied to other tomographic techniques, such as X-ray micro-CT and electron tomography.

An Algorithm for Electronic Image Stabilization

In most video systems,the carrier of camera expierences all kinds of motions which make the video ambiguous and unstable.Usually,the gray projection matching algorithm can only stabilize the translation motion between frames,but the rotation motion of frames in a video sequence.By zoning the image into few small regions,the rotation motion could be ignored,then, local movement vector is aqcuired by the grey projection matching method and compensate the movement between frames.Then for the rotation movement,it can be stabilized by the compensation with circle projection matching.

An Electronic Image Stabilizition Algorithm Based on Gray Projection and Circle Projection Matching

In most video systems,the carrier of camera expierences all kinds of motions which make the video ambiguous and unstable.Usually,the gray projection matching algorithm can only stabilize the translation motion between frames,but the rotation motion of frames in a video sequence.By zoning the image into few small regions,the rotation motion could be ignored,then, local movement vector is aqcuired by the grey projection matching method and compensate the movement between frames.Then for the rotation movement,it can be stabilized by the compensation with circle projection matching.

Aligning Projection Images from Binary Volumes

In tomography, slight differences between the geometry of the scanner hardware and the geometric model used in the reconstruction lead to alignment artifacts. To exploit high-resolution detectors used in many applications of tomography, alignment of the projection data is essential. Markerless alignment algorithms are the preferred choice over alignment with markers, in case a fully automatic tomography pipeline is required. Moreover, marker based alignment is often not feasible or even possible. At the same time, markerless alignment methods often fail in scenarios where only a small number of projections are available. In this case, the angular separation between projection images is large and therefore the correlation between them is low. This is a property that most markerless algorithms rely on. The intermediate reconstruction problem of alignment by projection matching is highly underdetermined in the limited data case. Therefore, we propose a projection matching method that incorporates prior knowledge of the ground truth. We focus on reconstructing binary volumes. A discrete tomography algorithm is employed to generate intermediate reconstructions. This type of reconstruction algorithm does not rely heavily on correlated projection images. Our numerical results suggest that alignment using discrete tomography projection matching produces much better results in the limited angle case, when compared to a projection matching method that employs an algebraic reconstruction method.

Obstacle detection of indoor robots based on monocular vision

In this paper,a new monocular vision system was proposed to improve obstacle detection capability of indoor mobile robot.In this system,firstly,the Hue,Saturation,Intensity(HSI) color space conversion of images was performed.Secondly,a small target threshold selection method was proposed to segment the images,which enhanced the precision of the image segmentation.Thirdly,the target scene matching method and target projection matching method were used to calculate the change of the target pixel and projection so as to judge whether the target is obstacles or ground graphs.The experimental results show that the monocular vision system is effective and feasible,and this system can be applied to the navigation for small indoor mobile robots.

Reconstruction of brachytherapy seed positions and orientations from cone-beam CT x-ray projections via a novel iterative forward projection matching method

To generalize and experimentally validate a novel algorithm for reconstructing the 3D pose (position and orientation) of implanted brachytherapy seeds from a set of a few measured 2D cone-beam CT (CBCT) x-ray projections. The iterative forward projection matching (IFPM) algorithm was generalized to reconstruct the 3D pose, as well as the centroid, of brachytherapy seeds from three to ten measured 2D projections. The gIFPM algorithm finds the set of seed poses that minimizes the sum-of-squared-difference of the pixel-by-pixel intensities between computed and measured autosegmented radiographic projections of the implant. Numerical simulations of clinically realistic brachytherapy seed configurations were performed to demonstrate the proof of principle. An in-house machined brachytherapy phantom, which supports precise specification of seed position and orientation at known values for simulated implant geometries, was used to experimentally validate this algorithm. The phantom was scanned on an ACUITY CBCT digital simulator over a full 660 sinogram projections. Three to ten x-ray images were selected from the full set of CBCT sinogram projections and postprocessed to create binary seed-only images. In the numerical simulations, seed reconstruction position and orientation errors were approximately 0.6 mm and 5 degrees, respectively. The physical phantom measurements demonstrated an absolute positional accuracy of (0.78 +/- 0.57) mm or less. The theta and phi angle errors were found to be (5.7 +/- 4.9) degrees and (6.0 +/- 4.1) degrees, respectively, or less when using three projections; with six projections, results were slightly better. The mean registration error was better than 1 mm/6 degrees compared to the measured seed projections. Each test trial converged in 10-20 iterations with computation time of 12-18 min/iteration on a 1 GHz processor. This work describes a novel, accurate, and completely automatic method for reconstructing seed orientations, as well as centroids, from a small number of radiographic projections, in support of intraoperative planning and adaptive replanning. Unlike standard back-projection methods, gIFPM avoids the need to match corresponding seed images on the projections. This algorithm also successfully reconstructs overlapping clustered and highly migrated seeds in the implant. The accuracy of better than 1 mm and 6 degrees demonstrates that gIFPM has the potential to support 2D Task Group 43 calculations in clinical practice.

Motion Detection and Projection Based Block Motion Estimation Using the Radon Transform for Video Coding

Problem statement: Motion estimation and compensation is the most computationally complex module of video coder. In this study, an innovative algorithm was proposed for a further complexity reduction of the Motion Estimation (ME) module of video coder by employing motion detection prior to motion compensation. Approach: A Motion Detection (MD) module can be added to the video coder in order to decide whether the current block contains motion or is with zero motion. This study propounded a MD module that depends on motion activity. Generally, the correlation of the two consecutive frames is a good criterion to measure motion activity. We applied the correlation as a threshold to detect the motion activity. To assure the correct motion vector and thus better video quality, to calculate motion vector of motion blocks this study also proposed a new block matching motion estimation criterion based on the Radon transform using projection-matching method. However, computationally complex, the method had the ability to be implemented in real time by using pipeline architecture. Results: A comparative result showed that the MD module reduced the number of search points for motion estimation and compared with some well-known algorithm that uses minimum absolute difference criterion, the new criterion can provide much higher performance. Conclusion: The result showed that this proposed scheme can simplify the encoder complexity maintaining good video quality.

Three-dimensional Reconstruction of Human Cystic Fibrosis Transmembrane Conductance Regulator Chloride Channel Revealed an Ellipsoidal Structure with Orifices beneath the Putative Transmembrane Domain

The cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel is a membrane-integral protein that belongs to an ATP-binding cassette superfamily. Mutations in the CFTR gene cause cystic fibrosis in which salt, water, and protein transports are defective in various tissues. Here we expressed wild-type human CFTR as a FLAG-fused protein in HEK293 cells heterologously and purified it in three steps: anti-FLAG and wheat germ agglutinin affinity chromatographies and size exclusion chromatography. The stoichiometry of the protein was analyzed using various biochemical approaches, including chemical cross-linking, blue-native PAGE, size exclusion chromatography, and electron microscopy (EM) observation of antibody-decorated CFTR. All these data support a dimeric assembly of CFTR. Using 5,039 automatically selected particles from negatively stained EM images, the three-dimensional structure of CFTR was reconstructed at 2-nm resolution assuming a 2-fold symmetry. CFTR, presumably in a closed state, was shown to be an ellipsoidal particle with dimensions of 120 x 106 x 162 A. It comprises a small dome-shaped extracellular and membrane-spanning domain and a large cytoplasmic domain with orifices beneath the putative transmembrane domain. EM observation of CFTR.anti-regulatory domain antibody complex confirmed that two regulatory domains are located around the bottom end of the larger oval cytoplasmic domain.

Diffraction of monochromatic waves on a metallic cylinder with longitudinal slots

The projection matching method is applied to the calculation of a circular cylindrical slot line with one and two slots. The diffraction problem is studied at near-resonance frequencies.

Fast projection matching for cryo-electron microscopy image reconstruction

A new FFT-accelerated projection matching method is presented and tested. The electron microscopy images are represented by their Fourier-Bessel transforms and the 3D model by its expansion in spherical harmonics, or more specifically in terms of symmetry-adapted functions. The rotational and translational properties of these representations are used to quickly access all the possible 2D projections of the 3D model, which allow an exhaustive inspection of the whole five-dimensional domain of parameters associated to each particle.

The Motor Protein Prestin Is a Bullet-shaped Molecule with Inner Cavities

Prestin is a transmembrane motor protein localized at the outer hair cells (OHCs) of the mammalian inner ear. Voltage-dependent conformational changes in prestin generate changes in the length of OHCs. A loss of prestin function is reported to induce severe auditory deficiencies, suggesting prestin-dependent changes of OHC length may be at least a part of cochlear amplification. Here we expressed the recombinant FLAG-fused prestin proteins in Sf9 cells and purified to particles of a uniform size in EM. The square-shaped top view of purified prestin, the binding of multiple anti-FLAG antibodies to each prestin particle, the native-PAGE analysis, and the much larger molecular weight obtained from size exclusion chromatography than the estimation for the monomer all support that prestin is a tetramer (Zheng, J., Du, G. G., Anderson, C. T., Keller, J. P., Orem, A., Dallos, P., and Cheatham, M. (2006) J. Biol. Chem. 281, 19916-19924). From negatively stained prestin particles, the three-dimensional structure was reconstructed at 2 nm resolution assuming 4-fold symmetry. Prestin is shown to be a bullet-shaped particle with a large cytoplasmic domain. The surface representation demonstrates indentations on the molecule, and the slice images indicate the inner cavities of sparse densities. The dimensions, 77 x 77 x 115 A, are consistent with the previously reported sizes of motor proteins on the surface of OHCs.

Three-dimensional Reconstruction Using Transmission Electron Microscopy Reveals a Swollen, Bell-shaped Structure of Transient Receptor Potential Melastatin Type 2 Cation Channel

Transient receptor potential melastatin type 2 (TRPM2) is a redox-sensitive, calcium-permeable cation channel activated by various signals, such as adenosine diphosphate ribose (ADPR) acting on the ADPR pyrophosphatase (ADPRase) domain, and cyclic ADPR. Here, we purified the FLAG-tagged tetrameric TRPM2 channel, analyzed it using negatively stained electron microscopy, and reconstructed the three-dimensional structure at 2.8-nm resolution. This multimodal sensor molecule has a bell-like shape of 18 nm in width and 25 nm in height. The overall structure is similar to another multimodal sensor channel, TRP canonical type 3 (TRPC3). In both structures, the small extracellular domain is a dense half-dome, whereas the large cytoplasmic domain has a sparse, double-layered structure with multiple internal cavities. However, a unique square prism protuberance was observed under the cytoplasmic domain of TRPM2. The FLAG epitope, fused at the C terminus of the ADPRase domain, was assigned by the antibody to a position close to the protuberance. This indicates that the agonist-binding ADPRase domain and the ion gate in the transmembrane region are separately located in the molecule.

An Archaeal Peptidase Assembles into Two Different Quaternary Structures

Cellular proteolysis involves large oligomeric peptidases that play key roles in the regulation of many cellular processes. The cobalt-activated peptidase TET1 from the hyperthermophilic Archaea Pyrococcus horikoshii (PhTET1) was found to assemble as a 12-subunit tetrahedron and as a 24-subunit octahedral particle. Both quaternary structures were solved by combining x-ray crystallography and cryoelectron microscopy data. The internal organization of the PhTET1 particles reveals highly self-compartmentalized systems made of networks of access channels extended by vast catalytic chambers. The two edifices display aminopeptidase activity, and their organizations indicate substrate navigation mechanisms different from those described in other large peptidase complexes. Compared with the tetrahedron, the octahedron forms a more expanded hollow structure, representing a new type of giant peptidase complex. PhTET1 assembles into two different quaternary structures because of quasi-equivalent contacts that previously have only been identified in viral capsids.

Patient position verification using small IMRT fields

A commonly used approach to quantify and minimize patient setup errors is by using electronic portal imaging devices (EPIDs). The position of the tumor can be verified indirectly by matching the bony anatomy to a reference image containing the same structures. In this paper we present two off-line methods for detecting the position of the bony anatomy automatically, even if every single portal image of each segment of an IMRT treatment beam contains insufficient matching information. Extra position verification fields will no longer be necessary, which reduces the total dose to the patient. The first method, the stack matching method (SMM), stacks the portal image of each segment of a beam to a three dimensional (3D) volume, and this volume is subsequently used during the matching phase. The second method [the averaged projection matching method (APMM)], is a simplification of the first one, since the initially created volume is reduced again to a 2D artificial image, which speeds up the matching procedure considerably, without a significant loss of accuracy. Matching is based on normalized mutual information. We demonstrate our methods by comparing them to existing matching routines, such as matching based on the largest segment. Both phantom and patient experiments show that our methods are comparable with the results obtained from standard position verification methods. The matches are verified by means of visual inspection. Furthermore, we show that when a distinct area of 40-60 cm2 of the EPID is exposed during one treatment beam, both SMM and APMM are able to deliver a good matching result.