Alignment Correction Algorithm Research Articles

Original article: Validity and reliability of gait metrics derived from researcher-placed and self-placed wearable inertial sensors

To compare the inter-session placement reliability for researcher-placed and self-placed sensors, and to evaluate the validity and reliability of waveforms and discrete variables from researcher-placed and self-placed sensors following a previously described alignment correction algorithm. Fourteen healthy, pain-free participants underwent gait analysis over two data collection sessions. Participants self-placed an inertial sensor on their left tibia and a researcher placed one on their right tibia, before completing 10 overground walking trials. Following an axis correction from a principal component analysis-based algorithm, validity and reliability were assessed within and between days for each sensor placement type through Euclidean distances, waveforms, and discrete outcomes. The placement location of researcher-placed sensors exhibited good inter-session reliability (ICC = 0.85) in comparison to self-placed sensors (ICC = 0.55). Similarly, waveforms from researcher-placed sensors exhibited excellent validity across all variables (CMC ≥ 0.90), while self-placed sensors saw high validity for most axes with reductions in validity for mediolateral acceleration and frontal plane angular velocity. Discrete outcomes saw good to excellent reliability across both sensor placement types. A simple alignment correction algorithm for inertial sensor gait data demonstrated good to excellent validity and reliability in self-placed sensors with no additional data or measures. This method can be used to align sensors easily and effectively despite sensor placement errors during straight, level walking to improve 3D gait data outcomes in data collected with self-placed sensors.

Concrete crack detection and quantification using deep learning and structured light

Considering the deterioration of civil infrastructures, the evaluation of structural safety by detecting cracks is becoming increasingly essential. In this paper, the advanced technologies of deep learning and structured light composed of vision and two laser sensors have been applied to detect and quantify cracks on surfaces of concrete structures. The YOLO (You Only Look Once) algorithm has been used for real-time detection, and the sizes of the detected cracks have been calculated based on the positions of the projected laser beams on the structural surface. Since laser beams may not be projected in parallel due to installation or manufacturing errors, the laser alignment correction algorithm with a specially designed jig module and a distance sensor is applied to increase the accuracy of the size measurement. The performance of the algorithm has been verified through simulations and experimental tests, and the results show that the cracks on the structural surfaces can be detected and quantified with high accuracy in real-time.

Experimental application of an automated alignment correction algorithm for geological CT imaging: phantom study and application to sediment cores from cold-water coral mounds

BackgroundIn computed tomography (CT) quality assurance, alignment of image quality phantoms is crucial for quantitative and reproducible evaluation and may be improved by alignment correction. Our goal was to develop an alignment correction algorithm to facilitate geological sampling of sediment cores taken from a cold-water coral mount.MethodsAn alignment correction algorithm was developed and tested with a CT acquisition at 120 kVp and 150 mAs of an image quality phantom. Random translation (maximum 15 mm) and rotation (maximum 2.86°) were applied and ground-truth was compared to parameters determined by alignment correction. Furthermore, mean densities were evaluated in four regions of interest (ROIs) placed in the phantom low-contrast section, comparing values before and after correction to ground truth. This process was repeated 1000 times. After validation, alignment correction was applied to CT acquisitions (140 kVp, 570 mAs) of sediment core sections up to 1 m in length, and sagittal reconstructions were calculated for sampling planning.ResultsIn the phantom, average absolute differences between applied and detected parameters after alignment correction were 0.01 ± 0.06 mm (mean ± standard deviation) along the x-axis, 0.11 ± 0.08 mm along the y-axis, 0.15 ± 0.07° around the x-axis, and 0.02 ± 0.02° around the y-axis, respectively. For ROI analysis, differences in densities were 63.12 ± 30.57, 31.38 ± 32.10, 18.27 ± 35.57, and 9.59 ± 26.37 HU before alignment correction and 1.22 ± 1.40, 0.76 ± 0.9, 0.45 ± 0.86, and 0.36 ± 0.48 HU after alignment correction, respectively. For sediment core segments, average absolute detected parameters were 3.93 ± 2.89 mm, 7.21 ± 2.37 mm, 0.37 ± 0.33°, and 0.21 ± 0.22°, respectively.ConclusionsThe alignment correction algorithm was successfully evaluated in the phantom and allowed a correct alignment of sediment core segments, thus aiding in sampling planning. Application to other tasks, like image quality analysis, seems possible.

An Efficient Correction Algorithm for Eliminating Image Misalignment Effects on Co-Phasing Measurement Accuracy for Segmented Active Optics Systems.

The misalignment between recorded in-focus and out-of-focus images using the Phase Diversity (PD) algorithm leads to a dramatic decline in wavefront detection accuracy and image recovery quality for segmented active optics systems. This paper demonstrates the theoretical relationship between the image misalignment and tip-tilt terms in Zernike polynomials of the wavefront phase for the first time, and an efficient two-step alignment correction algorithm is proposed to eliminate these misalignment effects. This algorithm processes a spatial 2-D cross-correlation of the misaligned images, revising the offset to 1 or 2 pixels and narrowing the search range for alignment. Then, it eliminates the need for subpixel fine alignment to achieve adaptive correction by adding additional tip-tilt terms to the Optical Transfer Function (OTF) of the out-of-focus channel. The experimental results demonstrate the feasibility and validity of the proposed correction algorithm to improve the measurement accuracy during the co-phasing of segmented mirrors. With this alignment correction, the reconstructed wavefront is more accurate, and the recovered image is of higher quality.

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-Based Coordinates into Frame Coordinates for Image-Guided Functional Neurosurgery

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-Based Coordinates into Frame Coordinates for Image-Guided Functional Neurosurgery

Alignment correction algorithm for transformation of stereotactic anterior commissure/posterior commissure-based coordinates for image-guided functional neurosurgery

To the Editor: I would like to add some comments to the article of Krauss et al. (6). We routinely use a trigonometric method of calculation of the stereotactic coordinates of invisible targets for functional neurosurgery, based only on axial CT scans. From December 1996 to November 1998, we performed 39 implantations for deep brain stimulation (28 subthalamic nuclei, 10 globus pallidus internus, 1 ventralis intermedius, in 20 Parkinsonian patients), with postoperative CT/MRI and clinical (improvement in UPDRS, Schwab-England, Hoehn-Yahr scales) confirmation of the accuracy of the procedure. This trigonometric method was presented at the 12th Congress of the European Society for Stereotactic and Functional Neurosurgery (Milan, June 12-15, 1996) and published in Acta Neurochirurgica (Wien) (138:615, 1996) and Rivista di Neuroradiologia (11:43-50, 1998) . We emphasize the points in which our method differs from that of Krauss et al., and we described how our method enlarges the applicability of the method itself. Besides the anterior and posterior commissures (AC and PC), only on axial CT scans, we calculate a third point, I, the center of the infundibular recess of the third ventricle. This enables us to fix a reference plane (the midsagittal plane of the third ventricle), to which we refer the distances of the "invisible targets" (subthalamic nucleus, inferior parietal gyrus, ventralis intermedius) codified by the Stereotactic Atlases(2,9). In this way, we have our own "internal" reference system that enables us not "to fix the base line (y axis) as parallel to the intercommissural (IC) line as possible," even though it is reassuring to aim at a parallelism between the y axis and the IC line. For this reason, we position our stereotactic frame (Leksell G; Elekta Instruments, Stockholm, Sweden) with a +12-degree angulation to the O-M line (11). Sagittal and coronal reformatted CT images are not necessary to identify AC and P. We use only axial CT scans to obtain the coordinates of AC and PC, which usually are found on different slices and the coordinates of the third point I. The exact IC length is obtained by the "double target method" (1), avoiding sagittal MRI scans and/or CT reformatted images, which are less reliable. It is then unnecessary "to check with the length measured on the MRI scans." Obviously proportional corrections are made in comparison with the standard values (IC length, distances of the invisible targets from the midpoint of IC, MP, on axial, sagittal, and coronal planes) of the Stereotactic Atlases, and the whole procedure is repeated, for a double check, according to the atlas of Andrew-Watkins (2), referring to the posteroinferior border of the foramen of Monro (FM) to the anterior border of PC and to the point I (FM-PC-I plane). As we refer to AC-PC-I and/or FM-PC-I planes instead of the IC line, it is unnecessary to correct the tilt of IC separately (roll, pitch, yaw) on the different planes. The target's coordinates (Tx, Ty, Tz) are directly calculated applying the formulas of the Appendix, and it is unnecessary to transfer the values of the CT coordinates to the center of the frame. They are directly referred to the frame. Tx and Ty values are projected on the axial CT slice corresponding with Tz. In this way, the calculated target is visualized before the time of surgery. All the values are calculated in real time through a simple QBASIC PC program. Our conclusions are as follows: 1) using a third point, I, and referring to a plane (AC-PC-I or FM-PC-I), the meticulous positioning of the frame with IC parallel to the y axis is not critical; 2) the third point, I, enables us to use our method, in contrast with Krauss, even in the case of "evident roll (tilting of the head relative to the frame along the y axis)"; 3) we do not agree with Krauss et al. that their method "could be used with stereotactic MRI," because we consider MRI not fully reliable (3-5,7,8,10,12); 4) neurophysiological and clinical intraoperative confirmation through microrecording or semi-microrecording and stimulation is mandatory in each case. Franco Ammannati Lorenzo Bordi Paolo Gronchi Florence, Italy

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-Based Coordinates into Frame Coordinates for Image-Guided Functional Neurosurgery

To the Editor: I would like to add some comments to the article of Krauss et al. (6). We routinely use a trigonometric method of calculation of the stereotactic coordinates of invisible targets for functional neurosurgery, based only on axial CT scans. From December 1996 to November 1998, we performed 39 implantations for deep brain stimulation (28 subthalamic nuclei, 10 globus pallidus internus, 1 ventralis intermedius, in 20 Parkinsonian patients), with postoperative CT/MRI and clinical (improvement in UPDRS, Schwab-England, Hoehn-Yahr scales) confirmation of the accuracy of the procedure. This trigonometric method was presented at the 12th Congress of the European Society for Stereotactic and Functional Neurosurgery (Milan, June 12-15, 1996) and published in Acta Neurochirurgica (Wien) (138:615, 1996) and Rivista di Neuroradiologia (11:43-50, 1998) . We emphasize the points in which our method differs from that of Krauss et al., and we described how our method enlarges the applicability of the method itself. Besides the anterior and posterior commissures (AC and PC), only on axial CT scans, we calculate a third point, I, the center of the infundibular recess of the third ventricle. This enables us to fix a reference plane (the midsagittal plane of the third ventricle), to which we refer the distances of the "invisible targets" (subthalamic nucleus, inferior parietal gyrus, ventralis intermedius) codified by the Stereotactic Atlases(2,9). In this way, we have our own "internal" reference system that enables us not "to fix the base line (y axis) as parallel to the intercommissural (IC) line as possible," even though it is reassuring to aim at a parallelism between the y axis and the IC line. For this reason, we position our stereotactic frame (Leksell G; Elekta Instruments, Stockholm, Sweden) with a +12-degree angulation to the O-M line (11). Sagittal and coronal reformatted CT images are not necessary to identify AC and P. We use only axial CT scans to obtain the coordinates of AC and PC, which usually are found on different slices and the coordinates of the third point I. The exact IC length is obtained by the "double target method" (1), avoiding sagittal MRI scans and/or CT reformatted images, which are less reliable. It is then unnecessary "to check with the length measured on the MRI scans." Obviously proportional corrections are made in comparison with the standard values (IC length, distances of the invisible targets from the midpoint of IC, MP, on axial, sagittal, and coronal planes) of the Stereotactic Atlases, and the whole procedure is repeated, for a double check, according to the atlas of Andrew-Watkins (2), referring to the posteroinferior border of the foramen of Monro (FM) to the anterior border of PC and to the point I (FM-PC-I plane). As we refer to AC-PC-I and/or FM-PC-I planes instead of the IC line, it is unnecessary to correct the tilt of IC separately (roll, pitch, yaw) on the different planes. The target's coordinates (Tx, Ty, Tz) are directly calculated applying the formulas of the Appendix, and it is unnecessary to transfer the values of the CT coordinates to the center of the frame. They are directly referred to the frame. Tx and Ty values are projected on the axial CT slice corresponding with Tz. In this way, the calculated target is visualized before the time of surgery. All the values are calculated in real time through a simple QBASIC PC program. Our conclusions are as follows: 1) using a third point, I, and referring to a plane (AC-PC-I or FM-PC-I), the meticulous positioning of the frame with IC parallel to the y axis is not critical; 2) the third point, I, enables us to use our method, in contrast with Krauss, even in the case of "evident roll (tilting of the head relative to the frame along the y axis)"; 3) we do not agree with Krauss et al. that their method "could be used with stereotactic MRI," because we consider MRI not fully reliable (3-5,7,8,10,12); 4) neurophysiological and clinical intraoperative confirmation through microrecording or semi-microrecording and stimulation is mandatory in each case. Franco Ammannati Lorenzo Bordi Paolo Gronchi Florence, Italy

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-based Coordinates into Frame Coordinates for Image-guided Functional Neurosurgery

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-based Coordinates into Frame Coordinates for Image-guided Functional Neurosurgery

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-based Coordinates into Frame Coordinates for Image-guided Functional Neurosurgery

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-based Coordinates into Frame Coordinates for Image-guided Functional Neurosurgery

Alignment correction algorithm for transformation of stereotactic anterior commissure/posterior commissure-based coordinates into frame coordinates for image-guided functional neurosurgery.

The goal was to describe an alignment correction algorithm for the transformation of stereotactic atlas-derived anterior commissure/posterior commissure-based coordinates into frame coordinates for image-guided functional stereotactic neurosurgery. The algorithm was developed for the calculation of targets that are referenced to the intercommissural line. It corrects for deviations of the axis of the intercommissural line in relation to the stereotactic frame (x, y, and z coordinates). The algorithm is easily implemented on a personal computer with a spreadsheet program. The calculation is fast and effective. The procedure is universally applicable for functional stereotactic neurosurgery, and it can be used with different stereotactic frames, different imaging techniques, and different workstations.

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-based Coordinates into Frame Coordinates for Image-guided Functional Neurosurgery

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-based Coordinates into Frame Coordinates for Image-guided Functional Neurosurgery

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-based Coordinates into Frame Coordinates for Image-guided Functional Neurosurgery

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-based Coordinates into Frame Coordinates for Image-guided Functional Neurosurgery

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-based Coordinates into Frame Coordinates for Image-guided Functional Neurosurgery

Alignment Correction Algorithm for Transformation of Stereotactic Anterior Commissure/Posterior Commissure-based Coordinates into Frame Coordinates for Image-guided Functional Neurosurgery