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Motion Tracking for Medical Imaging: A Nonvisible Structured Light Tracking Approach

We present a system for head motion tracking in 3D brain imaging. The system is based on facial surface reconstruction and tracking using a structured light (SL) scanning principle. The system is designed to fit into narrow 3D medical scanner geometries limiting the field of view. It is tested in a clinical setting on the high resolution research tomograph (HRRT), Siemens PET scanner with a head phantom and volunteers. The SL system is compared to a commercial optical tracking system, the Polaris Vicra system, from NDI based on translatory and rotary ground truth motions of the head phantom. The accuracy of the systems was similar, with root mean square (rms) errors of 0.09° for ±20° axial rotations, and rms errors of 0.24 mm for ± 25 mm translations. Tests were made using 1) a light emitting diode (LED) based miniaturized video projector, the Pico projector from Texas Instruments, and 2) a customized version of this projector replacing a visible light LED with a 850 nm near infrared LED. The latter system does not provide additional discomfort by visible light projection into the patients eyes. The main advantage over existing head motion tracking devices, including the Polaris Vicra system, is that it is not necessary to place markers on the patient. This provides a simpler workflow and eliminates uncertainties related to marker attachment and stability. We show proof of concept of a marker less tracking system especially designed for clinical use with promising results.

Spatial resolution of the HRRT PET scanner using 3D-OSEM PSF reconstruction

In this paper, the resolution of the Siemens high resolution research tomograph (HRRT) was centrally (r < 60 mm) homogenous with a FWHM of 1.4 mm for 18F-FDG in air. This was where the main part of the brain is located if the patient has been positioned correctly. The 1.4 mm resolution was obtained using the newly develop 3D-OSEM PSF reconstruction algorithm, which was a significant improvement over 3D-OSEM reconstruction without PSF. The algorithm uses a simple PSF model that was the same for all the pixels in the FOV and does not regulate for the circular/octagonal scanner geometry. This supports that the FWHM of the radial axis is increasing with the distance from the center for r > 60mm.

Methods for Motion Correction Evaluation Using 18F-FDG Human Brain Scans on a High-Resolution PET Scanner

Many authors have reported the importance of motion correction (MC) for PET. Patient motion during scanning disturbs kinetic analysis and degrades resolution. In addition, using misaligned transmission for attenuation and scatter correction may produce regional quantification bias in the reconstructed emission images. The purpose of this work was the development of quality control (QC) methods for MC procedures based on external motion tracking (EMT) for human scanning using an optical motion tracking system. Methods: Two scans with minor motion and 5 with major motion (as reported by the optical motion tracking system) were selected from 18F-FDG scans acquired on a PET scanner. The motion was measured as the maximum displacement of the markers attached to the subjects head and was considered to be major if larger than 4 mm and minor if less than 2 mm. After allowing a 40- to 60-min uptake time after tracer injection, we acquired a 6-min transmission scan, followed by a 40-min emission list-mode scan. Each emission list-mode dataset was divided into 8 frames of 5 min. The reconstructed time-framed images were aligned to a selected reference frame using either EMT or the AIR (automated image registration) software. The following 3 QC methods were used to evaluate the EMT and AIR MC: a method using the ratio between 2 regions of interest with gray matter voxels (GM) and white matter voxels (WM), called GM/WM; mutual information; and cross correlation. Results: The results of the 3 QC methods were in agreement with one another and with a visual subjective inspection of the image data. Before MC, the QC method measures varied significantly in scans with major motion and displayed limited variations on scans with minor motion. The variation was significantly reduced and measures improved after MC with AIR, whereas EMT MC performed less well. Conclusion: The 3 presented QC methods produced similar results and are useful for evaluating tracer-independent external-tracking motion-correction methods for human brain scans.

List-Mode PET Motion Correction Using Markerless Head Tracking: Proof-of-Concept With Scans of Human Subject

A custom designed markerless tracking system was demonstrated to be applicable for positron emission tomography (PET) brain imaging. Precise head motion registration is crucial for accurate motion correction (MC) in PET imaging. State-of-the-art tracking systems applied with PET brain imaging rely on markers attached to the patients head. The marker attachment is the main weakness of these systems. A healthy volunteer participating in a cigarette smoking study to image dopamine release was scanned twice for 2 h with 11C-racolopride on the high resolution research tomograph (HRRT) PET scanner. Head motion was independently measured, with a commercial marker-based device and the proposed vision-based system. A list-mode event-by-event reconstruction algorithm using the detected motion was applied. A phantom study with hand-controlled continuous random motion was obtained. Motion was time-varying with long drift motions of up to 18 mm and regular step-wise motion of 1-6 mm. The evaluated measures were significantly better for motion-corrected images compared to no MC. The demonstrated system agreed with a commercial integrated system. Motion-corrected images were improved in contrast recovery of small structures.

Structured light 3D tracking system for measuring motions in PET brain imaging

Patient motion during scanning deteriorates image quality, especially for high resolution PET scanners. A new proposal for a 3D head tracking system for motion correction in high resolution PET brain imaging is set up and demonstrated. A prototype tracking system based on structured light with a DLP projector and a CCD camera is set up on a model of the High Resolution Research Tomograph (HRRT). Methods to reconstruct 3D point clouds of simple surfaces based on phase-shifting interferometry (PSI) are demonstrated. The projector and camera are calibrated using a simple stereo vision procedure where the projector is treated as a camera. Additionally, the surface reconstructions are corrected for the non-linear projector output prior to image capture. The results are convincing and a first step toward a fully automated tracking system for measuring head motions in PET imaging.

Accurate and simple calibration of DLP projector systems

Much work has been devoted to the calibration of optical cameras, and accurate and simple methods are now available which require only a small number of calibration targets. The problem of obtaining these parameters for light projectors has not been studied as extensively and most current methods require a camera and involve feature extraction from a known projected pattern. In this work we present a novel calibration technique for DLP Projector systems based on phase shifting profilometry projection onto a printed calibration target. In contrast to most current methods, the one presented here does not rely on an initial camera calibration, and so does not carry over the error into projector calibration. A radial interpolation scheme is used to convert features coordinates into projector space, thereby allowing for a very accurate procedure. This allows for highly accurate determination of parameters including lens distortion. Our implementation acquires printed planar calibration scenes in less than 1s. This makes our method both fast and convenient. We evaluate our method in terms of reprojection errors and structured light image reconstruction quality.

Motion tracking in narrow spaces: a structured light approach

We present a novel tracking system for patient head motion inside 3D medical scanners. Currently, the system is targeted at the Siemens High Resolution Research Tomograph (HRRT) PET scanner. Partial face surfaces are reconstructed using a miniaturized structured light system. The reconstructed 3D point clouds are matched to a reference surface using a robust iterative closest point algorithm. A main challenge is the narrow geometry requiring a compact structured light system and an oblique angle of observation. The system is validated using a mannequin head mounted on a rotary stage. We compare the system to a standard optical motion tracker based on a rigid tracking tool. Our system achieves an angular RMSE of 0.11 degrees demonstrating its relevance for motion compensated 3D scan image reconstructions as well as its competitiveness against the standard optical system with an RMSE of 0.08 degrees. Finally, we demonstrate qualitative result on real face motion estimation.

New attenuation correction for the HRRT using transmission scatter correction and total variation regularization

In the standard software for the Siemens HRRT PET scanner the most commonly used segmentation in the ¿-map reconstruction for human brain scans is MAP-TR. Problems with bias in the lower cerebellum and pons in HRRT brain images have been reported. The main source of the problem is poor bone / soft tissue segmentation in these regions and the lack of scatter correction in the ¿-map reconstruction. In this paper we describe and validate the new TXTV segmentation method (included in the HRRTU 1.0 and 1.1 user software) aimed at solving the bias problem.

Real time surface registration for PET motion tracking

Head movement during high resolution Positron Emission Tomography brain studies causes blur and artifacts in the images. Therefore, attempts are being made to continuously monitor the pose of the head and correct for this movement. Specifically, our method uses a structured light scanner system to create point clouds representing parts of the patients face. The movement is estimated by a rigid registration of the point clouds. The registration should be done using a robust algorithm that can handle partial overlap and ideally operate in real time. We present an optimized Iterative Closest Point algorithm that operates at 10 frames per second on partial human face surfaces.

Automatic thresholding for frame-repositioning using external tracking in PET brain imaging

Motion correction (MC) in positron emission tomography (PET) brain imaging become of higher importance with increasing scanner resolution. Several motion correction methods have been suggested and so far the Polaris Vicra tracking system has been the preferred one for motion registration. We present an automated algorithm for dividing PET acquisitions into subframes based on the registered head motion to correct for intra-frame motion with the frame repositioning MC method. The method is tested on real patient data (five 11C-SB studies and five 11C-PIB studies) and compared with an image based registration method (AIR). Quantitative evaluation was done using a correlation measure. The study shows that MC improves the correlation of the PET images and that AIR performed slightly better than the Polaris Vicra. We found significant intra-frame motion of 1–5 mm in 9 frames but the correlation was not significantly improved using intra-frame MC.