Two-photon calcium imaging (TPCI) is a functional neuroimaging technique that simultaneously reveals the function of small populations of cells as well as the structure of surrounding brain tissue. These unique properties cause TPCI to be increasingly popular for experimental basic neuroscience. Unfortunately, methodological development for data processing has not kept pace with experimental needs. I address this lack by developing and testing new methodology for several key tasks. Specifically, I address two primary analysis steps which are nearly universally required in early data processing: region of interest segmentation and motion correction. For each task I organize the sparse existing literature, clearly define the requirements of the problem, propose a solution, and evaluate it on experimental data. I develop MaSCS, an automated adaptable multi-class segmentation system that improves with use. I carefully define and describe the impact of motion artifacts on imaging data, and quantify the effects of standard and innovative motion correction approaches. Finally, I apply my work on segmentation and motion correction to explore one scientific target, namely discovering correlation-based cell clustering. I show that estimating such correlation-based clustering remains an open question, as it is highly sensitive to motion artifacts, even after motion correction techniques are applied. The contributions of this work include the organization of existing resources, methodological advances in segmentation, motion correction and clustering, and the development of prototype analysis software.

"Single Photon Emission Computed Tomography (SPECT) is a medical imaging modality that allows us to visualize functional information about a patient's specific organ or body systems. During 20 minute scan, patients may move. Such motion will cause misalignment in the reconstruction, degrade the quality of 3D images and potentially lead to errors in diagnosis. Body bend and twist are types of patient motion that may occur during SPECT imaging and which has been generally ignored in SPECT motion correction strategies. To correct for these types of motion we propose a deformation model and its inclusion within an iterative reconstruction algorithm. One simulation and three experiments were conducted to investigate the applicability of our model. The simulation employed simulated projections of the MCAT phantom formed using an analytical projector which includes attenuation and distance-dependent resolution to investigate applications of our model in reconstruction. We demonstrate in the simulation studies that twist and bend can significantly degrade SPECT image quality visually. Our correction strategy is shown to be able to greatly diminish the degradation seen in the slices, provided the parameters are estimated accurately. To verify the correctness of our deformation model, we design the first experiment. In this experiment, the return of the post-motion-compensation locations of markers on the body-surface of a volunteer to approximate their original coordinates is used to examine our method of estimating the parameters of our model and the parameters' use in undoing deformation. Then, we design an MRI based experiment to validate our deformation model without any reconstruction. We use the surface marker motion to alter an MRI body volume to compensate the deformation the volunteer undergoes during data acquisition, and compare the motion-compensated volume with the motionless volume. Finally, an experiment with SPECT acquisitions and modified MLEM algorithm is designed to show the contribution of our deformation correction for clinical SPECT imaging. We view this work as a first step towards being able to estimate and correct patient deformation based on information obtained from marker tracking data."

Magnetic resonance imaging (MRI) is a non-invasive technique used to produce high-quality images of the interior of the human body. Compared to other imaging modalities, however, MRI requires a relatively long data acquisition time to form an image. Patients often have difficulty staying still during this period. This is problematic as motion produces artifacts in the image. This thesis explores the methods of imaging a moving object using MRI. Testing is performed using simulations, a moving phantom, and human subjects. Several strategies developed to avoid motion artifact problems are presented. Emphasis is placed on techniques that provide motion correction without penalty in terms of acquisition time. The most significant contribution presented is the development and assessment of the 'TRELLIS' pulse sequence and reconstruction algorithm. TRELLIS is a unique approach to motion correction in MRI. Orthogonal overlapping strips fill k-space and phase-encode and frequency-encode directions are alternated such that the frequency-encode direction always runs lengthwise along each strip. The overlap between pairs of orthogonal strips is used for signal averaging and to produce a system of equations that, when solved, quantifies the rotational and translational motion of the object. Acquired data is then corrected using this motion estimation. The advantage of TRELLIS over existing techniques is that k-space is sampled uniformly and all collected data is used for both motion detection and image reconstruction. This thesis presents a number of other contributions: a proposed means of motion correction using parallel imaging; an extension to the phase-correlation method for determining displacement between two objects; a metric to quantify the level of motion artifacts; a moving phantom; a physical version of the ubiquitous Shepp-Logan head phantom; a motion resistant data acquisition technique; and a means of correcting for T2 blurring artifacts.

Indiana University-Purdue University Indianapolis (IUPUI) / The advances in health care technology help physicians make more accurate diagnoses about the health conditions of their patients. Positron Emission Tomography/Computed Tomography (PET/CT) is one of the many tools currently used to diagnose health and disease in patients. PET/CT explorations are typically used to detect: cancer, heart diseases, disorders in the central nervous system. Since PET/CT studies can take up to 60 minutes or more, it is impossible for patients to remain motionless throughout the scanning process. This movements create motion-related artifacts which alter the quantitative and qualitative results produced by the scanning process. The patient's motion results in image blurring, reduction in the image signal to noise ratio, and reduced image contrast, which could lead to misdiagnoses. In the literature, software and hardware-based techniques have been studied to implement motion correction over medical files. Techniques based on the use of an external motion tracking system are preferred by researchers because they present a better accuracy. This thesis proposes a motion correction system that uses 3D affine registrations using particle swarm optimization and an off-the-shelf Microsoft Kinect camera to eliminate or reduce errors caused by the patient's motion during a medical imaging study.

This thesis is a study of the air-sea fluxes of momentum, sensible heat and latent heat. Fluxes are estimated using the covariance, COARE2.6b bulk flux algorithm, and inertial dissipation methods. The bulk algorithm is validated against the covariance fluxes for the first time in a light-wind, shallow tropical sea, with strong atmospheric instability and low sea state conditions. The removal of ship motion contamination is investigated. This is the first study to quantify the errors associated with corrections for ship motion contamination, and the effects of motion contamination on the covariance calculated heat fluxes. Flow distortion is investigated. Bulk transfer coefficients and roughness lengths are computed and related to the sea state. Ship motion contamination is successfully removed in 86% of the runs. Error analysis of the motion removal algorithm indicates maximum uncertainties of 15% in the wind fluctuations, and 0.002 N/m/m for the wind stress. Motion correction changes the stress by more than 15% in half of the runs analysed. The ship is found to accelerate the mean air flow and deflect it above the horizontal. A correction is developed for the air flow acceleration. The scalar fluxes show good agreement on average for all the methods. As wind speed approaches zero, covariance wind stress is significantly larger than the bulk and inertial dissipation derived wind stress. The non-zero covariance wind stress is reflected in the drag coefficient, CdN10, and momentum roughness length, z0, which are much larger than the parameterisations used in the bulk algorithm. The MCTEX CdN10, wind speed (u10N) relation is 1000 x Cd10N = 1.03 + 7.88/(u10N)^2 0.8 &lt u10N &lt 7.5 m/s z0 is primarily a function of wind speed rather than sea state, with largest roughness lengths occurring as wind speed approaches zero. This relation is used in the bulk algorithm, yielding good agreement between covariance and bulk derived wind stress. A new parameterisation for the effects of gustiness, based on wind variance is developed. This brings the bulk wind stress into agreement with the covariance derived wind stress.

Magnetic resonance imaging (MRI) is one of medical images used by doctors for diagnosing diseases. MRI shows higher quality in displaying soft tissues and tumors. Pharmacokinetic dynamic gadolinium-enhanced MR imaging and functional MR imaging (fMRI) were used in this dissertation. Dynamic MR images are obtained using fast spin-echo sequences at consecutive time after the injection of gadolinium-diethylene-triamine penta-acetic (Gd-DTPA) acid. A pharmacokinetic model analyzes time-signal intensity curves of suspected lesions. Functional MR imaging produces images of activated brain regions by detecting the indirect effects of neuronal activity on local blood volume, flow, and oxygen saturation. Thus it is a promising tool for further understanding the relationships between brain structure, function, and pathology. Because of patients' movement during imaging, serially acquired MR images do not correspond in the same pixel position. Therefore, matching corresponding points from MR images is one of fundamental tasks in this dissertation. Least-squares estimation is a standard method for parameter estimation. However, outliers (due to non-Gaussian noise, lesion evolution, motion-related artifacts, etc.) may exist and thus may cause the motion parameter estimation result to deteriorate. In this dissertation, we describe two robust estimation algorithms for the registration of serially acquired MR images. The first estimation algorithm is based on the Newton method and uses the Tukey's biweight objective function. The second estimation algorithm is based on the Levenberg-Marquardt technique and uses a skipped mean objective function. The robust M-estimators can suppress the effects of the outliers by scaling down their error magnitudes or completely rejecting outliers using a weighting function. Experimental results show the accuracy of the proposed robust estimation algorithms is within subpixel. MR imaging has been used to evaluate nasal papilloma. However, postoperative MR imaging of nasal papilloma becomes more complicated because repair with granulation and fibrosis occurs after surgery. Therefore, it is possible to misclassify recurrences as postoperative changes or to misclassify postoperative changes as recurrences. Recently, dynamic gadolinium-enhanced MR imaging with pharmacokinetic analysis has been successfully used to identify the post-treatment recurrence or postoperative changes in rectal and cervical carcinoma. Nasopharyngeal carcinoma (NPC) comprising malignant tumors is a disease more common in Asia than in other parts of the world. Hence, in this dissertation, we evaluate the feasibility of dynamic gadolinium-enhanced MR imaging with pharmacokinetic analysis in detecting NPC and distinguishing recurrent nasal papilloma from postoperative changes (fibrosis or granulation tissue). In this dissertation, a new approach to differentiate recurrent nasal papilloma from postoperative changes using pharmacokinetic dynamic gadolinium-enhanced MR imaging and robust motion correction is presented. First, a robust estimation technique is incorporated into nonlinear minimization method to robustly register dynamic gadolinium-enhanced MR images. Next, user roughly selects the region of interest (ROI) and an active contour technique is used to extract a more precise ROI. Then, the relative signal increase (RSI) is calculated. We use a three-parameter mathematical model for pharmacokinetic analysis. The pharmacokinetic parameters A (enhancement amplitude) and Tc (tissue distribution time) are calculated by a nonlinear least-squares fitting technique. The calculated A and Tc are used to characterize tissue. Pharmacokinetic analysis shows that recurrent nasal papilloma has faster tissue distribution time (Tc, 41 versus 88 seconds) and higher enhancement amplitude (A, 2.4 versus 1.2 arbitrary units) than do postoperative changes. A cut-off of 65 seconds for tissue distribution time and 1.6 units for enhancement amplitude yields an accuracy of 100% for differentiating recurrent nasal papilloma from postoperative changes. Though the above methods obtained good results, finding the region of interest (ROI) was done in a semi-automatic manner. For diagnosing NPC and improve the drawback, a system that automatically detects and labels NPC with dynamic gadolinium-enhanced MR imaging is presented. This system is a multistage process, involving motion correction, gadolinium-enhanced MR data quantitative evaluation, rough segmentation, and rough segmentation refinement. Three approaches, a relative signal increase method, a slope method and a relative signal change method, are proposed for the quantitative evaluation of gadolinium-enhanced MR data. After the quantitative evaluation, a rough NPC outline is determined. Morphological operations are applied to refine the rough segmentation into a final mask. The NPC detection results obtained using the proposed methods had a rating of 85% in match percent compared with these lesions identified by an experienced radiologist. However, the proposed methods can identify the NPC regions quickly and effectively. In this dissertation, the proposed methods provide significant improvement in correcting the motion-related artifacts and can enhance the detection of real brain activation and provide a fast, valuable diagnostic tool for detecting NPC and differentiating recurrent nasal papilloma from postoperative changes.

In this work, a major effort has been made to establish an Internet accessible system for medical imaging simulation as a convenient service under the cloud computing environment. First, an Internet accessible, medical imaging education platform has been developed. It includes teaching and dynamic assessment tracking system for five commonly used imaging modalities. The system is integrated by the open source MySQL database software that manages updating materials and also tracks students’ learning engagements, which allow the reliability and appropriateness of the on-line teaching material and assessment methods to be optimized. The evaluation results have shown increased learning gains promisingly. Second, a prototype simulation service platform has been established. It is based on a job-oriented work flow to provide different kinds of service to users to perform medical imaging simulation. These simulations not only include the straightforward CT data reconstruction based on Radon transform, but also the sophisticated PET imaging simulation based on GATE as well. The QGATE’s client-server configuration can manage the GATE system to queue and monitor the submitted simulation scripts and return simulation results. The system is suitable for classroom training and easy to use for students or new users to the field of nuclear medicine imaging simulation. Finally, based on the developed simulation platform, a simulation study on PET imaging has been carried out. Event-based dynamic justification method has been tested based on the phantoms generated by NCAT associated with different breathing signals. The results show its potential capability of motion correction for PET data acquisition.

Although motion correction in medical imaging is well established and has attracted much interest and research funding, a gap still exists in that there is a lack of reliable, low-cost hardware to enable such techniques to be widely adopted in healthcare. Motion correction of brain Positron Emission Tomography (PET) data for instance is an important step in realising the potential offered by modern high resolution PET scanners. Since it is not likely that subjects can remain stationary throughout the PET scan, which can last 60 minutes or more, accurate and reliable motion tracking is needed to correct the PET data for any observed motion. A commercially available marker based motion tracking system was evaluated and found to produce unreliable data. This was due to the possibility of the tracking tool slipping from the subject. This thesis describes the investigations into alternative and novel tracking techniques for use in PET. These included a markerless tracking system using the Microsoft Kinect (a low cost depth sensor) as well as a multiple target marker tracking system. The performance characteristics of both systems (low cost, high spatial and temporal accuracy, and real-time operation) were evaluated using phantom and clinical experiments. Investigations into using these two tracking techniques in whole body PET, specifically measuring the respiratory rate during lung imaging, were developed and compared against current commercially available solutions.

Motion artifacts are often a significant component of the measured signal in functional near-infrared spectroscopy (fNIRS) experiments. A variety of methods have been proposed to address this issue, including principal component analyses (PCA), Kalman filtering, correlation-based signal improvement (CBSI), wavelet filtering, spline interpolation, and autoregressive algorithms. The efficacy of these techniques has been compared using simulated data; however, our understanding of how these techniques fare when dealing with task-based cognitive data is limited. Recently, Brigadoi et al. (2014) quantitatively compared 6 motion correction techniques in a sample of adult data measured during a simple cognitive task. Wavelet filtering showed the most promise as an optimal technique for motion correction. Because fNIRS is often used with infants and young children, it is critical to evaluate the effectiveness of motion correction techniques directly with data from these age groups. Here we examined which techniques are most effective with data from young children. The efficacy of each technique was compared quantitatively using objective metrics related to the physiological properties of the hemodynamic response using two different sets of parameters to ensure maximum retention of included trials. Results showed that targeted PCA (tPCA) and CBSI retained a higher number of trials. These techniques also performed well in direct head-to-head comparisons with the other approaches using both quantitative metrics and a qualitative assessment. The CBSI technique corrected many of the artifacts present in our data; however, this technique was highly influenced by the parameters used to detect motion. The tPCA technique, by contrast, was robust across changes in parameters while also performing well across all comparison metrics. We conclude, therefore, that tPCA is an effective technique for the correction of motion artifacts in fNIRS data from young children.

No description available.