Testing SPECT Motion Correction Algorithms

Frequently, testing of Single Photon Emission Computed Tomography (SPECT) motion correction algorithms is done either by using simplistic deformations that do not accurately simulate true patient motion or by applying the algorithms directly to data acquired from a real patient, where the true internal motion is unknown. In this work, we describe a way to combine these two approaches by using imaging data acquired from real volunteers to simulate the data that the motion correction algorithms would normally observe. The goal is to provide an assessment framework which can both: simulate realistic SPECT acquisitions that incorporate realistic body deformations and provide a ground truth volume to compare against. Every part of the motion correction algorithm needs to be exercised: from parameter estimation of the motion model, to the final reconstruction results. In order to build the ground truth anthropomorphic numerical phantoms, we acquire high resolution MRI scans and motion observation data of a volunteer in multiple different configurations. We then extract the organ boundaries using thresholding, active contours, and morphology. Phantoms of radioactivity uptake and density inside the body can be generated from these boundaries to be used to simulate SPECT acquisitions. We present results on extraction of the ribs, lungs, heart, spine, and the rest of the soft tissue in the thorax using our segmentation approach. In general, extracting the lungs, heart, and ribs in images that do not contain the spine works well, but the spine could be better extracted using other methods that we discuss. We also go in depth into the software development component of this work, describing the C++ coding framework we used and the High Level Interactive GUI Language (HLING). HLING solved a lot of problems but introduced a fair bit of its own. We include a set of requirements to provide a foundation for the next attempt at developing a declarative and minimally restrictive methodology for writing interactive image processing applications in C++ based on lessons learned during the development of HLING

Multi-modality image simulation with the Virtual Imaging Platform: Illustration on cardiac echography and MRI

International audienceMedical image simulation is useful for biological modeling, image analysis, and designing new imaging devices but it is not widely available due to the complexity of simulators, the scarcity of object models, and the heaviness of the associated computations. This paper presents the Virtual Imaging Platform, an openly-accessible web platform for multi-modality image simulation. The integration of simulators and models is described and exemplified on simulated cardiac MRIs and ultrasonic images

Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data

The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes, and physiological variations. Alternatively, computational phantoms can be used to generate large data sets for different disease states, providing a ground truth. Several studies use registration of dynamic images to derive voxel deformations to create moving computational phantoms. These phantoms together with simulation software generate raw data. This paper proposes a method for the synthesis of dynamic PET data using a fast analytic method. This is achieved by incorporating realistic models of respiratory motion into a numerical phantom to generate datasets with continuous and variable motion with magnetic resonance imaging (MRI)-derived motion modeling and high resolution MRI images. In this paper, data sets for two different clinical traces are presented, ¹⁸F-FDG and ⁶⁸Ga-PSMA. This approach incorporates realistic models of respiratory motion to generate temporally and spatially correlated MRI and PET data sets, as those expected to be obtained from simultaneous PET-MRI acquisitions

Tissue mimicking materials for imaging and therapy phantoms: a review

Tissue mimicking materials (TMMs), typically contained within phantoms, have been used for many decades in both imaging and therapeutic applications. This review investigates the specifications that are typically being used in development of the latest TMMs. The imaging modalities that have been investigated focus around CT, mammography, SPECT, PET, MRI and ultrasound. Therapeutic applications discussed within the review include radiotherapy, thermal therapy and surgical applications. A number of modalities were not reviewed including optical spectroscopy, optical imaging and planar x-rays. The emergence of image guided interventions and multimodality imaging have placed an increasing demand on the number of specifications on the latest TMMs. Material specification standards are available in some imaging areas such as ultrasound. It is recommended that this should be replicated for other imaging and therapeutic modalities. Materials used within phantoms have been reviewed for a series of imaging and therapeutic applications with the potential to become a testbed for cross-fertilization of materials across modalities. Deformation, texture, multimodality imaging and perfusion are common themes that are currently under development

Relevance of accurate Monte Carlo modeling in nuclear medical imaging

Monte Carlo techniques have become popular in different areas of medical physics with advantage of powerful computing systems. In particular, they have been extensively applied to simulate processes involving random behavior and to quantify physical parameters that are difficult or even impossible to calculate by experimental measurements. Recent nuclear medical imaging innovations such as single-photon emission computed tomography (SPECT), positron emission tomography (PET), and multiple emission tomography (MET) are ideal for Monte Carlo modeling techniques because of the stochastic nature of radiation emission, transport and detection processes. Factors which have contributed to the wider use include improved models of radiation transport processes, the practicality of application with the development of acceleration schemes and the improved speed of computers. This paper presents derivation and methodological basis for this approach and critically reviews their areas of application in nuclear imaging. An overview of existing simulation programs is provided and illustrated with examples of some useful features of such sophisticated tools in connection with common computing facilities and more powerful multiple-processor parallel processing systems. Current and future trends in the field are also discussed

Characterization and Compensation of Hysteretic Cardiac Respiratory Motion in Myocardial Perfusion Studies Through MRI Investigations

Respiratory motion causes artifacts and blurring of cardiac structures in reconstructed images of SPECT and PET cardiac studies. Hysteresis in respiratory motion causes the organs to move in distinct paths during inspiration and expiration. Current respiratory motion correction methods use a signal generated by tracking the motion of the abdomen during respiration to bin list- mode data as a function of the magnitude of this respiratory signal. They thereby fail to account for hysteretic motion. The goal of this research was to demonstrate the effects of hysteretic respiratory motion and the importance of its correction for different medical imaging techniques particularly SPECT and PET. This study describes a novel approach for detecting and correcting hysteresis in clinical SPECT and PET studies. From the combined use of MRI and a synchronized Visual Tracking System (VTS) in volunteers we developed hysteretic modeling using the Bouc-Wen model with inputs from measurements of both chest and abdomen respiratory motion. With the MRI determined heart motion as the truth in the volunteer studies we determined the Bouc Wen model could match the behavior over a range of hysteretic cycles. The proposed approach was validated through phantom simulations and applied to clinical SPECT studies

RIGOROUS TASK-BASED OPTIMIZATION OF INSTRUMENTATION, ACQUISITION PARAMETERS AND RECONSTRUCTION METHODS FOR MYOCARDIAL PERFUSION SPECT

Coronary artery disease (CAD) is the most common type of heart disease and a major cause of death in the United States. Myocardial perfusion SPECT (MPS) is a well-established noninvasive diagnostic imaging technique for the detection and functional characterization of CAD. MPS involves intravenous injection of a radiopharmaceutical (e.g. Tc-99m sestamibi) followed by acquiring planar images of the 3-D distribution of the radioactive labeled agent, using one or more gamma cameras that are rotated around the patient, at different projection views. Transaxial reconstructed images are formed from these projections using tomographic image reconstruction methods. The quality of SPECT images is affected by instrumentation, acquisition parameters and reconstruction/compensation methods used. The overall goal of this dissertation was to perform rigorous optimization of MPS using task-based image quality assessment methods and metrics, in which image quality is evaluated based on the performance of an observer on diagnostic tasks relevant to MPS. In this work, we used three different model observers: the Ideal Observer (IO), and its extension, the Ideal Observer with Model Mismatch (IO-MM) and an anthropomorphic observer, the Channelized Hotelling Observer (CHO). The IO makes optimal use of the available information in the image data. However, due to its implicit perfect knowledge about the image formation process, using the IO to optimize imaging systems could lead to differences in optimal parameters compared to those optimized for humans (or CHO) interpreting images that are reconstructed with imperfect compensation for image-degrading factors. To address this, we developed the IO-MM that allows optimization of acquisition and instrumentation parameters in the absence of compensation or the presence of non-ideal compensation methods and evaluates them in terms of the IO. In order to perform clinically relevant optimization of MPS and due to radiation concerns that limit system evaluation using patient studies, we designed and developed a population of digital phantoms based on the 3-D eXtended CArdiac Torso (XCAT) phantom that provides an extremely realistic model of the human anatomy. To make the simulation of the population computationally feasible, we developed and used methods to efficiently simulate a database of Tc-99m and Tl-201 MPS projections using full Monte Carlo (MC) simulations. We used the phantom population and the projection database to optimize and evaluate the major acquisition and instrumentation parameters for MPS. An important acquisition parameter is the width of the acquisition energy window, which controls the tradeoff between scatter and noise. We used the IO, IO–MM and CHO to find the optimal acquisition energy window width and evaluate various scatter modeling and compensation methods, including the dual and triple energy window and the Effective Source Scatter Estimation (ESSE). Results indicated that the ESSE scatter estimation method provided very similar performance to the perfect scatter model implicit in the IO. Collimators are a major factor limiting image quality and largely determine the noise and resolution of SPECT images. We sought the optimal collimator with respect to the IO performance on two tasks related to MPS: binary detection and joint detection and localization. The results of this study suggested that higher sensitivity collimators than those currently used clinically appear optimal for both of the diagnostic tasks. In a different study, we evaluated and compared various CDR modeling and compensation methods using the IO (i.e. the observer implicitly used a true CDR model), IO-MM (using an approximate or no model of the CDR) and CHO, operating on images reconstructed using the same compensation methods. Results from the collimator and acquisition energy window optimization studies indicated that the IO-MM had good agreement with the CHO, in terms of the range of optimal Tc-99m acquisition energy window widths, optimal collimators, and the ranking of scatter and CDR compensation methods. The IO was in agreement with the CHO when model mismatch was small. Dual isotope simultaneous acquisition (DISA) rest Tl-201/stress Tc-99m MPS has the potential to provide reduced acquisition time, increased patient comfort, and perfectly registered images compared to separate acquisition protocols, the current clinical protocols of choice. However, crosstalk contamination, where photons emitted by one radionuclide contribute to the image of the other, degrades image quality. In this work, we optimized, compared and evaluated dual isotope MPS imaging with separate and simultaneous acquisition using the IO in the context of 3-class defect detection task. Optimal acquisition parameters were different for the two protocols. Results suggested that DISA methods, when used with accurate crosstalk compensation methods, could potentially provide image quality as good as that obtained with separate acquisition protocols

Non-invasive cardiac radiosurgery with MRI guidance: a ground-truth for real-time target localisation using the XCAT phantom

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia. The growing epidemic of AF already affects millions of patients around the world and millions more are forecast to develop the condition in coming decades. The standard non-pharmacological treatment for AF is catheter ablation, an invasive and time consuming procedure. Non-invasive treatment of AF with radiosurgery has recently been put forward but is challenged by complex cardiac and respiratory motion. Compensating for target motion and treating in real-time could be realised with a MRI linear accelerator (MRI-Linac). A recent study developed methodology to track cardiac targets for this purpose but until now no measure of its accuracy has been accessible. In this investigation, the existing real-time cardiac tracking is quantified and developed on a digital phantom platform. It is first tested within a perfect digital scenario and then extended to a realistic anthropomorphic simulation. In a final experiment, developed tracking methods are applied to real-world anatomical data. A total number of twenty-one virtual patients were generated with the 4 dimensional extended cardiac-torso (XCAT) phantom software and received magnetic resonance imaging (MRI) simulated cardiac scans. A 3D volume representing a distinct cardiac phase is comprised of 2D slices which cover the entire target area. These template volumes are matched through pixel similarity to 2D orthogonal real-time MRI planes to localise the target volume in real-time. One virtual patient represented ideal and thus unrealistic MRI scans to initially test the cardiac tracking. Twenty virtual patients were subjected to MRI scans that closely model the proposed real-world scenario. An available ground-truth is compared to target motion trajectories output from the cardiac tracking algorithm for the twenty-one virtual patients. The cardiac tracking methodology is simultaneously developed as a result of the quantitative measures. Additionally, the correlation and significance of the virtual patients’ physiological parameters with tracking accuracy is investigated. Finally, the best performing tracking function is qualitatively assessed on a single patient’s real-world MRI scans. Employing a tracking method with the same basic methodology as the original tracking on the twenty virtual patient cohort resulted in a mean 3D tracking error of 3.2 ± 1.7 mm. The three anatomical plane constituent errors were 1.3 ± 0.9 mm in the superior inferior (SI) plane, 1.4 ± 0.9 mm in the anterior posterior (AP) plane and 2.2 ± 1.8 mm in the left right (LR) plane. This result is in strong agreement with the inferred error of 3 - 4 mm from the previous study that was based on 2D quantification. After tracking developments were implemented, the best performing mean 3D tracking error of 2.9 ± 1.6 mm was ascertained. A patient’s heart rate is the only anatomical parameter to show a significant linear relationship with tracking error (r=0.65, p-value = 0.0018). Comparing best performing tracking functions across the virtual patients show that the optimal tracking function is patient-specific. When the developed methods were reintroduced to a patient’s MRI data the tracking accuracy was qualitatively assessed to have improved. The results of the previous single patient treatment planning indicate that high-dose cardiac radiosurgery can be administered for the treatment of AF when safety margins are below 5 mm. The quantitative measures presented here demonstrate that real-time target localisation and motion compensation could successfully be implemented with an MRI-Linac. The conclusions of this work strongly encourage further development of the proposed AF treatment with non-invasive radiosurgery