Data-driven head motion correction for PET using time-of-flight and positron emission particle tracking techniques.

OBJECTIVES: Positron emission tomography (PET) is susceptible to patient movement during a scan. Head motion is a continuing problem for brain PET imaging and diagnostic assessments. Physical head restraints and external motion tracking systems are most commonly used to address to this issue. Data-driven methods offer substantial advantages, such as retroactive processing but typically require manual interaction for robustness. In this work, we introduce a time-of-flight (TOF) weighted positron emission particle tracking (PEPT) algorithm that facilitates fully automated, data-driven head motion detection and subsequent automated correction of the raw listmode data. MATERIALS METHODS: We used our previously published TOF-PEPT algorithm Dustin Osborne et al. (2017), Tasmia Rahman Tumpa et al., Tasmia Rahman Tumpa et al. (2021) to automatically identify frames where the patient was near-motionless. The first such static frame was used as a reference to which subsequent static frames were registered. The underlying rigid transformations were estimated using weak radioactive point sources placed on radiolucent glasses worn by the patient. Correction of raw event data were achieved by tracking the point sources in the listmode data which was then repositioned to allow reconstruction of a single image. To create a "gold standard" for comparison purposes, frame-by-frame image registration based correction was implemented. The original listmode data was used to reconstruct an image for each static frame detected by our algorithm and then applying manual landmark registration and external software to merge these into a single image. RESULTS: We report on five patient studies. The TOF-PEPT algorithm was configured to detect motion using a 500 ms window. Our event-based correction produced images that were visually free of motion artifacts. Comparison of our algorithm to a frame-based image registration approach produced results that were nearly indistinguishable. Quantitatively, Jaccard similarity indices were found to be in the range of 85-98% for the former and 84-98% for the latter when comparing the static frame images with the reference frame counterparts. DISCUSSION: We have presented a fully automated data-driven method for motion detection and correction of raw listmode data. Easy to implement, the approach achieved high temporal resolution and reliable performance for head motion correction. Our methodology provides a mechanism by which patient motion incurred during imaging can be assessed and corrected post hoc.

A data-driven respiratory motion estimation approach for PET based on time-of-flight weighted positron emission particle tracking.

PURPOSE: Respiratory motion of patients during positron emission tomography (PET)/computed tomography (CT) imaging affects both image quality and quantitative accuracy. Hardware-based motion estimation, which is the current clinical standard, requires initial setup, maintenance, and calibration of the equipment, and can be associated with patient discomfort. Data-driven techniques are an active area of research with limited exploration into lesion-specific motion estimation. This paper introduces a time-of-flight (TOF)-weighted positron emission particle tracking (PEPT) algorithm that facilitates lesion-specific respiratory motion estimation from raw listmode PET data. METHODS: The TOF-PEPT algorithm was implemented and investigated under different scenarios: (a) a phantom study with a point source and an Anzai band for respiratory motion tracking; (b) a phantom study with a point source only, no Anzai band; (c) two clinical studies with point sources and the Anzai band; (d) two clinical studies with point sources only, no Anzai band; and (e) two clinical studies using lesions/internal regions instead of point sources and no Anzai band. For studies with radioactive point sources, they were placed on patients during PET/CT imaging. The motion tracking was performed using a preselected region of interest (ROI), manually drawn around point sources or lesions on reconstructed images. The extracted motion signals were compared with the Anzai band when applicable. For the purposes of additional comparison, a center-of-mass (COM) algorithm was implemented both with and without the use of TOF information. Using the motion estimate from each method, amplitude-based gating was applied, and gated images were reconstructed. RESULTS: The TOF-PEPT algorithm is shown to successfully determine the respiratory motion for both phantom and clinical studies. The derived motion signals correlated well with the Anzai band; correlation coefficients of 0.99 and 0.94-0.97 were obtained for the phantom study and the clinical studies, respectively. TOF-PEPT was found to be 13-38% better correlated with the Anzai results than the COM methods. Maximum Standardized Uptake Values (SUVs) were used to quantitatively compare the reconstructed-gated images. In comparison with the ungated image, a 14-39% increase in the max SUV across several lesion areas and an 8.7% increase in the max SUV on the tracked lesion area were observed in the gated images based on TOF-PEPT. The distinct presence of lesions with reduced blurring effect and generally sharper images were readily apparent in all clinical studies. In addition, max SUVs were found to be 4-10% higher in the TOF-PEPT-based gated images than in those based on Anzai and COM methods. CONCLUSION: A PEPT- based algorithm has been presented for determining movement due to respiratory motion during PET/CT imaging. Gating based on the motion estimate is shown to quantifiably improve the image quality in both a controlled point source phantom study and in clinical data patient studies. The algorithm has the potential to facilitate true motion correction where the reconstruction algorithm can use all data available.

DirectPET: full-size neural network PET reconstruction from sinogram data.

Purpose: Neural network image reconstruction directly from measurement data is a relatively new field of research, which until now has been limited to producing small single-slice images (e.g., 1 × 128 × 128 ). We proposed a more efficient network design for positron emission tomography called DirectPET, which is capable of reconstructing multislice image volumes (i.e., 16 × 400 × 400 ) from sinograms. Approach: Large-scale direct neural network reconstruction is accomplished by addressing the associated memory space challenge through the introduction of a specially designed Radon inversion layer. Using patient data, we compare the proposed method to the benchmark ordered subsets expectation maximization (OSEM) algorithm using signal-to-noise ratio, bias, mean absolute error, and structural similarity measures. In addition, line profiles and full-width half-maximum measurements are provided for a sample of lesions. Results: DirectPET is shown capable of producing images that are quantitatively and qualitatively similar to the OSEM target images in a fraction of the time. We also report on an experiment where DirectPET is trained to map low-count raw data to normal count target images, demonstrating the method's ability to maintain image quality under a low-dose scenario. Conclusion: The ability of DirectPET to quickly reconstruct high-quality, multislice image volumes suggests potential clinical viability of the method. However, design parameters and performance boundaries need to be fully established before adoption can be considered.

CNN-based PET sinogram repair to mitigate defective block detectors.

Positron emission tomography (PET) scanners continue to increase sensitivity and axial coverage by adding an ever expanding array of block detectors. As they age, one or more block detectors may lose sensitivity due to a malfunction or component failure. The sinogram data missing as a result thereof can lead to artifacts and other image degradations. We propose to mitigate the effects of malfunctioning block detectors by carrying out sinogram repair using a deep convolutional neural network. Experiments using whole-body patient studies with varying amounts of raw data removed are used to show that the neural network significantly outperforms previously published methods with respect to normalized mean squared error for raw sinograms, a multi-scale structural similarity measure for reconstructed images and with regard to quantitative accuracy.

Respiratory Motion Correction Using A Novel Positron Emission Particle Tracking Technique: A Framework Towards Individual Lesion-Based Motion Correction.

Respiratory motion during PET/CT imaging is a matter of concern due to degraded image quality and reduced quantitative accuracy caused by motion artifacts. One class of motion correction methods relies on hardware-based respiratory motion tracking systems in order to use respiratory cycles for correcting motion artifacts. Another class of hardware-free methods extract motion information from the reconstructed images or sinograms. Hardware-based methods, however, are limited by calibration requirement, patient discomfort, lack of adaptability during scanning, presence of electronic drift during respiratory monitoring etc. Extracting motion information from reconstructed images is also limited by the fact that the original raw information requires significant processing before it can be used. Hence the motivation behind this work is to introduce a software-based approach that can be applied on raw 64-bit listmode data. The basic design of the proposed method is based on the fundamentals of Positron Emission Particle Tracking (PEPT) with additional incorporation of Time of Flight (TOF) information. Respiratory motion of patients has been extracted from the raw PET data by tracking a point source attached to the patient in areas on and near the chest. The key objective of this work is to describe a new process by which this particle tracking based motion correction system can eventually be lesion specific and correct the motion for a particular lesion within the patient. This work thus serves as a framework for lesion specific motion correction.

Comparison of SIRT and SQS for Regularized Weighted Least Squares Image Reconstruction.

Tomographic image reconstruction is often formulated as a regularized weighted least squares (RWLS) problem optimized by iterative algorithms that are either inherently algebraic or derived from a statistical point of view. This paper compares a modified version of SIRT (Simultaneous Iterative Reconstruction Technique), which is of the former type, with a version of SQS (Separable Quadratic Surrogates), which is of the latter type. We show that the two algorithms minimize the same criterion function using similar forms of preconditioned gradient descent. We present near-optimal relaxation for both based on eigenvalue bounds and include a heuristic extension for use with ordered subsets. We provide empirical evidence that SIRT and SQS converge at the same rate for all intents and purposes. For context, we compare their performance with an implementation of preconditioned conjugate gradient. The illustrative application is X-ray CT of luggage for aviation security.

GATE validation of standard dual energy corrections in small animal SPECT-CT.

This paper addresses 123I and 125I dual isotope SPECT imaging, which can be challenging because of spectrum overlap in the low energy spectrums of these isotopes. We first quantify the contribution of low-energy photons from each isotope using GATE-based Monte Carlo simulations for the MOBY mouse phantom. We then describe and analyze a simple, but effective method that uses the ratio of detected low and high energy 123I activity to separate the mixed low energy 123I and 125I activities. Performance is compared with correction methods used in conventional tissue biodistribution techniques. The results indicate that the spectrum overlap effects can be significantly reduced, if not entirely eliminated, when attenuation and scatter is either absent or corrected for using standard methods. In particular, we show that relative activity levels of the two isotopes can be accurately estimated for a wide range of organs and provide quantitative validation that standard methods for spectrum overlap correction provide reasonable estimates for reasonable corrections in small-animal SPECT/CT imaging.

Development and validation of a complete GATE model of the Siemens Inveon trimodal imaging platform.

This article presents and validates a newly developed GATE model of the Siemens Inveon trimodal imaging platform. Fully incorporating the positron emission tomography (PET), single-photon emission computed tomography (SPECT), and computed tomography (CT) data acquisition subsystems, this model enables feasibility studies of new imaging applications, the development of reconstruction and correction algorithms, and the creation of a baseline against which experimental results for real data can be compared. Model validation was based on comparing simulation results against both empirical and published data. The PET modality was validated using the NEMA NU-4 standard. Validations of SPECT and CT modalities were based on assessment of model accuracy compared to published and empirical data on the platform. Validation results show good agreement between simulation and empirical data of approximately ± 5%.

Computational analysis and improvement of SIRT.

Iterative X-ray computed tomography (CT) algorithms have the potential for producing high-quality images but are computationally very demanding, especially when applied to high-resolution problems. Focusing on simultaneous iterative reconstruction technique (SIRT), we provide an eigenvalue based scheme for automatically determining a near-optimal value of the relaxation parameter. This accelerates the convergence rate of SIRT to the point where only half the number of iterations normally required is needed. We also modify the way SIRT uses preconditioning to solve a weighted least squares problem. The resulting algorithm, which we call PSIRT, is associated with a smaller memory footprint and calls for less data to be communicated in a distributed-memory implementation. Experimental residual norm and timing results are provided based on cone-beam micro-CT mouse data, including for an ordered subsets study.

Radioimaging of light chain amyloid with a fibril-reactive monoclonal antibody.

UNLABELLED: Currently, there are no available means in the United States to document objectively the location and extent of amyloid deposits in patients with systemic forms of amyloidosis. To address this limitation, we have developed a novel diagnostic strategy, namely, the use of a radiolabeled fibril-reactive murine monoclonal antibody (mAb) as an amyloid-specific imaging agent. The goal of this study was to determine the pharmacokinetics, biodistribution, and ability of this reagent to target the type of amyloid that is formed from immunoglobulin light chains, that is, AL. METHODS: Subcutaneous tumors (amyloidomas) were induced in BALB/c mice by injection of human AL fibrils. The IgG1 mAb designated 11-1F4 and an isotype-matched control antibody were radioiodinated, and the pharmacokinetics and localization of these reagents were determined from blood and tissue samples. Amyloidoma-bearing animals that received (125)I- or (124)I-labeled antibodies were imaged by whole-body small-animal SPECT/CT or small-animal PET/CT technology, respectively. RESULTS: Radioiodinated mAb 11-1F4 retained immunoreactivity, as evidenced by its subnanomolar affinity for light chains immobilized on 96-well microtiter plates and for beads conjugated with a light chain-related peptide. Additionally, after intravenous administration, the labeled reagents had the expected biologic half-life of murine IgG1, with monoexponential whole-body clearance kinetics. In the amyloidoma mouse model, (125)I-11-1F4 was predominately localized in the tumors, as demonstrated in biodistribution and autoradiographic analyses. The mean uptake of this reagent, that is, the percentage injected dose per gram of tissue, 72 h after injection was significantly higher for amyloid than for skeletal muscle, spleen, kidney, heart, liver, or other tissue samples. Notably, the accumulation within the amyloidomas of (125)I- or (124)I-11-1F4 was readily visible in the fused small-animal SPECT/CT or small-animal PET/CT images, respectively. CONCLUSION: Our studies demonstrate the amyloid-imaging capability of a radiolabeled fibril-reactive mAb and provide the basis for a clinical trial designed to determine its diagnostic potential in patients with AL amyloidosis and other systemic amyloidoses.

Micro-imaging of amyloid in mice.

Scintigraphic imaging of radioiodinated serum amyloid P-component is a proven method for the clinical detection of peripheral amyloid deposits (Hawkins et al., 1990). However, the inability to perform comparably high-resolution studies in experimental animal models of amyloid disease has impacted not only basic studies into the pathogenesis of amyloidosis but also in the preclinical in vivo evaluation of potential anti-amyloid therapeutic agents. We have developed microimaging technologies, implemented novel computational methods, and established protocols to generate high-resolution images of amyloid deposits in mice. (125)I-labeled serum amyloid P component (SAP) and an amyloid-fibril reactive murine monoclonal antibody (designated 11-1F4) have been used successfully to acquire high-resolution single photon emission computed tomographic (SPECT) images that, when fused with x-ray computed tomographic (CT) data, have provided precise anatomical localization of secondary (AA) and primary (AL) amyloid deposits in mouse models of these diseases. This chapter will provide detailed protocols for the radioiodination and purification of amyloidophilic proteins and the generation of mouse models of AA and AL amyloidosis. A brief description of the available hardware and the parameters used to acquire high-resolution microSPECT and CT images is presented, and the tools used to perform image reconstruction and visualization that permit the analysis and presentation of image data are discussed. Finally, we provide established methods for measuring organ- and tissue-specific activities with which to corroborate the microSPECT and CT images.

Quantitative high-resolution microradiographic imaging of amyloid deposits in a novel murine model of AA amyloidosis.

The mouse model of experimentally induced systemic AA amyloidosis is long established, well validated, and closely analogous to the human form of this disease. However, the induction of amyloid by experimental inflammation is unpredictable, inconsistent, and difficult to modulate. We have previously shown that murine AA amyloid deposits can be imaged using iodine-123 labeled SAP scintigraphy and report here substantial refinements in both the imaging technology and the mouse model itself. In this regard, we have generated a novel prototype of AA amyloid in which mice expressing the human interleukin 6 gene, when given amyloid enhancing factor, develop extensive and progressive systemic AA deposition without an inflammatory stimulus, i.e., a transgenic rapidly inducible amyloid disease (TRIAD) mouse. Additionally, we have constructed high-resolution micro single photon emission computed tomography (SPECT)/computed tomography (CT) instrumentation that provides images revealing the precise anatomic location of amyloid deposits labeled by radioiodinated serum amyloid P component (SAP). Based on reconstructed microSPECT/CT images, as well as autoradiographic, isotope biodistribution, and quantitative histochemical analyses, the (125)I-labeled SAP tracer bound specifically to hepatic and splenic amyloid in the TRIAD animals. The ability to discern radiographically the extent of amyloid burden in the TRIAD model provides a unique opportunity to evaluate the therapeutic efficacy of pharmacologic compounds designed to inhibit fibril formation or effect amyloid resolution.