Quantitative Image Reconstruction Research Articles

Evaluation and imaging of biodistribution of magnetic nanoparticles in a model of hepatic cirrhosis via alternating current biosusceptometry

In recent years, magnetic nanoparticles (MNPs) have exhibited theragnostic characteristics which confer a wide range of applications in the biomedical field. Consequently, through Alternating Current Biosusceptometry (ACB), magnetic nanoparticles can be used as tracers, allowing the study of healthy and cirrhotic livers and providing the ability to differentiate them through the reconstruction of quantitative images. The ACB system consists of a developing biomagnetic technique that has the ability to magnetize and measure the magnetic susceptibility of a material such as MNPs, thereby offering quantitative information about biological systems with magnetic tracers.

Quantitative assessment of cardiac 123iodo-metaiodobenzylguanidine SPECT/CT in patients with arrhythmogenic right ventricular cardiomyopathy: Novel insight in disease monitoring

BackgroundThe heart-to-mediastinum ratio (H/M-Ratio) of 123iodo-metaiodobenzylguanidine (123I-MIBG) represents state-of-the-art assessment for sympathetic dysfunction in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC). This study aims to evaluate quantitative reconstruction of 123I-MIBG uptake and to demonstrate its correlation with echocardiographic parameters. MethodsCardiac innervation was assessed in 23 patients diagnosed with definite ARVC or borderline ARVC and 12 patients with other cardiac disease presenting arrhythmia, using quantitative 123I-MIBG Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) imaging. Tracer uptake was evaluated in the left (LV) and right ventricle (RV) based on a CT scan after quantitative image reconstruction. The relationship between tracer uptake and echocardiographic parameter data was examined. ResultsAbsolute quantification of 123I-MIBG uptake in the LV and RV is feasible and correlates accurately with the gold standard H/M Ratio. When comparing sensitivity and specificity, the area under the curve (AUC) favors standardized uptake value (SUV) of the RV over the right-ventricle-to-mediastinum-ratio (RV/M-Ratio) for diagnosing ARVC. A reduced RV-SUV in patients with definite ARVC is associated with reduced RV function. RV polar maps revealed globally reduced 123I-MIBG uptake without segment-specific reduction in the RV. ConclusionsQuantitative 123I-MIBG SPECT in ARCV patients offers robust potential for clinical reporting and demonstrates a significant correlation with RV function. Segmental RV analysis needs to be evaluated in larger samples. In summary, cardiac 123I-MIBG imaging using SUV could facilitate image-guided therapy in patients diagnosed with ARVC.

A dataset of digital holograms of normal and thalassemic cells

Digital holographic microscopy (DHM) is an intriguing medical diagnostic tool due to its label-free and quantitative nature, providing high-contrast images of phase samples. By capturing both intensity and phase information, DHM enables the numerical reconstruction of quantitative phase images. However, the lateral resolution is limited by the diffraction limit, which prompted the recent suggestion of microsphere-assisted DHM to enhance the DHM resolution straightforwardly. The use of such a technique as a medical diagnostic tool requires testing and validation of the proposed assays to prove their feasibility and viability. This paper publishes 760 and 609 microsphere-assisted DHM images of normal and thalassemic red blood cells obtained from a normal and thalassemic male individual, respectively.

Prototyping optimization-based image reconstructions from limited-angular-range data in dual-energy CT

Image reconstruction from data collected over full-angular range (FAR) in dual-energy CT (DECT) is well-studied. There exists interest in DECT with advanced scan configurations in which data are collected only over limited-angular ranges (LARs) for meeting unique workflow needs in certain practical imaging applications, and thus in the algorithm development for image reconstruction from such LAR data. The objective of the work is to investigate and prototype image reconstructions in DECT with LAR scans. We investigate and prototype optimization programs with various designs of constraints on the directional-total-variations (DTVs) of virtual monochromatic images and/or basis images, and derive the DTV algorithms to numerically solve the optimization programs for achieving accurate image reconstruction from data collected in a slew of different LAR scans. Using simulated and real data acquired with low- and high-kV spectra over LARs, we conduct quantitative studies to demonstrate and evaluate the optimization programs and their DTV algorithms developed. As the results of the numerical studies reveal, while the DTV algorithms yield images of visual quality and quantitative accuracy comparable to that of the existing algorithms from FAR data, the former reconstruct images with improved visualization, reduced artifacts, and also enhanced quantitative accuracy when applied to LAR data in DECT. Optimization-based, one-step algorithms, including the DTV algorithms demonstrated, can be developed for quantitative image reconstruction from spectral data collected over LARs of extents that are considerably smaller than the FAR in DECT. The theoretical and numerical results obtained can be exploited for prototyping designs of optimization-based reconstructions and LAR scans in DECT, and they may also yield insights into the development of reconstruction procedures in practical DECT applications. The approach and algorithms developed can naturally be applied to investigating image reconstruction from LAR data in multi-spectral and photon-counting CT.

A retrospective evaluation of Bayesian-penalized likelihood reconstruction for [15O]H2O myocardial perfusion imaging

BackgroundNew Block-Sequential-Regularized-Expectation-Maximization (BSREM) image reconstruction technique has been introduced for clinical use mainly for oncologic use. Accurate and quantitative image reconstruction is essential in myocardial perfusion imaging with positron emission tomography (PET) as it utilizes absolute quantitation of myocardial blood flow (MBF). The aim of the study was to evaluate BSREM reconstruction for quantitation in patients with suspected coronary artery disease (CAD). Methods and ResultsWe analyzed cardiac [15O]H2O PET studies of 177 patients evaluated for CAD. Differences between BSREM and Ordered-Subset-Expectation-Maximization with Time-Of-Flight (TOF) and Point-Spread-Function (PSF) modeling (OSEM-TOF-PSF) in terms of MBF, perfusable tissue fraction, and vascular volume fraction were measured. Classification of ischemia was assessed between the algorithms. OSEM-TOF-PSF and BSREM provided similar global stress MBF in patients with ischemia (1.84 ± 0.21 g⋅ml−1⋅min−1 vs 1.86 ± 0.21 g⋅ml−1⋅min−1) and no ischemia (3.26 ± 0.34 g⋅ml−1⋅min−1 vs 3.28 ± 0.34 g⋅ml−1⋅min−1). Global resting MBF was also similar (0.97 ± 0.12 g⋅ml−1⋅min−1 and 1.12 ± 0.06 g⋅ml−1⋅min−1). The largest mean relative difference in MBF values was 7%. Presence of myocardial ischemia was classified concordantly in 99% of patients using OSEM-TOF-PSF and BSREM reconstructions ConclusionOSEM-TOF-PSF and BSREM image reconstructions produce similar MBF values and diagnosis of myocardial ischemia in patients undergoing [15O]H2O PET due to suspected obstructive coronary artery disease

Quantitative image reconstruction for microscopy with correct sub-pixel photon statistics.

The diffraction of light forces a lower limit on spatial resolution available with conventional optical microscopes. However, sophisticated computational techniques in conjunction with inhomogeneous illumination have facilitated image acquisition without this limitation. Typical image reconstruction algorithms for such superresolution microscopy techniques, including structured illumination microscopy (SIM), involve combining multiple diffraction-limited raw images in Fourier space via minimization of some cost function. This leads to undesired artifacts including loss of correct noise properties for the photons collected by the camera and loss of temporal resolution. Here, we propose a Bayesian framework performing reconstructions in real space while incorporating all sources of noise such as camera electronics and incoming photon Poisson statistics. Furthermore, this framework inherently allows corrections for inhomogeneous illumination incident on a biological sample. As a result, superresolution reconstructions can now be performed with fewer images otherwise needed by existing reconstruction methods while maintaining Poisson statistics for collected photons at the sub-pixel level. We benchmark our framework using both simulated and experimentally generated images.

Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

PurposeAttenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images.MethodsNon-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center).ResultsIn terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21–14.81%) and FL-PL (CI:11.82–13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32–12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34–26.10%). Furthermore, the Mann–Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R2 = 0.94), FL-SQ (R2 = 0.93), and FL-PL (R2 = 0.92), while CB model achieved a far lower coefficient of determination (R2 = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed.ConclusionDeep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers.

Accurate Range Migration for Fast Quantitative Fourier-Based Image Reconstruction With Monostatic Radar

Range migration (or range focusing) techniques are widely used in optical, acoustic, and microwave real-time image reconstruction methods. They have been successfully applied to far-field 3-D imaging where they rely on plane-wave assumptions, which ignore the data amplitude variation over the acquisition aperture. The accuracy of the plane-wave assumption, however, quickly degrades in close-range imaging, where amplitude variations are significant and where the range to the target is on the order of the range sampling step. Here, we present a range-focusing method of improved accuracy, which is applicable to both far-zone and close-range monostatic radar. It refocuses the system point-spread function (PSF) to any range location, taking into account both magnitude and phase changes. The approach can be applied with any Fourier-based imaging algorithm utilizing the Lippmann–Schwinger equation as the underlying scattering model. Here, it is validated through examples based on simulated and measured data where the images are reconstructed with quantitative microwave holography (QMH). QMH employs measured PSFs to achieve quantitative imaging in real-time. The proposed range-migration method is applicable with measured PSFs, too, leading to reduced system-calibration effort and the ability to focus an image at any desired range.

Simultaneous Use of the Born and Rytov Approximations in Real-Time Imaging With Fourier-Space Scattered Power Mapping

The Fourier-space scattered-power mapping (F-SPM) is proposed as a computationally efficient alternative to the original real-space SPM method for real-time quantitative image reconstruction with an emphasis on close-range and near-field applications. Similar to SPM, F-SPM can employ either the Born or the Rytov data approximations in a linear scattering model, resulting in two distinct algorithms. However, to exploit the complementarity of the two approximations, a strategy is proposed for their combined use in a single inversion process. The combined Born&#x2013;Rytov F-SPM method consistently improves the image quality in comparison with the images generated when using either approximation separately. The improvement is most significant when the limitations of either the Born or the Rytov approximations are violated. In the cases where neither or both of these limitations are violated, the images are of comparable quality to those generated by the standalone algorithms. The proposed Born&#x2013;Rytov F-SPM algorithm is verified and compared to the standalone Born-based F-SPM and Rytov-based F-SPM in examples utilizing simulated and measured data. The gain in computational speed compared to the original real-space SPM is also demonstrated.

Label‐free single‐shot imaging with on‐axis phase‐shifting holographic reflectance quantitative phase microscopy

Quantitative phase microscopy (QPM) has been emerged as an indispensable diagnostic and characterization tool in biomedical imaging with its characteristic nature of label-free, noninvasive, and real time imaging modality. The integration of holography to the conventional microscopy opens new advancements in QPM featuring high-resolution and quantitative three-dimensional image reconstruction. However, the holography schemes suffer in space-bandwidth and time-bandwidth issues in the off-axis and phase-shifting configuration, respectively. Here, we introduce an on-axis phase-shifting holography based QPM system with single-shot imaging capability. The technique utilizes the Fizeau interferometry scheme in combination with polarization phase-shifting and space-division multiplexing to achieve the single-shot recording of the multiple phase-shifted holograms. Moreover, the high-speed imaging capability with instantaneous recording of spatially phase shifted holograms offers the flexible utilization of the approach in dynamic quantitative phase imaging with robust phase stability. We experimentally demonstrated the validity of the approach by quantitative phase imaging and depth-resolved imaging of paramecium cells. Furthermore, the technique is applied to the phase imaging and quantitative parameter estimation of red blood cells. This integration of a Fizeau-based phase-shifting scheme to the optical microscopy enables a simple and robust tool for the investigations of engineered and biological specimen with real-time quantitative analysis.

An end-to-end AI-based framework for automated discovery of rapid CEST/MT MRI acquisition protocols and molecular parameter quantification (AutoCEST).

PurposeTo develop an automated machine‐learning‐based method for the discovery of rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting acquisition and reconstruction protocols.MethodsAn MR physics‐governed AI system was trained to generate optimized acquisition schedules and the corresponding quantitative reconstruction neural network. The system (termed AutoCEST) is composed of a CEST saturation block, a spin dynamics module, and a deep reconstruction network, all differentiable and jointly connected. The method was validated using a variety of chemical exchange phantoms and in vivo mouse brains at 9.4T.ResultsThe acquisition times for AutoCEST optimized schedules ranged from 35 to 71 s, with a quantitative image reconstruction time of only 29 ms. The resulting exchangeable proton concentration maps for the phantoms were in good agreement with the known solute concentrations for AutoCEST sequences (mean absolute error = 2.42 mM; Pearson’s r=0.992, p<0.0001), but not for an unoptimized sequence (mean absolute error = 65.19 mM; Pearson’s r=‐0.161, p=0.522). Similarly, improved exchange rate agreement was observed between AutoCEST and quantification of exchange using saturation power (QUESP) methods (mean absolute error: 35.8 Hz, Pearson’s r=0.971, p<0.0001) compared to an unoptimized schedule and QUESP (mean absolute error = 58.2 Hz; Pearson’s r=0.959, p<0.0001). The AutoCEST in vivo mouse brain semi‐solid proton volume fractions were lower in the cortex (12.77% ± 0.75%) compared to the white matter (19.80% ± 0.50%), as expected.ConclusionAutoCEST can automatically generate optimized CEST/MT acquisition protocols that can be rapidly reconstructed into quantitative exchange parameter maps.

Refining Enteric Neural Circuitry by Quantitative Morphology and Function in Mice.

RNA-Seq, electrophysiology and optogenetics in mouse models are used to assess function, identify disease related genes and model enteric neural circuits. Lacking a comprehensive quantitative description of the murine colonic enteric nervous system (ENS) makes it difficult to most effectively use mouse data to better understand ENS function or for development of therapeutic approaches for human motility disorders. Our goal was to provide a quantitative description of mouse colon to establish the extent to which mouse colon architecture, connectivity and function is a useful surrogate for human and other mammalian ENS. Using GCaMP imaging coupled with pharmacology and quantitative confocal and 3D image reconstruction, we present quantitative and functional data demonstrating that regional structural changes and variable distribution of neurons define neural circuit dynamics and functional connectivity responsible for colonic motor patterns and regional functional differences. Our results advance utility of multispecies and gut region-specific data.

Multiple Regression Fitting Electrical Impedance Spectro-Tomography for Quantitative Image Reconstruction of Dead Cell Fraction and Cell Concentration

A novel image reconstruction method called multiple regression fitting electrical impedance spectro-tomography (mrf-EIST) has been proposed in order to realize the quantitative image reconstruction of dead cell fraction &#x03D5;d and cell concentration cc in a huge amount of cell environment. mrf-EIST statistically selects frequencies to extract two variables &#x03C8;d and &#x03C8;c, which quantify&#x03D5;d and cc, respectively. The &#x03D5;d and cc images are reconstructed by solving the inverse problem using &#x03C8;d and &#x03C8;c. To validate the performance of mrf-EIST, the image reconstruction by mrf-EIST in the frequency range from 100 Hz to 1 MHz is carried out under the condition that the number of cells is over 109 cells. As a result, mrf-EIST shows that the image quality defined by the difference in pixel value from the true image is less than 0.050 in &#x03D5;d and 0.071 in cc, respectively. In comparison to frequency-difference EIT (fd-EIT) as a conventional EIST regarding a position error of center of gravity, mrf-EIST provides much more accurate images, qualitatively and quantitatively, compared to the fd-EIT.

ADMM approach for efficient iterative tomographic deconvolution reconstruction of 3D quantitative phase images.

Tomographic deconvolution phase microscopy (TDPM) is a promising approach for 3D quantitative imaging of phase objects such as biological cells and optical fibers. In the present work, the alternating direction method of multipliers (ADMM) is applied to TDPM to shorten its image acquisition and processing times while simultaneously improving its accuracy. ADMM-TDPM is used to optimize the image fidelity by minimizing Gaussian noise and by using total variation regularization with the constraints of nonnegativity and known zeros. ADMM-TDPM can reconstruct phase objects that are shift variant in three spatial dimensions. ADMM-TDPM achieves speedups of 5x in image acquisition time and greater than 10x in image processing time with accompanying higher accuracy compared to TDPM.

Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques.

The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (ML/DL) algorithms at the helm have stimulated the development of many applications involving the use of AI-based techniques in molecular imaging research. Applications reported in the literature encompass various areas, including innovative design concepts in positron emission tomography (PET) instrumentation, quantitative image reconstruction and analysis techniques, computer-aided detection and diagnosis, as well as modeling and prediction of outcomes. This review reflects the tremendous interest in quantitative molecular imaging using ML/DL techniques during the past decade, ranging from the basic principles of ML/DL techniques to the various steps required for obtaining quantitatively accurate PET data, including algorithms used to denoise or correct for physical degrading factors as well as to quantify tracer uptake and metabolic tumor volume for treatment monitoring or radiation therapy treatment planning and response prediction.This review also addresses future opportunities and current challenges facing the adoption of ML/DL approaches and their role in multimodality imaging.

Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging

Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated.For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.

Synthetic MRI of Preterm Infants at Term-Equivalent Age: Evaluation of Diagnostic Image Quality and Automated Brain Volume Segmentation.

Neonatal MR imaging brain volume measurements can be used as biomarkers for long-term neurodevelopmental outcome, but quantitative volumetric MR imaging data are not usually available during routine radiologic evaluation. In the current study, the feasibility of automated quantitative brain volumetry and image reconstruction via synthetic MR imaging in very preterm infants was investigated. Conventional and synthetic T1WIs and T2WIs from 111 very preterm infants were acquired at term-equivalent age. Overall image quality and artifacts of the conventional and synthetic images were rated on a 4-point scale. Legibility of anatomic structures and lesion conspicuity were assessed on a binary scale. Synthetic MR volumetry was compared with that generated via MANTiS, which is a neonatal tissue segmentation toolbox based on T2WI. Image quality was good or excellent for most conventional and synthetic images. The 2 methods did not differ significantly regarding image quality or diagnostic performance for focal and cystic WM lesions. Dice similarity coefficients had excellent overlap for intracranial volume (97.3%) and brain parenchymal volume (94.3%), and moderate overlap for CSF (75.6%). Bland-Altman plots demonstrated a small systematic bias in all cases (1.7%-5.9%) CONCLUSIONS: Synthetic T1WI and T2WI sequences may complement or replace conventional images in neonatal imaging, and robust synthetic volumetric results are accessible from a clinical workstation in less than 1 minute. Via the above-described methods, volume assessments could be routinely used in daily clinical practice.

Machine Learning in PET: from Photon Detection to Quantitative Image Reconstruction.

Machine learning has found unique applications in nuclear medicine from photon detection to quantitative image reconstruction. While there have been impressive strides in detector development for time-of-flight positron emission tomography, most detectors still make use of simple signal processing methods to extract the time and position information from the detector signals. Now with the availability of fast waveform digitizers, machine learning techniques have been applied to estimate the position and arrival time of high-energy photons. In quantitative image reconstruction, machine learning has been used to estimate various corrections factors, including scattered events and attenuation images, as well as to reduce statistical noise in reconstructed images. Here machine learning either provides a faster alternative to an existing time-consuming computation, such as in the case of scatter estimation, or creates a data-driven approach to map an implicitly defined function, such as in the case of estimating the attenuation map for PET/MR scans. In this article, we will review the abovementioned applications of machine learning in nuclear medicine.

Total-Body Dynamic Reconstruction and Parametric Imaging on the uEXPLORER.

The world's first 194-cm-long total-body PET/CT scanner (uEXPLORER) has been built by the EXPLORER Consortium to offer a transformative platform for human molecular imaging in clinical research and health care. Its total-body coverage and ultra-high sensitivity provide opportunities for more accurate tracer kinetic analysis in studies of physiology, biochemistry, and pharmacology. The objective of this study was to demonstrate the capability of total-body parametric imaging and to quantify the improvement in image quality and kinetic parameter estimation by direct and kernel reconstruction of the uEXPLORER data. Methods: We developed quantitative parametric image reconstruction methods for kinetic analysis and used them to analyze the first human dynamic total-body PET study. A healthy female subject was recruited, and a 1-h dynamic scan was acquired during and after an intravenous injection of 256 MBq of 18F-FDG. Dynamic data were reconstructed using a 3-dimensional time-of-flight list-mode ordered-subsets expectation maximization (OSEM) algorithm and a kernel-based algorithm with all quantitative corrections implemented in the forward model. The Patlak graphical model was used to analyze the 18F-FDG kinetics in the whole body. The input function was extracted from a region over the descending aorta. For comparison, indirect Patlak analysis from reconstructed frames and direct reconstruction of parametric images from the list-mode data were obtained for the last 30 min of data. Results: Images reconstructed by OSEM showed good quality with low noise, even for the 1-s frames. The image quality was further improved using the kernel method. Total-body Patlak parametric images were obtained using either indirect estimation or direct reconstruction. The direct reconstruction method improved the parametric image quality, having a better contrast-versus-noise tradeoff than the indirect method, with a 2- to 3-fold variance reduction. The kernel-based indirect Patlak method offered image quality similar to the direct Patlak method, with less computation time and faster convergence. Conclusion: This study demonstrated the capability of total-body parametric imaging using the uEXPLORER. Furthermore, the results showed the benefits of kernel-regularized reconstruction and direct parametric reconstruction. Both can achieve superior image quality for tracer kinetic studies compared with the conventional indirect OSEM for total-body imaging.

Quantitative phase imaging in common-path cross-referenced holographic microscopy using double-exposure method

This paper proposes an optimized implementation of the double-exposure method with emphasis on the uniformity and minimization of the residual phase imperfections in cross-referenced holographic microscopy (CRHM). The quantitative phase images are restored from single-shot cross-referenced holograms, which are separated in the Fourier space and processed to eliminate effects caused by imperfections of the optical path and sample background. CRHM is implemented in a microscope configuration supplemented by a Sagnac interference module providing splitting and shearing of the sample and reference waves. Utilization of the averaging process, which enhances precision of quantitative phase image (QPI) reconstruction, applicable in the methods with a replicated field of view is also presented. The high temporal stability of CRHM is verified in calibration measurements and its application potential demonstrated by a quantitative restoration of the phase resolution target and imaging of biological samples including cheek and sperm cells.