Efficient and multiplexed tracking of single cells using whole-body PET/CT

In vivo molecular imaging tools are crucially important for elucidating how cells move through complex biological systems, however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upwards of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a new tracking algorithm (PEPT-EM) to push the cellular detection threshold to below 4 Bq/cell, and a streamlined workflow to reliably label single cells with over 50 Bq/cell of 18F-fluorodeoxyglucose (FDG). To demonstrate the potential of method, we tracked the fate of over 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.

Measurement Technologies of Light Field Camera: An Overview

Visual measurement methods are extensively used in various fields, such as aerospace, biomedicine, agricultural production, and social life, owing to their advantages of high speed, high accuracy, and non-contact. However, traditional camera-based measurement systems, relying on the pinhole imaging model, face challenges in achieving three-dimensional measurements using a single camera by one shot. Moreover, traditional visual systems struggle to meet the requirements of high precision, efficiency, and compact size simultaneously. With the development of light field theory, the light field camera has garnered significant attention as a novel measurement method. Due to its special structure, the light field camera enables high-precision three-dimensional measurements with a single camera through only one shot. This paper presents a comprehensive overview of light field camera measurement technologies, including the imaging principles, calibration methods, reconstruction algorithms, and measurement applications. Additionally, we explored future research directions and the potential application prospects of the light field camera.

Improving 90Y PET Scan Image Quality Through Optimized Reconstruction Algorithms

This study aimed to improve the quality of ⁹⁰Y PET imaging by optimizing the reconstruction algorithm. Methods: We recruited 10 patients with neuroendocrine tumor metastatic to the liver or primary hepatocellular carcinoma who were qualified for ⁹⁰Y-labeled selective internal radiation therapy or peptide receptor radionuclide therapy. They underwent posttherapeutic PET/CT imaging using 3 different reconstruction parameters: VUE Point HD with a 6.4-mm filter cutoff, 24 subsets, and 2 iterations (algorithm A); VUE Point FX with a 6.0-mm filter cutoff, 18 subsets, and 3 iterations using time of flight (algorithm B); and VUE Point HD (LKYG) with a 5-mm filter cutoff, 32 subsets, and 1 iteration (algorithm C). The reconstructed PET/CT images were assessed by 10 nuclear medicine physicians using 4-point semiqualitative scoring criteria. A P value of less than 0.05 was considered significant. Results: The median quality assessment scores for algorithm C were consistently scored the highest, with algorithms A, B, and C, scoring 3, 2, and 4, respectively. The ⁹⁰Y PET scans using algorithm C were deemed diagnostic 91% of the time. There was a statistically significant difference in quality assessment scores among the algorithms by the Kruskal-Wallis rank sum test ([Formula: see text] = 86.5, P < 0.001), with a mean rank quality score of 130.03 for algorithm A, 109.76 for algorithm B, and 211.71 for algorithm C. Subgroup analysis for quality assessment scoring of post-peptide receptor radionuclide therapy imaging alone showed a statistically significant difference between different scanning algorithms ([Formula: see text] = 35.35, P < 0.001), with mean rank quality scores of 45.85 for algorithm A, 50.05 for algorithm B, and 85.6 for algorithm C. Similar results were observed for quality assessment scoring of imaging after selective internal radiation therapy ([Formula: see text] = 79.90, P < 0.001), with mean ranks of 82.33 for algorithm A, 55.79 for algorithm B, and 133.38 for algorithm C. Conclusion: The new LKYG algorithm that was featured by decreasing the number of iterations, decreasing the cutoff of the filter thickness, and increasing the number of subsets successfully improved image quality.

Reconstruction algorithms for grain mapping by laboratory X-ray diffraction contrast tomography

X-ray-based non-destructive 3D grain mapping techniques are well established at synchrotron facilities. To facilitate everyday access to grain mapping instruments, laboratory diffraction contrast tomography (LabDCT), using a laboratory-based conical polychromatic X-ray beam, has been developed and commercialized. Yet the currently available LabDCT grain reconstruction methods are either ill-suited for handling a large number of grains or require a commercial licence bound to a specific instrument. To promote the availability of LabDCT, grain reconstruction methods have been developed with multiple reconstruction algorithms based on both forward and back calculations. The different algorithms are presented in detail and their efficient implementation using parallel computing is described. The performance of different reconstruction methods is assessed on synthetic data. The code to implement all the described algorithms has been made publicly accessible with the intention of fostering the development of grain mapping techniques on widely available laboratory instruments.

QSM reconstruction challenge 2.0: Design and report of results

PURPOSE

The aim of the second quantitative susceptibility mapping (QSM) reconstruction challenge (Oct 2019, Seoul, Korea) was to test the accuracy of QSM dipole inversion algorithms in simulated brain data.

METHODS

A two-stage design was chosen for this challenge. The participants were provided with datasets of multi-echo gradient echo images synthesized from two realistic in silico head phantoms using an MR simulator. At the first stage, participants optimized QSM reconstructions without ground truth data available to mimic the clinical setting. At the second stage, ground truth data were provided for parameter optimization. Submissions were evaluated using eight numerical metrics and visual ratings.

RESULTS

A total of 98 reconstructions were submitted for stage 1 and 47 submissions for stage 2. Iterative methods had the best quantitative metric scores, followed by deep learning and direct inversion methods. Priors derived from magnitude data improved the metric scores. Algorithms based on iterative approaches and total variation (and its derivatives) produced the best overall results. The reported results and analysis pipelines have been made public to allow researchers to compare new methods to the current state of the art.

CONCLUSION

The synthetic data provide a consistent framework to test the accuracy and robustness of QSM algorithms in the presence of noise, calcifications and minor voxel dephasing effects. Total Variation-based algorithms produced the best results among all metrics. Future QSM challenges should assess whether this good performance with synthetic datasets translates to more realistic scenarios, where background fields and dipole-incompatible phase contributions are included.

Comparison of Regularized Reconstruction and Ordered Subset Expectation Maximization Reconstruction in the Diagnostics of Prostate Cancer Using Digital Time-of-Flight 68Ga-PSMA-11 PET/CT Imaging

In prostate cancer, the early detection of distant spread has been shown to be of importance. Prostate-specific membrane antigen (PSMA)-binding radionuclides in positron emission tomography (PET) is a promising method for precise disease staging. PET diagnostics depend on image reconstruction techniques, and ordered subset expectation maximization (OSEM) is the established standard. Block sequential regularized expectation maximization (BSREM) is a more recent reconstruction algorithm and may produce fewer equivocal findings and better lesion detection. Methods: ⁶⁸Ga PSMA-11 PET/CT scans of patients with de novo or suspected recurrent prostate cancer were retrospectively reformatted using both the OSEM and BSREM algorithms. The lesions were counted and categorized by three radiologists. The intra-class correlation (ICC) and Cohen’s kappa for the inter-rater reliability were calculated. Results: Sixty-one patients were reviewed. BSREM identified slightly fewer lesions overall and fewer equivocal findings. ICC was excellent with regards to definitive lymph nodes and bone metastasis identification and poor with regards to equivocal metastasis irrespective of the reconstruction algorithm. The median Cohen’s kappa were 0.66, 0.74, 0.61 and 0.43 for OSEM and 0.61, 0.63, 0.66 and 0.53 for BSREM, with respect to the tumor, local lymph nodes, metastatic lymph nodes and bone metastasis detection, respectively. Conclusions: BSREM in the setting of ⁶⁸Ga PMSA PET staging or restaging is comparable to OSEM.

Early-photon reflectance fluorescence molecular tomography for small animal imaging: Mathematical model and numerical experiment

The paper presents an original approach to time-domain reflectance fluorescence molecular tomography (FMT) of small animals. It is based on the use of early arriving photons and state-of-the-art compressed-sensing-like reconstruction algorithms and aims to improve the spatial resolution of fluorescent images. We deduce the fundamental equation that models the imaging operator and derive analytical representations for the sensitivity functions which are responsible for the reconstruction of the fluorophore absorption coefficient. The idea of fluorescence lifetime tomography with our approach is also discussed. We conduct a numerical experiment on 3D reconstruction of box phantoms with spherical fluorescent inclusions of small diameters. For modeling measurement data and constructing the sensitivity matrix we assume a virtual fluorescence tomograph with a scanning fiber probe that illuminates and collects light in reflectance geometry. It provides for large source-receiver separations which correspond to the macroscopic regime. Two compressed-sensing-like reconstruction algorithms are used to solve the inverse problem. These are the algebraic reconstruction technique with total variation regularization and our modification of the fast iterative shrinkage-thresholding algorithm. Results of our numerical experiment show that our approach is capable of achieving as good spatial resolution as 0.2 mm and even better at depths to 9 mm inclusive.

Association of a CT-Based Clinical and Radiomics Score of Non-Small Cell Lung Cancer (NSCLC) with Lymph Node Status and Overall Survival

Background: To evaluate whether a model based on radiomic and clinical features may be associated with lymph node (LN) status and overall survival (OS) in lung cancer (LC) patients; to evaluate whether CT reconstruction algorithms may influence the model performance. Methods: patients operated on for LC with a pathological stage up to T3N1 were retrospectively selected and divided into training and validation sets. For the prediction of positive LNs and OS, the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was used; univariable and multivariable logistic regression analysis assessed the association of clinical-radiomic variables and endpoints. All tests were repeated after dividing the groups according to the CT reconstruction algorithm. p-values < 0.05 were considered significant. Results: 270 patients were included and divided into training (n = 180) and validation sets (n = 90). Transfissural extension was significantly associated with positive LNs. For OS prediction, high- and low-risk groups were different according to the radiomics score, also after dividing the two groups according to reconstruction algorithms. Conclusions: a combined clinical–radiomics model was not superior to a single clinical or single radiomics model to predict positive LNs. A radiomics model was able to separate high-risk and low-risk patients for OS; CTs reconstructed with Iterative Reconstructions (IR) algorithm showed the best model performance.

Tomographic reconstruction with a generative adversarial network

This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps. The GAN has been developed to fit the input sinogram with the model sinogram generated from the predicted reconstruction. Good quality reconstructions can be obtained during the minimization of the fitting errors. The reconstruction is a self-training procedure based on the physics model, instead of on training data. The algorithm showed significant improvements in the reconstruction accuracy, especially for missing-wedge tomography acquired at less than 180° rotational range. It was also validated by reconstructing a missing-wedge X-ray ptychographic tomography (PXCT) data set of a macroporous zeolite particle, for which only 51 projections over 70° could be collected. The GANrec recovered the 3D pore structure with reasonable quality for further analysis. This reconstruction concept can work universally for most of the ill-posed inverse problems if the forward model is well defined, such as phase retrieval of in-line phase-contrast imaging.

Limited-View X-Ray Tomography Combining Attenuation and Compton Scatter Data: Approach and Experimental Results

X-ray inspection systems are critical in medical, non-destructive testing, and security applications, with systems typically measuring attenuation along straight-line paths connecting sources and detectors. Computed tomography (CT) systems can provide higher-quality images than single- or dual-view systems, but the need to measure many projections leads to greater system cost and complexity. Typically, off-angle Compton scattered photons are treated as noise during tomographic inversion. We seek to maximize the image quality of limited-view systems by combining attenuation data with measurements of Compton-scattered photons, exploiting the fact that the broken-ray paths followed by scattered photons provide additional geometric sampling of the scene. We describe a single-scatter forward model for Compton-scatter data measured with energy-resolving detectors, and demonstrate a reconstruction algorithm for density that combines both attenuation and scatter measurements. The experimental results suggest that including Compton-scattered data in the reconstruction process can improve image quality for density reconstruction using limited-view systems.

New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers

Background

The image quality of digital breast tomosynthesis (DBT) volumes depends greatly on the reconstruction algorithm.

Purpose

To compare two DBT reconstruction algorithms used by the Siemens Mammomat Inspiration system, filtered back projection (FBP), and FBP with iterative optimizations (EMPIRE), using qualitative analysis by human readers and detection performance of machine learning algorithms.

Material and Methods

Visual grading analysis was performed by four readers specialized in breast imaging who scored 100 cases reconstructed with both algorithms (70 lesions). Scoring (5-point scale: 1 = poor to 5 = excellent quality) was performed on presence of noise and artifacts, visualization of skin-line and Cooper’s ligaments, contrast, and image quality, and, when present, lesion visibility. In parallel, a three-dimensional deep-learning convolutional neural network (3D-CNN) was trained (n = 259 patients, 51 positives with BI-RADS 3, 4, or 5 calcifications) and tested (n = 46 patients, nine positives), separately with FBP and EMPIRE volumes, to discriminate between samples with and without calcifications. The partial area under the receiver operating characteristic curve (pAUC) of each 3D-CNN was used for comparison.

Results

EMPIRE reconstructions showed better contrast (3.23 vs. 3.10, P = 0.010), image quality (3.22 vs. 3.03, P < 0.001), visibility of calcifications (3.53 vs. 3.37, P = 0.053, significant for one reader), and fewer artifacts (3.26 vs. 2.97, P < 0.001). The 3D-CNN-EMPIRE had better performance than 3D-CNN-FBP (pAUC-EMPIRE = 0.880 vs. pAUC-FBP = 0.857; P < 0.001).

Conclusion

The new algorithm provides DBT volumes with better contrast and image quality, fewer artifacts, and improved visibility of calcifications for human observers, as well as improved detection performance with deep-learning algorithms.

Techniques for robot-aided intraocular surgery using monocular vision

This paper presents techniques for robot-aided intraocular surgery using monocular vision in order to overcome erroneous stereo reconstruction in an intact eye. We propose a new retinal surface estimation method based on a structured-light approach. A handheld robot known as the Micron enables automatic scanning of a laser probe, creating projected beam patterns on the retinal surface. Geometric analysis of the patterns then allows planar reconstruction of the surface. To realize automated surgery in an intact eye, monocular hybrid visual servoing is accomplished through a scheme that incorporates surface reconstruction and partitioned visual servoing. We investigate the sensitivity of the estimation method according to relevant parameters and also evaluate its performance in both dry and wet conditions. The approach is validated through experiments for automated laser photocoagulation in a realistic eye phantom in vitro. Finally, we present the first demonstration of automated intraocular laser surgery in porcine eyes ex vivo.

Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge

PURPOSE

The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully.

METHODS

Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations. Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions.

RESULTS

Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance.

CONCLUSION

Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria. Magn Reson Med 79:1661-1673, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network

Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction algorithms are one of the most promising way to compensate for the increased noise due to reduction of photon flux. Most iterative reconstruction algorithms incorporate manually designed prior functions of the reconstructed image to suppress noises while maintaining structures of the image. These priors basically rely on smoothness constraints and cannot exploit more complex features of the image. The recent development of artificial neural networks and machine learning enabled learning of more complex features of image, which has the potential to improve reconstruction quality. In this letter, K-sparse auto encoder was used for unsupervised feature learning. A manifold was learned from normal-dose images and the distance between the reconstructed image and the manifold was minimized along with data fidelity during reconstruction. Experiments on 2016 Low-dose CT Grand Challenge were used for the method verification, and results demonstrated the noise reduction and detail preservation abilities of the proposed method.

Comparative analysis of iterative reconstruction algorithms with resolution recovery and time of flight modeling for 18F-FDG cardiac PET: A multi-center phantom study

BACKGROUND

The purpose of this study was to evaluate the image quality in cardiac 18F-FDG PET using the time of flight (TOF) and/or point spread function (PSF) modeling in the iterative reconstruction (IR).

METHODS

Three scanners and an anthropomorphic cardiac phantom with an insert simulating a transmural defect (TD) were used. Two sets of scans (with/without TD) were acquired, and four reconstruction schemes were considered: (1) IR; (2) IR + PSF, (3) IR + TOF, and (4) IR + TOF + PSF. LV wall thickness (FWHM), contrast between LV wall and inner chamber (C IC), and TD contrast in LV wall (C TD) were evaluated.

RESULTS

Tests of the reconstruction protocols showed a decrease in FWHM from IR (13 mm) to IR + PSF (11 mm); an increase in the C IC from IR (65%) to IR + PSF (71%) and from IR + TOF (72%) to IR + TOF + PSF (77%); and an increase in the C TD from IR + PSF (72%) to IR + TOF (75%) and to IR + TOF + PSF (77%). Tests of the scanner/software combinations showed a decrease in FWHM from Gemini_TF (13 mm) to Biograph_mCT (12 mm) and to Discovery_690 (11 mm); an increase in the C IC from Gemini_TF (65%) to Biograph_mCT (73%) and to Discovery_690 (75%); and an increase in the C TD from Gemini_TF/Biograph_mCT (72%) to Discovery_690 (77%).

CONCLUSION

The introduction of TOF and PSF increases image quality in cardiac 18F-FDG PET. The scanner/software combinations exhibit different performances, which should be taken into consideration when making cross comparisons.

Recent Advances in X-ray Cone-beam Computed Laminography

X-ray computed tomography is an established volume imaging technique used routinely in medical diagnosis, industrial non-destructive testing, and a wide range of scientific fields. Traditionally, computed tomography uses scanning geometries with a single axis of rotation together with reconstruction algorithms specifically designed for this setup. Recently there has however been increasing interest in more complex scanning geometries. These include so called X-ray computed laminography systems capable of imaging specimens with large lateral dimensions or large aspect ratios, neither of which are well suited to conventional CT scanning procedures. Developments throughout this field have thus been rapid, including the introduction of novel system trajectories, the application and refinement of various reconstruction methods, and the use of recently developed computational hardware and software techniques to accelerate reconstruction times. Here we examine the advances made in the last several years and consider their impact on the state of the art.

Breast tomosynthesis using the multiple projection algorithm adapted for stationary detectors

OBJECTIVE

The aim of this study is to investigate the validity of using the Multiple Projection Algorithm (MPA) for Breast Tomosynthesis (BT) using real projection images acquired with phantoms at a clinical setting.

METHODS

The CIRS-BR3D phantom with ranging thicknesses between 3 cm and 6 cm was used for all image quality evaluations. Five sets of measurements were acquired, each comprised of a 2D mammographic image followed by a set of 25 projections within an arc length of 50°. A reconstruction algorithm based on the MPA was adapted for partial isocentric rotation using a stationary detector. For reference purposes, a Back Projection (BP) algorithm was also developed for this geometry. The performance of the algorithms was evaluated, in combination with pre-filtering of the projections, in comparative studies that involved also a comparison between tomosynthesis slices and 2D mammograms.

RESULTS

Evaluation of tomosynthesis slices reconstructed with BP and MPA showed close performance for the two algorithms with no considerable differences in feature detection, size and appearance of the background tissue with the MPA running faster the overall process. Pre-filtering of the projections, led to better BT images compared to non-filtering. Increased thickness resulted in limited detection of the features of interest, especially the smaller sized ones. In these cases, the filtered BT slices allowed improved visualization due to removed superimposed tissue compared to the 2D images. The different breast-like slab arrangements in phantoms of the same thickness demonstrated a slight influence on the quality of reconstructed features.

CONCLUSIONS

The MPA which had been applied previously to reconstruct tomograms from projections acquired at synchrotron facilities, is a time efficient algorithm, and is fully compliant with and can be successfully used in BT clinical systems. Compared to 2D mammography, BT shows advantage in visualizing features of small size and for increased phantom thickness or features within a dense background with superimposed structures.

A software tool for tomographic axial superresolution in STED microscopy

A method for generating three-dimensional tomograms from multiple three-dimensional axial projections in STimulated Emission Depletion (STED) superresolution microscopy is introduced. Our STED< method, based on the use of a micromirror placed on top of a standard microscopic sample, is used to record a three-dimensional projection at an oblique angle in relation to the main optical axis. Combining the STED< projection with the regular STED image into a single view by tomographic reconstruction, is shown to result in a tomogram with three-to-four-fold improved apparent axial resolution. Registration of the different projections is based on the use of a mutual-information histogram similarity metric. Fusion of the projections into a single view is based on Richardson-Lucy iterative deconvolution algorithm, modified to work with multiple projections. Our tomographic reconstruction method is demonstrated to work with real biological STED superresolution images, including a data set with a limited signal-to-noise ratio (SNR); the reconstruction software (SuperTomo) and its source code will be released under BSD open-source license.

Dependence of image quality on acquisition time for the PET/CT Biograph mCT

The impact of acquisition time on reconstructed PET image quality is analyzed for different acquisition times (1, 2, 3 and 4min). Image quality was tested according to the National Electrical Manufacturers Association (NEMA) NU 2-2007, the evaluation for the signal to noise ratio (SNR) and the reconstructed activity ratio (RAR) for three algorithms, i.e. OSEM, TrueX and TOF applying different effective iteration numbers. The present work shows that the image quality of 3 and 4min acquisition time for spherical lesions of 10mm diameter are not significantly different between TrueX, TOF and OSEM. The 2min acquisition time should be used carefully for the TrueX and OSEM algorithms in small lesions, because the levels of background noise are high compared to 3 or 4min measurements. Also, the reconstructed activity ratio is underestimated to be approximately half of the expected value. For large lesions the three algorithms perform similarly for all acquisition durations, however, OSEM has the advantage of a more accurately reconstructed activity ratio compared to TrueX and TOF, which are more strongly influenced by noise.

Calculation of three-dimensional left ventricular strains from biplanar tagged MR images

The noninvasive measurement of time-resolved three-dimensional (3D) strains throughout the myocardium could greatly improve the clinical evaluation of cardiac disease and the ability to mathematically model the heart. On the basis of orthogonal arrays of tagged magnetic resonance (MR) images taken at several times during systole, such strains can be determined, but only after heart motion through the image planes is taken into account. An iterative material point-tracking algorithm is presented to solve this problem. It is tested by means of mathematical models of the heart with cylindric and spherical geometries that undergo deformations and bulk motions. Errors introduced by point-tracking interpolation were found to be negligible compared with those due to marker identification on the images. In a human heart studied with this technique, the corrected radial strains at the left ventricular base were approximately 2.5 times the two-dimensional estimates derived from the fixed image planes. The authors conclude that material point tracking allows accurate, time-resolved 3D strains to be calculated from tagged MR images, and that prior correction for motion of the heart through image planes is necessary.