Physical Phantom Research Articles

Attenuation coefficient in the energy range 14–36 keV of 3D printing materials for physical breast phantoms

Objective. To measure the monoenergetic x-ray linear attenuation coefficient, μ, of fused deposition modeling (FDM) colored 3D printing materials (ABS, PLAwhite, PLAorange, PET and NYLON), used as adipose, glandular or skin tissue substitutes for manufacturing physical breast phantoms. Approach. Attenuation data (at 14, 18, 20, 24, 28, 30 and 36 keV) were acquired at Elettra synchrotron radiation facility, with step-wedge objects, using the Lambert–Beer law and a CCD imaging detector. Test objects were 3D printed using the Ultimaker 3 FDM printer. PMMA, Nylon-6 and high-density polyethylene step objects were also investigated for the validation of the proposed methodology. Printing uniformity was assessed via monoenergetic and polyenergetic imaging (32 kV, W/Rh). Main results. Maximum absolute deviation of μ for PMMA, Nylon-6 and HD-PE was 5.0%, with reference to literature data. For ABS and NYLON, μ differed by less than 6.1% and 7.1% from that of adipose tissue, respectively; for PET and PLAorange the difference was less than 11.3% and 6.3% from glandular tissue, respectively. PLAorange is a good substitute of skin (differences from −9.4% to +1.2%). Hence, ABS and NYLON filaments are suitable adipose tissue substitutes, while PET and PLAorange mimick the glandular tissue. PLAwhite could be printed at less than 100% infill density for matching the attenuation of glandular tissue, using the measured density calibration curve. The printing mesh was observed for sample thicknesses less than 60 mm, imaged in the direction normal to the printing layers. Printing dimensional repeatability and reproducibility was less 1%. Significance. For the first time an experimental determination was provided of the linear attenuation coefficient of common 3D printing filament materials with estimates of μ at all energies in the range 14–36 keV, for their use in mammography, breast tomosynthesis and breast computed tomography investigations.

Projection decomposition via univariate optimization for dual-energy CT.

Dual-energy computed tomography (DECT) acquires two x-ray projection datasets with different x-ray energy spectra, performs material-specific image reconstruction based on the energy-dependent non-linear integral model, and provides more accurate quantification of attenuation coefficients than single energy spectrum CT. In the diagnostic energy range, x-ray energy-dependent attenuation is mainly caused by photoelectric absorption and Compton scattering. Theoretically, these two physical components of the x-ray attenuation mechanism can be determined from two projection datasets with distinct energy spectra. Practically, the solution of the non-linear integral equation is complicated due to spectral uncertainty, detector sensitivity, and data noise. Conventional multivariable optimization methods are prone to local minima. In this paper, we develop a new method for DECT image reconstruction in the projection domain. This method combines an analytic solution of a polynomial equation and a univariate optimization to solve the polychromatic non-linear integral equation. The polynomial equation of an odd order has a unique real solution with sufficient accuracy for image reconstruction, and the univariate optimization can achieve the global optimal solution, allowing accurate and stable projection decomposition for DECT. Numerical and physical phantom experiments are performed to demonstrate the effectiveness of the method in comparison with the state-of-the-art projection decomposition methods. As a result, the univariate optimization method yields a quality improvement of 15% for image reconstruction and substantial reduction of the computational time, as compared to the multivariable optimization methods.

A multi-modality physical phantom for mimicking tumor heterogeneity patterns in PET/CT and PET/MRI.

BackgroundHybrid imaging (e.g., positron emission tomography [PET]/computed tomography [CT], PET/magnetic resonance imaging [MRI]) helps one to visualize and quantify morphological and physiological tumor characteristics in a single study. The noninvasive characterization of tumor heterogeneity is essential for grading, treatment planning, and following‐up oncological patients. However, conventional (CONV) image‐based parameters, such as tumor diameter, tumor volume, and radiotracer activity uptake, are insufficient to describe tumor heterogeneities. Here, radiomics shows promise for a better characterization of tumors. Nevertheless, the validation of such methods demands imaging objects capable of reflecting heterogeneities in multi‐modality imaging. We propose a phantom to simulate tumor heterogeneity repeatably in PET, CT, and MRI.MethodsThe phantom consists of three 50‐ml plastic tubes filled partially with acrylic spheres of S1: 1.6 mm, S2: 50%(1.6 mm)/50%(6.3 mm), or S3: 6.3‐mm diameter. The spheres were fixed to the bottom of each tube by a plastic grid, yielding one sphere free homogeneous region and one heterogeneous (S1, S2, or S3) region per tube. A 3‐tube phantom and its replica were filled with a fluorodeoxyglucose (18F) solution for test–retest measurements in a PET/CT Siemens TPTV and a PET/MR Siemens Biograph mMR system. A number of 42 radiomic features (10 first order and 32 texture features) were calculated for each phantom region and imaging modality. Radiomic features stability was evaluated through coefficients of variation (COV) across phantoms and scans for PET, CT, and MRI. Further, the Wilcoxon test was used to assess the capability of stable features to discriminate the simulated phantom regions.ResultsThe different patterns (S1–S3) did present visible heterogeneity in all imaging modalities. However, only for CT and MRI, a clear visual difference was present between the different patterns. Across all phantom regions in PET, CT, and MR images, 10, 16, and 21 features out of 42 evaluated features in total had a COV of 10% or less. In particular, CONV, histogram, and gray‐level run length matrix features showed high repeatability for all the phantom regions and imaging modalities. Several of repeatable texture features allowed the image‐based discrimination of the different phantom regions (p < 0.05). However, depending on the feature, different pattern discrimination capabilities were found for the different imaging modalities.ConclusionThe proposed phantom appears suitable for simulating heterogeneities in PET, CT, and MRI. We demonstrate that it is possible to select radiomic features for the readout of the phantom. Most of these features had been shown to be relevant in previous clinical studies.

Salivary Dysfunctions and Consequences After Radioiodine Treatment for Thyroid Cancer: Protocol for a Self-Controlled Study (START Study).

BackgroundFollowing radioiodine (131I) therapy of differentiated thyroid cancer, the salivary glands may become inflamed, leading to dysfunctions and decreases in patients’ nutritional status and quality of life. The incidence of these dysfunctions after 131I-therapy is poorly known, and no clinical or genetic factors have been identified to date to define at-risk patients, which would allow the delivered activity to be adapted to the expected risk of salivary dysfunctions.ObjectiveThe aims of this study are to estimate the incidence of salivary dysfunctions, and consequences on the quality of life and nutritional status for patients after 131I-therapy; to characterize at-risk patients of developing posttreatment dysfunctions using clinical, biomolecular, and biochemical factors; and to validate a dosimetric method to calculate the dose received at the salivary gland level for analyzing the dose-response relationship between absorbed doses to salivary glands and salivary dysfunctions.MethodsThis prospective study aims to include patients for whom 131I-therapy is indicated as part of the treatment for differentiated thyroid cancer in a Paris hospital (40 and 80 patients in the 1.1 GBq and 3.7 GBq groups, respectively). The follow-up is based on three scheduled visits: at inclusion (T0, immediately before 131I-therapy), and at 6 months (T6) and 18 months (T18) posttreatment. For each visit, questionnaires on salivary dysfunctions (validated French tool), quality of life (Hospital Anxiety and Depression scale, Medical Outcomes Study 36-Item Short Form Survey), and nutritional status (visual analog scale) are administered by a trained clinical research associate. At T0 and T6, saliva samples and individual measurements of the salivary flow, without and with salivary glands stimulation, are performed. External thermoluminescent dosimeters are positioned on the skin opposite the salivary glands and at the sternal fork immediately before 131I administration and removed after 5 days. From the doses recorded by the dosimeters, an estimation of the dose received at the salivary glands will be carried out using physical and computational phantoms. Genetic and epigenetic analyses will be performed to search for potential biomarkers of the predisposition to develop salivary dysfunctions after 131I-therapy.ResultsA total of 139 patients (99 women, 71.2%; mean age 47.4, SD 14.3 years) were enrolled in the study between September 2020 and April 2021 (45 and 94 patients in the 1.1 GBq and 3.7G Bq groups, respectively). T6 follow-up is complete and T18 follow-up is currently underway. Statistical analyses will assess the links between salivary dysfunctions and absorbed doses to the salivary glands, accounting for associated factors. Moreover, impacts on the patients’ quality of life will be analyzed.ConclusionsTo our knowledge, this study is the first to investigate the risk of salivary dysfunctions (using both objective and subjective indicators) in relation to organ (salivary glands) doses, based on individual dosimeter records and dose reconstructions. The results will allow the identification of patients at risk of salivary dysfunctions and will permit clinicians to propose a more adapted follow-up and/or countermeasures to adverse effects.Trial RegistrationClinicalTrials.gov NCT04876287; https://clinicaltrials.gov/ct2/show/NCT04876287International Registered Report Identifier (IRRID)DERR1-10.2196/35565

Automated image quality assessment of mammography phantoms: a systematic review.

Computerized image analysis is a viable technique for evaluating image quality as a complement to human observers. To systematically review the image analysis software used in the assessment of 2D image quality using mammography phantoms. A systematic search of multiple databases was performed from inception to July 2020 for articles that incorporated computerized analysis of 2D images of physical mammography phantoms to determine image quality. A total of 26 studies were included, 12 were carried out using direct digital imaging and 14 using screen film mammography. The ACR phantom (model-156) was the most frequently evaluated phantom, possibly due to the lack of accepted standard software. In comparison to the inter-observer variations, the computerized image analysis was more consistent in scoring test objects. The template matching method was found to be one of the most reliable algorithms, especially for high-contrast test objects, while several algorithms found low-contrast test objects to be harder to distinguish due to the smaller contrast variations between test objects and their backgrounds. This was particularly true for small object sizes. Image analysis software was in agreement with human observers but demonstrated higher consistency and reproducibility of quality evaluation. Additionally, using computerized analysis, several quantitative metrics such as contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) could be used to complement the conventional scoring method. Implementing a computerized approach for monitoring image quality over time would be crucial to detect any deteriorating mammography system before clinical images are impacted.

Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset

BackgroundIn recent years, a lot of effort has been put in the enhancement of medical imaging using artificial intelligence. However, limited patient data in combination with the unavailability of a ground truth often pose a challenge to a systematic validation of such methodologies. The goal of this work was to investigate a recently proposed method for an artificial intelligence-based generation of synthetic SPECT projections, for acceleration of the image acquisition process based on a large dataset of realistic SPECT simulations.MethodsA database of 10,000 SPECT projection datasets of heterogeneous activity distributions of randomly placed random shapes was simulated for a clinical SPECT/CT system using the SIMIND Monte Carlo program. Synthetic projections at fixed angular increments from a set of input projections at evenly distributed angles were generated by different u-shaped convolutional neural networks (u-nets). These u-nets differed in noise realization used for the training data, number of input projections, projection angle increment, and number of training/validation datasets. Synthetic projections were generated for 500 test projection datasets for each u-net, and a quantitative analysis was performed using statistical hypothesis tests based on structural similarity index measure and normalized root-mean-squared error. Additional simulations with varying detector orbits were performed on a subset of the dataset to study the effect of the detector orbit on the performance of the methodology. For verification of the results, the u-nets were applied to Jaszczak and NEMA physical phantom data obtained on a clinical SPECT/CT system.ResultsNo statistically significant differences were observed between u-nets trained with different noise realizations. In contrast, a statistically significant deterioration was found for training with a small subset (400 datasets) of the 10,000 simulated projection datasets in comparison with using a large subset (9500 datasets) for training. A good agreement between synthetic (i.e., u-net generated) and simulated projections before adding noise demonstrates a denoising effect. Finally, the physical phantom measurements show that our findings also apply for projections measured on a clinical SPECT/CT system.ConclusionOur study shows the large potential of u-nets for accelerating SPECT/CT imaging. In addition, our analysis numerically reveals a denoising effect when generating synthetic projections with a u-net. Clinically interesting, the methodology has proven robust against camera orbit deviations in a clinically realistic range. Lastly, we found that a small number of training samples (e.g., ~ 400 datasets) may not be sufficient for reliable generalization of the u-net.

Age-specific calibration for in vivo monitoring of thyroid: is it necessary?

Usually, an age-specific calibration of detectors used for in vivo monitoring of 131I thyroid radioactivity is not performed in practice. This study aimed to investigate the reduction in uncertainty that one can expect if an age-specific calibration is performed. For this, voxel and stylized computational phantoms of the thyroid, corresponding to children at different age groups, were used to simulate the calibration process of 131I detectors used for thyroid monitoring. SCK•CEN physical phantoms were also used for this purpose. Both analytical and Monte Carlo methods (MCNPX version 2.6.0) were used to estimate the counting efficiencies of the considered detectors. The results show that the uncertainties in the assessment of thyroid activity at a distance of 20cm would be reduced from a range of +8% to +30%, to a range from -6% to +15% when age-specific calibration was performed. Using a calibration based on thyroids of adults would result in an overestimation of the thyroid activity for children by up to 30% at a detector-neck distance of about 20cm; a larger overestimation may be expected at closer distances. It is concluded that age-specific calibration of in vivo monitoring systems for the thyroid is important and has to be taken into consideration to improve the reliability of thyroid dose assessment for children.

Investigating uncertainty in augmented reality enhanced renal navigation using in vitro patient-specific tissue-mimicking phantoms.

To improve the outcome of minimally invasive renal interventions, traditional video-guided needle navigation can be enhanced by tracking the needle, guiding the needle using video imaging, and augmenting the surgical scene with pre-procedural images or models of the anatomy. In our previous work we studied, both through simulations and in vitro experiments, the uncertainty associated with the model-to-phantom registration, as well as the camera-tracker calibration and video-guided navigation. In this work, we characterize the overall navigation uncertainty using tissue emulating patient-specific kidney phantoms featuring both virtual and physical internal targets. Pre-procedural models of the kidney phantoms and internal targets are generated from cone-beam CT images, and are registered to their intra-operative physical counter-parts. The user then guides the needle insertion to reach the internal targets using video-based imaging augmented with a virtual representation of the needle tracked in real time. Following navigation, we acquire post-procedural cone-beam CT images of the phantoms and inserted needles. These images are used to determine the ground truth needle navigation accuracy (i.e., needle to target distance) against which the intra-operative navigation accuracy (i.e., intra-op needle tip to target distance) is assessed. We also explore a method to update the pre-procedural model to physical phantom registration intra-operatively using tracked video imaging, with the overall goal to improve overall navigation accuracy in the event of sub-optimal initial image-to-phantom registration. Our results showed a navigation error of less than 3.5 mm in gelatin phantoms and less than 6.5 mm in PVA phantoms. Following registration correction intra-operatively, we showed an overall improvement in navigation from roughly 6 mm RMS to approximately 2 mm RMS error, which is acceptable given the inherent tracking, 3D printing and phantom manufacturing limitations.

Single-shot T2 mapping via multi-echo-train multiple overlapping-echo detachment planar imaging and multitask deep learning.

Quantitative magnetic resonance imaging provides robust biomarkers in clinics. Nevertheless, the lengthy scan time reduces imaging throughput and increases the susceptibility of imaging results to motion. In this context, a single-shot T2 mapping method based on multiple overlapping-echo detachment (MOLED) planar imaging was presented, but the relatively small echo time range limits its accuracy, especially in tissues with large T2 . In this work we proposed a novel single-shot method, Multi-Echo-Train Multiple OverLapping-Echo Detachment (METMOLED) planar imaging, to accommodate a large range of T2 quantification without additional measurements to rectify signal degeneration arisen from refocusing pulse imperfection. Multiple echo-train techniques were integrated into the MOLED sequence to capture larger TE information. Maps of T2 , B1 , and spin density were reconstructed synchronously from acquired METMOLED data via multitask deep learning. A typical U-Net was trained with 3000/600 synthetic data with geometric/brain patterns to learn the mapping relationship between METMOLED signals and quantitative maps. The refocusing pulse imperfection was settled through the inherent information of METMOLED data and auxiliary tasks. Experimental results on the digital brain (structural similarity (SSIM) index=0.975/0.991/0.988 for MOLED/METMOLED-2/METMOLED-3, hyphenated number denotes the number of echo-trains), physical phantom (the slope of linear fitting with reference T2 map=1.047/1.017/1.006 for MOLED/METMOLED-2/METMOLED-3), and human brain (Pearson's correlation coefficient (PCC)=0.9581/0.9760/0.9900 for MOLED/METMOLED-2/METMOLED-3) demonstrated that the METMOLED improved the quantitative accuracy and the tissue details in contrast to the MOLED. These improvements were more pronounced in tissues with large T2 and in application scenarios with high temporal resolution (PCC=0.8692/0.9465/0.9743 for MOLED/METMOLED-2/METMOLED-3). Moreover, the METMOLED could rectify the signal deviations induced by the non-ideal slice profiles of refocusing pulses without additional measurements. A preliminary measurement also demonstrated that the METMOLED is highly repeatable (mean coefficient of variation (CV)=1.65%). METMOLED breaks the restriction of echo-train length to TE and implements unbiased T2 estimates in an extensive range. Furthermore, it corrects the effect of refocusing pulse inaccuracy without additional measurements or signal post-processing, thus retaining its single-shot characteristic. This technique would be beneficial for accurate T2 quantification.

Algorithmic insights of camera-based respiratory motion extraction

Objective. Measuring the respiratory signal from a video based on body motion has been proposed and recently matured in products for contactless health monitoring. The core algorithm for this application is the measurement of tiny chest/abdominal motions induced by respiration (i.e. capturing sub-pixel displacement caused by subtle motion between subsequent video frames), and the fundamental challenge is motion sensitivity. Though prior art reported on the validation with real human subjects, there is no thorough or rigorous benchmark to quantify the sensitivities and boundary conditions of motion-based core respiratory algorithms. Approach. A set-up was designed with a fully-controllable physical phantom to investigate the essence of core algorithms, together with a mathematical model incorporating two motion estimation strategies and three spatial representations, leading to six algorithmic combinations for respiratory signal extraction. Their promises and limitations are discussed and clarified through the phantom benchmark. Main results. With the variation of phantom motion intensity between 0.5 mm and 8 mm, the recommended approach obtains an average precision, recall, coverage and MAE of 88.1%, 91.8%, 95.5% and 2.1 bpm in the day-light condition, and 81.7%, 90.0%, 93.9% and 4.4 bpm in the night condition. Significance. The insights gained in this paper are intended to improve the understanding and applications of camera-based respiration measurement in health monitoring. The limitations of this study stem from the used physical phantom that does not consider human factors like body shape, sleeping posture, respiratory diseases, etc., and the investigated scenario is focused on sleep monitoring, not including scenarios with a sitting or standing patient like in clinical ward and triage.

Autonomous Robotic Screening of Tubular Structures Based Only on Real-Time Ultrasound Imaging Feedback

Ultrasound (US) imaging is widely employed for diagnosis and staging of vascular diseases, mainly due to its high availability and the fact it does not emit ionizing radiation. However, high interoperator variability limits the repeatability of US image acquisition. To address this challenge, we propose an end-to-end workflow for automatic robotic US screening of tubular structures using only real-time US imaging feedback. First, a U-Net was trained for real-time segmentation of vascular structure from cross-sectional US images. Then, we represented the detected vascular structure as a 3-D point cloud, which was used to estimate the centerline of the target structure and its local radius by solving a constrained nonlinear optimization problem. Iterating the previous processes, the US probe was automatically aligned to the normal direction of the target structure, while the object was constantly maintained in the center of the US view. The real-time segmentation result was evaluated both on a phantom and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> on brachial arteries of volunteers. In addition, the whole process was validated using both simulation and physical phantoms. The mean absolute orientation, centering, and radius error ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> SD) on a gel phantom were <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{3.7}\pm \text{1.6}^{\circ }$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.2\pm \text{0.2}\,\text{mm}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.8\pm \text{0.4}\,\text{mm}$</tex-math></inline-formula> , respectively. The results indicate that the method can automatically screen tubular structures with an optimal probe orientation (i.e., normal to the vessel) and accurately estimate the radius of the target structure.

A new approach to overcome the inconsistency between SPECT and the anatomical map in maximum A-posterior expectation-maximization reconstruction algorithm

Noise reduction while preserving spatial resolution is one of the most important challenges in the reconstructing of emission tomography images. One of the resolving methods is the Bowsher maximum a-posteriori expectation-maximization reconstruction (MAPEM) algorithm. This method considers a binary selection of the neighbors of each voxel based on the prior anatomical values to use in the regularization function. This method is particularly susceptible to imposing the wrong data into the reconstructed image due to the spatial or functional inconsistencies between the anatomical image and the actual activity distribution. Because of the poor spatial resolution of single-photon emission tomography (SPECT) images and the different nature of emission and anatomical imaging, there is not enough certainty of inconsistency with anatomical images. Therefore, we proposed a new weighted Bowsher method that can overcome this weakness while the image quality indexes, especially the spatial resolution, are almost preserved. In the proposed method, each of the neighbors of a specific voxel takes a constant weight depending on the order of its value and independent of its intensity quantity. The proposed method was evaluated using some different physical phantoms and a patient scan. The results show that the proposed method has superiority in the presence of inconsistency; moreover, the proposed method gives nearly similar results to the regular Bowsher MAPEM in case of consistency. In conclusion, we show that using a suitable constant weighting factor in Bowsher MAPEM, one can operatively reduce the image noise while preserving the image quality parameters where the emission tomography images are either consistent or inconsistent with the prior anatomical map.

Lung-Optimized Deep-Learning-Based Reconstruction for Ultralow-Dose CT

To evaluate the image properties of lung-specialized deep-learning-based reconstruction (DLR) and its applicability in ultralow-dose CT (ULDCT) relative to hybrid- (HIR) and model-based iterative-reconstructions (MBIR). An anthropomorphic chest phantom was scanned on a 320-row scanner at 50-mA (low-dose-CT 1 [LDCT-1]), 25-mA (LDCT-2), and 10-mA (ULDCT). LDCT were reconstructed with HIR; ULDCT images were reconstructed with HIR (ULDCT-HIR), MBIR (ULDCT-MBIR), and DLR (ULDCT-DLR). Image noise and contrast-to-noise ratio (CNR) were quantified. With the LDCT images as reference standards, ULDCT image qualities were subjectively scored on a 5-point scale (1=substantially inferior to LDCT-2, 3=comparable to LDCT-2, 5=comparable to LDCT-1). For task-based image quality analyses, a physical evaluation phantom was scanned at seven doses to achieve the noise levels equivalent to chest phantom; noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated. Clinical ULDCT (10-mA) images obtained in 14 nonobese patients were reconstructed with HIR, MBIR, and DLR; the subjective acceptability was ranked. Image noise was lower and CNR was higher in ULDCT-DLR and ULDCT-MBIR than in LDCT-1, LDCT-2, and ULDCT-HIR (p < 0.01). The overall quality of ULDCT-DLR was higher than of ULDCT-HIR and ULDCT-MBIR (p < 0.01), and almost comparable with that of LDCT-2 (mean score: 3.4 ± 0.5). DLR yielded the highest NPS peak frequency and TTF50% for high-contrast object. In clinical ULDCT images, the subjective acceptability of DLR was higher than of HIR and MBIR (p < 0.01). DLR optimized for lung CT improves image quality and provides possible greater dose optimization opportunity than HIR and MBIR.

PIMA-CT: Physical Model-Aware Cyclic Simulation and Denoising for Ultra-Low-Dose CT Restoration.

A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.

Fabrication of 3D printed patient-derived anthropomorphic breast phantoms for mammography and digital breast tomosynthesis: Imaging assessment with clinical X-ray spectra

PurposeTo design, fabricate and characterize 3D printed, anatomically realistic, compressed breast phantoms for digital mammography (DM) and digital breast tomosynthesis (DBT) x-ray imaging. MaterialsWe realized 3D printed phantoms simulating healthy breasts, via fused deposition modeling (FDM), with a layer resolution of 0.1 mm and 100% infill density, using a dual extruder printer. The digital models were derived from a public dataset of segmented clinical breast computed tomography scans. Three physical phantoms were printed in polyethylene terephthalate (PET), acrylonitrile–butadiene–styrene (ABS), or in polylactic-acid (PLA) materials, using ABS as a substitute for adipose tissue, and PLA or PET filaments for replicating glandular and skin tissues. 3D printed phantoms were imaged at three clinical centers with DM and DBT scanners, using typical spectra. Anatomical noise of the manufactured phantoms was evaluated via the estimates of the β parameter both in DM images and in images acquired via a clinical computed tomography (CT) scanner. ResultsDM and DBT phantom images showed an inner texture qualitatively similar to the images of a clinical DM or DBT exam, suitably reproducing the glandular structure of their computational phantoms. β parameters evaluated in DM images of the manufactured phantoms ranged between 2.84 and 3.79; a lower β was calculated from the CT scan. ConclusionsFDM 3D printed compressed breast phantoms have been fabricated using ABS, PLA and PET filaments. DM and DBT images with clinical x-ray spectra showed realistic textures. These phantoms appear promising for clinical applications in quality assurance, image quality and dosimetry assessments.

Challenges and advances in optical 3D mesoscale imaging.

Optical mesoscale imaging is a rapidly developing field that allows the visualisation of larger samples than is possible with standard light microscopy, and fills a gap between cell and organism resolution. It spans from advanced fluorescence imaging of micrometric cell clusters to centimetre‐size complete organisms. However, with larger volume specimens, new problems arise. Imaging deeper into tissues at high resolution poses challenges ranging from optical distortions to shadowing from opaque structures. This manuscript discusses the latest developments in mesoscale imaging and highlights limitations, namely labelling, clearing, absorption, scattering, and also sample handling. We then focus on approaches that seek to turn mesoscale imaging into a more quantitative technique, analogous to quantitative tomography in medical imaging, highlighting a future role for digital and physical phantoms as well as artificial intelligence.

Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction

BackgroundThe GE Discovery NM (DNM) 530c/570c are dedicated cardiac SPECT scanners with 19 detector modules designed for stationary imaging. This study aims to incorporate additional projection angular sampling to improve reconstruction quality. A deep learning method is also proposed to generate synthetic dense-view image volumes from few-view counterparts. MethodsBy moving the detector array, a total of four projection angle sets were acquired and combined for image reconstructions. A deep neural network is proposed to generate synthetic four-angle images with 76 (4 × 19) projections from corresponding one-angle images with 19 projections. Simulated data, pig, physical phantom, and human studies were used for network training and evaluation. Reconstruction results were quantitatively evaluated using representative image metrics. The myocardial perfusion defect size of different subjects was quantified using an FDA-cleared clinical software. ResultsMulti-angle reconstructions and network results have higher image resolution, improved uniformity on normal myocardium, more accurate defect quantification, and superior quantitative values on all the testing data. As validated against cardiac catheterization and diagnostic results, deep learning results showed improved image quality with better defect contrast on human studies. ConclusionIncreasing angular sampling can substantially improve image quality on DNM, and deep learning can be implemented to improve reconstruction quality in case of stationary imaging.

Evaluation of Wavelet Kernel-Based PET Image Reconstruction

Positron emission tomography (PET) imaging is used to track biochemical processes in the human body. PET image quality is limited by noise and several methods have been implemented to improve the quality. Kernel-based image reconstruction is among the methods implemented to increase PET image quality and commonly uses a Gaussian kernel to include spatial correlations from image priors into the forward projection model of PET. Unfortunately, the Gaussian kernel tends to smooth details in the reconstructed image. To reduce noise without losing contrast details, a different kernel is needed. Wavelet kernels can be more efficient than the Gaussian kernel in reducing noise while keeping contrast details by better separating signal from noise and thus, it does not over smooth peak values in the final reconstructed images. In this work, we evaluate a wavelet kernel for kernel-based PET image reconstruction. For this goal, a wavelet kernel approach has been tested using simulated brain data, physical phantom data, and patient data. Reconstruction results are presented and discussed in detail comparing the wavelet kernel method with the Gaussian kernel method. Our results suggest that a wavelet kernel performs better in contrast recovery for phantoms and also results in higher signal-to-noise ratio (SNR) for real patient data.

General orientation transform for the estimation offiberorientations in white matter tissues.

The orientation distribution function (ODF), which is obtained from the radial integral of the probability density function weighted by ( is the radial length), has been used to estimate fiber orientations of white matter tissues. Currently, there is no general expression of the ODF that is suitable for any n value in the HARDI methods. A novel methodology is proposed to calculate the ODF for any through the Taylor series expansion and a generalized expression for is provided. Then a series of single-shell HARDI methods, termed the general orientation transform (GOT), is developed based on the obtained expression. By combining complementary GOTs, a composite estimator is obtained and further optimized via constrained optimization to take full advantage of individual merits. The final optimized HARDI method is termed the combined GOT with constrained optimization (coGOT). The proposed method is compared with other commonly used HARDI methods on the simulated data, the physical phantom data, the ISMRM 2015 Tractography challenge data, and in vivo HCP datasets. coGOT can resolve crossing fibers with higher resolution, performs better robustness, generates fewer spurious lobes in glyphs, and thus provides distinct improvement in the tractography. The evaluations show coGOT's superior capability in reconstructing the fiber orientations from dMRI signals. Generalization of the ODF allows us to obtain a wide range of HARDI estimators to select suitable candidates for composite formulation. The optimized estimator coGOT has great potential for studying neural architecture and serving as fiber tracking tools.

Scanner-specific validation of a CT simulator using a COPD-emulated anthropomorphic phantom.

Traditional methods of quantitative analysis of CT images typically involve working with patient data, which is often expensive and limited in terms of ground truth. To counter these restrictions, quantitative assessments can instead be made through Virtual Imaging Trials (VITs) which simulate the CT imaging process. This study sought to validate DukeSim (a scanner-specific CT simulator) utilizing clinically relevant biomarkers for a customized anthropomorphic chest phantom. The physical phantom was imaged utilizing two commercial CT scanners (Siemens Somatom Force and Definition Flash) with varying imaging parameters. A computational version of the phantom was simulated utilizing DukeSim for each corresponding real acquisition. Biomarkers were computed and compared between the real and virtually acquired CT images to assess the validity of DukeSim. The simulated images closely matched the real images both qualitatively and quantitatively, with the average biomarker percent difference of 3.84% (range 0.19% to 18.27%). Results showed that DukeSim is reasonably well validated across various patient imaging conditions and scanners, which indicates the utility of DukeSim for further VIT studies where real patient data may not be feasible.