Digital anthropomorphic phantoms of non-rigid human respiratory and voluntary body motion for investigating motion correction in emission imaging

Noninvasive Electrical Stimulation Neuromodulation and Digital Brain Technology: A Review

We review the research progress on noninvasive neural regulatory systems through system design and theoretical guidance. We provide an overview of the development history of noninvasive neuromodulation technology, focusing on system design. We also discuss typical cases of neuromodulation that use modern noninvasive electrical stimulation and the main limitations associated with this technology. In addition, we propose a closed-loop system design solution of the "time domain", "space domain", and "multi-electrode combination". For theoretical guidance, this paper provides an overview of the "digital brain" development process used for noninvasive electrical-stimulation-targeted modeling and the development of "digital human" programs in various countries. We also summarize the core problems of the existing "digital brain" used for noninvasive electrical-stimulation-targeted modeling according to the existing achievements and propose segmenting the tissue. For this, the tissue parameters of a multimodal image obtained from a fresh cadaver were considered as an index. The digital projection of the multimodal image of the brain of a living individual was implemented, following which the segmented tissues could be reconstructed to obtain a "digital twin brain" model with personalized tissue structure differences. The "closed-loop system" and "personalized digital twin brain" not only enable the noninvasive electrical stimulation of neuromodulation to achieve the visualization of the results and adaptive regulation of the stimulation parameters but also enable the system to have individual differences and more accurate stimulation.

Chinese Digital Arm (CDA): A High-Precision Digital Arm for Electrical Stimulation Simulation

To effectively analyze the diffusion and accumulation of signals on the surface and inside the human body under electrical stimulation, we used the gray threshold of the Chinese Digital Human image dataset to segment an arm image and reconstruct the tissue to obtain its three-dimensional cloud point dataset. Finally, a semirefined digital arm entity model with the geometric characteristics of the actual human arm tissue was constructed using reverse engineering technology. Further input of the current signal stimulation under tDCS and tACS with additional analysis of the signal diffusion in the transient mode via model calculation revealed that DC electrical stimulation is likely to cause high-voltage burns. The effective depth achieved using the AC stimulation signal is considerable, and provides reference for the electrical stimulation selection. Simultaneously, in the digital arm model, the signal diffusion and tissue damage inside the arm can be analyzed by changing the field, which provides a theoretical basis for the experimental study of the human body.

External Hardware and Sensors, for Improved MRI

Complex engineered systems are often equipped with suites of sensors and ancillary devices that monitor their performance and maintenance needs. MRI scanners are no different in this regard. Some of the ancillary devices available to support MRI equipment, the ones of particular interest here, have the distinction of actually participating in the image acquisition process itself. Most commonly, such devices are used to monitor physiological motion or variations in the scanner's imaging fields, allowing the imaging and/or reconstruction process to adapt as imaging conditions change. "Classic" examples include electrocardiography (ECG) leads and respiratory bellows to monitor cardiac and respiratory motion, which have been standard equipment in scan rooms since the early days of MRI. Since then, many additional sensors and devices have been proposed to support MRI acquisitions. The main physical properties that they measure may be primarily "mechanical" (eg acceleration, speed, and torque), "acoustic" (sound and ultrasound), "optical" (light and infrared), or "electromagnetic" in nature. A review of these ancillary devices, as currently available in clinical and research settings, is presented here. In our opinion, these devices are not in competition with each other: as long as they provide useful and unique information, do not interfere with each other and are not prohibitively cumbersome to use, they might find their proper place in future suites of sensors. In time, MRI acquisitions will likely include a plurality of complementary signals. A little like the microbiome that provides genetic diversity to organisms, these devices can provide signal diversity to MRI acquisitions and enrich measurements. Machine-learning (ML) algorithms are well suited at combining diverse input signals toward coherent outputs, and they could make use of all such information toward improved MRI capabilities. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

An update on computational anthropomorphic anatomical models

The prevalent availability of high-performance computing coupled with validated computerized simulation platforms as open-source packages have motivated progress in the development of realistic anthropomorphic computational models of the human anatomy. The main application of these advanced tools focused on imaging physics and computational internal/external radiation dosimetry research. This paper provides an updated review of state-of-the-art developments and recent advances in the design of sophisticated computational models of the human anatomy with a particular focus on their use in radiation dosimetry calculations. The consolidation of flexible and realistic computational models with biological data and accurate radiation transport modeling tools enables the capability to produce dosimetric data reflecting actual setup in clinical setting. These simulation methodologies and results are helpful resources for the medical physics and medical imaging communities and are expected to impact the fields of medical imaging and dosimetry calculations profoundly.

Development of a new Python-based cardiac phantom for myocardial SPECT imaging

PURPOSE

The aim of this work was to develop a digital dynamic cardiac phantom able to mimic gated myocardial perfusion single photon emission computed tomography (SPECT) images.

METHODS

A software code package was written to construct a cardiac digital phantom based on mathematical ellipsoidal model utilizing powerful numerical and mathematic libraries of python programing language. An ellipsoidal mathematical model was adopted to create the left ventricle geometrical volume including myocardial boundaries, left ventricular cavity, with incorporation of myocardial wall thickening and motion. Realistic myocardial count density from true patient studies was used to simulate statistical intensity variation during myocardial contraction. A combination of different levels of defect extent and severity were precisely modeled taking into consideration defect size variation during cardiac contraction. Wall thickening was also modeled taking into account the effect of partial volume.

RESULTS

It has been successful to build a python-based software code that is able to model gated myocardial perfusion SPECT images with variable left ventricular volumes and ejection fraction. The recent flexibility of python programming enabled us to manipulate the shape and control the functional parameters in addition to creating variable sized-defects, extents and severities in different locations. Furthermore, the phantom code also provides different levels of image filtration mimicking those filters used in image reconstruction and their influence on image quality. Defect extent and severity were found to impact functional parameter estimation in consistence to clinical examinations.

CONCLUSION

A python-based gated myocardial perfusion SPECT phantom has been successfully developed. The phantom proved to be reliable to assess cardiac software analysis tools in terms of perfusion and functional parameters. The software code is under further development and refinement so that more functionalities and features can be added.

Recent advances in cardiac SPECT instrumentation and imaging methods

Cardiac SPECT continues to play a critical role in detecting and managing cardiovascular disease, in particularly coronary artery disease (CAD) (Jaarsma et al 2012 J. Am. Coll. Cardiol. 59 1719-28), (Agostini et al 2016 Eur. J. Nucl. Med. Mol. Imaging 43 2423-32). While conventional dual-head SPECT scanners using parallel-hole collimators and scintillation crystals with photomultiplier tubes are still the workhorse of cardiac SPECT, they have the limitations of low photon sensitivity (~130 count s^-1 MBq^-1), poor image resolution (~15 mm) (Imbert et al 2012 J. Nucl. Med. 53 1897-903), relatively long acquisition time, inefficient use of the detector, high radiation dose, etc. Recently our field observed an exciting growth of new developments of dedicated cardiac scanners and collimators, as well as novel imaging algorithms for quantitative cardiac SPECT. These developments have opened doors to new applications with potential clinical impact, including ultra-low-dose imaging, absolute quantification of myocardial blood flow (MBF) and coronary flow reserve (CFR), multi-radionuclide imaging, and improved image quality as a result of attenuation, scatter, motion, and partial volume corrections (PVCs). In this article, we review the recent advances in cardiac SPECT instrumentation and imaging methods. This review mainly focuses on the most recent developments published since 2012 and points to the future of cardiac SPECT from an imaging physics perspective.

Accuracy of SPECT/CT-based lung dose calculation for Holmium-166 hepatic radioembolization before OSEM convergence

PURPOSE

In intra-arterial hepatic radioembolization using Holmium-166 (166 Ho) microspheres, a predicted lung-absorbed dose of more than 30 Gy is a contraindication for therapy. Therefore, scout imaging by means of quantitative SPECT of the lungs after a low-dose pretreatment session is essential. Earlier we showed the superiority of Monte Carlo-based iterative SPECT reconstructions over conventional reconstructions due to its quantitative nature, required for dosimetry, at the cost of substantial computation times. In clinical routine, however, the limited available time between scout imaging and therapy constrains its application. To reduce computation times, we investigated the minimum number of iterations required to guarantee a clinical acceptable accuracy in lung dose estimation using patient and phantom data.

METHODS

166 Ho scout SPECT data (range: 222-283 MBq) were used from 10 patients. SPECT images were Monte Carlo-based OSEM reconstructed (effective iterations: 240). Additionally, the 4D XCAT anthropomorphic phantom was used to mimic studies with an injected scout activity of 250 MBq and with varying lung-absorbed doses ranging from 0.9 to 225 Gy for a therapeutic dosage of 15 GBq. These studies were reconstructed in the same way as the patient data, and were also reconstructed using a clinically available, standard OSEM algorithm for comparison. Lung-absorbed dose was determined using VOI analysis as a function of iterations.

RESULTS

The estimated lung-absorbed dose in nine patients ranged upon MC-based OSEM convergence from 0 to 0.26 Gy for a therapeutic dosage. One patient had an estimated lung absorbed-dose for a therapeutic dosage of 20.3 Gy upon MC-based OSEM convergence, or 18.4 Gy after 40 iterations (-9%). The phantom data showed that the lung-absorbed dose upon OSEM convergence was underestimated by 15% as compared to the actual simulated lung dose, and the dose after 40 iterations was underestimated by 9% as compared to the dose upon convergence. Both underestimations were irrespective of the magnitude of the lung-absorbed dose (0.9 to 225 Gy) and thus can be easily corrected for. The quantitative accuracy of the MC-based OSEM reconstructions (40 iterations, before convergence) outperformed the clinical OSEM reconstruction while estimating the lung dose.

CONCLUSIONS

The number of effective iterations necessary for quantitative estimation of the lung dose using MC-based OSEM can be reduced from 240 to 40. The resulting sixfold reduction in calculation time enables processing of the scout images before therapy administration.

Virtual Human Models for Electromagnetic Studies and Their Applications

Numerical simulation of electromagnetic, thermal, and mechanical responses of the human body to different stimuli in magnetic resonance imaging safety, antenna research, electromagnetic tomography, and electromagnetic stimulation is currently limited by the availability of anatomically adequate and numerically efficient cross-platform computational models or "virtual humans." The objective of this study is to provide a comprehensive review of modern human models and body region models available in the field and their important features.

Enhancing ejection fraction measurement through 4D respiratory motion compensation in cardiac PET imaging

ECG gated cardiac PET imaging measures functional parameters such as left ventricle (LV) ejection fraction (EF), providing diagnostic and prognostic information for management of patients with coronary artery disease (CAD). Respiratory motion degrades spatial resolution and affects the accuracy in measuring the LV volumes for EF calculation. The goal of this study is to systematically investigate the effect of respiratory motion correction on the estimation of end-diastolic volume (EDV), end-systolic volume (ESV), and EF, especially on the separation of normal and abnormal EFs. We developed a respiratory motion incorporated 4D PET image reconstruction technique which uses all gated-frame data to acquire a motion-suppressed image. Using the standard XCAT phantom and two individual-specific volunteer XCAT phantoms, we simulated dual-gated myocardial perfusion imaging data for normally and abnormally beating hearts. With and without respiratory motion correction, we measured the EDV, ESV, and EF from the cardiac-gated reconstructed images. For all the phantoms, the estimated volumes increased and the biases significantly reduced with motion correction compared with those without. Furthermore, the improvement of ESV measurement in the abnormally beating heart led to better separation of normal and abnormal EFs. The simulation study demonstrated the significant effect of respiratory motion correction on cardiac imaging data with motion amplitude as small as 0.7 cm. The larger the motion amplitude the more improvement respiratory motion correction brought about on the EF measurement. Using data-driven respiratory gating, we also demonstrated the effect of respiratory motion correction on estimating the above functional parameters from list mode patient data. Respiratory motion correction has been shown to improve the accuracy of EF measurement in clinical cardiac PET imaging.

An evaluation of data-driven motion estimation in comparison to the usage of external-surrogates in cardiac SPECT imaging

Motion estimation methods in single photon emission computed tomography (SPECT) can be classified into methods which depend on just the emission data (data-driven), or those that use some other source of information such as an external surrogate. The surrogate-based methods estimate the motion exhibited externally which may not correlate exactly with the movement of organs inside the body. The accuracy of data-driven strategies on the other hand is affected by the type and timing of motion occurrence during acquisition, the source distribution, and various degrading factors such as attenuation, scatter, and system spatial resolution. The goal of this paper is to investigate the performance of two data-driven motion estimation schemes based on the rigid-body registration of projections of motion-transformed source distributions to the acquired projection data for cardiac SPECT studies. Comparison is also made of six intensity based registration metrics to an external surrogate-based method. In the data-driven schemes, a partially reconstructed heart is used as the initial source distribution. The partially-reconstructed heart has inaccuracies due to limited angle artifacts resulting from using only a part of the SPECT projections acquired while the patient maintained the same pose. The performance of different cost functions in quantifying consistency with the SPECT projection data in the data-driven schemes was compared for clinically realistic patient motion occurring as discrete pose changes, one or two times during acquisition. The six intensity-based metrics studied were mean-squared difference, mutual information, normalized mutual information (NMI), pattern intensity (PI), normalized cross-correlation and entropy of the difference. Quantitative and qualitative analysis of the performance is reported using Monte-Carlo simulations of a realistic heart phantom including degradation factors such as attenuation, scatter and system spatial resolution. Further the visual appearance of motion-corrected images using data-driven motion estimates was compared to that obtained using the external motion-tracking system in patient studies. Pattern intensity and normalized mutual information cost functions were observed to have the best performance in terms of lowest average position error and stability with degradation of image quality of the partial reconstruction in simulations. In all patients, the visual quality of PI-based estimation was either significantly better or comparable to NMI-based estimation. Best visual quality was obtained with PI-based estimation in one of the five patient studies, and with external-surrogate based correction in three out of five patients. In the remaining patient study there was little motion and all methods yielded similar visual image quality.