Set Of Phantoms Research Articles

Design and Development of Children's Calibration Phantoms for Whole and Mobile Body Counters

Abstract The experiences from several past radiological accidents (e.g., Fukushima Daiichi, Chernobyl, and Goiania) have shown that not only occupationally radiation-exposed adults but also children of all age groups can be affected by such an accident. Moreover, in asymmetric conflicts, children could possibly be affected by the explosion of a radiological dispersal device. Whole-body counting facilities all over the world are well prepared to measure children and adults but suffer in the case of newly born babies and toddlers owing to a lack of corresponding calibrations. A series of calibration phantoms representative of adult persons and larger children is commercially available or can be constructed from easily accessible materials, but for newly born and children up to 20 kg, only very few phantoms have been described which approximate the infantile body, and only very roughly. The aim of this work was to improve the current unsatisfactory situation. Two main steps toward achieving this aim included the compilation of anthropomorphic data for children and the construction of a modular set of calibration phantoms for the weight classes between 3 and 20 kg from simple, readily accessible, and cheap materials. The phantoms are made from commercially available freezer packs, which can be filled with a radionuclide solution. By using calibrations based on these purposely-built realistic weight class phantoms, internal radioactive contamination in children can be assessed more reliably than would be otherwise possible with the standard adult phantom calibrations.

Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography

Chemical staining of biological specimens is commonly utilised to boost contrast in soft tissue structures, but unambiguous identification of staining location and distribution is difficult without confirmation of the elemental signature, especially for chemicals of similar density contrast. Hyperspectral X-ray computed tomography (XCT) enables the non-destructive identification, segmentation and mapping of elemental composition within a sample. With the availability of hundreds of narrow, high resolution (~ 1 keV) energy channels, the technique allows the simultaneous detection of multiple contrast agents across different tissue structures. Here we describe a hyperspectral imaging routine for distinguishing multiple chemical agents, regardless of contrast similarity. Using a set of elemental calibration phantoms, we perform a first instance of direct stain concentration measurement using spectral absorption edge markers. Applied to a set of double- and triple-stained biological specimens, the study analyses the extent of stain overlap and uptake regions for commonly used contrast markers. An improved understanding of stain concentration as a function of position, and the interaction between multiple stains, would help inform future studies on multi-staining procedures, as well as enable future exploration of heavy metal uptake across medical, agricultural and ecological fields.

A Comparative Study on a Novel Quality Assessment Protocol Based on Image Analysis Methods for Color Doppler Ultrasound Diagnostic Systems.

Color Doppler (CD) imaging is widely used in diagnostics since it allows real-time detection and display of blood flow superimposed on the B-mode image. Nevertheless, to date, a shared worldwide standard on Doppler equipment testing is still lacking. In this context, the study herein proposed would give a contribution focusing on the combination of five test parameters to be included in a novel Quality Assessment (QA) protocol for CD systems testing. A first approach involving the use of the Kiviat diagram was investigated, assuming the diagram area, normalized with respect to one of the gold standards, as an index of the overall Doppler system performance. The QA parameters were obtained from the post-processing of CD data through the implementation of custom-written image analysis methods and procedures, here applied to three brand-new high-technology-level ultrasound systems. Experimental data were collected through phased and convex array probes, in two configuration settings, by means of a Doppler flow phantom set at different flow rate regimes. The outcomes confirmed that the Kiviat diagram might be a promising tool applied to quality controls of Doppler equipment, although further investigations should be performed to assess the sensitivity and specificity of the proposed approach.

Вычислительный фантом для дозиметрии красного костного мозга новорожденного ребенка от инкорпорированных бета-излучателей

Active (red) bone marrow (AM) exposure due to ingested bone-seeking radionuclides can lead to grave medical consequences. For example, a radioactive contamination of the Techa River in the 1950s caused exposure to AM for riverside residents and led to chronic radioactive exposure syndrome in some of them, with higher risk of leukemia. The main sources of the marrow exposure were the bone-seeking beta emitters 89,90Sr. Improving the dosimetry of AM internal exposure is an important step in clarifying the risks of chronic radiation exposure for riverside residents. To evaluate the energy absorbed by AM from incorporated 90Sr it is customary to use computational phantoms where radiation transport can be emulated. A phantom is a representative digital representation of skeletal bone geometry and AM The goal of this work was to develop a computational phantom of a newborn skeleton for dosimetry of AM from incorporated 90Sr. The researchers have used the Stochastic Parametric Skeletal Dosimetry method (SPSD), where hematopoietic sites were modeled as a set of phantoms of simple geometric shape describing individual skeletal bone areas. The AM content in the skeleton as well as the phantom parameters were evaluated on the basis of published measurements of real bones. As a result, a computational phantom of the main skeletal hematopoietic sites was generated for a newborn baby, including 34 phantoms of bone areas. The simulated phantom simulates the bone structure as well as the variability of skeletal parameters within the population and corresponds well to measurements of real bones.

Simulation of a tissue backscatter coefficient measurement experiment using the finite element method

Ultrasound has been shown to be an effective imaging modality for investigating tissue health. For example, the backscatter coefficient (BSC) has been shown to be a biomarker of tumour response to therapy but is limited in its application clinically due to the difficulty of accurately acquiring attenuation and diffraction corrections. To assess the variability in BSC assessment, we present a finite element tool simulating a singleelement planar reflector substitution method estimate of the BSC of a set of simulated phantoms. BSC estimates were computed with errors <5% of the theoretical value for a range of source apertures and over a set of phantoms with varying scatterer number densities to within 10%. In addition, the firstorder amplitude envelope statistics of backscattered waves were shown to be commensurate with Rayleigh scattering models. These results indicate that the tool is accurate in its replication of soft tissue-like scattering, suggesting that it could be an opportune testing ground for investigating sources of variability in BSC measurements and the testing of algorithms and hypotheses for interrogating the scattering behaviour of soft tissues.

Hybrid computational pregnant female phantom construction for radiation dosimetry applications

The number of patients undergoing diagnostic radiology and radiation therapy procedures has increased drastically owing to improvements in cancer diagnosis and treatment, and consequently, patient survival. However, the risk of secondary malignancies owing to radiation exposure remains a matter of concern. We previously published three hybrid computational fetal phantoms, which contained 27 fetal organs, as a starting point for developing the whole hybrid computational pregnant phantom set, which is the final objective of this study. An International Commission on Radiological Protection (ICRP) reference female voxel model was converted to a non-uniform rational B-spline (NURBS) surface model to construct a hybrid computational female phantom as a pregnant mother for each fetal model. Both fetal and maternal organs were matched with the ICRP- 89 reference data. To create a complete standard pregnant computational phantom set at 20, 30, and 35 weeks of pregnancy, the model mother’s reproductive organs were removed, and fetal phantoms with appropriate placental and uterine models were added to the female pelvis using a 3D-modeling software. With the aid of radiological image sets that had originally been used to construct the fetal models, each fetal position and rotation inside the uterus were carefully adjusted to represent the real fetal locations inside the uterus. The major abdominal soft tissue organs below the diaphragm, namely the small intestine, large intestine, liver, gall bladder, stomach, pancreas, uterus, and urinary bladder, were removed from non-pregnant females. The resulting fetal phantom was positioned in the appropriate location, matching the original radiological image sets. An obstetrician-gynecologist reviewed the complete internal anatomy of all fetus phantoms and the pregnant women for accuracy, and suggested changes were implemented as needed. The remaining female anatomical tissues were reshaped and modified to accommodate the location of the fetus inside the uterus. This new series of hybrid computational pregnant phantom models provides realistic anatomical details that can be useful in evaluating fetal radiation doses in pregnant patients undergoing diagnostic imaging or radiotherapy procedures where realistic fetal computational human phantoms are required.

Investigating the lesion detectability of Tc-99m planar scintigraphy acquired with LEHRS collimator for patients with different body sizes: A phantom study.

PurposeThe aim of this work was to investigate the lesion detectability of Tc‐99m planar scintigraphy acquired with a low‐energy high‐resolution and sensitivity (LEHRS) collimator and processed by Clarity 2D for patients with different body sizes through phantom study.MethodsA NEMA IEC body phantom set was covered by two layers of 25‐mm‐thick bolus to construct phantom in three different sizes. All image data were performed on a Discovery NM/CT 870 DR with an LEHRS collimator and processed by Clarity 2D with blend ratio a of 0%, 20%, 40%, 60%, 80%, and 100%. The lesion detectability in gamma scintigraphy was evaluated by calculating the contrast‐to‐noise ratio (CNR). Multiple linear regression methods were used to analyze the impact of body size, target size, and Clarity 2D blending weight on the lesion detectability of Tc‐99m planar scintigraphy.ResultsIt was found that changing the blend ratio could improve CNR, and this phenomenon was more significant in anterior view than in posterior view. Our results also suggested that the blend ratio should be selected according to patient body size in order to maintain consistent CNR. Hence, when a blend ratio of 60% was used for a patient before cancer treatment, a lower blend ratio should be used for the same patient experiencing treatment‐related weight loss to achieve consistent lesion detectability in Tc‐99m planar scintigraphy acquired with LEHRS and processed by Clarity 2D.ConclusionThe magnitude of photon attenuation and scattering is higher in patients with larger body size, so Tc‐99m planar scintigraphy usually has lower lesion detectability in obese patients. Although photon attenuation and scattering are inevitable during image formation, their impacts on image quality can be eased by employing appropriate image protocol parameters.

Fabrication of Customizable Intraplaque Hemorrhage Phantoms for Magnetic Resonance Imaging

PurposeMagnetic resonance (MR) imaging detection of methemoglobin, a molecular marker of intraplaque hemorrhage (IPH), in atherosclerotic plaque is a promising method of assessing stroke risk. However, the multicenter imaging studies required to further validate this technique necessitate the development of IPH phantoms to standardize images acquired across different scanners. This study developed a set of phantoms that modeled methemoglobin-laden IPH for use in MR image standardization.ProceduresA time-stable material mimicking the MR properties of methemoglobin in IPH was created by doping agarose hydrogel with gadolinium and sodium alginate. This material was used to create a phantom that consisted of 9 cylindrical IPH sites (with sizes from 1 to 8 mm). Anatomical replicas of IPH-positive atherosclerosis were also created using 3D printed molds. These plaque replicas also modeled other common plaque components including a lipid core and atheroma cap. T1 mapping and a magnetization-prepared rapid acquisition gradient echo (MPRAGE) carotid imaging protocol were used to assess phantom realism and long-term stability.ResultsCylindrical phantom IPH sites possessed a T1 time of 335 ± 51 ms and exhibited little change in size or MPRAGE signal intensity over 31 days; the mean (SD) magnitude of changes in size and signal were 6.4 % (2.7 %) and 7.3 % (6.7 %), respectively. IPH sites incorporated into complex anatomical plaque phantoms exhibited contrast comparable to clinical images.ConclusionsThe cylindrical IPH phantom accurately modeled the short T1 time characteristic of methemoglobin-laden IPH, with the IPH sites exhibiting little variation in imaging properties over 31 days. Furthermore, MPRAGE images of the anatomical atherosclerosis replicas closely matched those of clinical plaques. In combination, these phantoms will allow for IPH imaging protocol standardization and thus facilitate future multicenter IPH imaging.

Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors.

.SignificanceDiffuse optical tomography is an ill-posed problem. Combination with ultrasound can improve the results of diffuse optical tomography applied to the diagnosis of breast cancer and allow for classification of lesions.AimTo provide a simulation pipeline for the assessment of reconstruction and classification methods for diffuse optical tomography with concurrent ultrasound information.ApproachA set of breast digital phantoms with benign and malignant lesions was simulated building on the software VICTRE. Acoustic and optical properties were assigned to the phantoms for the generation of B-mode images and optical data. A reconstruction algorithm based on a two-region nonlinear fitting and incorporating the ultrasound information was tested. Machine learning classification methods were applied to the reconstructed values to discriminate lesions into benign and malignant after reconstruction.ResultsThe approach allowed us to generate realistic US and optical data and to test a two-region reconstruction method for a large number of realistic simulations. When information is extracted from ultrasound images, at least 75% of lesions are correctly classified. With ideal two-region separation, the accuracy is higher than 80%.ConclusionsA pipeline for the generation of realistic ultrasound and diffuse optics data was implemented. Machine learning methods applied to a optical reconstruction with a nonlinear optical model and morphological information permit to discriminate malignant lesions from benign ones.

Synthetic 4DCT(MRI) lung phantom generation for 4D radiotherapy and image guidance investigations.

PurposeRespiratory motion is one of the major challenges in radiotherapy. In this work, a comprehensive and clinically plausible set of 4D numerical phantoms, together with their corresponding “ground truths,” have been developed and validated for 4D radiotherapy applications.MethodsThe phantoms are based on CTs providing density information and motion from multi‐breathing‐cycle 4D Magnetic Resonance imagings (MRIs). Deformable image registration (DIR) has been utilized to extract motion fields from 4DMRIs and to establish inter‐subject correspondence by registering binary lung masks between Computer Tomography (CT) and MRI. The established correspondence is then used to warp the CT according to the 4DMRI motion. The resulting synthetic 4DCTs are called 4DCT(MRI)s. Validation of the 4DCT(MRI) workflow was conducted by directly comparing conventional 4DCTs to derived synthetic 4D images using the motion of the 4DCTs themselves (referred to as 4DCT(CT)s). Digitally reconstructed radiographs (DRRs) as well as 4D pencil beam scanned (PBS) proton dose calculations were used for validation.ResultsBased on the CT image appearance of 13 lung cancer patients and deformable motion of five volunteer 4DMRIs, synthetic 4DCT(MRI)s with a total of 871 different breathing cycles have been generated. The 4DCT(MRI)s exhibit an average superior–inferior tumor motion amplitude of 7 ± 5 mm (min: 0.5 mm, max: 22.7 mm). The relative change of the DRR image intensities of the conventional 4DCTs and the corresponding synthetic 4DCT(CT)s inside the body is smaller than 5% for at least 81% of the pixels for all studied cases. Comparison of 4D dose distributions calculated on 4DCTs and the synthetic 4DCT(CT)s using the same motion achieved similar dose distributions with an average 2%/2 mm gamma pass rate of 90.8% (min: 77.8%, max: 97.2%).ConclusionWe developed a series of numerical 4D lung phantoms based on real imaging and motion data, which give realistic representations of both anatomy and motion scenarios and the accessible “ground truth” deformation vector fields of each 4DCT(MRI). The open‐source code and motion data allow foreseen users to generate further 4D data by themselves. These numeric 4D phantoms can be used for the development of new 4D treatment strategies, 4D dose calculations, DIR algorithm validations, as well as simulations of motion mitigation and different online image guidance techniques for both proton and photon radiation therapy.

Quality and dose optimization in canine chest radiography using a digital radiography system

Radiography is becoming increasingly popular as a diagnostic tool in veterinary radiology services. During radiological procedures, containment of the animal is often necessary to ensure a safe examination. This work aimed to establish a methodology to optimize canine radiography protocols in canines of different sizes. Here, was used a set of four homogeneous canine chest phantoms with two different views: dorsoventral and lateral. The authors developed this set of homogeneous canine chest phantoms previously. Effective detective quantum efficiency and entrance skin dose for each test technique was estimated. Then, all test techniques were applied to canine subjects. This allowed subjective evaluations through visual grading analysis (VGA), by the veterinary radiologists. The VGA chooses which test techniques include relevant structures to ensure a medical diagnosis, selecting them as optimized techniques. Based on the ALARA (As Low As Reasonably Achievable) principle, a set of gold standard techniques was chosen among all optimized techniques. Physical parameters were used to identify the image quality of the gold standard for all sizes of patients (small-S, medium-M, large-L, and Giant-G). This work presented a new methodology to optimize digital radiography in veterinary procedures. This optimization process provides a better risk-benefit ratio for professionals and animal owners and a better cost-benefit ratio for radiology services. This work can be performed on any radiological equipment or other anatomical structures.

Deep-learning based image reconstruction for MRI-guided near-infrared spectral tomography.

Non-invasive near-infrared spectral tomography (NIRST) can incorporate the structural information provided by simultaneous magnetic resonance imaging (MRI), and this has significantly improved the images obtained of tissue function. However, the process of MRI guidance in NIRST has been time consuming because of the needs for tissue-type segmentation and forward diffuse modeling of light propagation. To overcome these problems, a reconstruction algorithm for MRI-guided NIRST based on deep learning is proposed and validated by simulation and real patient imaging data for breast cancer characterization. In this approach, diffused optical signals and MRI images were both used as the input to the neural network, and simultaneously recovered the concentrations of oxy-hemoglobin, deoxy-hemoglobin, and water via end-to-end training by using 20,000 sets of computer-generated simulation phantoms. The simulation phantom studies showed that the quality of the reconstructed images was improved, compared to that obtained by other existing reconstruction methods. Reconstructed patient images show that the well-trained neural network with only simulation data sets can be directly used for differentiating malignant from benign breast tumors.

Reduction of Compton Background Noise for X-ray Fluorescence Computed Tomography with Deep Learning

For bench-top X-ray fluorescence computed tomography (XFCT), the X-ray tube source will bring extreme Compton background noise, resulting in a low signal-to-noise ratio and low contrast detection limit. In this paper, a noise2noise denoising algorithm based on the UNet deep learning network is proposed. The network can use noise image learning to convert the noise image into a clean image. Two sets of phantoms (high concentration Gd phantom and low concentration Bi phantom) are used for scanning to simulate the imaging process under different noise levels and generate the required data set. Additionally, the data set is generated by Geant4 simulation. In the training process, the L1 loss function is used for its good convergence. The image quality is evaluated according to CNR and pixel profile, which shows that our algorithm is better than BM3D, both visually and quantitatively.

Validation of 4D Flow based relative pressure maps in aortic flows.

While the clinical gold standard for pressure difference measurements is invasive catheterization, 4D Flow MRI is a promising tool for enabling a non-invasive quantification, by linking highly spatially resolved velocity measurements with pressure differences via the incompressible Navier-Stokes equations. In this work we provide a validation and comparison with phantom and clinical patient data of pressure difference maps estimators. We compare the classical Pressure Poisson Estimator (PPE) and the new Stokes Estimator (STE) against catheter pressure measurements under a variety of stenosis severities and flow intensities. Specifically, we use several 4D Flow data sets of realistic aortic phantoms with different anatomic and hemodynamic severities and two patients with aortic coarctation. The phantom data sets are enriched by subsampling to lower resolutions, modification of the segmentation and addition of synthetic noise, in order to study the sensitivity of the pressure difference estimators to these factors. Overall, the STE method yields more accurate results than the PPE method compared to catheterization data. The superiority of the STE becomes more evident at increasing Reynolds numbers with a better capacity of capturing pressure gradients in strongly convective flow regimes. The results indicate an improved robustness of the STE method with respect to variation in lumen segmentation. However, with heuristic removal of the wall-voxels, the PPE can reach a comparable accuracy for lower Reynolds' numbers.

IPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and Its Application to CT Organ Dosimetry.

This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Given a volume of patient CT images, iPhantom segments selected anchor organs and structures (e.g., liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs which are challenging to segment (e.g., intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting digital-twin phantoms are used to assess organ doses during routine CT exams. iPhantom was validated on both with a set of XCAT digital phantoms (n = 50) and an independent clinical dataset (n = 10) with similar accuracy. iPhantom precisely predicted all organ locations yielding Dice Similarity Coefficients (DSC) 0.6 - 1 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed <10% errors in estimated radiation dose for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors). iPhantom enables automated and accurate creation of patient-specific phantoms and, for the first time, provides sufficient and automated patient-specific dose estimates for CT dosimetry. The new framework brings the creation and application of CHPs (computational human phantoms) to the level of individual CHPs through automation, achieving wide and precise organ localization, paving the way for clinical monitoring, personalized optimization, and large-scale research.

Speed-of-sound imaging using diverging waves

Purpose.Due to its safe, low-cost, portable, and real-time nature, ultrasound is a prominent imaging method in computer-assisted interventions. However, typical B-mode ultrasound images have limited contrast and tissue differentiation capability for several clinical applications.Methods.Recent introduction of imaging speed-of-sound (SoS) in soft tissues using conventional ultrasound systems and transducers has great potential in clinical translation providing additional imaging contrast, e.g., in intervention planning, navigation, and guidance applications. However, current pulse-echo SoS imaging methods relying on plane wave (PW) sequences are highly prone to aberration effects, therefore suboptimal in image quality. In this paper we propose using diverging waves (DW) for SoS imaging and study this comparatively to PW.Results.We demonstrate wavefront aberration and its effects on the key step of displacement tracking in the SoS reconstruction pipeline, comparatively between PW and DW on a synthetic example. We then present the parameterization sensitivity of both approaches on a set of simulated phantoms. Analyzing SoS imaging performance comparatively indicates that using DW instead of PW, the reconstruction accuracy improves by over 20% in root-mean-square-error (RMSE) and by 42% in contrast-to-noise ratio (CNR). We then demonstrate SoS reconstructions with actual US acquisitions of a breast phantom. With our proposed DW, CNR for a high contrast tumor-representative inclusion is improved by 42%, while for a low contrast cyst-representative inclusion a 2.8-fold improvement is achieved.Conclusion.SoS imaging, so far only studied using a plane wave transmission scheme, can be made more reliable and accurate using DW. The high imaging contrast of DW-based SoS imaging will thus facilitate the clinical translation of the method and utilization in computer-assisted interventions such as ultrasound-guided biopsies, where B-Mode contrast is often to low to detect potential lesions.

Evaluation of the Accuracy of Standardized Uptake Values of 18F-fluorodeoxyglucose in Lung Lesions Based on Phantom Studies.

The aim of the study was to estimate the accuracy of standardized uptake values of 18F-fluorodeoxyglucose (18F-FDG) in lung lesions during positron emission tomography combined with computed tomography (PET/CT) imaging, based on phantom studies performed for different PET/CT scanners.Materials and MethodsThe analysis of the PET/CT with 18F-FDG data was performed for 86 patients newly diagnosed with the lung lesions: malignant tumors (n=37), benign tumors and inflammatory diseases (n=49). The criteria for inclusion in the study were developed considering the recommendations of the Fleischner Society (2017). The characteristics of the lesions on CT met the following requirements: a round shape or close to it; total size of 8 to 30 mm; solid or subsolid structure (with the exception of lesion with ground-glass opacity); a solid part size of ≥8 mm. All the patients had no signs of pleurisy, lymphadenopathy, or cancer history. PET/CT imaging with 18F-FDG was performed with three scanners: Discovery 690 (General Electric, USA), Biograph mCT 128 (Siemens, Germany), and Biograph mCT 40 (Siemens); the preparation of patients prior to the scan was standardized. To determine the reference accumulation of a radiopharmaceutical in the pathological lesion, four scans of a specialized NEMA IEC PET Body Phantom Set (USA) were performed for each scanner. For each unit, the recovery coefficients (RCs) of radioactivity, maximum and recovered (corrected) standardized uptake values (SUVs) were determined. Statistical relationship between the size of lesions, SUVmax and SUVcorrect was evaluated. Data processing was performed using MedCalc v. 19.2.0 software.ResultsDuring the phantom study, the underestimation of the radioactivity was determined in the spheres with the diameters of 10 and 13 mm, overestimation was observed in the sphere with the diameter of 28 mm. Both underestimation and overestimation of radioactivity were determined for the spheres with a diameter of 17 and 22 mm.SUVmax differed from the reference values for 85 patients (98.8%). The underestimation of these values was found for 63 patients (73.2%) due to the partial volume effect. The greatest underestimation was observed for the patients with 8 mm diameter lesions. Depending on the scanner, the underestimation of the SUVmax in these patients reached up to 54–73%. For 9 patients (25%) with malignant tumors of 9–12 mm, the utility of RC made it possible to avoid false negative results. For the lesions with a diameter of 30 mm, an overestimation of SUVmax up to 22% was determined due to the negative influence of the reconstruction algorithms.ConclusionThe use of RC eliminates the influence of the partial volume effect and reconstruction methods on the accuracy of estimating the SUVmax in lung lesions, which ensures reproducibility, increase in the information content of the method, as well as the comparability of the results of PET/CT with 18F-FDG obtained on the different models of PET/CT units with different technological characteristics.

Diffusion-informed spatial smoothing of fMRI data in white matter using spectral graph filters

Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detectability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject’s unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project’s 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.

Narrowband diffuse reflectance spectroscopy in the 900-1000 nm wavelength region to quantify water and lipid content of turbid media.

We report a narrow wavelength band diffuse reflectance spectroscopy (nb-DRS) method to determine water and fat ratios of scattering media in the 900-1000 nm range. This method was shown to be linearly correlated with absolute water and fat concentrations as tested on a set of turbid emulsion phantoms with a range of water and lipid compositions. Robustness to scattering assumptions was demonstrated and compared against measured scattering by a frequency-domain photon migration system. nb-DRS was also tested on ex-vivo porcine samples and compared against direct tissue water extraction by analytical chemistry methods. We speculate nb-DRS has potential applications in portable devices such as clinical and digital health wearables.

Learning Optical Scattering Through Symmetrical Orthogonality Enforced Independent Components for Unmixing Deep Tissue Photoacoustic Signals

Noninvasive mapping of chromophore distribution in deep tissue is highly desired in numerous biomedical applications. Multispectral photoacoustic (PA) imaging fused with linear spectral unmixing is employed to generate 2-D and 3-D maps of tissue chromophores. However, wavelength and depth dependent attenuation of optical fluence leads to nonuniform variations in the spectral behavior of biomolecules affecting the accuracy of conventional linear unmixing methods. To address this, a modified independent component analysis (ICA) model is proposed to construct adaptive and statistically independent components for each chromophore from the given spectral PA data. The model was trained with custom designed multispectral PA imaging simulations consisting of both endogenous (oxy- and deoxy-hemoglobin) and exogenous (indocyanine green - ICG) chromophores inside 30 mm deep tissue. End-to-end unsupervised nature of the proposed approach made the process independent of human labeling and outperformed the standard linear spectral unmixing method when tested on a set of simulated phantoms, experimental phantoms, and in vivo mouse imaging data.