We aimed at reviewing design and realisation of perfusion/flow phantoms for validating quantitative perfusion imaging (PI) applications to encourage best practices.

Tissue-equivalent phantoms that mimic the optical properties of human and animal tissues are commonly used in diffuse optical imaging research to characterize instrumentation or evaluate an image reconstruction method. Although many recipes have been produced for generating solid phantoms with specified absorption and transport scattering coefficients at visible and near-infrared wavelengths, the construction methods are generally time-consuming and are unable to create complex geometries. We present a method of generating phantoms using a standard 3D printer. A simple recipe was devised which enables printed phantoms to be produced with precisely known optical properties. To illustrate the capability of the method, we describe the creation of an anatomically accurate, tissue-equivalent premature infant head optical phantom with a hollow brain space based on MRI atlas data. A diffuse optical image of the phantom is acquired when a high contrast target is inserted into the hollow space filled with an aqueous scattering solution.

Multicenter clinical trials that use positron emission tomography (PET) imaging frequently rely on stable bias in imaging biomarkers to assess drug effectiveness. Many well-documented factors cause variability in PET intensity values. Two of the largest scanner-dependent errors are scanner calibration and reconstructed image resolution variations. For clinical trials, an increase in measurement error significantly increases the number of patient scans needed. We aim to provide a robust quality assurance system using portable PET/computed tomography "pocket" phantoms and automated image analysis algorithms with the goal of reducing PET measurement variability. A set of the "pocket" phantoms was scanned with patients, affixed to the underside of a patient bed. Our software analyzed the obtained images and estimated the image parameters. The analysis consisted of 2 steps, automated phantom detection and estimation of PET image resolution and global bias. Performance of the algorithm was tested under variations in image bias, resolution, noise, and errors in the expected sphere size. A web-based application was implemented to deploy the image analysis pipeline in a cloud-based infrastructure to support multicenter data acquisition, under Software-as-a-Service (SaaS) model. The automated detection algorithm localized the phantom reliably. Simulation results showed stable behavior when image properties and input parameters were varied. The PET "pocket" phantom has the potential to reduce and/or check for standardized uptake value measurement errors.

Tissue-mimicking phantoms are widely used for the calibration, evaluation and standardisation of medical imaging systems, and for clinical training. For photoacoustic imaging, tissue-mimicking materials (TMMs) that have tuneable optical and acoustic properties, high stability, and mechanical robustness are highly desired. In this study, gel wax is introduced as a TMM that satisfies these criteria for developing photoacoustic imaging phantoms. The reduced scattering and optical absorption coefficients were independently tuned with the addition of TiO2 and oil-based inks. The frequency-dependent acoustic attenuation obeyed a power law; for native gel wax, it varied from 0.71 dB/cm at 3 MHz to 9.93 dB/cm at 12 MHz. The chosen oil-based inks, which have different optical absorption spectra in the range of 400 to 900 nm, were found to have good photostability under pulsed illumination with photoacoustic excitation light. Optically heterogeneous phantoms that comprised of inclusions with different concentrations of carbon black and coloured inks were fabricated, and multispectral photoacoustic imaging was performed with an optical parametric oscillator and a planar Fabry-Pérot sensor. We conclude that gel wax is well suited as a TMM for multispectral photoacoustic imaging.

The increasing availability of rodent models of human cardiovascular disease has led to a need to translate noninvasive imaging techniques such as magnetic resonance imaging (MRI) from the clinic to the animal laboratory. The aim of this study was to develop phantoms simulating the short-axis view of left ventricular motion of rats and mice, thus reducing the need for live animals in the development of MRI. Cylindrical phantoms were moulded from polyvinyl alcohol (PVA) Cryogel and attached via stiff water-filled tubing to a gear pump. Pulsatile distension of the phantoms was effected by suitable programming of the pump. Cine MRI scanning was carried out at 7 T and compared with in vivo rodent cardiac imaging. Suitable pulsatile performance was achieved with phantoms for which the PVA material had been subjected to two freeze-thaw cycles, resulting in T1 and T2 relaxation time constants of 1656±124 ms and 55±10 ms, respectively. For the rat phantom operating at 240 beats per min (bpm), the dynamic range of the outer diameter was from 10.3 to 12.4 mm with the wall thickness varying between 1.9 and 1.2 mm. Corresponding figures for the mouse phantom at 480 bpm were outer diameter range from 5.4 to 6.4 mm and wall thickness from 1.5 to 1.2 mm. Dynamic cardiac phantoms simulating rodent left ventricular motion in the short-axis view were successfully developed and compared with in vivo imaging. The phantoms can be used for future development work with reduced need of live animals.

As photoacoustic imaging (PAI) begins to mature and undergo clinical translation, there is a need for well-validated, standardized performance test methods to support device development, quality control, and regulatory evaluation. Despite recent progress, current PAI phantoms may not adequately replicate tissue light and sound transport over the full range of optical wavelengths and acoustic frequencies employed by reported PAI devices. Here we introduce polyacrylamide (PAA) hydrogel as a candidate material for fabricating stable phantoms with well-characterized optical and acoustic properties that are biologically relevant over a broad range of system design parameters. We evaluated suitability of PAA phantoms for conducting image quality assessment of three PAI systems with substantially different operating parameters including two commercial systems and a custom system. Imaging results indicated that appropriately tuned PAA phantoms are useful tools for assessing and comparing PAI system image quality. These phantoms may also facilitate future standardization of performance test methodology.

The authors previously developed the 4D extended cardiac-torso (XCAT) phantom for multimodality imaging research. The XCAT consisted of highly detailed whole-body models for the standard male and female adult, including the cardiac and respiratory motions. In this work, the authors extend the XCAT beyond these reference anatomies by developing a series of anatomically variable 4D XCAT adult phantoms for imaging research, the first library of 4D computational phantoms.

We previously developed a set of highly detailed 4D reference pediatric extended cardiac-torso (XCAT) phantoms at ages of newborn, 1, 5, 10, and 15 yr with organ and tissue masses matched to ICRP Publication 89 values. In this work, we extended this reference set to a series of 64 pediatric phantoms of varying age and height and body mass percentiles representative of the public at large. The models will provide a library of pediatric phantoms for optimizing pediatric imaging protocols.

The current work features process parameters for the ultrasound (25 kHz)-assisted fabrication of polydopamine-shelled perfluorocarbon (PDA/PFC) emulsion droplets with bimodal (modes at 100-600 nm and 1-6 µm) and unimodal (200-600 nm) size distributions. Initial screening of these materials revealed that only PDA/PFC emulsion droplets with bimodal distributions showed photoacoustic signal enhancement due to large size of their optically absorbing PDA shells. Performance of this particular type of emulsion droplets as photoacoustic agents were evaluated in Intralipid®-India ink media, mimicking the optical scattering and absorbanceof various tissuetypes. From these measurements, it was observed that PDA/PFC droplets with bimodal size distributions can enhance the photoacoustic signal of blood-mimicking phantom by up to five folds in various tissue-mimicking phantoms with absorption coefficients from 0.1 to 1.0 cm-1. Furthermore, using the information from enhanced photoacoustic images at 750 nm, the ultimate imaging depth was explored for polydopamine-shelled, perfluorohexane (PDA/PFH) emulsion droplets by photon trajectory simulations in 3D using a Monte Carlo approach. Based on these simulations, maximal tissue imaging depths for PDA/PFH emulsion droplets range from 10 to 40 mm, depending on the tissue type. These results demonstrate for the first time that ultrasonically fabricated PDA/PFC emulsion droplets have great potential as photoacoustic imaging agents that can be complemented with other reported characteristics of PDA/PFC emulsion droplets for extended applications in theranostics and other imaging modalities.

Interventional fluorescence imaging is increasingly being utilized to quantify cancer biomarkers in both clinical and preclinical models, yet absolute quantification is complicated by many factors. The use of optical phantoms has been suggested by multiple professional organizations for quantitative performance assessment of fluorescence guidance imaging systems. This concept can be further extended to provide standardized tools to compare and assess image analysis metrics.

Additive manufacturing or 3-dimensional printing has become a widespread technology with many applications in medicine. We have conducted a systematic review of its application in radiation oncology with a particular emphasis on the creation of phantoms for image quality assessment and radiation dosimetry. Traditionally used phantoms for quality assurance in radiotherapy are often constraint by simplified geometry and homogenous nature to perform imaging analysis or pretreatment dosimetric verification. Such phantoms are limited due to their ability in only representing the average human body, not only in proportion and radiation properties but also do not accommodate pathological features. These limiting factors restrict the patient-specific quality assurance process to verify image-guided positioning accuracy and/or dose accuracy in "water-like" condition.

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that have realistic image texture and densities, which are critical in evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized stone-based filament to increase Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in texture and contrast. Measured differences between patient and phantom were less than 15 HU for soft tissue and bone marrow. The stone-based filament accurately represented bony tissue structures across different X-ray energies, as measured by spectral CT. In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

To test whether our proposed denoising approach with deep learning-based reconstruction (dDLR) can effectively denoise brain MR images.

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that have realistic image texture and densities, which are critical in evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized stone-based filament to increase Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in texture and contrast. Measured differences between patient and phantom were less than 15 HU for soft tissue and bone marrow. The stone-based filament accurately represented bony tissue structures across different X-ray energies, as measured by spectral CT. In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that possess accurate densities and exhibit visually realistic image textures. These qualities are crucial for evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized calcium-doped filament to increase the Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility, and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in visual texture and contrast. Micro-CT analysis revealed minimal variations between prints, with an overall deviation of ± 0.8% in filament line spacing and ± 0.022 mm in line width. Measured differences between patient and phantom were less than 12 HU for soft tissue and 15 HU for bone marrow, and 514 HU for cortical bone. The calcium-doped filament accurately represented bony tissue structures across different X-ray energies in spectral CT (RMSE ranging from ± 3 to ± 28 HU, compared to 400 mg/ml hydroxyapatite). In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

The aim of this study was to establish the repeatability measures of quantitative Gaussian and non-Gaussian diffusion metrics using diffusion-weighted imaging (DWI) data from phantoms and patients with head-and-neck and papillary thyroid cancers. The Quantitative Imaging Biomarker Alliance (QIBA) DWI phantom and a novel isotropic diffusion kurtosis imaging phantom were scanned at 3 different sites, on 1.5T and 3T magnetic resonance imaging systems, using standardized multiple b-value DWI acquisition protocol. In the clinical component of this study, a total of 60 multiple b-value DWI data sets were analyzed for test-retest, obtained from 14 patients (9 head-and-neck squamous cell carcinoma and 5 papillary thyroid cancers). Repeatability of quantitative DWI measurements was assessed by within-subject coefficient of variation (wCV%) and Bland-Altman analysis. In isotropic diffusion kurtosis imaging phantom vial with 2% ceteryl alcohol and behentrimonium chloride solution, the mean apparent diffusion (Dapp × 10-3 mm2/s) and kurtosis (Kapp, unitless) coefficient values were 1.02 and 1.68 respectively, capturing in vivo tumor cellularity and tissue microstructure. For the same vial, Dapp and Kapp mean wCVs (%) were ≤1.41% and ≤0.43% for 1.5T and 3T across 3 sites. For pretreatment head-and-neck squamous cell carcinoma, apparent diffusion coefficient, D, D*, K, and f mean wCVs (%) were 2.38%, 3.55%, 3.88%, 8.0%, and 9.92%, respectively; wCVs exhibited a higher trend for papillary thyroid cancers. Knowledge of technical precision and bias of quantitative imaging metrics enables investigators to properly design and power clinical trials and better discern between measurement variability versus biological change.

Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) provides a novel method for analyzing biomolecule concentrations in tissues without exogenous contrast agents. Despite its potential, achieving a high signal-to-noise ratio (SNR) is imperative for detecting small CEST effects. Traditional metrics such as Magnetization Transfer Ratio Asymmetry (MTRasym) and Lorentzian analyses are vulnerable to image noise, hampering their precision in quantitative concentration estimations. Recent noise-reduction algorithms like principal component analysis (PCA), nonlocal mean filtering (NLM), and block matching combined with 3D filtering (BM3D) have shown promise, as there is a burgeoning interest in the utilization of neural networks (NNs), particularly autoencoders, for imaging denoising. This study uses the Bloch-McConnell equations, which allow for the synthetic generation of CEST images and explores NNs efficacy in denoising these images. Using synthetically generated phantoms, autoencoders were created, and their performance was compared with traditional denoising methods using various datasets. The results underscored the superior performance of NNs, notably the ResUNet architectures, in noise identification and abatement compared to analytical approaches across a wide noise gamut. This superiority was particularly pronounced at elevated noise intensities in the in vitro data. Notably, the neural architectures significantly improved the PSNR values, achieving up to 35.0, while some traditional methods struggled, especially in low-noise reduction scenarios. However, the application to the in vivo data presented challenges due to varying noise profiles. This study accentuates the potential of NNs as robust denoising tools, but their translation to clinical settings warrants further investigation.

Gold nanoclusters (AuNCs) have attracted extensive attention as light-emissive materials with unique advantages such as high photostability, large Stoke shifts and low toxicity. However, a better understanding of their solid-state photoluminescence properties is still needed. Herein, we investigated for the first time the intrinsic photoluminescence properties of lyophilized bovine serum albumin stabilized AuNCs (BSA-AuNCs) via fluorescence lifetime imaging microscopy (FLIM) studies performed under both one and two photon excitations (OPE and TPE) on individual microflakes, combined with fluorescence spectroscopic investigations. Both in solution and solid-state, the synthesized BSA-AuNCs exhibit photoluminescence in the first biological window with an absolute quantum yield of 6% and high photostability under continuous irradiation. Moreover, under both OPE and TPE conditions, solid BSA-AuNCs samples exhibited a low degree of photobleaching, while FLIM assays prove the homogeneous distribution of the photoluminescence signal inside the microflakes. Finally, we demonstrate the ability of BSA-AuNCs to perform as reliable bright and photostable contrast agents for the visualization of cancer tissue mimicking agarose-phantoms using FLIM approach under non-invasive TPE. Therefore, our results emphasize the great potential of the as synthesized BSA-AuNCs for ex vivo and in vivo non-invasive NIR imaging applications.

Introduction: 3D printed patient-specific vascular phantoms provide superior anatomical insights for simulating complex endovascular procedures. Currently, lack of exposure to the technology poses a barrier for adoption. We offer an accessible, low-cost guide to producing vascular anatomical models using routine CT angiography, open source software packages and a variety of 3D printing technologies. Methods: Although applicable to all vascular territories, we illustrate our methodology using Abdominal Aortic Aneurysms (AAAs) due to the strong interest in this area. CT aortograms acquired as part of routine care were converted to representative patient-specific 3D models, and then printed using a variety of 3D printing technologies to assess their material suitability as aortic phantoms. Depending on the technology, phantoms cost $20-$1,000 and were produced in 12-48 h. This technique was used to generate hollow 3D printed thoracoabdominal aortas visible under fluoroscopy. Results: 3D printed AAA phantoms were a valuable addition to standard CT angiogram reconstructions in the simulation of complex cases, such as short or very angulated necks, or for positioning fenestrations in juxtarenal aneurysms. Hollow flexible models were particularly useful for device selection and in planning of fenestrated EVAR. In addition, these models have demonstrated utility other settings, such as patient education and engagement, and trainee and anatomical education. Further study is required to establish a material with optimal cost, haptic and fluoroscopic fidelity. Conclusion: We share our experiences and methodology for developing inexpensive 3D printed vascular phantoms which despite material limitations, successfully mimic the procedural challenges encountered during live endovascular surgery. As the technology continues to improve, 3D printed vascular phantoms have the potential to disrupt how endovascular procedures are planned and taught.

Fiber tractography is widely used to non-invasively map white-matter bundles in vivo using diffusion-weighted magnetic resonance imaging (dMRI). As it is the case for all scientific methods, proper validation is a key prerequisite for the successful application of fiber tractography, be it in the area of basic neuroscience or in a clinical setting. It is well-known that the indirect estimation of the fiber tracts from the local diffusion signal is highly ambiguous and extremely challenging. Furthermore, the validation of fiber tractography methods is hampered by the lack of a real ground truth, which is caused by the extremely complex brain microstructure that is not directly observable non-invasively and that is the basis of the huge network of long-range fiber connections in the brain that are the actual target of fiber tractography methods. As a substitute for in vivo data with a real ground truth that could be used for validation, a widely and successfully employed approach is the use of synthetic phantoms. In this work, we are providing an overview of the state-of-the-art in the area of physical and digital phantoms, answering the following guiding questions: "What are dMRI phantoms and what are they good for?", "What would the ideal phantom for validation fiber tractography look like?" and "What phantoms, phantom datasets and tools used for their creation are available to the research community?". We will further discuss the limitations and opportunities that come with the use of dMRI phantoms, and what future direction this field of research might take.