X-ray Imaging Modalities Research Articles

Radiation shielding transmission data for modern digital radiography.

Radiography is one of the most widely used x-ray imaging modalities. In National Council on Radiation Protection and Measurements (NCRP) Report No. 147, transmission data for radiographic systems were evaluated on those installed before 2000. For x-ray systems (except intraoral dental) manufactured on or after June 10, 2006, the U.S. required minimum half-value layer (HVL) was increased; for example, 2.9 (2.3) mm Al at 80kV, where the value in parenthesis denotes the earlier requirement before the above date. To calculate the transmission of the broad x-ray beam of modern digital radiography (DR) through shielding materials. X-ray beam HVLs on two DR systems (Agfa DR 600, GE Revolution XR/d) were measured in 10kV increments between 60 and 120kV, with a calibrated ionization chamber (Radcal model 10×5-60) and varying thickness of aluminum 1100 plates. Monte Carlo (Geant4) simulation was performed to calculate the transmission of broad x-ray beams through lead, concrete, gypsum, and steel, with x-ray HVLs matching those of the DR 600 at two beam filtrations (default, 1mm Al plus 0.1mm Cu added filtration). The transmission data were fitted to the Archer equation. HVLs on two DR systems with default beam filtration were consistent (median difference, 2.1%; maximum difference, 5.5%). An additional beam filtration option (1mm Al plus 0.1mm Cu) on the DR 600 substantially increased HVLs by 45.2%-61.2%. Transmission fitting parameters were provided for seven tube voltages (60-120kV) at two beam filtrations. This work presents transmission data for modern DR systems, indicating increased x-ray beam filtration compared to the primary x-ray beam in NCRP Report No. 147. The updated transmission data can enhance structural shielding evaluations at individual tube voltages or with a workload distribution.

3D Printing Materials Mimicking Human Tissues after Uptake of Iodinated Contrast Agents for Anthropomorphic Radiology Phantoms.

(1) Background: 3D printable materials with accurately defined iodine content enable the development and production of radiological phantoms that simulate human tissues, including lesions after contrast administration in medical imaging with X-rays. These phantoms provide accurate, stable and reproducible models with defined iodine concentrations, and 3D printing allows maximum flexibility and minimal development and production time, allowing the simulation of anatomically correct anthropomorphic replication of lesions and the production of calibration and QA standards in a typical medical research facility. (2) Methods: Standard printing resins were doped with an iodine contrast agent and printed using a consumer 3D printer, both (resins and printer) available from major online marketplaces, to produce printed specimens with iodine contents ranging from 0 to 3.0% by weight, equivalent to 0 to 3.85% elemental iodine per volume, covering the typical levels found in patients. The printed samples were scanned in a micro-CT scanner to measure the properties of the materials in the range of the iodine concentrations used. (3) Results: Both mass density and attenuation show a linear dependence on iodine concentration (R2 = 1.00), allowing highly accurate, stable, and predictable results. (4) Conclusions: Standard 3D printing resins can be doped with liquids, avoiding the problem of sedimentation, resulting in perfectly homogeneous prints with accurate dopant content. Iodine contrast agents are perfectly suited to dope resins with appropriate iodine concentrations to radiologically mimic tissues after iodine uptake. In combination with computer-aided design, this can be used to produce printed objects with precisely defined iodine concentrations in the range of up to a few percent of elemental iodine, with high precision and anthropomorphic shapes. Applications include radiographic phantoms for detectability studies and calibration standards in projective X-ray imaging modalities, such as contrast-enhanced dual energy mammography (abbreviated CEDEM, CEDM, TICEM, or CESM depending on the equipment manufacturer), and 3-dimensional modalities like CT, including spectral and dual energy CT (DECT), and breast tomosynthesis.

20 µm resolution multipixel ghost imaging with high-energy x-rays

Hard x-ray imaging is indispensable across diverse fields owing to its high penetrability. However, the resolution of traditional x-ray imaging modalities, such as computed tomography (CT) systems, is constrained by factors including beam properties, the limitations of optical components, and detection resolution. As a result, the typical resolution in commercial imaging systems that provide full-field imaging is limited to a few hundred microns, and scanning CT systems are too slow for many applications. This study advances high-photon-energy imaging by extending the concept of computational ghost imaging to multipixel ghost imaging with x-rays. We demonstrate a remarkable resolution of approximately 20 µm for an image spanning 0.9 by 1 cm2, comprised of 400,000 pixels and involving only 1000 realizations. Furthermore, we present a high-resolution CT reconstruction using our method, revealing enhanced visibility and resolution. Our achievement is facilitated by an innovative x-ray lithography technique and the computed tiling of images captured by each detector pixel. Importantly, this method maintains reasonable timeframes and can be scaled up for larger images without sacrificing the short measurement time, thereby opening intriguing possibilities for noninvasive high-resolution imaging of small features that are invisible with the present modalities.

RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison with CNN Transformer and Quantum Computing.

The aim and objective of the research are to develop an automated diagnosis system for the prediction of rheumatoid arthritis (RA) based on artificial intelligence (AI) and quantum computing for hand radiographs and thermal images. The hand radiographs and thermal images were segmented using a UNet++ model and color-based k-means clustering technique, respectively. The attributes from the segmented regions were generated using the Speeded-Up Robust Features (SURF) feature extractor and classification was performed using k-star and Hoeffding classifiers. For the ground truth and the predicted test image, the study utilizing UNet++ segmentation achieved a pixel-wise accuracy of 98.75%, an intersection over union (IoU) of 0.87, and a dice coefficient of 0.86, indicating a high level of similarity. The custom RA-X-ray thermal imaging (XTNet) surpassed all the models for the detection of RA with a classification accuracy of 90% and 93% for X-ray and thermal imaging modalities, respectively. Furthermore, the study employed quantum support vector machine (QSVM) as a quantum computing approach which yielded an accuracy of 93.75% and 87.5% for the detection of RA from hand X-ray and thermal images. In addition, vision transformer (ViT) was employed to classify RA which obtained an accuracy of 80% for hand X-rays and 90% for thermal images. Thus, depending on the performance measures, the RA-XTNet model can be used as an effective automated diagnostic method to diagnose RA accurately and rapidly in hand radiographs and thermal images.

Covid-19 Diagnosis using Deep Learning Approaches: A Systematic Review

The utilisation of deep learning techniques has witnessed a surge in popularity within the realm of medical image analysis, particularly in the context of identifying COVID-19. Following the occurrence of the COVID-19 pandemic, extensive investigations have been conducted to identify the existence of Sars-Cov-2 through the utilisation of several deep learning algorithms. The objective of this study is to conduct a comprehensive review of deep learning techniques utilised for the detection of COVID-19. "Can deep learning methodologies serve as a viable substitute for radiologists in the diagnostic process of COVID-19?" is the research inquiry. In order to compile research articles for the purpose of conducting a systematic review, two scientific databases were employed as primary sources. Databases such as PubMed and IEEE Xplore have been utilised for this purpose till January 2022. The published studies were examined in accordance with the PRISMA guidelines. The study established predetermined criteria for exclusion and inclusion, and subsequently identified relevant works based on these criteria. The findings indicated that a total of 543 out of the 634 articles that were initially retrieved were excluded due to their lack of conformity with the predetermined criteria. Conversely, 87 articles met the inclusion criteria and were retained for further analysis. The research articles presented in this compilation are categorised into three distinct groups: the types of visual representations utilised, the methods employed for applying deep learning techniques, and the programming languages that are most frequently utilised. The exclusive reliance on deep learning algorithms is insufficient for substituting the visual diagnostic performed by physicians and radiologists in the detection of COVID-19. Due to the lack of substantiation by the medical establishment. CT and x-ray imaging modalities are commonly utilised in various fields. However, alternative imaging techniques, such as Optical Coherence Tomography (OCT) and Ultrasonic imaging, are either overlooked or not given due consideration. The predominant focus of study is on retrospective (theoretical) rather than prospective (pragmatic) investigations. Consequently, there exists a significant need for researchers to enhance the practicality of their investigations.

Correcting directional dark field x-ray imaging artefacts using position dependent image deblurring and attenuation removal

In recent years, a novel x-ray imaging modality has emerged that reveals unresolved sample microstructure via a “dark-field image”, which provides complementary information to conventional “bright-field” images, such as attenuation and phase-contrast modalities. This x-ray dark-field signal is produced by unresolved microstructures scattering the x-ray beam resulting in localised image blur. Dark-field retrieval techniques extract this blur to reconstruct a dark-field image. Unfortunately, the presence of non-dark-field blur such as source-size blur or the detector point-spread-function can affect the dark-field retrieval as they also blur the experimental image. In addition, dark-field images can be degraded by the artefacts induced by large intensity gradients from attenuation and propagation-based phase contrast, particularly around sample edges. By measuring any non-dark-field blurring across the image plane and removing it from experimental images, as well as removing attenuation and propagation-based phase contrast, we show that a directional dark-field image can be retrieved with fewer artefacts and more consistent quantitative measures. We present the details of these corrections and provide “before and after” directional dark-field images of samples imaged at a synchrotron source. This paper utilises single-grid directional dark-field imaging, but these corrections have the potential to be broadly applied to other x-ray imaging techniques.

Deep-learning-based Attenuation Correction for 68Ga-DOTATATE Whole-body PET Imaging: A Dual-center Clinical Study.

Attenuation correction is a critical phenomenon in quantitative positron emission tomography (PET) imaging with its own special challenges. However, computed tomography (CT) modality which is used for attenuation correction and anatomical localization increases patient radiation dose. This study was aimed to develop a deep learning model for attenuation correction of whole-body 68Ga-DOTATATE PET images. Non-attenuation-corrected and computed tomography-based attenuation-corrected (CTAC) whole-body 68Ga-DOTATATE PET images of 118 patients from two different imaging centers were used. We implemented a residual deep learning model using the NiftyNet framework. The model was trained four times and evaluated six times using the test data from the centers. The quality of the synthesized PET images was compared with the PET-CTAC images using different evaluation metrics, including the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), mean square error (MSE), and root mean square error (RMSE). Quantitative analysis of four network training sessions and six evaluations revealed the highest and lowest PSNR values as (52.86±6.6) and (47.96±5.09), respectively. Similarly, the highest and lowest SSIM values were obtained (0.99±0.003) and (0.97±0.01), respectively. Additionally, the highest and lowest RMSE and MSE values fell within the ranges of (0.0117±0.003), (0.0015±0.000103), and (0.01072±0.002), (0.000121±5.07xe-5), respectively. The study found that using datasets from the same center resulted in the highest PSNR, while using datasets from different centers led to lower PSNR and SSIM values. In addition, scenarios involving datasets from both centers achieved the best SSIM and the lowest MSE and RMSE. Acceptable accuracy of attenuation correction on 68Ga-DOTATATE PET images using a deep learning model could potentially eliminate the need for additional X-ray imaging modalities, thereby imposing a high radiation dose on the patient.

High-resolution computed tomography with scattered X-ray radiation and a single pixel detector

X-ray imaging is a prevalent technique for non-invasively visualizing the interior of the human body and other opaque samples. In most commercial X-ray modalities, an image is formed by measuring the X-rays that pass through the object of interest. However, despite the potential of scattered radiation to provide additional information about the object, it is often disregarded due to its inherent tendency to cause blurring. Consequently, conventional imaging modalities do not measure or utilize these valuable data. In contrast, we propose and experimentally demonstrate a high resolution technique for X-ray computed tomography (CT) that measures scattered radiation by exploiting computational ghost imaging (CGI). We show that the resolution of our method can exceed 500 µm, which is approximately an order of magnitude higher than the typical resolution of X-ray imaging modalities based on scattered radiation. Our research reveals a promising technique for incorporating scattered radiation data in CT scans to improve image contrast and resolution while minimizing radiation exposure for patients. The findings of our study suggest that our technique could represent a significant advancement in the fields of medical and industrial imaging, with the potential to enhance the accuracy and safety of diagnostic imaging procedures.

Typical values statistical analysis for adult chest and abdomen-pelvis CT examinations

The use of DRLs has been extensively documented in the literature as a tool for protocol optimization across different x-ray imaging modalities in different countries. It recognizes the importance of developing and validating methods capable of correlating DRL quantities (CTDIvol, DLP and SSDE) with the technical parameters employed in CT studies. Such correlations must be supported by robust statistical methodologies in order to ensure the adoption of adequate optimization decisions. The aim of this work was to apply the Generalized Additive Model (GAM) statistical analysis in adult non-contrast chest and abdomen-pelvis CT typical values. These patient cohorts were statistically evaluated to identify correlations with key-parameters associated to the demographic patient information and machine dependent data, taking into account patients’ effective diameters, d, and body mass indexes (BMI). GAM was implemented considering each anatomical region in order to correlate the log-transformed DRL quantities (DRLq's) as outcomes given different key predictors related to image acquisition and patient characteristics. A total of 956 CT patient data were collected in this retrospective single-center study. Demographic variables demonstrate that age is not or it is just weakly-correlated to the DRLq's resulting from chest procedures, but it is strongly correlated when considering abdomen-pelvis examinations. Gender is correlated to the DRLq's for chest examinations adopting d as a key predictor but it is only correlated with DLP adopting the BMI as a key predictor. The level of accuracy provided by the GAM was adequate for interpreting the large fluctuations of the DRLq's, technical parameters and demographic data observed for the studied patient cohorts. Our results reflect the importance of a comprehensive statistical evaluation of typical values. The domain of this technique is important to different CT imaging chain stakeholders and its application can be a key tool for decision-making process to effective optimization strategies.

Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images.

Osteoporosis is a word used to describe a condition in which bone density has been diminished as a result of inadequate bone tissue development to counteract the elimination of old bone tissue. Osteoporosis diagnosis is made possible by the use of medical imaging technologies such as CT scans, dual X-ray, and X-ray images. In practice, there are various osteoporosis diagnostic methods that may be performed with a single imaging modality to aid in the diagnosis of the disease. The proposed study is to develop a framework, that is, to aid in the diagnosis of osteoporosis which agrees to all of these CT scans, X-ray, and dual X-ray imaging modalities. The framework will be implemented in the near future. The proposed work, CBTCNNOD, is the integration of 3 functional modules. The functional modules are a bilinear filter, grey-level zone length matrix, and CB-CNN. It is constructed in a manner that can provide crisp osteoporosis diagnostic reports based on the images that are fed into the system. All 3 modules work together to improve the performance of the proposed approach, CBTCNNOD, in terms of accuracy by 10.38%, 10.16%, 7.86%, and 14.32%; precision by 11.09%, 9.08%, 10.01%, and 16.51%; sensitivity by 9.77%, 10.74%, 6.20%, and 12.78%; and specificity by 11.01%, 9.52%, 9.5%, and 15.84%, while requiring less processing time of 33.52%, 17.79%, 23.34%, and 10.86%, when compared to the existing techniques of RCETA, BMCOFA, BACBCT, and XSFCV, respectively.

Novel reconstruction method of angle-limited backprojection (ALBP) for low-dose dental panoramic X-ray imaging

Panoramic radiography is a popular two-dimensional X-ray imaging modality in dentistry. It produces a single image of the overall facial view, including the maxillary and mandibular arches and supporting structures. Panoramic images are typically reconstructed using the shift-and-add (SAA) algorithm, in which only structures within a certain image layer are in focus, and others are out of focus on the panoramic image. As the clinical use of panoramic radiography has grown rapidly, radiologists are continuously seeking ways to reduce the radiation dose to patients. This study proposes a novel panoramic reconstruction method called the angle-limited backprojection (ALBP) algorithm for low-dose panoramic X-ray imaging. In ALBP, X-rays that meet the angular criterion established in this study are selected from the measured panoramic projection data and then backprojected onto the spherical voxels placed along the dental arch, as in a typical computed tomography reconstruction. The angular criterion selects X-rays that pass through a given spherical voxel and simultaneously have directional vectors within a prespecified angular range concerning the directional vector at the corresponding source position. To validate the efficacy of the proposed reconstruction method, we conducted a numerical simulation and experiment and investigated the image characteristics. Panoramic images were reconstructed using the SAA and ALBP algorithms with full and half (i.e., half radiation dose) panoramic projections, and their image quality was quantitatively evaluated using the intensity profile and universal quality index (UQI). The image quality of the ALBP with half projections was similar to that of the SAA with full projections, indicating the efficacy of the proposed method. Accordingly, our results demonstrated the potential for radiation dose reduction in panoramic X-ray imaging.

Fighting against COVID‐19: Innovations and applications

Fighting against <scp>COVID</scp>‐19: Innovations and applications

SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images

Medical images are usually degraded by numerous noises during acquisition or transmission, which often causes low contrast leading to deterioration of image quality. As such, medical image denoising and enhancement has become a paramount routine task. To overcome this problem, we propose a cutting-edge joint statistical and morphological model for the denoising and enhancement operation. Firstly, we propose a statistical model in formulating the marginal distribution of the wavelet coefficients. This model is integrated into a Bayesian inference framework to develop a maximum a posterior (MAP) estimator of the noise-free coefficient. Based on the statistical model, we eliminate the need for noise level estimation, and allows the model to automatically adapts to the observed image data. Secondly, we propose an adjustable morphological reconstruction model to eliminate known and unknown noises associated with medical images, while preserving the image details. After these operations, the image is decomposed into several wavelet subbands to extract the illumination and detail components. The image is then reconstructed based on the inverse wavelet to generate the enhanced noise-free image.Experimental results show that the proposed framework obtained high EME values of 41.04, 48.81, 47.81, and 45.75 for OCTA, FFA, CT, and X-ray imaging modalities, and performs better than the state-of-the-art methods. The proposed algorithm can effectively and efficiently enhance medical images, which will assist the clinicians in disease diagnosis, monitoring, and treatment.

Noise correlation and its impact on the performance of multi-material decomposition-based spectral imaging in photon-counting CT.

It has been known that noise correlation plays an important role in the determination of the performance of spectral imaging based on two-material decomposition (2-MD). To further understand the basics of spectral imaging in photon-counting CT toward optimal design and implementation, we study the noise correlation in multi-MD (m-MD) and its impact on the performance of spectral imaging. We derive the equations that characterize the noise and noise correlation in the material-specific (basis) images in m-MD, followed by a simulation study to verify the derived equations and study the noise correlation's impact on the performance of spectral imaging. Using a specially designed digital phantom, the study of noise correlation runs over the cases of two-, three-, and four-MD (2-MD, 3-MD, and 4-MD). Then, the noise correlation's impact on the performance of spectral imaging in photon-counting CT is investigated, using a modified Shepp-Logan phantom. The results in 2-MD show that, in-line with what has been reported in the literature, the noise correlation coefficient between the material-specific images corresponding to the basis materials approaches -1. The results in m-MD (m≥3) are more complicated and interesting, as the noise correlation coefficients between a pair of the material-specific images alternate between ±1, and so do in the case of 4-MD. The m-MD data show that the noise in virtual monochromatic imaging (a form of spectral imaging) is moderate even though the noises in material-specific (basis) images vary drastically. The observation of noise correlation in 3-MD, 4-MD, and beyond (i.e., m-MD) is informative to the community. The relationship between noise correlation and the performance of spectral imaging revealed in this work may help clinical medical physicists understand the fundamentals of spectral imaging based on MD and optimize the performance of spectral imaging in photon-counting CT and other X-ray imaging modalities.

Fragmentation of hunting bullets observed with synchrotron radiation: Lighting up the source of a lesser-known lead exposure pathway.

Bullet fragments have been previously observed in the remains and edible portions of big game animals that were harvested using rifles. The fragmentation issue has attracted attention because traditional hunting bullets are more than 70% lead, which is toxic to humans and scavengers in the ecosystem. We prepared gunshot wounds in ballistic gelatin blocks, and then applied synchrotron X-ray imaging technology to the bullet fragmentation process for the first time. The K edge subtraction (KES) imaging method allowed a clear separation of lead in an image from false positives, including the other major bullet component, copper, and non-lead objects such as bone fragments. The superior brightness of synchrotron radiation was also harnessed to resolve thousands of embedded sub-10 μm fragments, a size range not previously observed using commonly applied X-ray imaging modalities. The results challenge the current understanding of the maximum extent that fragments may be distributed, and the effectiveness of imaging methods used to screen wild game donations at food banks for lead bullet fragments.

Recovery of complete time density curves from incomplete angiographic data using recurrent neural networks.

Quantitative angiography is a 2D/3D x-ray imaging modality that summarizes hemodynamic information using time density curve (TDC) based parameters. Estimation of the TDC parameters are susceptible to errors due to various factors including, patient motion, incomplete temporal data, imaging trigger errors etc. In this study, we tested the feasibility of using recurrent neural networks (RNN) to recover complete TDC temporal information from incomplete sequences and evaluate quantitative parameters generated from the corrected TDCs. Digital subtraction angiograms (DSAs) were collected from patients undergoing endovascular treatments and angiographic parametric imaging (API) parameters were calculated from each DSA. Each set of API parameters was used to simulate a TDC resulting in a dataset of 760 TDCs. One-third of each TDC was continuously masked from pseudo-random points past the peak height (PH) point to simulate missing/artifact information. An RNN was developed, trained and tested to generate completed/corrected TDCs. The RNN recovered complete TDC temporal information with an average mean squared error of 0.0086±0.002. Average mean absolute errors were calculated between each API parameter generated from the ground truth TDCs and RNN corrected TDCs, these were 11.02%±0.91 for time to peak, 10.97%±0.69 for mean transit time, 5.65%±0.76 for PH, and 15.08%±0.98 for area under the TDC. The change in API parameters was not clinically significant and the predictive power of the API parameters was retained. This study proved the feasibility of using RNNs to mitigate motion artifacts and incomplete angiographic acquisitions to extract accurate quantitative parameters.

Descriptive analysis of dental X-ray images using various practical methods: A review.

In dentistry, practitioners interpret various dental X-ray imaging modalities to identify tooth-related problems, abnormalities, or teeth structure changes. Another aspect of dental imaging is that it can be helpful in the field of biometrics. Human dental image analysis is a challenging and time-consuming process due to the unspecified and uneven structures of various teeth, and hence the manual investigation of dental abnormalities is at par excellence. However, automation in the domain of dental image segmentation and examination is essentially the need of the hour in order to ensure error-free diagnosis and better treatment planning. In this article, we have provided a comprehensive survey of dental image segmentation and analysis by investigating more than 130 research works conducted through various dental imaging modalities, such as various modes of X-ray, CT (Computed Tomography), CBCT (Cone Beam Computed Tomography), etc. Overall state-of-the-art research works have been classified into three major categories, i.e., image processing, machine learning, and deep learning approaches, and their respective advantages and limitations are identified and discussed. The survey presents extensive details of the state-of-the-art methods, including image modalities, pre-processing applied for image enhancement, performance measures, and datasets utilized.

Comparative dosimetry of radiography device, MSCT device and two CBCT devices in the elbow region.

The aim of the study was to estimate and to compare effective doses in the elbow region resulting from four different x‐ray imaging modalities. Absorbed organ doses were measured using 11 metal oxide field effect transistor (MOSFET) dosimeters that were placed in a custom‐made anthropomorphic elbow RANDO phantom. Examinations were performed using Shimadzu FH‐21 HR radiography device, Siemens Sensation Open 24‐slice MSCT‐device, NewTom 5G CBCT device, and Planmed Verity CBCT device, and the effective doses were calculated according to ICRP 103 recommendations. The effective dose for the conventional radiographic device was 1.5 µSv. The effective dose for the NewTom 5G CBCT ranged between 2.0 and 6.7 µSv, for the Planmed Verity CBCT device 2.6 µSv and for the Siemens Sensation MSCT device 37.4 µSv. Compared with conventional 2D radiography, this study demonstrated a 1.4–4.6 fold increase in effective dose for CBCT and 25‐fold dose for standard MSCT protocols. When compared with 3D CBCT protocols, the study showed a 6‐19 fold increase in effective dose using a standard MSCT protocol. CBCT devices offer a feasible low‐dose alternative for elbow 3D imaging when compared to MSCT.

Adaptive Image Rescaling for Weakly Contrast-Enhanced Lesions in Dedicated Breast CT: A Phantom Study.

PurposeDedicated breast CT is an emerging volumetric X-ray imaging modality for diagnosis that does not require any painful breast compression. To improve the detection rate of weakly enhanced lesions, an adaptive image rescaling (AIR) technique was proposed.Materials and MethodsTwo disks containing five identical holes and five holes of different diameters were scanned using 60/100 kVp to obtain single-energy CT (SECT), dual-energy CT (DECT), and AIR images. A piece of pork was also scanned as a subclinical trial. The image quality was evaluated using image contrast and contrast-to-noise ratio (CNR). The difference of imaging performances was confirmed using student's t test.ResultsTotal mean image contrast of AIR (0.70) reached 74.5% of that of DECT (0.94) and was higher than that of SECT (0.22) by 318.2%. Total mean CNR of AIR (5.08) was 35.5% of that of SECT (14.30) and was higher than that of DECT (2.28) by 222.8%. A similar trend was observed in the subclinical study.ConclusionThe results demonstrated superior image contrast of AIR over SECT, and its higher overall image quality compared to DECT with half the exposure. Therefore, AIR seems to have the potential to improve the detectability of lesions with dedicated breast CT.

Chemical Modifications of Porous Shape Memory Polymers for Enhanced X-ray and MRI Visibility.

The goal of this work was to develop a shape memory polymer (SMP) foam with visibility under both X-ray and magnetic resonance imaging (MRI) modalities. A porous polymeric material with these properties is desirable in medical device development for applications requiring thermoresponsive tissue scaffolds with clinical imaging capabilities. Dual modality visibility was achieved by chemically incorporating monomers with X-ray visible iodine-motifs and MRI visible monomers with gadolinium content. Physical and thermomechanical characterization showed the effect of increased gadopentetic acid (GPA) on shape memory behavior. Multiple compositions showed brightening effects in pilot, T1-weighted MR imaging. There was a correlation between the polymeric density and X-ray visibility on expanded and compressed SMP foams. Additionally, extractions and indirect cytocompatibility studies were performed to address toxicity concerns of gadolinium-based contrast agents (GBCAs). This material platform has the potential to be used in a variety of medical devices.