Improve Imaging Accuracy Research Articles

In vivo fluorescence imaging of nanocarriers in near-infrared window II based on aggregation-caused quenching

Accurate fluorescence imaging of nanocarriers in vivo remains a challenge owing to interference derived mainly from biological tissues and free probes. To address both issues, the current study explored fluorophores in the near-infrared (NIR)-II window with aggregation-caused quenching (ACQ) properties to improve imaging accuracy. Candidate fluorophores with NIR-II emission, ACQ984 (λem = 984 nm) and IR-1060 (λem = 1060 nm), from the aza-BODIPY and cyanine families, respectively, were compared with the commercial fluorophore ICG with NIR-II tail emission and the NIR-I fluorophore P2 from the aza-BODIPY family. ACQ984 demonstrates high water sensitivity with complete fluorescence quenching at a water fraction greater than 50%. Physically embedding the fluorophores illuminates various nanocarriers, while free fluorophores cause negligible interference owing to the ACQ effect. Imaging based on ACQ984 revealed fine structures in the vascular system at high resolution. Moreover, good in vivo and ex vivo correlations in the monitoring of blood nanocarriers can be established, enabling real-time noninvasive in situ investigation of blood pharmacokinetics and dynamic distribution in various tissues. IR-1060 also has a good ACQ effect, but the lack of sufficient photostability and steady post-labeling fluorescence undermines its potential for nanocarrier bioimaging. P2 has an excellent ACQ effect, but its NIR-I emission only provides nondiscriminative ambiguous images. The failure of the non-ACQ probe ICG to display the biodistribution details serves as counterevidence for the improved imaging accuracy by NIR-II ACQ probes. Taken together, it is concluded that fluorescence imaging of nanocarriers based on NIR-II ACQ probes enables accurate in vivo bioimaging and real-time in situ pharmacokinetic analysis.

Damage imaging method based on circular DE-IDT array for phased array data fusion

Plate-like structures, including composite material plates, are extensively employed in major engineering structures such as aerospace, high-speed rail, and civil engineering. The growing utilization of curved panel structures and the increasing complexity of these structures have emphasized the need for flexible sensors capable of efficient signal front-end reception. Addressing the limitations of conventional sensors made of brittle piezoelectric materials in adhering to the surface of structures for health monitoring, a self-developed flexible dielectric elastomer-interdigital transducer (DE-IDT) array is employed to achieve damage imaging in isotropic aluminum curved panel structures and orthotropic composite material plates using linear frequency modulation (chirp) signal excitation. In addition, a proposed fusion method is utilized to obtain weighted imaging results, and evaluation and analysis are performed using relevant indicators. Experimental findings highlight the improved imaging accuracy achieved through phased array data fusion using the linear chirp signal, attributed to its wideband characteristics and ability to capture more damage signals. Moreover, the effectiveness of the circular DE-IDT array for damage detection and imaging in curved panel structures and composite material plates is successfully verified.

434. Improving Image Accuracy in Low-Field Magnetic Resonance Imaging to Make Accessible Biomarkers for Psychiatric Disorders

434. Improving Image Accuracy in Low-Field Magnetic Resonance Imaging to Make Accessible Biomarkers for Psychiatric Disorders

Abstract 4163: Improving the dynamic range of pH low insertion peptides for targeted imaging of pancreatic tumors

Abstract Objective: Current intraoperative imaging modalities such as near-infrared fluorescent imaging (NIR) or ultrasound are limited due to operator experience and depth, and targeted imaging of pancreatic tumors would be beneficial. However, specific pancreatic tumor biomarkers available for targeting in a novel imaging protocol are lacking. As such, we focus on developing a novel targeted imaging paradigm utilizing multispectral optoacoustic imaging (MSOT) to target the acidic microenvironment of pancreatic tumors. Peptides know as pH low insertion peptides (pHLIPs) when bound to dye allow for contrasted imaging selectively based on pH using MSOT. We have identified the need for a pHLIP with improved dynamic range to improve imaging accuracy. Here, we synthesize the novel pHLIP V7FS for comparison to V3 with aims of improving the dynamic range of imaging with MSOT in pancreatic cancer. Methods: Using microwave chemistry, V7FS and V3 were synthesized with >90% purity. Peptides were conjugated to HiLyte 750 dye, and peptide-dye conjugation confirmed via spectroscopy. Intraoperative measurements of human pancreatic tumor and surrounding uninvolved tissue were obtained using a microsensor. Near-infrared fluorescent imaging was used to measure probe uptake in S2VP10 and S2013 pancreatic cancer cells lines grown in pH specific media (7.4, 6.8, 6.6) in replicates. MSOT was then used to image probes in phantoms mimicking tissue and then in mice with pancreatic tumors from orthotopically implanted S2VP10. Statistical analysis was performed using ANOVA. Results: Compared to surrounding uninvolved pancreatic tissue, human pancreatic tumor was more acidic with pH 6.2-6.7 vs 7.2-7.3 (p<0.01). Signal at pH 7.4, 6.8 and 6.6 was higher for V7FS-750 compared to V3-750 (p=0.0105). Signal for V7FS-750 was centered around pH 6.6 and signal for V3-750 centered around pH 6.8. Interaction between probe and pH did not reach significance (p=0.745). Peak uptake in orthotopic murine models was observed at 3 hours following intravenous injection. Conclusions: V7FS-750 was shown to have higher signal accumulation compared to V3-750 favoring a more acidic environment (pH 6.6 vs pH 6.8). This novel pHLIP was demonstrated to be viable for use in an imaging paradigm utilizing MSOT in an orthotopic murine model. Continued improvement in the dynamic range of these probes is promising for the use of pHLIPS in imaging of pancreatic cancer with MSOT. Citation Format: Ryan Bynum, Emma Sanderson, Barish H. Edil, Ajay Jain, Lacey R. McNally. Improving the dynamic range of pH low insertion peptides for targeted imaging of pancreatic tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4163.

Improving image accuracy of ambient noise data by temporary seismic arrays at different observation periods

When using ambient noise data to invert velocity and anisotropic structures, the two-station inter-correlation method requires synchronous stations. If there are multiple temporary seismic arrays with different observation periods in the study area, the seismic arrays are usually used selectively. This paper takes the Sanjiang lateral collision zone as an example, and utilizes the ambient noise data of multiple temporary seismic arrays at different observation periods to improve the accuracy of regional velocity structure and anisotropy by anchoring permanent seismic stations. In this paper, notable enhancements in S-wave velocity and azimuthal anisotropy imaging accuracy are achieved by integrating data from three temporary seismic arrays (SJ-Array, SL-Array, and ChinArray-I) with the permanent seismic network. The imaging resolutions for the S-wave velocity and azimuthal anisotropy above 40 ​km are 0.4° ​× ​0.4° and 0.5° ​× ​0.5°, respectively. In the region of the most concentrated array coverage, the imaging resolution of S-wave velocity can reach 0.33° ​× ​0.33° at depths of less than 30 ​km. These findings underscore the significant improvement in deep structure imaging accuracy by the synergistic integration of ambient noise data from multiple temporary seismic arrays.

Automated surface profile measurement with digital holographic microscopy and large mask inpainting networks

Digital holographic microscopy (DHM) allows for highly precise 3D surface measurements in a non-invasive way, but phase aberrations from off-axis DHM recordings can compromise image accuracy. Traditional compensation methods require manual intervention, hindering further automated use of DHM. Other methods based on background segmentation and Zernike polynomials have been proposed, but identifying the sample and background regions can lead to inaccurate compensation results. Additionally, traditional image restoration algorithms struggle with restoring sample-free holograms involving large or multiple vacant areas. A new automated aberration compensation method is proposed using large-mask inpainting networks. This method restores sample-free holograms and compensates for phase aberrations, leveraging deep learning to enable real-time measurements. In the study, the network was trained with holograms of varying fringes, and experimental results show its effectiveness in improving image accuracy and detail. This approach could have wide applications in industries such as micro-electromechanical systems and micro- integrated circuits.

An encoder–decoder and modified U‐Net network for microwave imaging of stroke

AbstractMicrowave imaging has been widely used in stroke diagnosis as a non‐invasive, ionizing radiation‐free imaging method. However, the electromagnetic inverse scattering problems of microwave imaging are nonlinear and ill‐posed. To further improve the accuracy of microwave imaging algorithms to identify and reconstruct stroke regions. A novel Encoder‐Decoder and Modified U‐Net (ED‐MUNET) network for microwave imaging of stroke is proposed. The newly proposed ED‐MUNET method accomplishes the initial imaging from scattered data to stroke images through an encoder–decoder in the first step. In the second step, the feature information of the reconstructed image is extracted through the modified U‐Net network, and the reconstruction from the rough stroke image to the high‐resolution image is realized. The second step avoids black‐box operations and improves image accuracy. ED‐MUNET has fewer artifacts, the relative reconstruction error is less than 0.05, and the reconstructed images are clearer, according to the comparative experiments with other networks. The experimental results showed the superiority of the proposed method for reconstructing stroke images.

IDGAN: Information-Driven Generative Adversarial Network of Coverless Image Steganography

Traditional image steganography techniques complete the steganography process by embedding secret information into cover images, but steganalysis tools can easily detect detectable pixel changes that lead to the leakage of confidential information. The use of a generative adversarial network (GAN) makes it possible to embed information using a combination of information and noise in generating images to achieve steganography. However, this approach is usually accompanied by issues such as poor image quality and low steganography capacity. To address these challenges, we propose a steganography model based on a novel information-driven generative adversarial network (IDGAN), which fuses a GAN, attention mechanisms, and image interpolation techniques. We introduced an attention mechanism on top of the original GAN model to improve image accuracy. In the generation model, we replaced some transposed convolution operations with image interpolation for better quality of dense images. In contrast to traditional steganographic methods, the IDGAN generates images containing confidential information without using cover images and utilizes GANs for information embedding, thus having better anti-detection capability. Moreover, the IDGAN uses an attention mechanism to improve the image details and clarity and optimizes the steganography effect through an image interpolation algorithm. Experimental results demonstrate that the IDGAN achieves an accuracy of 99.4%, 95.4%, 93.2%, and 100% on the MNIST, Intel Image Classification, Flowers, and Face datasets, respectively, with an embedding rate of 0.17 bpp. The model effectively protects confidential information while maintaining high image quality.

PDGFB-targeted functional MRI nanoswitch for activatable T1–T2 dual-modal ultra-sensitive diagnosis of cancer

As one of the most significant imaging modalities currently available, magnetic resonance imaging (MRI) has been extensively utilized for clinically accurate cancer diagnosis. However, low signal-to-noise ratio (SNR) and low specificity for tumors continue to pose significant challenges. Inspired by the distance-dependent magnetic resonance tuning (MRET) phenomenon, the tumor microenvironment (TME)-activated off–on T1–T2 dual-mode MRI nanoswitch is presented in the current study to realize the sensitive early diagnosis of tumors. The tumor-specific nanoswitch is designed and manufactured on the basis of PDGFB-conjugating ferroferric oxide coated by Mn-doped silica (PDGFB-FMS), which can be degraded under the high-concentration GSH and low pH in TME to activate the T1–T2 dual-mode MRI signals. The tumor-specific off–on dual-mode MRI nanoswitch can significantly improve the SNR and is used successfully for the accurate diagnosis of early-stage tumors, particularly for orthotopic prostate cancer. In addition, the systemic delivery of the nanoswitch did not cause blood or tissue damage, and it can be excreted out of the body in a timely manner, demonstrating excellent biosafety. Overall, the strategy is a significant step in the direction of designing off–on dual-mode MRI nanoprobes to improve imaging accuracy, which opens up new avenues for the development of new MRI probes.

A High-Precision Elastic Reverse-Time Migration for Complex Geologic Structure Imaging in Applied Geophysics

High-precision elastic reverse-time migration (ERTM) imaging has always been one of the trends in the development of geophysics. However, current wavefield simulations using time-domain finite-difference (FD) approaches in ERTM have second-order temporal accuracy, resulting in travel time changes and waveform distortion in wavefield propagation with large time steps, i.e., temporal dispersion. Errors caused by the temporal dispersion can lead to erroneous imaging locations and out-of-focus diffraction events. A new ERTM and its workflow are established here using temporal and spatial high-order FD accuracy wavefields and the vector-based imaging condition. Our method computes elastic vector-based wavefields by solving a P- and S-wave decomposition form of a quasi-stress–velocity equation. An advanced finite-difference scheme is employed in the wavefield solution to achieve simulation with temporal fourth-order accuracy and spatial arbitrary even-order accuracy. The normalized dot-product imaging condition of the source and receiver P/S wavefields is then applied to generate high-quality images. The elastic wavefield simulation and ERTM numerical examples presented here reveal that the anti-dispersion workflow can improve modeling and imaging accuracy. In addition, the field data application shows that our method can achieve reasonable and reliable ERTM images. This method can integrate the most advanced imaging techniques into this computational framework to improve imaging accuracy.

Magnetic Resonance Image under the Low-Rank Matrix Denoising Algorithm in Evaluating the Efficacy of Neoadjuvant Chemo-Radiotherapy for Rectal Cancer

This study was to explore the application value of magnetic resonance imaging (MRI) images obtained by low-rank matrix recovery algorithm (LRMR algorithm) in evaluating the curative effect of rectal cancer patients receiving the neoadjuvant chemo-radiotherapy (nCRT). In this study, an image denoising model was designed based on the LRMR algorithm, the original low-rank data matrix was recovered from the error, and the low-rank matrix was restored by solving the optimal kernel norm, so as to effectively separate the image data information and the interference noise. In addition, the model was applied to 60 patients with rectal cancer who received nCRT to extract the texture parameters and lesion-related data from the MRI images. The results showed that the MRI images optimized by LRMR algorithm were clearer than the original images, contained less excess noise, and had improved imaging accuracy and image quality. The results of typical cases suggested that the front of the rectal wall membrane of a patient in the T-downstage group was not smooth before treatment, the internal angiography was blurred, and the wall membrane was thickened, but the wall membrane became thinner after treatment, the highest position was reduced from 1.46 cm to 0.38 cm, the average value of the apparent diffusion coefficient (ADC) increased from 0.732 × 10−3 mm2/s to 1.196 × 10−3 mm2/s, and the lesion tissue was thicker. It was found that the height, length, and ADC of the lesion after the nCRT showed statistically great difference in contrast to the values before the treatment P < 0.05 . Such results indicated that the nCRT showed obvious effects in the clinical treatment of rectal cancer. In short, the LRMR algorithm could remove the interference noise in the MRI image, and from the information about rectal cancer tumor lesions extracted from that, the height value and length value of tumor lesions in patients given neoadjuvant chemo-radiotherapy were reduced compared with those before treatment, and the apparent diffusion coefficient value was increased, indicating that neoadjuvant chemo-radiotherapy has a significant effect in the clinical treatment of rectal cancer.

Real-time automatic feedback by deep learning to reduce apical foreshortening in echocardiography

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Norwegian University of Science and Technology, St. Olavs University Hospital, Central-Norway Health Authority Background Left ventricular (LV) foreshortening is common in echocardiography and may impair reproducibility and estimation of LV function. Feedback of foreshortening metrics such as LV length during scanning could improve accuracy, however, such a tool has not been available. Purpose To evaluate the effect of feedback of LV length during imaging for experienced sonographers, measured in real-time using a robust deep learning (DL) based tool. Methods Consecutive patients (n = 108) with mixed cardiac pathology were included during two separate periods. Each patient underwent three echocardiograms. The first and second examinations were performed by two (of three) randomized sonographers and the third exam by one (of four) randomized cardiologists. All examinations included the three standard apical views and the cardiologists’ exam included tri-plane recordings for reference. The data collection was divided in two phases. In the first period, the sonographers were told to provide high quality echocardiograms for analyses of LV function. Subsequently, sonographers were trained in use of the real-time DL tool on 10 patients each. In period two, the algorithm was used during scanning by the sonographer performing the second exam. One expert reader measured LV length (subendocardial apex to mitral annular plane) in all exams. The cardiologists’ tri-plane recordings were analyzed and used as reference. LV foreshortening was calculated at end-diastole (reference minus the operators LV length). Each exam was classified as foreshortened if the difference was ≥4 mm. Results After excluding those not in sinus rhythm (n = 9) and with need for contrast echocardiography (n = 11), 88 patients (45% women) were included (41 in period 1 and 47 in period 2). Age was mean (SD) 63 (16) years (45% women). Main findings are shown in the Table. LV length by both sonographer groups was significantly foreshortened in period 1 (mean 2.6 and 3.5 mm), and both sonographer groups reduced LV foreshortening in period 2 (both p ≤0.02). Improvement was best for sonographers using the real-time DL algorithm. These were not significantly foreshortened compared to the reference (p = 0.53), and less foreshortened compared to sonographer 1 (p = 0.001). Similarly, sonographers had more foreshortened examinations than cardiologist in period 1 (sonographer groups; 15 (37%) and 13 (32%) vs. cardiologist; 5 (12%), both p ≤0.03). In period 2, there was no difference in proportion of foreshortening between cardiologists and sonographer 2 (cardiologist; 0 vs sonographer; 2 (4%, p = 0.5)), while sonographer 1 foreshortened more 11 (23%), p ≤0.003). Conclusion Feedback of LV length during imaging significantly reduced foreshortening in echocardiographic acquisitions by sonographers, and has the potential to improve image accuracy even in the hands of experienced operators. Abstract Table Abstract Figure. Deep Learning foreshortening application

Improve correlation matrix of Discrete Fourier Transformation technique for finding the missing values of MRI images.

Missing values in the k-NN algorithm are a significant research concern, especially in low-grade tumours and CSF fluid, which are commonly identified in MRI scans. Missing values are usually ignored, but when data is mined, they can lead to bias and errors. In addition, the data is not missing at random. This study improves image accuracy, boosts the efficiency of missing k-NN hybrid values, and develops a research technique for detecting CSF fluid deposits in brain areas separated from non-tumor areas. We also offer a new method for detecting low-grade tumours or cerebrospinal fluid (CSF) formation in its early stages. In this study, we combine the hybrid K-Nearest Neighbor algorithm with the Discrete Fourier transform (DFT), as well as Time-Lagged analysis of four-dimensional (4D) MRI images. These dependencies exist in both space and time, but present techniques do not account for both sequential linkages and numerous types of missingness. To address this, we propose the DFLk-NN imputation method, which combines two imputation approaches based on a hybrid k-NN extension and the DFT to capture time-lag correlations both within and across variables. There are several types of missingness are enables the imputation of missing values across the variable even when all the data for a given time point is missing. The proposed method gives high accuracies of MRI datasets and retrieves the missing data in the images.

Pemetaan Habitat Dasar Perairan Dangkal Menggunakan Citra Satelit SPOT-7 di Pulau Nusa Lembongan, Bali

Shallow water habitat is one of the regions that has high dynamics and has an important role are ecologically and economically. The high dynamics of the ideal shallow water habitat is always followed by updating information so that an overview of the area is obtained in accordance with reality. Remote sensing technology is one of the technologies that can be used for mapping natural resource studies such as mapping of shallow water habitats with the satellite imagery. This study aims to map the distribution of shallow water habitats using SPOT-7 satellite imagery on Nusa Lembongan Island, Bali and test the level of accuracy. The method used true color composite and DII (Depth Invariant Index) transformation and uses the maximum likelihood classification. The classification scheme used 6 classes, namely sand, seagrass, macro algae, rubble, live coral, and dead coral. The results of this study indicate the distribution of shallow water habitat on Nusa Lembongan Island, Bali spread equally based on the level of water depth with a total area of shallow water habitat of 453.41 ha. The results of mapping accuracy test showed the overall accuracy of the DII transformation classification results is better than the composite image classification results with an overall accuracy of 75.43% and a kappa coefficient is 0.71. So from these results can be said that used of a water column correction with the DII method can improve image accuracy in mapping shallow water habitats.

Elastic least-squares reverse time migration based on decoupled wave equations

Elastic reverse time migration (ERTM) is developed for better characterization of complex structures by imaging multicomponent seismic data. However, conventional ERTM is subject to limitations such as finite recording aperture, limited bandwidth, and imperfect illumination. Elastic least-squares reverse time migration (ELSRTM) can improve imaging accuracy gradually with iterations by minimizing the residuals between the observed and calculated multicomponent data. Conventional ELSRTM suffers from crosstalk artifacts caused by coupled elastic wavefields with different wave modes. Decomposing the coupled elastic wavefields into pure P- and S-waves is an effective method to suppress these crosstalk artifacts. Considering the trade-off between calculation accuracy and efficiency, we have developed a new ELSRTM scheme for isotropic media based on decoupled wave equations to suppress these wave mode-related crosstalk artifacts in the images of conventional ELSRTM. Pure wavefields are obtained by solving the decoupled wave equations using the finite-difference method in our new ELSRTM method. We also derive new decoupled adjoint-state wave equations that are suitable for the elastic velocity-stress equations in isotropic media. Furthermore, we use the gradient equations based on pure wavefields to update the reflectivity images. Synthetic examples demonstrate that our new ELSRTM method can generate images that better represent the subsurface when compared with conventional ERTM and conventional ELSRTM.

Defect Imaging Enhancement through Optimized Shape Factors of the RAPID Algorithm Based on Guided Wave Beam Pattern Analysis.

In this study, a modified imaging algorithm was implemented to improve the imaging accuracy for defects located on a structure. Based on analysis of the Lamb wave mode, a guided ultrasonic wave inspection technique was applied, which was able to illustrate images of defects in a 6 mm steel plate simulating containment liner plate (CLP) in nuclear power plants. The dominant Lamb wave mode was determined through short-time Fourier transform waveform analysis and imaging verification. Following tomography verification, limitations of the antisymmetric mode in the thick steel plate were identified. In addition, a modified shape factor, based on the energy distribution factor according to the beam pattern and beam width, was suggested for field applications and improved imaging accuracy. Results of the analysis revealed a beam skewing phenomenon for the Lamb wave mode. In the case of S0 2.7 MHz·mm, skewing as well as distortion effects are not observed in the experiment, while the S0 modes at 2.64 and 2.74 MHz·mm show either of them. Considering skewing width, the size of the shape function was modified. Application of the modified shape function allows us to obtain more accurate image to actual defect shape.

Nonlinear ultrasonic imaging of damage in composite materials using a higher harmonic and modulated multi-path reciprocal method

Structural health monitoring has become an important factor in the assessment of defects/damage in material components. Ultrasonic methods generally incorporate a sparse array of sensors/transducers as they provide a low number of piezoelectric sensors per area, thus providing savings with regard to system costs and weight. Many structural health monitoring techniques rely on linear ultrasonic effects such as reflections, amplitude changes, time of arrival and wave scattering effects which rely on precise baseline measurements. In this work, a nonlinear ultrasonic method based on a sparse array of surface-bonded ultrasonic transducers was used to evaluate the second harmonic and modulated elastic responses from a damaged medium. A complex composite stiffened panel with barely visible impact damage was evaluated. The points closest to damage are found on the paths between transmitter–receiver pairs through a reciprocal relationship of nonlinear elastic parameters and a statistical approach was used to select a cloud of points so that a two-dimensional image of the damaged region is created. Experimental results revealed that the second-order nonlinear parameter provided accurate damage localisation and imaging and the use of modulation bands further improved imaging accuracy and damage localisation. The maximum error between the calculated and the real damage area centres was only 1.3 mm. The proposed nonlinear elastic multi-path imaging technique based on higher harmonic generation and modulation coupled with a statistical approach provides damage detection without a priori knowledge of the material characteristics, which is in direct contrast to conventional linear ultrasonic methods which rely on precise measurement of elastic wave effects both before and after the initiation of damage.

Reconstructing the Position and Intensity of Multiple Gamma-Ray Point Sources With a Sparse Parametric Algorithm

We present an experimental demonstration of Additive Point Source Localization (APSL), a sparse parametric imaging algorithm that reconstructs the 3D positions and activities of multiple gamma-ray point sources. Using a handheld gamma-ray detector array and up to four $8$ ${\mu}$Ci $^{137}$Cs gamma-ray sources, we performed both source-search and source-separation experiments in an indoor laboratory environment. In the majority of the source-search measurements, APSL reconstructed the correct number of sources with position accuracies of ${\sim}20$ cm and activity accuracies (unsigned) of ${\sim}20\%$, given measurement times of two to three minutes and distances of closest approach (to any source) of ${\sim}20$ cm. In source-separation measurements where the detector could be moved freely about the environment, APSL was able to resolve two sources separated by $75$ cm or more given only ${\sim}60$ s of measurement time. In these source-separation measurements, APSL produced larger total activity errors of ${\sim}40\%$, but obtained source separation distances accurate to within $15$ cm. We also compare our APSL results against traditional Maximum Likelihood-Expectation Maximization (ML-EM) reconstructions, and demonstrate improved image accuracy and interpretability using APSL over ML-EM. These results indicate that APSL is capable of accurately reconstructing gamma-ray source positions and activities using measurements from existing detector hardware.

An Activatable X‐Ray Scintillating Luminescent Nanoprobe for Early Diagnosis and Progression Monitoring of Thrombosis in Live Rat

AbstractThrombosis is a frequent predisposing factor in high‐mortality cardiovascular diseases, which underscores the urgent need to precisely diagnose thrombosis formation at a less severe stage. However, it is still challenging to realize in vivo early thrombosis imaging due to lack of high sensitivity/‐specificity molecular imaging strategy. To address such problems, thrombin‐activatable scintillating nanoprobes are developed for background‐free X‐ray‐excited luminescence (XEL) imaging of in vivo early thrombosis. The nanoprobes are constructed by designing bright XEL‐emitting lanthanide‐doped scintillator nanocrystals (NCs) with dye labeled‐peptide modification. Such nanoprobes show XEL‐off originally and enable robust turn‐on XEL upon thrombin‐specific cleavage of the peptide, resulting from the rational manipulation of energy transfer between dye and NCs. Specially, due to high tissue penetration depth and background‐free attributes in XEL imaging, this strategy achieves high‐efficiency XEL imaging of the early thrombosis on the basis of in situ elevated thrombin levels. Moreover, XEL and magnetic resonance imaging capabilities can be integrated to further improve imaging accuracy and monitor thrombosis progression. As such, the exploited strategy demonstrates a novel paradigm for realizing timely thrombosis imaging and provides a new readily tailorable platform for sensitive in vivo deep‐tissue imaging.

Carbon dioxide-angiography for patients with peripheral arterial disease at risk of contrast-induced nephropathy.

Patients with peripheral arterial disease (PAD) and critical limb ischemia are at risk for limb amputation and require urgent management to restore blood flow. Patients with PAD often have several comorbidities, including chronic kidney disease, diabetes mellitus, and hypertension. Diagnostic and interventional angiography using iodinated contrast agents provides excellent image resolution but can be associated with contrast-induced nephropathy (CIN). The use of carbon dioxide (CO2) as a contrast agent reduces the volume of iodine contrast required for angiography and reduces the incidence of CIN. However, CO2 angiography has been underutilized due to concerns regarding safety and image quality. Modern CO2 delivery systems with advanced digital subtraction angiography techniques and hybrid angiography have improved imaging accuracy and reduced the incidence of CIN. Awareness of the need for optimal imaging conditions, contraindications, and potential complications have improved the safety of CO2 angiography. This review aims to highlight current technological advances in the delivery of CO2 in vascular angiography for patients with PAD and critical limb ischemia, which result in limb preservation while preventing kidney damage.