Image Reconstruction Techniques Research Articles

Advanced image reconstruction for electrostatic tomography in gas-solid two-phase flow based on convolutional autoencoder neural network

The image reconstruction of flowing charged particles in gas-solid two-phase (GSTP) flow can be achieved through electrostatic tomography (EST). Accurate image reconstruction is crucial for detecting the movement patterns of the particles. In order to improve the quality of reconstructed images, a unique convolutional autoencoder neural network (CANN) is proposed. This study uses an image set generated by the linear backprojection (LBP) algorithm to train the CANN, which consists of an encoder and a decoder. The encoder utilizes convolutional and max-pooling layers to reduce the dimensionality of the images and extract key features, while the decoder restores and reconstructs the images through up-sampling and convolutional operations to closely approximate the reference image. To prevent overfitting, dropout layers are introduced after each max-pooling layer in the encoder. To verify the anti-noise capability of the network, Gaussian white noise ranging from 10 dB to 20 dB is added to the test set. The proposed CANN has been validated through simulations and experiments, demonstrating its effectiveness in overcoming noticeable artifacts and noise in reconstructed images when identifying GSTP flow patterns. Furthermore, it shows significant enhancements in imaging outcomes compared to conventional image reconstruction techniques and some current deep learning algorithms.

All-optical image transmission through a dynamically perturbed multimode fiber and a ring-core fiber using diffractive deep neural networks.

In this study, we present an all-optical image reconstruction technique leveraging a diffractive deep neural network (D2NN) within a ring-core fiber (RCF) architecture. Orbital angular momentum (OAM) modes are employed to facilitate imaging transmission. We experimentally validate the efficacy of our approach for complex field diffractive image reconstruction through a multimode fiber (MMF) and RCF at a 1550 nm operating wavelength. The experimental results demonstrate the superior performance of the proposed method in mitigating RCF scattering-to-restoration transformation issues, significantly outperforming traditional MMF-based imaging correction techniques.

Design and Construction of Experimental System for Validation of a New Image Reconstruction Technique with Limited-view-angle Projection Data for BNCT-SPECT

Abstract. Boron Neutron Capture Therapy (BNCT) is expected to be a promising radiation therapy for cancer, and research on various issues associated with it is still in progress. One of the unsolved issues is the construction of a system for observing the effect of the treatment (local boron dose) in real time under high-background circumstances. Thus, we set out to establish a method for measuring gamma-rays emitted promptly from the body following the boron neutron capture reaction and reconstructing images in a SPECT-like manner. We call this method BNCT-SPECT. We have been developing a new image reconstruction method based on Bayesian estimation for the BNCT-SPECT method. In the present study, the aims were to design and construct an experimental system capable of validating BNCT-SPECT and its image reconstruction method and to confirm BNCT-SPECTs performance. The crucial takeaway from this study is that we should consider the use of extremely high background radiation during BNCT. For the validation of BNCT-SPECT, we attempted to reproduce these conditions, despite this being quite challenging. We first constructed an experimental system capable of reproducing such conditions. Theoretical investigation was carried out to facilitate the design of the system using the Monte Carlo transport calculation code MCNP. Finally, we performed experiments with some standard gamma-ray sources to complete the system.

Ultrasonic Total Focusing Method Technology in Carbon Steel Butt Weld Application

Carbon steel butt welds are widely used in aircraft carrier, shipbuilding, railway, aerospace and other industries. As a new technology of ultrasonic non-destructive testing, Total Focusing Method (TFM) technology is a method of image reconstruction in which the value of each constituent datum of the image results from focused ultrasound. TFM may also be understood as a broad term encompassing a family of processing techniques for image reconstruction from full matrix capture (FMC). This paper compares resolution and focus between TFM and conventional phased array technology, by using phased array test block type B. The paper also introduces the testing system, including equipment, probe, wedge, calibration block as per ASME, PC analysis software, for detection of 21mm thickness carbon steel welds. In order to improve the defect detection ability, a customized software on board was designed based on TFM technology, and an appropriate propagation mode was adopted for different types of defects, such as longitudinal cracks on surface, lack of fusion, porosities, slag inclusions. The test results showing that compared with conventional phased array technology, the defect information such as length and depth detected by TFM are nearer to the actual situation, and the shape is closer to the physical one. TFM technology has more advantages in quantitative and qualitative capabilities in testing carbon steel butt welds, and can meet the requirements of on-site testing.

Optimization of image reconstruction technique for respiratory-gated lung stereotactic body radiotherapy treatment planning using four-dimensional CT: a phantom study.

Patient respiration is characterized by respiratory parameters, such as cycle, amplitude, and baseline drift. In treatment planning using four-dimensional computed tomography (4DCT) images, the target dose may be affected by variations in image reconstruction techniques and respiratory parameters. This study aimed to optimize 4DCT image reconstruction techniques for the treatment planning of lung stereotactic body radiotherapy (SBRT) based on respiratory parameters using respiratory motion phantom. We quantified respiratory parameters using 30 respiratory motion datasets. The 4DCT images were acquired, and the phase- and amplitude-based reconstruction images (RI) were created. The target dose was calculated based on these reconstructed images. Statistical analysis was performed using Pearson's correlation coefficient (r) to determine the relationship between respiratory parameters and target dose in each reconstructed technique and respiratory region. In the inhalation region of phase-based RI, r of the target dose and baseline drift was -0.52. In particular, the target dose was significantly reduced for respiratory parameters with a baseline drift of 0.8mm/s and above. No other respiratory parameters or respiratory regions were significantly correlated with target dose in phase-based RI. In amplitude-based RI, there were no significant differences in the correlation between all respiratory parameters and target dose in the exhalation or inhalation regions. These results showed that the target dose of the amplitude-based RI did not depend on changes in respiratory parameters or respiratory regions, compared to the phase-based RI. However, it is possible to guarantee the target dose by considering respiratory parameters during the inhalation region of the phase-based RI.

Differential Hemodynamic Responses to Motor and Tactile Imagery: Insights from Multichannel fNIRS Mapping.

Tactile and motor imagery are crucial components of sensorimotor functioning and cognitive neuroscience research, yet the neural mechanisms of tactile imagery remain underexplored compared to motor imagery. This study employs multichannel functional near-infrared spectroscopy (fNIRS) combined with image reconstruction techniques to investigate the neural hemodynamics associated with tactile (TI) and motor imagery (MI). In a study of 15 healthy participants, we found that MI elicited significantly greater hemodynamic responses (HRs) in the precentral area compared to TI, suggesting the involvement of different cortical areas involved in two different types of sensorimotor mental imagery. Concurrently, the HRs in S1 and parietal areas exhibited comparable patterns in both TI and MI. During MI, both motor and somatosensory areas demonstrated comparable HRs. However, in TI, somatosensory activation was observed to be more pronounced. Our results highlight the distinctive neural profiles of motor versus tactile imagery and indicate fNIRS technique to be sensitive for this. This distinction is significant for fundamental understanding of sensorimotor integration and for developing advanced neurotechnologies, including imagery-based brain-computer interfaces (BCIs) that can differentiate between different types of mental imagery.

Applications of artificial intelligence in radiology

Artificial intelligence (AI) is increasingly finding its way into routine radiological work. Presentation of the current advances and applications of AI along the entire radiological patient journey. Systematic literature review of established AI techniques and current research projects, with reference to consensus recommendations. The applications of AI in radiology cover awide range, starting with AI-supported scheduling and indications assessment, extending to AI-enhanced image acquisition and reconstruction techniques that have the potential to reduce radiation doses in computed tomography (CT) or acquisition times in magnetic resonance imaging (MRI), while maintaining comparable image quality. These include computer-aided detection and diagnosis, such as fracture recognition or nodule detection. Additionally, methods such as worklist prioritization and structured reporting facilitated by large language models enable arethinking of the reporting process. The use of AI promises to increase the efficiency of all steps of the radiology workflow and an improved diagnostic accuracy. To achieve this, seamless integration into technical workflows and proven evidence of AI systems are necessary. Applications of AI have the potential to profoundly influence the role of radiologists in the future.

CT-based measurement and analysis of distal humerus morphology in healthy adults from Northern China

BackgroundThis study aims to investigate the morphological characteristics of the distal humerus in healthy adults from northern China using computed tomography and three-dimensional reconstruction techniques and compared whether there were diferences in morphology among populations from diferent geographical regions.MethodsThe CT data of 80 patients were imported into Mimics software for three-dimensional reconstruction and measurement. The differences in distal humeral morphological parameters between different genders and sides were compared, and the correlation between the parameters was explored. The distal humeral morphological parameters between Western and Chinese populations based on current and previous pooled results were compared.ResultsThirty-one morphological parameters were measured and analyzed in this study. The average (and standard deviation) of capitellum depth, capitellum width, capitellum height, distal humerus width, epitrochlea width, and humeral metaphyseal width was 10.83 ± 1.18 mm, 17.60 ± 2.06 mm, 21.10 ± 2.03 mm, 44.38 ± 4.07 mm, 12.02 ± 1.90 mm and 58.95 ± 4.86 mm, these parameters were significantly higher (P < 0.001*) in males than females. The capitellum width (r = -0.300, P = 0.007*), anterior lateral trochlear depth (r =-0.227, P = 0.043*), medial crest coronal tangential angle (r = 0.307, P = 0.006*), olecranon fossa volume (r = -0.408, P < 0.001*), olecranon fossa surface area (r = -0.345, P = 0.002*) and coronoid fossa surface area (r = -0.279, P = 0.012*) were significantly correlated with the age of the subjects. In the comparison of people from different regions, the capitellum height, lateral trochlear high, trochlear groove high, trochlear depth and medial trochlear high of the Western population were 23.25 ± 2.56 m, 21.6 ± 2.20 mm, 17.8 ± 2.00 mm, 17.80 ± 2.00 mm, 29.9 ± 4.10 mm, are significantly higher than those in the Chinese population. while capitellum width (15.55 ± 2.68 mm) and capitellum depth (9.00 ± 1.00 mm) were slightly lower.ConclusionThe findings provide a basis for the design of distal humeral orthopaedic implants, ensuring greater alignment with the anatomical structure of the distal humerus and improved surgical outcomes. Furthermore, the study provides a reference point for the diagnosis and classification of distal humeral diseases, as well as guidance for patient rehabilitation.

Salient Region Guided Colour Image Restoration using Deep Learning with Adaptive Compressive Sensing

In this research, a unique colour image reconstruction method based on Compressive Sensing is presented, aimed at enhancing efficiency and quality. The proposed method incorporates deep learning techniques, adaptive measurement rate setting, and salient region detection. The process begins with the collection of an input RGB images, followed by pre-processing to convert them to the Y+ colour space., including optimized Faster R-CNN (OFRCNN), are employed to detect and segment salient regions in the Y-channel. An adaptive measurement rate setting using Deep Q-Network (DQN) dynamically adjusts measurement rates based on input data characteristics. To optimize the Y-channel compression, a Dual attention-based CS Network (CSNet) model is applied. Moreover, adaptive weighted reconstruction is introduced, utilizing salient region masks and a hybrid the Walrus Optimization Algorithm (WaOA) and mountain gazelle optimizer (MGO), called Hybrid Walrus with Mountain Gazelle Optimizer (HWMGO). Finally, the restored Y-channel is combined with the reconstructed U and V channels to reconstruct full-colour in the RGB colour space. Experimental evaluations on a diverse dataset demonstrate the effectiveness of the proposed algorithm in accurately reconstructing high-quality colour images from CS data. This research contributes to the advancement of colour image reconstruction techniques, offering a comprehensive and efficient framework for various applications.

3D Imaging Reconstruction and Laparoscopic Robotic Surgery Approach to Disseminated Peritoneal Leiomyomatosis

ObjectivesThis video article explores the synergistic approach of 3D imaging reconstruction and laparoscopic robotic surgery for the management of a complex case of disseminated peritoneal leiomyomatosis (1). The primary focus lies in the capability of the reconstruction model to provide diagnostic support to identify fibroids during surgical procedures, potentially enhancing surgical precision, reducing operating times, minimizing uterine incisions, and limiting blood loss. 3D imaging reconstruction techniques were used to facilitate the identification of multiple parasitic and non-serosal myomas, which is particularly challenging when operating with a robotic surgical platform that lacks haptic feedback. SettingTertiary referral center. ParticipantsA case report design was employed, focusing on a 43-year-old nulliparous infertile woman with multiple symptomatic uterine myomas. Our institution has made a further diagnosis of disseminated peritoneal leiomyomatosis(4-5). InterventionsDue to the widespread nature of peritoneal leiomyomatosis and numerous uterine fibroids, robotic surgery was considered a preferable option based on our experience to operate within confined anatomical spaces. 3D imaging reconstruction technology was utilized for preoperative and intraoperative planning, enabling precise determination of the fibroids' location, size, and volume obtained through MRI imaging. Real-time 3D imaging guided rapid myoma localization and surgical strategy adjustment (2-3). The procedure resulted in the removal of 15 fibroids, with minimal blood loss (250 mL) and a total operative time of 120 minutes. Multilayer running hysterorraphy was performed using a barbed monofilament suture to ensure effective hemostasis, incorporating serosal introflection to reduce the risk of post-operative adhesion development. ConclusionsThe combined approach of 3D imaging reconstruction and laparoscopic robotic surgery holds significant potential for the management of disseminated peritoneal leiomyomatosis. This approach can overcome some robotic surgery limitations, particularly the absence of haptic feedback, providing accurate preoperative planning and real-time intraoperative guidance, facilitating efficient fibroid localization, minimizing uterine incisions, and reducing blood loss. Further research is needed to fully evaluate the clinical impact of this promising technology.

Intensity-sensitive Quality Assessment of Extended Sources in Astronomical Images

Radio astronomy studies the Universe by observing the radio emissions of celestial bodies. Different methods can be used to recover the sky brightness distribution (SBD), which describes the distribution of celestial sources from recorded data, with the output dependent on the method used. Image quality assessment (IQA) indexes can be used to compare the differences between restored SBDs produced by different image reconstruction techniques to evaluate their effectiveness. However, reconstructed images (for the same SBD) can appear to be very similar, especially when observed by the human visual system (HVS). Hence, current structural similarity methods, inspired by the HVS, are not effective. In the past, we have proposed two methods to assess point-source images, where low amounts of concentrated information are present in larger regions of noise-like data. But for images that include extended source(s), the increase in complexity of the structure makes the IQA methods for point sources oversensitive because the important objects cannot be described by isolated point sources. Therefore, in this article we propose the augmented low-information similarity index (augLISI), an improved version of LISI, to assess images including extended source(s). Experiments have been carried out to illustrate how this new IQA method can help with the development and study of astronomical imaging techniques. Note that although we focus on radio astronomical images herein, these IQA methods are also applicable to other astronomical images and imaging techniques.

Super-resolution deep learning image reconstruction: image quality and myocardial homogeneity in coronary computed tomography angiography

BackgroundThe recently introduced super-resolution (SR) deep learning image reconstruction (DLR) is potentially effective in reducing noise level and enhancing the spatial resolution. We aimed to investigate whether SR-DLR has advantages in the overall image quality and intensity homogeneity on coronary computed tomography (CT) angiography with four different approaches: filtered-back projection (FBP), hybrid iterative reconstruction (IR), DLR, and SR-DLR.MethodsSixty-three patients (mean age, 61 ± 11 years; range, 18–81 years; 40 men) who had undergone coronary CT angiography between June and October 2022 were retrospectively included. Image noise, signal to noise ratio, and contrast to noise ratio were quantified in both proximal and distal segments of the major coronary arteries. The left ventricle myocardium contrast homogeneity was analyzed. Two independent reviewers scored overall image quality, image noise, image sharpness, and myocardial homogeneity.ResultsImage noise in Hounsfield units (HU) was significantly lower (P < 0.001) for the SR-DLR (11.2 ± 2.0 HU) compared to those associated with other image reconstruction methods including FBP (30.5 ± 10.5 HU), hybrid IR (20.0 ± 5.4 HU), and DLR (14.2 ± 2.5 HU) in both proximal and distal segments. SR-DLR significantly improved signal to noise ratio and contrast to noise ratio in both the proximal and distal segments of the major coronary arteries. No significant difference was observed in the myocardial CT attenuation with SR-DLR among different segments of the left ventricle myocardium (P = 0.345). Conversely, FBP and hybrid IR resulted in inhomogeneous myocardial CT attenuation (P < 0.001). Two reviewers graded subjective image quality with SR-DLR higher than other image reconstruction techniques (P < 0.001).ConclusionsSR-DLR improved image quality, demonstrated clearer delineation of distal segments of coronary arteries, and was seemingly accurate for quantifying CT attenuation in the myocardium.

Image Analysis Artificial Intelligence Technologies for Plant Phenotyping: Current State of the Art

Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended.

Impact of Emerging Deep Learning-Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features.

This study aims to evaluate, on one MRI vendor's platform, the impact of deep learning (DL)-based reconstruction techniques on MRI radiomic features compared to conventional image reconstruction techniques. Under IRB approval and informed consent, we prospectively collected undersampled coronal T2-weighted MR images of the abdomen (1.5 T; Philips Healthcare) from 17 pediatric and adult subjects and reconstructed them using a conventional image reconstruction technique (compressed sensitivity encoding [C-SENSE]) and two DL-based reconstruction techniques (SmartSpeed [Philips Healthcare, US FDA cleared] and SmartSpeed with Super Resolution [SmartSpeed-SuperRes, not US FDA cleared to date]). Eight regions of interest (ROIs) across organs/tissues (liver, spleen, kidney, pancreas, fat, and muscle) were manually placed. Eighty-six MRI radiomic features were then extracted. Pearson's correlation coefficients (PCCs) and intraclass correlation coefficients (ICCs) were calculated between (A) C-SENSE versus SmartSpeed, and (B) C-SENSE versus SmartSpeed-SuperRes. To quantify the impact from the perspective of the whole MR image, cross-ROI mean PCCs and ICCs were calculated for individual radiomic features. The impact of image reconstruction on individual radiomic features in different organs/tissues was evaluated using ANOVA analyses. According to cross-ROI mean PCCs, 50 out of 86 radiomic features were highly correlated (PCC, ≥0.8) between SmartSpeed and C-SENSE, whereas only 15 radiomic features were highly correlated between SmartSpeed-SuperRes and C-SENSE reconstructions. According to cross-ROI mean ICCs, 58 out of 86 radiomic features had high agreements (ICC ≥0.75) between SmartSpeed and C-SENSE, whereas only 9 radiomic features had high agreements between SmartSpeed-SuperRes and C-SENSE reconstructions. For SmartSpeed reconstruction, the psoas muscle ROI appeared to be impacted most with the lowest median (IQR) correlation of 0.57 (0.25). The circular liver ROI was impacted most by SmartSpeed-SuperRes (PCC, 0.60 [0.22]). ANOVA analyses suggest that the impact of DL reconstruction algorithms on radiomic features varies significantly among different organs/tissues (P < 0.001). MRI radiomic features are significantly altered by DL-based reconstruction compared to a conventional reconstruction technique. The impact of DL reconstruction algorithms on radiomic features varies significantly between different organs/tissues.

The Usefulness of Low-Kiloelectron Volt Virtual Monochromatic Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction Technique in Improving the Delineation of Pancreatic Ductal Adenocarcinoma.

To evaluate the usefulness of low-keV multiphasic computed tomography (CT) with deep learning image reconstruction (DLIR) in improving the delineation of pancreatic ductal adenocarcinoma (PDAC) compared to conventional hybrid iterative reconstruction (HIR). Thirty-five patients with PDAC who underwent multiphasic CT were retrospectively evaluated. Raw data were reconstructed with two energy levels (40keV and 70keV) of virtual monochromatic imaging (VMI) using HIR (ASiR-V50%) and DLIR (TrueFidelity-H). Contrast-to-noise ratio (CNRtumor) was calculated from the CT values within regions of interest in tumor and normal pancreas in the pancreatic parenchymal phase images. Lesion conspicuity of PDAC in pancreatic parenchymal phase on 40-keV HIR, 40-keV DLIR, and 70-keV DLIR images was qualitatively rated on a 5-point scale, using 70-keV HIR images as reference (score 1 = poor; score 3 = equivalent to reference; score 5 = excellent) by two radiologists. CNRtumor of 40-keV DLIR images (median 10.4, interquartile range (IQR) 7.8-14.9) was significantly higher than that of the other VMIs (40keV HIR, median 6.2, IQR 4.4-8.5, P < 0.0001; 70-keV DLIR, median 6.3, IQR 5.1-9.9, P = 0.0002; 70-keV HIR, median 4.2, IQR 3.1-6.1, P < 0.0001). CNRtumor of 40-keV DLIR images were significantly better than those of the 40-keV HIR and 70-keV HIR images by 72 ± 22% and 211 ± 340%, respectively. Lesion conspicuity scores on 40-keV DLIR images (observer 1, 4.5 ± 0.7; observer 2, 3.4 ± 0.5) were significantly higher than on 40-keV HIR (observer 1, 3.3 ± 0.9, P < 0.0001; observer 2, 3.1 ± 0.4, P = 0.013). DLIR is a promising reconstruction method to improve PDAC delineation in 40-keV VMI at the pancreatic parenchymal phase compared to conventional HIR.

Correction: Randomized recursive techniques for image reconstruction in diffuse optical tomography

Correction: Randomized recursive techniques for image reconstruction in diffuse optical tomography

Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques.

To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement. DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR)showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight≥90 kg) yielded similar findings to the overall study population.

Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction

BackgroundDeep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction. This study aims to compare the diagnostic performance of DLIR and iterative reconstruction (IR) in the evaluation of focal hepatic lesions. MethodsWe conducted a retrospective study of 216 focal hepatic lesions in 109 adult participants who underwent abdominal CT scanning at our institution. We used DLIR (low, medium, and high strength) and IR (0 %, 10 %, 20 %, and 30 %) techniques for image reconstruction. Four experienced abdominal radiologists independently evaluated focal hepatic lesions based on five qualitative aspects (lesion detectability, lesion border, diagnostic confidence level, image artifact, and overall image quality). Quantitatively, we measured and compared the level of image noise for each technique at the liver and aorta. ResultsThere were significant differences (p < 0.001) among the seven reconstruction techniques in terms of lesion borders, image artifacts, and overall image quality. Low-strength DLIR (DLIR-L) exhibited the best overall image quality. Although high-strength DLIR (DLIR-H) had the least image noise and fewest artifacts, it also had the lowest scores for lesion borders and overall image quality. Image noise showed a weak to moderate positive correlation with participants’ body mass index and waist circumference. ConclusionsThe optimal-strength DLIR significantly improved overall image quality for evaluating focal hepatic lesions compared to the IR technique. DLIR-L achieved the best overall image quality while maintaining acceptable levels of image noise and quality of lesion borders.

Co-Transplantation-Based Human-Mouse Chimeric Brain Models to Study Human Glial-Glial and Glial-Neuronal Interactions.

Human-mouse chimeric brain models, generated by transplanting human induced pluripotent stem cell (hiPSC)-derived neural cells, are valuable for studying the development and function of human neural cells in vivo. Understanding glial-glial and glial-neuronal interactions is essential for unraveling the complexities of brain function and developing treatments for neurological disorders. To explore these interactions between human neural cells within an intact brain environment, we employe a co-transplantation strategy involving the engraftment of hiPSC-derived neural progenitor cells along with primitive macrophage progenitors into the neonatal mouse brain. This approach creates human-mouse chimeric brains containing human microglia, macroglia (astroglia and oligodendroglia), and neurons. Using super-resolution imaging and 3D reconstruction techniques, we examine the dynamics between human neurons and glia, unveiling human microglia engulfing immature human neurons, microglia pruning synapses of human neurons, and significant interactions between human oligodendrocytes and neurons. Single-cell RNA sequencing analysis of the chimeric brain uncovers a close recapitulation of the human glial progenitor cell population, along with a dynamic stage in astroglial development that mirrors the processes found in the human brain. Furthermore, cell-cell communication analysis highlights significant neuronal-glial and glial-glial interactions, especially the interaction between adhesion molecules neurexins and neuroligins. This innovative co-transplantation model opens up new avenues for exploring the complex pathophysiological mechanisms underlying human neurological diseases. It holds particular promise for studying disorders where glial-neuronal interactions and non-cell-autonomous effects play crucial roles.

Randomized recursive techniques for image reconstruction in diffuse optical tomography

Randomized recursive techniques for image reconstruction in diffuse optical tomography