Image Sequences Research Articles

Human Pose Classification Based on Pose Long Short-Term Memory

Objective: At present, pose classification research is a hot topic in the field of computer vision, playing a crucial role in surveillance security, and motion data mining. The research serves as a crucial follow-up to three dimensional (3D) pose estimation and makes a significant contribution to the advancement of artificial intelligence. Pose classification is a complex problem involving a large amount of complicated data, making its digital modeling and further processing challenging. Methods: This article proposes the Pose long short - term memory (LSTM) method to classify 3D poses reconstructed from 2D image sequences. Result: Compared with traditional methods, the experimental tests show that the performance of the Pose LSTM is more superior in all aspects. Conclusion: Pose LSTM is particularly effective for recognizing continuous action poses because they can capture temporal dependencies in sequential data. In the context of pose recognition, it can analyze the sequence of poses over time and understand how these poses transition from one to another, making it possible to accurately classify or predict actions that unfold over a series of frames.

Acoustic noise in a small-format 3.0-T neonatal MRI system.

Today, magnetic resonance imaging (MRI) is rarely used in managing the care of premature neonates. This is in large part due to the medical and logistical challenges associated with moving neonates from the neonatal intensive care unit (NICU) to the radiology department. Furthermore, acoustic noise associated with MR scanning poses safety concerns for both practitioners and neonatal patients. A small-format 3.0-T neonatal scanner was recently developed and placed within the NICU to address these logistical and acoustic challenges. To compare acoustic noise measurements of a small-format 3.0-T neonatal MRI scanner with conventional adult-sized 1.5-T and 3.0-T MRI scanners using identical neonatal head imaging protocols. Sound pressure level (SPL) measurements of a standard imaging protocol were made in a small-format neonatal 3.0-T MRI scanner as well as in adult-sized 1.5-T and 3.0-T scanners. SPL measurements were made with a Brüel & Kjær sound level meter model 2250. The statistical significance of the differences in SPL between scanners was determined using one-way ANOVA. Average sound pressure level values were measured in unweighted decibels (dB) and A-weighted decibels (dBA) for all imaging sequences in the protocol. The average A-weighted SPLs for the NICU from 1.5-T and 3.0-T MRI scanners were 81.02 ± 0.28 dBA, 87.00 ± 0.85 dBA, and 94.91 ± 0.65 dBA, respectively. SPLs at the isocenter of the NICU MRI scanner were 5.98 dBA quieter than in the 1.5-T scanner (P=0.007), and 13.89 dBA quieter than in the 3.0-T scanner (P<0.001). For staff standing next to the scanner, the NICU scanner was 20.24 dBA quieter than the 1.5-T scanner (P<0.001) and 19.28 dBA quieter than the 3.0-T scanner (P<0.001). The NICU 3.0-T MRI system is significantly quieter than conventional adult-sized MRI systems, improving safety for neonatal patients. Significant reductions in SPL were also noted inside the screen room where clinicians may be present during scanning.

POSSIBILITIES OF RADIOLOGICAL SCAN METHODS WITH POST-PROCESSING IMAGE RECONSTRUCTION IN THE ASSESSMENT OF ATHEROSCLEROTIC PLAQUES OF THE CAROTID ARTERIES

The aim. To establish the informativeness of the methods of computed tomography angiography, ultrasound diagnostics and magnetic resonance arteriography without contrast in patients with atherosclerosis of carotid arteries. Materials and methods. During July 2022 - July 2024, 82 male patients were examined on the basis of the Neurological Pathology Diagnostic Center of the KNP "Regional Clinical Hospital of the Ivano-Frankivsk Regional Council". Forty-six of them were included in the study group with clinical signs of atherosclerotic lesions of the carotid arteries. Thirty-six patients with asymptomatic course of the carotid arteries atherosclerosis were chosen as the control group. Patients of both groups were aged 51-79 years, and were homogeneous in terms of concomitant diseases, duration of smoking and lipidogram results. Exclusion criteria from both cohorts were: the presence of arrhythmia, radiation treatment of oncological processes in the projection of the head and neck, diagnosed Alzheimer's disease or Fara's disease, lack of written informed consent to participate in the study, impaired legal capacity of the person, when the written consent cannot be considered truly conscious. CTA was performed using a 160-slice computer tomograph Aquilion Prime Toshiba Scanner TSX-302A (Toshiba) with SUREPlaque software to analyze atherosclerotic plaques and vessels in the Carotid CTA fast scanning mode. MRI of the head and neck with non-contrast MR-arteriography was performed using a 1.5 Tesla Phillips Achieva device using blood flow imaging sequences: s3DI MC (three-dimensional time of flight MR-angiography). Ultrasound of the carotid arteries was performed using the Esaote MyLab9eXP device using B-mode, Doppler mapping and elastography. Atherosclerotic plaques were assessed according to the new imaging-based classification - Carotid Plaque-RADS from 2024. The results. According to the classification of Carotid Plaque-RADS, the following results were obtained according to visualization by ultrasound and CT methods: a combination of plaques of categories from Plaque-RADS 2 to Plaque-RADS 4, considering the types of each category, was detected in patients of both groups. There was no significant difference between the groups in the assessment of the structure of the atherosclerotic plaque (p=0.616). For the study group, the detection of the higher percentage of the stenotic lumen of the vessel was statistically different (median - 68.5% (interquartile range 9.75%)) compared to the control group (median - 60.0% (interquartile range 7.5%)) (p =0.048). A comparison of the degree of stenosis by ultrasound and CTA methods was carried out, where a direct correlation of very high strength according to the Chaddock scale was established (r=0.934 (confidence interval: 0.881 – 0.964), p˂0.001). Atherosclerotic lesions were characterized by indirect signs on MRI, namely: wall filling defects, which most likely correspond to plaques, increased bends of main arteries. Conclusions. Ultrasound diagnostics and reconstructive post-processing capabilities of computed tomography were practically equivalent in assessing the degree of stenosis with high correlation strength (r=0.934) and highly informative images of atherosclerotic masses of carotid arteries, while non-contrast magnetic resonance arteriography allowed to suspect atherosclerotic lesions as an area of ​​a filling defect without detailed tissue structures.

Human Fall Motion Prediction: Fall Motion Forecasting and Detection with GRU

The human fall motion prediction system is a preventive tool aimed at reducing the risk of falls. In our research, we developed a deep learning model that utilizes pose estimation to track human body posture and integrated this with a Gated Recurrent Unit (GRU) to forecast human motion and predict falls. GRU, an enhancement of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models offers improved memorization and more efficient memory usage and performance. Our study presents the human fall motion prediction, which combines the forecasting and classification of potential falls.The CAUCAFall dataset is used as the benchmark of this study, which contains the image sequences of single human motion with ten actions conducted by ten actors. We employed the YOLOv8 Pose model to track the 2D human body pose as the input in our system. A thorough evaluation of the CAUCAFall dataset highlights the effectiveness of our proposed system. Evaluation using the CAUCAFall dataset demonstrates that the model achieved a Mean Per Joint Position Error (MPJPE) of 4.65 pixels from the ground truth, with a 70% accuracy rate in fall prediction. However, the model also exhibited a Mean Relative Error (MRE) of 0.3, indicating that 30% of the predictions were incorrect. These findings underscore the potential of the GRU-based system in fall prevention

Prediction of Malignancy and Pathological Types of Solid Lung Nodules on CT Scans Using a Volumetric SWIN Transformer.

Lung adenocarcinoma and squamous cell carcinoma are the two most common pathological lung cancer subtypes. Accurate diagnosis and pathological subtyping are crucial for lung cancer treatment. Solitary solid lung nodules with lobulation and spiculation signs are often indicative of lung cancer; however, in some cases, postoperative pathology finds benign solid lung nodules. It is critical to accurately identify solid lung nodules with lobulation and spiculation signs before surgery; however, traditional diagnostic imaging is prone to misdiagnosis, and studies on artificial intelligence-assisted diagnosis are few. Therefore, we introduce a volumetric SWIN Transformer-based method. It is a multi-scale, multi-task, and highly interpretable model for distinguishing between benign solid lung nodules with lobulation and spiculation signs, lung adenocarcinomas, and lung squamous cell carcinoma. The technique's effectiveness was improved by using 3-dimensional (3D) computed tomography (CT) images instead of conventional 2-dimensional (2D) images to combine as much information as possible. The model was trained using 352 of the 441 CT image sequences and validated using the rest. The experimental results showed that our model could accurately differentiate between benign lung nodules with lobulation and spiculation signs, lung adenocarcinoma, and squamous cell carcinoma. On the test set, our model achieves an accuracy of 0.9888, precision of 0.9892, recall of 0.9888, and an F1-score of 0.9888, along with a class activation mapping (CAM) visualization of the 3D model. Consequently, our method could be used as a preoperative tool to assist in diagnosing solitary solid lung nodules with lobulation and spiculation signs accurately and provide a theoretical basis for developing appropriate clinical diagnosis and treatment plans for the patients.

Asymmetric spin echo multi-echo echo planar imaging (ASEME-EPI) sequence for pre-clinical high-field fMRI.

In functional magnetic resonance imaging (fMRI) of the blood oxygen level-dependent (BOLD) contrast, gradient-recalled echo (GRE) acquisitions offer high sensitivity but suffer from susceptibility-induced signal loss and lack specificity to microvasculature. In contrast, spin echo (SE) acquisitions provide improved specificity at the cost of reduced sensitivity. This study introduces Asymmetric Spin Echo Multi-Echo Echo Planar Imaging (ASEME-EPI), a technique designed to combine the benefits of both GRE and SE for high-field preclinical fMRI. ASEME-EPI employs a spin echo readout followed by two asymmetric spin echo (ASE) GRE readouts, providing an initial T2-weighted SE image and subsequent T2 * -weighted ASE images. A feasibility study for the technique was implemented on a 9.4 T pre-clinical MRI system and tested using a visual stimulation in northern tree shrews. Comparing ASEME-EPI with conventional GRE echo planar imaging (GRE-EPI) and SE echo planar imaging (SE-EPI) acquisitions, results showed that ASEME-EPI achieved BOLD contrast-to-noise ratio (CNR) comparable to GRE-EPI while offering improved specificity in activation maps. ASEME-EPI activation was more confined to the primary visual cortex (V1), unlike GRE-EPI which showed activation extending beyond anatomical boundaries. Additionally, ASEME-EPI demonstrated the ability to recover signal in areas of severe field inhomogeneity where GRE-EPI suffered from signal loss. The performance of ASEME-EPI is attributed to its multi-echo nature, allowing for SNR-optimized combination of echoes, effectively denoising the data. The inclusion of the initial SE also contributes to signal recovery in areas prone to susceptibility artifacts. This feasibility study demonstrates the potential of ASEME-EPI for high-field pre-clinical fMRI, offering a promising compromise between GRE sensitivity and SE specificity while addressing challenges of T2 * decay at high field strengths.

Sorghum segmentation and leaf counting using in silico trained deep neural model

AbstractThis paper introduces a novel deep neural model for segmenting and tracking the number of leaves in sorghum plants in phenotyping facilities. Our algorithm inputs a sequence of images of a sorghum plant and outputs the segmented images and the number of leaves. The key novelty of our approach is in training the deep neural model. Manual annotations are tedious, and we have developed a procedural three‐dimensional (3D) sorghum model that provides detailed geometry and texture to generate photorealistic 3D models. The overall shape of the sorghum leaf geometry is determined by its skeleton, and it is detailed by a procedural model that varies its curvature, width, length, and overall shape. The color is determined by using a Monte Carlo path tracer. We mimic the illumination of the phenotyping facility and use reflectance and transmittance on sorghum surfaces to determine the color of the leaves. The 3D procedural model allows us to generate photorealistic and segmented images that we use to train a deep neural model. Our segmentation provides a mean intersection over union score of 0.51, resulting in leaf counting accuracy within the 90% confidence interval for the human counts.

High-precision geometric positioning for optical remote sensing satellite in dynamic imaging

ABSTRACT Dynamic imaging of optical remote sensing satellites refers to the active acquisition of images while the satellite is maneuvering at a high angular velocity, which significantly enhances the efficiency and application value of remote sensing imagery. However, due to the influence of rapid maneuvering, the random error of star sensors significantly increases, resulting in a decrease in the geometric positioning accuracy of dynamic imaging remote sensing images. This paper proposes a dynamic fusion method for multisource attitude measurement data based on the noise adaptive estimation and bidirectional filter to achieve high-precision attitude determination and geometric positioning in dynamic imaging. Based on the measurement error model of star sensors, the weights of the star sensor and gyroscope are adaptively adjusted in multisource data fusion to reduce the impact of star sensor measurement errors on the gyroscope. Moreover, a bidirectional fusion filter that includes a low-velocity maneuvering stage is proposed to realize the optimal estimation of the satellite attitude parameter. The simulation data and onboard data of the Luojia3-01 (LJ3–01) satellite were tested to verify the effectiveness of the proposed method. The geometric positioning accuracy of the staring images of LJ3–01 improved from 10.048 m to 7.538 m. The registration accuracy of the sequential images improved from 3.568 pixels to 1.179 pixels. The proposed method can significantly improve the attitude determination accuracy and the geometric positioning accuracy of LJ3–01 satellite staring images. Moreover, for simulation data with various angular velocities, the attitude determination accuracies of the proposed method are better than 0.93”. The experimental results show that the proposed method can achieve high-precision attitude determination in dynamic imaging, reaching the accuracy in the traditional passive imaging.

Artefacts on magnetic resonance imaging with Osia®2 bone conduction hearing aid: A cadaver study

Objectives: Hearing implants often limit the assessment of magnetic resonance examinations due to susceptibility artefacts. Our aim was to evaluate the impact of artefacts attributed to the Osia®2 implant system in terms of utility in visualizing selected cranial structures. Methods: A BI300 implant and an OSI200 actuator were implanted into a human cadaver head in the audiologically most favourable position according to the manufacturer’s guidelines. Scanning was accomplished using the institutional head and inner ear protocol with a General Electric 1.5 Tesla scanner with retained and removed implant magnet, extended with T1 and T2 weighted sequences with metal-artefact reduction (MAVRIC SL). Image quality was evaluated by three radiologists. Results: The Osia®2 produced significant artefacts in most of the series of standard imaging sequences predominantly on the ipsilateral side of the head. The majority of the artefacts were caused by the implant magnet. Even without removing the magnet, MAVRIC SL improved image quality to such an extent that it became comparable with that after magnet removal. Conclusions: The standard sequences suffer considerable quality loss due to the artefacts, attributed predominantly to the magnetic component. Metal-artefact reduction sequences are effective in obtaining sufficient-to-good quality images without surgical magnet removal.

Advancements in sarcopenia diagnosis: from imaging techniques to non-radiation assessments.

Sarcopenia is a prevalent condition with significant clinical implications, and it is expected to escalate globally, demanding for effective diagnostic strategies, possibly at an early stage of the disease. Imaging techniques play a pivotal role in comprehensively evaluating sarcopenia, offering insights into both muscle quantity and quality. Among all the imaging techniques currently used for the diagnosis and follow up of sarcopenia, it is possible to distinguish two classes: Rx based techniques, using ionizing radiations, and non-invasive techniques, which are based on the use of safe and low risk diagnostic procedures. Dual-energy x-ray Absorptiometry and Computed Tomography, while widely utilized, entail radiation exposure concerns. Ultrasound imaging offers portability, real-time imaging, and absence of ionizing radiation, making it a promising tool Magnetic Resonance Imaging, particularly T1-weighted and Dixon sequences, provides cross- sectional and high-resolution images and fat-water separation capabilities, facilitating precise sarcopenia quantification. Bioelectrical Impedance Analysis (BIA), a non-invasive technique, estimates body composition, including muscle mass, albeit influenced by hydration status. Standardized protocols, such as those proposed by the Sarcopenia through Ultrasound (SARCUS) Working Group, are imperative for ensuring consistency across assessments. Future research should focus on refining these techniques and harnessing the potential of radiomics and artificial intelligence to enhance diagnostic accuracy and prognostic capabilities in sarcopenia.

Magnetic resonance microscopy for submillimeter samples in a horizontal MR scanner

The spatial resolution in magnetic resonance imaging is mainly limited by low SNR, which is commonly addressed by long measurement times or dedicated hardware. In single digit micron resolutions, diffusion becomes a further limiting factor since depending on the gradient strength, the diffusion length of particles may approach the target resolutions. Spatial resolution improvement has been addressed by microscopy inserts comprising dedicated gradient systems and RF-coils, usually designed for NMR spectrometers that are often equipped with a deuterium lock for field drift compensations. The presented microscopy insert has been designed to provide single-digit micron resolutions on horizontal preclinical imaging systems utilizing their full imaging and user interface capabilities. The incorporated gradient provides an efficiency of 0.135 T/(m∙A) which in combination with the system’s gradient amplifiers yields a maximum of 27 T/m. With the additional low noise amplifier added to the RF-path a three-fold SNR improvement could be achieved for small samples. Furthermore, a modified constant time imaging sequence was introduced to improve the capability of the setup for ultra‐high-resolution imaging demonstrated on zebrafish embryos at different development stages with (9 μm)³ resolution.

Fast autofocusing based on single-pixel moment detection

Traditional image processing-based autofocusing techniques require the acquisition, storage, and processing of large amounts of image sequences, constraining focusing speed and cost. Here we propose an autofocusing technique, which directly and exactly acquires the geometric moments of the target object in real time at different locations by means of a proper image modulation and detection by a single-pixel detector. An autofocusing criterion is then formulated using the central moments, and the fast acquisition of the focal point is achieved by searching for the position that minimizes the criterion. Theoretical analysis and experimental validation of the method are performed and the results show that the method can achieve fast and accurate autofocusing. The proposed method requires only three single-pixel detections for each focusing position of the target object to evaluate the focusing criterion without imaging the target object. The method does not require any active object-to-camera distance measurement. Comparing to local differential methods such as contrast or gradient measurement, our method is more stable to noise and requires very little data compared with the traditional image processing methods. It may find a wide range of potential applications and prospects, particularly in low-light imaging and near-infra imaging, where the level of noise is typically high.

Towards multimodal visualization of esophageal motility: fusion of manometry, impedance, and videofluoroscopic image sequences.

Dysphagia is the inability or difficulty to swallow normally. Standard procedures for diagnosing the exact disease are, among others, X-ray videofluoroscopy, manometry and impedance examinations, usually performed consecutively. In order to gain more insights, ongoing research is aiming to collect these different modalities at the same time, with the goal to present them in a joint visualization. One idea to create a combined view is the projection of the manometry and impedance values onto the right location in the X-ray images. This requires to identify the exact sensor locations in the images. This work gives an overview of the challenges associated with the sensor detection task and proposes a robust approach to detect the sensors in X-ray image sequences, ultimately allowing to project the manometry and impedance values onto the right location in the images. The developed sensor detection approach is evaluated on a total of 14 sequences from different patients, achieving a F1-score of 86.36%. To demonstrate the robustness of the approach, another study is performed by adding different levels of noise to the images, with the performance of our sensor detection method only slightly decreasing in these scenarios. This robust sensor detection provides the basis to accurately project manometry and impedance values onto the images, allowing to create a multimodal visualization of the swallow process. The resulting visualizations are evaluated qualitatively by domain experts, indicating a great benefit of this proposed fused visualization approach. Using our preprocessing and sensor detection method, we show that the sensor detection task can be successfully approached with high accuracy. This allows to create a novel, multimodal visualization of esophageal motility, helping to provide more insights into swallow disorders of patients.

Simulation of Dendrite Growth with a Diffusion-Limited Aggregation Model Validated by MRI of a Lithium Symmetric Cell during Charging

Lithium metal batteries offer high energy density but are challenged by dendrite growth, which can lead to short circuits and battery failure. Multiple models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. This study presents a simple model to simulate macroscale dendrite growth on lithium metal electrodes. The model uses a 3D single-particle Diffusion-Limited Aggregation (DLA) algorithm with an electric field bias to simulate dendrite growth. The electric field bias was introduced into the model with an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. Before performing the simulation with the proposed model, the dendrite growth in a lithium symmetric cell during charging was measured by sequential 3D magnetic resonance imaging (MRI). These data were then used to validate the simulation, as the dendrite structure in each measured MRI time frame was used a starting point for a new simulation, the results of which were then validated with the measured dendrite structure of the next time frame. The best agreement between the simulated and measured dendrite structures using the overlap and displacement of deposition sites metrics was obtained at the biasing factor c = 0.7. This agreement was also good in terms with the fractal dimension of the dendrite structures. The proposed method offers a simple, accurate, and scalable framework for predicting dendrite growth over long deposition periods, making it a valuable tool for studying dendrite suppression under real-world battery charging conditions.

Panoramic single-pixel imaging with megapixel resolution based on rotational subdivision.

Single-pixel imaging (SPI) using a single-pixel detector is an unconventional imaging method that has great application prospects in many fields to realize high-performance imaging. In particular, the recently proposed catadioptric panoramic ghost imaging (CPGI) extends the application potential of SPI to high-performance imaging at a wide field of view (FOV) with recent growing demands. However, the resolution of CPGI is limited by the hardware parameters of the digital micromirror device (DMD), which cannot meet ultrahigh-resolution panoramic imaging needs that require detailed information. Therefore, to overcome the resolution limitation of CPGI, we propose a panoramic SPI based on rotational subdivision (RSPSI). The key of RSPSI is to obtain the entire panoramic scene by the rotation-scanning of a rotating mirror tilted 45°, so that one single pattern that only covers one sub-FOV with a small FOV can complete an uninterrupted modulation on the entire panoramic FOV during a once-through pattern projection. Then, based on temporal resolution subdivision, the image sequence of sub-FOVs subdivided from the entire panoramic FOV can be reconstructed with pixel-level or even subpixel-level horizontal shifting adjacently. Experimental results using a proof-of-concept setup show that the panoramic image can be obtained with 10428 × 543 of 5,662,404 pixels, which is more than 9.6 times higher than the resolution limit of the CPGI using the same DMD. To the best of our knowledge, the proposed RSPSI is the first to achieve a megapixel resolution via SPI, which can provide potential applications in fields requiring imaging with ultrahigh-resolution and wide FOV.

Characterizing brain mechanics through 7 tesla magnetic resonance elastography

Magnetic resonance elastography (MRE) is a non-invasive method for determining the mechanical response of tissues using applied harmonic deformation and motion-sensitive MRI. MRE studies of the human brain are typically performed at conventional field strengths, with a few attempts at the ultra-high field strength, 7T, reporting increased spatial resolution with partial brain coverage. Achieving high-resolution human brain scans using 7T MRE presents unique challenges of decreased octahedral shear strain-based signal-to-noise ratio (OSS-SNR) and lower shear wave motion sensitivity. In this study, we establish high resolution MRE at 7T with a custom 2D multi-slice single-shot spin-echo echo-planar imaging sequence, using the Gadgetron advanced image reconstruction framework, applying Marchenko-Pastur Principal component analysis denoising, and using nonlinear viscoelastic inversion. These techniques allowed us to calculate the viscoelastic properties of the whole human brain at 1.1 mm isotropic imaging resolution with high OSS-SNR and repeatability. Using phantom models and 7T MRE data of eighteen healthy volunteers, we demonstrate the robustness and accuracy of our method at high-resolution while quantifying the feasible tradeoff between resolution, OSS-SNR, and scan time. Using these post-processing techniques, we significantly increased OSS-SNR at 1.1 mm resolution with whole-brain coverage by approximately 4-fold and generated elastograms with high anatomical detail. Performing high-resolution MRE at 7T on the human brain can provide information on different substructures within brain tissue based on their mechanical properties, which can then be used to diagnose pathologies (e.g. Alzheimer's disease), indicate disease progression, or better investigate neurodegeneration effects or other relevant brain disorders,in vivo.

Tracing proliferating potential of lung tumours by [11C]Nicotinamide

Many tumours rely on aerobic glycolysis, known as the Warburg effect, to satisfy energy needs and sustain rapid proliferation [1]. High demands of glucose in tumours can be measured by [18F[fluorodeoxyglucose, [18F]FDG, positron emission tomography (PET). Nicotinamide adenine dinucleotide (NAD) is a coenzyme involved in redox reactions in many metabolic pathways, including glycolysis. To maintain the high rates of glycolysis and proliferation, tumours need increased NAD+ levels and use nicotinamide for the upregulated NAD+ salvage pathway that is the main source of NAD+ in proliferative cancer cells [2]. Herein, we show an improved synthesis method of [11C]Nicotinamide, [11C]NAM, and studied the uptake of [11C]NAM and [18F]FDG to measure proliferating capacity in a lung cancer model. [Carboxyl-11C]nicotinamide was synthesized by a one-pot radiolabeling method [3] on the SYNTHRA synthesizer (Synthra GmbH, Germany) (see Figure 1A). We used a KrasG12D/+ genetically engineered mouse model of lung cancer and evaluated the uptake of [11C]nicotinamide and [18F]FDG with dual-tracer sequential imaging protocol (see Figure 1B) using a NanoScan PET/MRI scanner (Mediso Medical Imaging Systems, Hungary). We obtained [carboxyl-11C]nicotinamide in a radiochemical yield of 15 ± 5 %, volumic activity of 50 ± 10 MBq/ml, and radiochemical purity was &gt; 99 %. We optimized the imaging protocol and found the uptake of [11C]nicotinamide co-localized with the uptake of [18F]FDG in the KrasG12D/+ model of lung cancer as shown in Figure 1C suggesting the higher NAM uptake was associated with high NAD+ production in highly proliferating tumours. We demonstrated that metabolic PET/MRI imaging with [11C]NAM can complement [18F]FDG PET and be useful for probing proliferative potential in lung cancer. This method could be translated and applied in clinical practice to enable visualization of NAD+ metabolism in lung tumours and targeted therapeutically. Please click on the 'PDF' for the full abstract!

A Modified Bleaching Method for Multiplex Immunofluorescence Staining of FFPE Tissue Sections.

Multiplex immunofluorescence (mIF) staining plays an important role in profiling biomarkers and allows investigation of co-relationships between multiple biomarkers in the same tissue section. The Cell DIVE mIF platform (Leica Microsystems) employs an alkaline solution of hydrogen peroxide as a fluorophore inactivation reagent in the sequential staining, imaging, and bleaching protocol for use on FFPE sections. Suboptimal bleaching efficiency, degradation of tissue structure, and loss of antigen immunogenicity occasionally are encountered with the standard bleaching process. To overcome these impediments, we adopted a modified photochemical bleaching method, which utilizes an intense LED light exposure concurrent with the application of hydrogen peroxide. Repeated stain/bleach rounds with different antibodies were performed on breast tissue and other tissue sections. Residual signal after conventional bleaching and the modified technique were compared and tissue integrity and antigen immunogenicity were assessed. The modified technique effectively eliminates fluorescence signal from previous staining rounds and produces consistent results for multiple rounds of staining and imaging. With the modified method, photochemical treatments did not destroy tissue sub-cellular contents, and the tissue antigenicity was well preserved during the entire mIF process. Overall processing time was reduced from 36 to 30 hours in an mIF procedure with 8 rounds. With the conventional method, tissue quality was highly degraded after 8 rounds. The new technique allows reduced turn-around time, provides reliable fluorophore removal in mIF with excellent maintenance of tissue integrity, facilitating studies of the co-localization of multiple biomarkers in tissues of interest.

Vendor-Specific Correction Software for Apparent Diffusion Coefficient Bias Due to Gradient Nonlinearity in Breast Diffusion-Weighted Imaging Using Ice-Water Phantom.

This study aimed to evaluate a vendor-specific correction software for apparent diffusion coefficient (ADC) bias due to gradient nonlinearity in breast diffusion-weighted magnetic resonance imaging using an ice-water phantom. The phantom consists of 5 plastic tubes with a length of 100 mm and a diameter of 15 mm, filled with distilled water and immersed in an ice-water bath. Diffusion-weighted images were acquired by echo-planar imaging sequence on a 3.0-T scanner. ADC maps with and without correction were calculated using 4 b -values (0, 100, 600, and 800 s/mm 2 ). The mean ADCs were measured using a rectangular profile with 5 × 40 pixels in the anterior-posterior (AP) and a square region of interest with 5 × 5 pixels in the right-left (RL) and superior-inferior (SI) directions on the ADC map. ADC was compared with and without correction using a paired t test. Additionally, ADC of the ice-water phantom was measured at the magnet isocenter. ADC increased in the AP and RL directions and decreased in the SI direction with increasing distance from the isocenter before correction. After the correction, ADC at the off-center positions in the AP, RL, and SI directions was reduced to within 5% of the expected value. There were significant differences in the ADC at the off-center positions without and with correction ( P < 0.001); however, ADC at the magnet isocenter did not vary after correction (1.08 ± 0.02 × 10 -3 mm 2 /s). The vendor-specific software corrected the ADC bias due to gradient nonlinearity at the off-center positions in the AP, RL, and SI directions. Therefore, the software will contribute to the accurate ADC assessment in breast DWI.

Safety Clearance and Artifact Testing of a Nitinol Breast Biopsy Clip in an Ultra-High Resolution (7 Tesla) Magnetic Resonance Imaging Environment

Abstract Background The lack of safety clearance of several metallic breast biopsy clips in 7 Tesla (T) poses a significant hurdle to using advanced magnetic resonance imaging (MRI) techniques in clinical management or cancer research. Aims This article assesses the Ultracor Twirl clip for safety and imaging artifacts in a 7T MRI scanner. Setting and Design This study can be categorized as a phantom study. Materials and Methods Tests for magnetic susceptibility (translational attraction and torque), MRI-related heating, and artifacts were conducted based on the American Society for Testing and Materials standards. The magnetic susceptibility tests evaluated the scanner's magnetic force that can cause clip movement and rotation. The heating test was conducted with customized MRI parameters of short TR and maximum echo-train length, designed to induce temperature change. The artifact test, using T1-weighted spin and gradient echo imaging sequences, evaluated potential image misrepresentations (localized signal loss) caused by the clip's metallic properties. Statistical Tests None. Results and Conclusion The magnetic susceptibility tests indicated no noticeable translational or rotational force exerted by the MRI scanner. The heating test indicated no significant temperature change (&lt;0.3°C) in the testing gel when the clip was absent/present, both within the safety threshold (&lt;1°C). The artifact test's clip images all contained an artifact (largest radius = 10.7 mm). These cumulative results indicate that this clip is safe in 7T scanners. Scanning at least 10.7 mm away from the clip avoids potential signal loss in the region of interest.