Decrease In Image Quality Research Articles

Accurate 3D single-molecule localization via vectorial in situ point spread function retrieval and aberration assessment

Single-molecule localization microscopy (SMLM) gradually plays an important role in deep tissue imaging. However, current SMLM methods primarily rely on fiducial marks, neglecting aberrations introduced by thick samples, thereby resulting in decreased image quality in deep tissues. Here, we introduce vectorial in situ point spread function (PSF) retrieval (VISPR), a method that retrieves a precise PSF model considering both system- and sample-induced aberrations under SMLM conditions. By employing the theory of vectorial PSF model and maximum likelihood estimation (MLE) phase retrieval, VISPR is capable of reconstructing an accurate in situ 3D PSF model achieving the theoretically minimum uncertainty and accurately reflecting three-dimensional information of single molecules. This capability enables accurate 3D super-resolution reconstruction in deep regions away from the coverslips. Additionally, VISPR demonstrates applicability in low signal-to-noise ratio scenarios and compatibility with various SMLM microscope modalities. From both simulations and experiments, we verified the superiority and effectiveness of VISPR. We anticipate that VISPR will become a pivotal tool for advancing deep tissue SMLM imaging.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective
Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.

Method
The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.

Results
DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR. 

Conclusion
DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Channel Self-Attention Based Low-Light Image Enhancement Network

Low-light image enhancement is crucial in applications such as traffic safety and medical imaging. Besides having characteristics like low luminance and poor visibility, low-light images are inevitably affected by noise. Noise not only covers image details, introduces artifacts, and decreases image quality, but also interferes with downstream computer vision tasks like object detection, image segmentation, and object tracking. Previous methods in image enhancement often overlook noise handling or fail to accurately suppress noise in adaptive denoising processes, resulting in more severe noise artifacts in the enhanced images. To effectively suppress noise, this paper proposes a Channel Self-Attention Based Low-Light Image Enhancement Network (CAENet), which leverages Transformers and CNNs to model long-range and short-range pixel dependencies, extract global and local features, and construct a Noise Suppression Transformer Block that adaptively suppresses noise regions guided by signal-to-noise ratio priors and attention maps. After adaptive noise suppression, the resulting images exhibit fewer noise artifacts and improved details. The experimental results show that the network in this paper outperforms other state-of-the-art methods overall on five representative paired datasets as well as six unpaired datasets, improving the image quality while effectively suppressing the noise.

AIE-YOLO: Effective object detection method in extreme driving scenarios via adaptive image enhancement.

The widespread research and implementation of visual object detection technology have significantly transformed the autonomous driving industry. Autonomous driving relies heavily on visual sensors to perceive and analyze the environment. However, under extreme weather conditions, such as heavy rain, fog, or low light, these sensors may encounter disruptions, resulting in decreased image quality and reduced detection accuracy, thereby increasing the risk for autonomous driving. To address these challenges, we propose adaptive image enhancement (AIE)-YOLO, a novel object detection method to enhance road object detection accuracy under extreme weather conditions. To tackle the issue of image quality degradation in extreme weather, we designed an improved adaptive image enhancement module. This module dynamically adjusts the pixel features of road images based on different scene conditions, thereby enhancing object visibility and suppressing irrelevant background interference. Additionally, we introduce a spatial feature extraction module to adaptively enhance the model's spatial modeling capability under complex backgrounds. Furthermore, a channel feature extraction module is designed to adaptively enhance the model's representation and generalization abilities. Due to the difficulty in acquiring real-world data for various extreme weather conditions, we constructed a novel benchmark dataset named extreme weather simulation-rare object dataset. This dataset comprises ten types of simulated extreme weather scenarios and is built upon a publicly available rare object detection dataset. Extensive experiments conducted on the extreme weather simulation-rare object dataset demonstrate that AIE-YOLO outperforms existing state-of-the-art methods, achieving excellent detection performance under extreme weather conditions.

Optimisation of virtual monoenergetic reconstructions for the diagnosis of pulmonary embolism using photon-counting detector computed tomography angiography

PurposeComputed tomography (CT) pulmonary angiography is considered the gold standard for pulmonary embolism (PE) diagnosis, relying on the discrimination between contrast and embolus. Photon-counting detector CT (PCD-CT) generates monoenergetic reconstructions through energy-resolved detection. Virtual monoenergetic images (VMI) at low keV can be used to improve pulmonary artery opacification. While studies have assessed VMI for PE diagnosis on dual-energy CT (DECT), there is a lack of literature on optimal settings for PCD-CT-PE reconstructions, warranting further investigation.Material and methodsTwenty-five sequential patients who underwent PCD-CT pulmonary angiography for suspicion of acute PE were retrospectively included in this study. Quantitative metrics including signal-to-noise ratio (SNR) and contrast-to-noise (CNR) ratio were calculated for 4 VMI values (40, 60, 80, and 100 keV). Qualitative measures of diagnostic quality were obtained for proximal to distal pulmonary artery branches by 2 cardiothoracic radiologists using a 5-point modified Likert scale.ResultsSNR and CNR were highest for the 40 keV VMI (49.3 ± 22.2 and 48.2 ± 22.1, respectively) and were inversely related to monoenergetic keV. Qualitatively, 40 and 60 keV both exhibited excellent diagnostic quality (mean main pulmonary artery: 5.0 ± 0 and 5.0 ± 0; subsegmental pulmonary arteries 4.9 ± 0.1 and 4.9 ± 0.1, respectively) while distal segments at high (80-100) keVs had worse quality.Conclusions40 keV was the best individual VMI for the detection of pulmonary embolism by quantitative metrics. Qualitatively, 40-60 keV reconstructions may be used without a significant decrease in subjective quality. VMIs at higher keV lead to reduced opacification of the distal pulmonary arteries, resulting in decreased image quality.

Classifier-guided multi-style tile image generation method

Image generative models for ceramic tile design lack style diversity and controllability of high-quality generated styles. It is difficult to find a series of ceramic tiles with the same texture but distinct styles, that makes it a challenging for users to select from a limited number of tiles with a single style. Although, Generative Adversarial Networks (GANs) can slightly increase the style diversity of tile images, the style controllability remains very weak. Additionally, concatenating generated tile image blocks to obtain a larger texture region can easily result in seams at the boundaries that decrease image quality. In this paper, we propose a style transfer method for ceramic tiles texture generation that combines a classifier-guided StyleGAN with AdaIN-GAN to overcome the above limitations. Firstly, we introduce a new conditional classifier-guided module into the StyleGAN. With the guidance of the input condition vector, the output image is made to have the tile style characteristics that match the vector. At the same time, the fusion of the condition vectors realizes the style gradient effect of the tile image to expand the style diversity. Secondly, we use the AdaIN-GAN to color the original texture in tile style. The style images generated by StyleGAN are then used as a dataset for model training to enhance the generalization ability of the model and achieve a style transfer effect with fixed texture features but significantly diverse styles. Finally, a linear weighted image stitching method is adopted, which uses an adaptive kernel linear weighted matrix to cover and splice arbitrary seams with image blocks, thereby successfully eliminating seams and enhancing image continuity. When this method is applied to high-resolution tile image generation, the method still maintains higher continuity and clearer image quality. Extensive experiments and human evaluation confirm the superior performance of the proposed method compared with other SOTA methods. The experimental results also verify that the new tile images generated by the proposed algorithm have diverse styles and meet the design requirements for tile style diversity.

Wavelet transform and edge loss-based three-stage segmentation model for retinal vessel

Retinal vessel segmentation is a rapid method for the diagnosis of ocular diseases. By applying deep learning-based techniques to retinal images, more structural information about retinal vessels can be extracted to accurately assess the extent and classification of ocular diseases. However, current segmentation networks typically consist of a single network, making them vulnerable to noise, decreased image quality, and other interfering factors, resulting in erroneous segmentation outcomes. Additionally, the traditional skip connection mechanism introduces noise from the encoder features into the decoder, which reduces the refinement of the final segmentation result. A three-stage fundus vessel segmentation model called EWSNet is proposed to address these issues. The EWSNet utilizes two different models to extract and reconstruct coarse and fine blood vessels, respectively. The reconstructed results are fed into the refinement network to rebuild the edge portion of the retinal vessels, achieving higher segmentation performance. Within the framework of EWSNet, a wavelet-transformation-based sampling module is used to effectively suppress high-frequency noise in the features while using low-frequency features to reconstruct vascular information. Besides, a new edge loss function (E-BCE Loss) is designed to encourage more precise predictions at the segmentation edges. Experimental results on CHASE_DB1, HRF, STARE, and a newly collected ultra-wide-angle fundus dataset (UWF) demonstrate that EWSNet has more robust segmentation performance in the microvascular region compared to the current mainstream models. The code is available at: https://github.com/xuecheng990531/EWSNet.

Ultra-low-dose computed tomography with deep learning reconstruction for craniosynostosis at radiation doses comparable to skull radiographs: a pilot study.

Craniofacial computed tomography (CT) is the diagnostic investigation of choice for craniosynostosis, but high radiation dose remains a concern. To evaluate the image quality and diagnostic performance of an ultra-low-dose craniofacial CT protocol with deep learning reconstruction for diagnosis of craniosynostosis. All children who underwent initial craniofacial CT for suspected craniosynostosis between September 2021 and September 2022 were included in the study. The ultra-low-dose craniofacial CT protocol using 70 kVp, model-based iterative reconstruction and deep learning reconstruction techniques was compared with a routine-dose craniofacial CT protocol. Quantitative analysis of the signal-to-noise ratio and noise was performed. The 3-dimensional (D) volume-rendered images were independently evaluated by two radiologists with regard to surface coarseness, step-off artifacts and overall image quality on a 5-point scale. Sutural patency was assessed for each of six sutures. Radiation dose was compared between the two protocols. Among 29 patients (15 routine-dose CT and 14 ultra-low-dose CT), 23 patients had craniosynostosis. The 3-D volume-rendered images of ultra-low-dose CT without deep learning showed decreased image quality compared to routine-dose CT. The 3-D volume-rendered images of ultra-low-dose CT with deep learning reconstruction showed higher noise level, higher surface coarseness but decreased step-off artifacts, comparable signal-to-noise ratio and overall similar image quality compared to the routine-dose CT images. Diagnostic performance for detecting craniosynostosis at the suture level showed no significant difference between ultra-low-dose CT without deep learning reconstruction, ultra-low-dose CT with deep learning reconstruction and routine-dose CT. The estimated effective radiation dose for the ultra-low-dose CT was 0.05mSv (range, 0.03-0.06mSv), a 95% reduction in dose over the routine-dose CT at 1.15mSv (range, 0.54-1.74mSv). This radiation dose is comparable to 4-view skull radiography (0.05-0.1mSv) and lower than previously reported effective dose for craniosynostosis protocols (0.08-3.36mSv). In this pilot study, an ultra-low-dose CT protocol using radiation doses at a level similar to skull radiographs showed preserved diagnostic performance for craniosynostosis, but decreased image quality compared to the routine-dose CT protocol. However, by combining the ultra-low-dose CT protocol with deep learning reconstruction, image quality was improved to a level comparable to the routine-dose CT protocol, without sacrificing diagnostic performance for craniosynostosis.

Investigation of the Impact of Defective Ultrasound Transducers on Clinical Image Quality in Grayscale 2-D Still Images

There are several studies that show high defect rates of transducers in clinical use. The purpose of the present study was to investigate whether image quality and the risk for misdiagnosis is affected by using defective transducers. Four defective transducers with varying degrees of defect severity, still in clinical use, were selected. Forty artifact-affected clinical images from each transducer were compared with images acquired from fully functional transducers, of the same model, in an observer study where four experienced radiologists rated each of the 320 images. The rating tasks included if the artifacts were detectable, if the possible artifacts might affect the diagnosis, how well structural details were reproduced and, finally, an assessment of overall image quality. The artifacts in the images were detectable for three of the four transducers (p < 0.05), and in 121 of 640 assessments of the images from the defective transducers the observers were confident that the artifacts could affect the diagnosis. All four faulty transducers were assessed to have decreased ability to resolve structural details (p < 0.05), and three of the four transducers were assessed to have worse overall image quality (p < 0.05). The present study shows that image quality and the risk of misdiagnosis can be affected by using defective transducers. This highlights the importance of frequent quality control of the transducers to avoid decreased image quality and even misdiagnosis.

Single energy metal artifact reduction performs better than virtual monoenergetic dual-energy reconstruction in CT of the equine proximal phalanx.

Metal artifacts in CT negatively impact the evaluation of surgical implants and the surrounding tissues. The aim of this prospective experimental study was to evaluate the ability of a single energy metal artifact reduction (SEMAR™, Canon) algorithm and virtual monoenergetic (VM) dual-energy CT (DECT) scanning techniques to reduce metal artifacts from stainless steel screws surgically inserted into the equine proximal phalanx. Seven acquisitions of 18 cadaver limbs were performed on a Canon Aquilion One Vision CT scanner (Helical +SEMAR, Volume +SEMAR, Standard Helical, Standard Volume and VM DECT at 135, 120, and 105keV) and reconstructed in a bone kernel. Blinded subjective evaluation performed by three observers indicated a significant effect of acquisition in both adjacent tissues (P<0.001) and distant tissues (P<0.001) and the best metal artifact reduction was seen with Helical +SEMAR and Volume +SEMAR. The subjective overall preference of CT acquisition type was (1) Helical +SEMAR, (2) Volume +SEMAR, (3) VM DECT 135keV, (4) VM DECT 120keV, (5) VM DECT 105keV, (6) Standard Helical, (7) Standard Volume (P<0.001). Unblinded objective evaluation performed by one observer showed that VM DECT 120keV, Helical +SEMAR, and Volume +SEMAR performed similarly and were objectively the best at reducing blooming artifact. Overall, the best metal artifact reduction was obtained with SEMAR, followed by VM DECT. However, VM DECT performance varies with energy level and was associated with decreased image quality in distant tissues and artifactual overcorrection of metal artifacts at high energy levels.

Abstract P2-05-04: Safety of Breast MRI under General Anesthesia

Abstract INTRODUCTION: Breast MRI (bMRI) has become a standard of care (SOC) modality for both screening and diagnostic work-up of breast disease (1-2). Despite availability of larger bores, claustrophobia remains the most common reason for patients declining MRI (3). Anesthesia service can offer sedation during MRI improving patients’ acceptance. However, unlike routine MRI, bMRI is performed with patient positioned prone. Our anesthesiologists observed patients’ expectation of receiving general anesthesia, without understanding the risks associated with sedation/anesthesia in this particular setting and no prior trials of oral anxiolysis. OBJECTIVE: This project retrospectively reviewed bMRI exams with sedation for image quality and potential complication to identify areas for intervention such as provider education on anesthesiology risks in the bMRI environment and the implementation of an institution-approved oral anxiolysis program. METHODS: Patients receiving bMRI exams with sedation/anesthesia at a large academic cancer center were identified from the existing institutionally approved Report Imaging Quality Issue database after receiving institutional approval for this quality improvement project with waiver for consent obtained from Institution Review Board. Patient demographics, imaging data, and clinical notes were reviewed by two board certified breast radiologists with over 15 years of experience in MRI interpretation. Safety reports were reviewed in consensus between two radiologists and lead anesthesiologist for MRI department. RESULTS: Of 4844 bMRI exams, 33 were performed with sedation/anesthesia. Mean age was 60 years (range 37-77), with mean body mass index of 34 (range 21-45). Reason for anesthesia included claustrophobia in 13 (40%) patients, pain in 6 (18%), and 14 not clearly documented (42%). Eighteen of 33 (55%) exams were rated as poor quality, 7 (21%) as average, and 8 (24%) good or diagnostic. Most common reasons for poor quality included motion from snoring or pain and poor fat suppression due to body habitus. In 11/33 (33%), repeat imaging with still poor quality was documented in technologists’ comments. Length of exam appointment ranged from 52 – 155, average 90 minutes. Only one case documented facial bruising due to prone positioning and one case of positioning requiring up to 7 staff. DISCUSSION: Availability of on-site anesthesia has enabled some patients to receive bMRI. These patients present to MRI with expectation of sedation/anesthesia without understanding risks or prior trial of anxiolysis and most have a challenging body habitus contributing to difficulty in prone positioning and decreased image quality while under sedation/anesthesia. Development of an oral patient anxiolysis program and provider education may improve image quality and reduce utilization of anesthesia. Citation Format: H. T. Carisa Le-Petross, Shreyas Bhavsar, Marion E. Scoggins, Jia Sun, Thao Bui, Kyungmin Shin, Jason Stafford, Richard Carlson III, Beatriz Adrada. Safety of Breast MRI under General Anesthesia [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-05-04.

Automated 3D Defect Detectionm based on Simulated Reference

Bringing the Computed Tomography (CT) technology into production lines brings many advantages due to the complete and detailed 3D characterization and localization of defects. But compared to laboratory systems, Inline-CT systems must meet several challenges, the most significant one is certainly the limited available time for the measurement procedure and data evaluation. Visual inspection of 3D data sets of complex parts within the production rate is almost impossible, so automated image evaluation algorithms are mandatory for that task. Fast measurement procedures often mean short integration times and a lower number of projections which are two factors that increase quantum noise and thus decrease image quality significantly. In addition to that, scatter artifacts also have a negative influence on the image quality. All these conditions constitute challenges to the image evaluation algorithm. The Fraunhofer EZRT has developed a method for automated defect detection in cast parts by using reference (that means flawless) parts for comparison. The advantage of this method is that image artifacts are considered, since they appear in both data sets in the same way and have almost no negative influence on the reliability of the evaluation result. Most recent work took this method one step further by using not real flawless parts, but instead using simulations to generate reference data sets in a very convenient and quick way for any number of different parts.

Multi T1-weighted contrast imaging and T1 mapping with compressed sensing FLAWS at 3T.

The Fluid And White matter Suppression (FLAWS) MRI sequence provides multiple T1-weighted contrasts of the brain in a single acquisition. However, the FLAWS acquisition time is approximately 8min with a standard GRAPPA 3 acceleration factor at 3T. This study aims at reducing the FLAWS acquisition time by providing a new sequence optimization based on a Cartesian phyllotaxis k-space undersampling and a compressed sensing (CS) reconstruction. This study also aims at showing that T1 mapping can be performed with FLAWS at 3T. The CS FLAWS parameters were determined using a method based on a profit function maximization under constraints. The FLAWS optimization and T1 mapping were assessed with in-silico, in-vitro and in-vivo (10 healthy volunteers) experiments conducted at 3T. In-silico, in-vitro and in-vivo experiments showed that the proposed CS FLAWS optimization allows the acquisition time of a 1mm-isotropic full-brain scan to be reduced from [Formula: see text] to [Formula: see text] without decreasing image quality. In addition, these experiments demonstrate that T1 mapping can be performed with FLAWS at 3T. The results obtained in this study suggest that the recent advances in FLAWS imaging allow to perform multiple T1-weighted contrast imaging and T1 mapping in a single [Formula: see text] sequence acquisition.

Deep Learning MRI Reconstruction for Accelerating Turbo Spin Echo Hand and Wrist Imaging: A Comparison of Image Quality, Visualization of Anatomy, and Detection of Common Pathologies with Standard Imaging

Magnetic resonance imaging (MRI) of the hand and wrist is a routine MRI examination and takes about 15-20 minutes, which can lead to problems resulting from the relatively long scan time, such as decreased image quality due to motion artifacts and lower patient throughput. The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the hand and wrist regarding image quality, visualization of anatomy, and diagnostic performance concerning common pathologies. Twenty-one patients (mean age: 43 ± 19 [19-85] years, 10 men, 11 female) were prospectively enrolled in this study between October 2020 and June 2021. Each participant underwent two MRI protocols: first, standard fully sampled TSE sequences reconstructed with a standard GRAPPA reconstruction (TSES) and second, prospectively undersampled TSE sequences using a conventional parallel imaging undersampling pattern reconstructed with a DL reconstruction (TSEDL). Both protocols were acquired consecutively in one examination. Two experienced MSK-imaging radiologists qualitatively evaluated the images concerning image quality, noise, edge sharpness, artifacts, and diagnostic confidence, as well as the delineation of anatomical structures (triangular fibrocartilage complex, tendon of the extensor carpi ulnaris muscle, extrinsic and intrinsic ligaments, median nerve, cartilage) using a five-point Likert scale and assessed common pathologies. Wilcoxon signed-rank test and kappa statistics were performed to compare the sequences. Overall image quality, artifacts, delineation of anatomical structures, and diagnostic confidence of TSEDL were rated to be comparable to TSES (p > 0.05). Additionally, TSEDL showed decreased image noise (4.90, median 5, IQR 5-5) compared to TSES (4.52, median 5, IQR 4-5, p < 0.05) and improved edge sharpness (TSEDL: 4.10, median 4, IQR 3.5-5; TSES: 3.57, median 4, IQR 3-4; p < 0.05). Inter- and intrareader agreement was substantial to almost perfect (κ=0.632-1.000) for the detection of common pathologies. Time of acquisition could be reduced by more than 60% with the protocol using TSEDL. Compared to TSES, TSEDL provided decreased noise and increased edge sharpness, equal image quality, delineation of anatomical structures, detection of pathologies, and diagnostic confidence. Therefore, TSEDL may be clinically relevant for hand and wrist imaging, as it reduces examination time by more than 60%, thus increasing patient comfort and patient throughput.

Click-to-Release: Cleavable Radioimmunoimaging with [89Zr]Zr-DFO-Trans-Cyclooctene-Trastuzumab Increases Tumor-to-Blood Ratio.

One of the main challenges of PET imaging with 89Zr-labeled monoclonal antibodies (mAbs) remains the long blood circulation of the radiolabeled mAbs, leading to high background signals, decreasing image quality. To overcome this limitation, here we report the use of a bioorthogonal linker cleavage approach (click-to-release chemistry) to selectively liberate [89Zr]Zr-DFO from trans-cyclooctene-functionalized trastuzumab (TCO-Tmab) in blood, following the administration of a tetrazine compound (trigger) in BT-474 tumor-bearing mice. Methods: We created a series of TCO-DFO constructs and evaluated their performance in [89Zr]Zr-DFO release from Tmab in vitro using different trigger compounds. The in vivo behavior of the best performing [89Zr]Zr-TCO-Tmab was studied in healthy mice first to determine the optimal dose of the trigger. To find the optimal time for the trigger administration, the rate of [89Zr]Zr-TCO-Tmab internalization was studied in BT-474 cancer cells. Finally, the trigger was administered 6 h or 24 h after [89Zr]Zr-TCO-Tmab- administration in tumor-bearing mice to liberate the [89Zr]Zr-DFO fragment. PET scans were obtained of tumor-bearing mice that received the trigger 6 h post-[89Zr]Zr-TCO-Tmab administration. Results: The [89Zr]Zr-TCO-Tmab and trigger pair with the best in vivo properties exhibited 83% release in 50% mouse plasma. In tumor-bearing mice the tumor-blood ratios were markedly increased from 1.0 ± 0.4 to 2.3 ± 0.6 (p = 0.0057) and from 2.5 ± 0.7 to 6.6 ± 0.9 (p < 0.0001) when the trigger was administered at 6 h and 24 h post-mAb, respectively. Same day PET imaging clearly showed uptake in the tumor combined with a strongly reduced background due to the fast clearance of the released [89Zr]Zr-DFO-containing fragment from the circulation through the kidneys. Conclusions: This is the first demonstration of the use of trans-cyclooctene-tetrazine click-to-release chemistry to release a radioactive chelator from a mAb in mice to increase tumor-to-blood ratios. Our results suggest that click-cleavable radioimmunoimaging may allow for substantially shorter intervals in PET imaging with full mAbs, reducing radiation doses and potentially even enabling same day imaging.

Estimation of Shooting Part Using a Camera with Depth Sensors and Pose Estimation Method and Automatic Setting of Optimal X-ray Imaging Conditions

In this study, we propose a system that combines a depth camera with a deep learning model for estimating the human skeleton and a depth camera to estimate the shooting part to be radiographed and to acquire the thickness of the subject, thereby providing optimized X-ray imaging conditions. We propose a system that provides optimized X-ray imaging conditions by estimating the shooting part and measuring the thickness of the subject using an RGB camera and a depth camera. The system uses OpenPose, a posture estimation library, to estimate the shooting part. The recognition rate of the shooting part was 15.38% for the depth camera and 84.62% for the RGB camera at a distance of 100 cm, and 42.31% for the depth camera and 100% for the RGB camera at a distance of 120 cm. The measurement accuracy of the subject thickness was within ±10 mm except for a few cases, indicating that the X-ray imaging conditions were optimized for the subject thickness. The implementation of this system in an X-ray system is expected to enable automatic setting of X-ray imaging conditions. The system is also useful in preventing increased exposure dose due to excessive dose or decreased image quality due to insufficient dose caused by incorrect setting of X-ray imaging conditions.

Transforming a Mid-infrared Laser Profile from Gaussian to a Top-Hat with a Diffractive Optical Element for Mass Spectrometry Imaging.

Many mass spectrometry imaging (MSI) applications such as infrared matrix-assisted electrospray ionization (IR-MALDESI) employ an infrared (IR) laser with a Gaussian profile where laser irradiance is highest in the center and decreases exponentially. To enable full ablation of a square region of interest, oversampling is often needed, which results in nonuniform ablation and leads to decreased image quality. A diffractive optical element (DOE) was integrated into the optical path to generate homogeneous intensity distributions while maintaining laser energy above the ablation threshold, to enable complete sample removal from laser pulses without oversampling. 2D and 3D imaging with the DOE inserted show clear and sharp ablation patterns with satisfactory biological signals gained. Further improvements will optimize the beam profile and generate a square top-hat laser beam for MSI application at higher spatial resolution.

Low-Dose CT Imaging of the Pelvis in Follow-up Examinations-Significant Dose Reduction and Impact of Tin Filtration: Evaluation by Phantom Studies and First Systematic Retrospective Patient Analyses.

Low-dose (LD) computed tomography (CT) is still rarely used in musculoskeletal (MSK) radiology. This study evaluates the potentials of LD CT for follow-up pelvic imaging with special focus on tin filtration (Sn) technology for normal and obese patients with and without metal implants. In a phantom study, 5 different LD and normal-dose (ND) CT protocols with and without tin filtration were tested using a normal and an obese phantom. Iterative reconstruction (IR) and filtered back projection (FBP) were used for CT image reconstruction. In a subsequent retrospective patient study, ND CT images of 45 patients were compared with follow-up tin-filtered LD CT images with a 90% dose reduction. Sixty-four percent of patients contained metal implants at the follow-up examination. Computed tomography images were objectively (image noise, contrast-to-noise ratio [CNR], dose-normalized contrast-to-noise ratio [CNRD]) and subjectively, using a 6-point Likert score, evaluated. In addition, the figure of merit was calculated. For group comparisons, paired t tests, Wilcoxon signed rank test, analysis of variance, or Kruskal-Wallis tests were used, where applicable. The LD Sn protocol with 67% dose reduction resulted in equal values in qualitative (Likert score) and quantitative image analysis (image noise) compared with the ND protocol in the phantom study. For follow-up examinations, dose could be reduced up to 90% by using Sn LD CT scans without impairment in the clinical study. However, metal implants resulted in a mild impairment of Sn LD as well as ND CT images. Cancellous bone ( P < 0.001) was assessed worse and cortical bone ( P = 0.063) equally in Sn LD CT images compared with ND CT images. Figure of merit values were significant ( P ≤ 0.02) lower and hence better in Sn LD as in ND protocols. Obese patients benefited in particular from tin filtration in LD MSK imaging in terms of image noise and CNR ( P ≤ 0.05). Low-dose CT scans with tin filtration allow maximum dose reduction while maintaining high image quality for certain clinical purposes, for example, follow-up examinations, especially metal implant position, material loosening, and consolidation controls. Overweight patients benefit particularly from tin filter technology. Although metal implants decrease image quality in ND as well as in Sn LD CT images, this is not a relevant limitation for assessability.

Evaluation of PET List Data-Driven Gated Motion Correction Technique Applied in Lung Tumors

PurposePositron emission tomography/computed tomography (PET/CT) is an important tool for tumor staging or treatment response evaluation, especially for lung tumors. However, the captured static PET image could be blurry due to patients' free breathing, resulting in decreased image quality and incorrect quantitative values. This study aimed to evaluate whether the Q.Static scan mode with the novel PET list data-driven gated (DDG) technique decreases the lesion blurring problems in the PET/CT images of patients with lung cancer.MethodsData of 194 patients with lung tumors were retrospectively reviewed. DDG Q.Static scan mode was set up in three beds to cover the whole chest and the upper abdomen in the routine PET/CT scans and was activated automatically when sensing significant respiratory motion. Routine reconstruction algorithm was applied for data analysis. Only the lesions in the motion-corrected areas were measured and calculated for statistics.ResultsAmong the 194 patients, 124 had at least one bed that activated the DDG Q.Static procedure. However, only 49 out of the 124 patients showed lesions in their activated beds. Compared with the non-corrected data, the DDG Q.Static data showed improved accuracy with increased SUVmax and SUVmean of 8.52% (9.20 ± 5.42 to 9.74 ± 5.42) and 8.65% (6.11 ± 3.68 to 6.48 ± 3.68), respectively. In addition, metabolic tumor volume was reduced from 6.54 ± 8.58 to 5.55 ± 7.33 (14.79% reduction). For subjective image quality, the DDG Q.Static data scored higher than the non-corrected data.ConclusionThis study showed that the quantitative values and image quality were improved after the correction. Therefore, the DDG Q.Static technique is an effective method to correct motion artifacts in PET/CT scans.

Noise modelling of perfusion CT images for robust hemodynamic parameter estimations

Objective. The radiation dose of cerebral perfusion computed tomography (CPCT) imaging can be reduced by lowering the milliampere-second or kilovoltage peak. However, dose reduction can decrease image quality due to excessive x-ray quanta fluctuation and reduced detector signal relative to system electronic noise, thereby influencing the accuracy of hemodynamic parameters for patients with acute stroke. Existing low-dose CPCT denoising methods, which mainly focus on specific temporal and spatial prior knowledge in low-dose CPCT images, not take the noise distribution characteristics of low-dose CPCT images into consideration. In practice, the noise of low-dose CPCT images can be much more complicated. This study first investigates the noise properties in low-dose CPCT images and proposes a perfusion deconvolution model based on the noise properties. Approach. To characterize the noise distribution in CPCT images properly, we analyze noise properties in low-dose CPCT images and find that the intra-frame noise distribution may vary in the different areas and the inter-frame noise also may vary in low-dose CPCT images. Thus, we attempt the first-ever effort to model CPCT noise with a non-independent and identical distribution (i.i.d.) mixture-of-Gaussians (MoG) model for noise assumption. Furthermore, we integrate the noise modeling strategy into a perfusion deconvolution model and present a novel perfusion deconvolution method by using self-relative structural similarity information and MoG model (named as SR-MoG) to estimate the hemodynamic parameters accurately. In the presented SR-MoG method, the self-relative structural similarity information is obtained from preprocessed low-dose CPCT images. Main results. The results show that the presented SR-MoG method can achieve promising gains over the existing deconvolution approaches. In particular, the average root-mean-square error (RMSE) of cerebral blood flow (CBF), cerebral blood volume, and mean transit time was improved by 40.3%, 69.1%, and 40.8% in the digital phantom study, and the average RMSE of CBF can be improved by 81.0% in the clinical data study, compared with tensor total variation regularization deconvolution method. Significance. The presented SR-MoG method can estimate high-accuracy hemodynamic parameters andachieve promising gains over the existing deconvolution approaches.