Fluid suppression in amide proton transfer-weighted (APTw) CEST imaging: New theoretical insights and clinical benefits.

PURPOSE: Amide proton transfer-weighted (APTw) MRI at 3T provides a unique contrast for brain tumor imaging. However, APTw imaging suffers from hyperintensities in liquid compartments such as cystic or necrotic structures and provides a distorted APTw signal intensity. Recently, it has been shown that heuristically motivated fluid suppression can remove such artifacts and significantly improve the readability of APTw imaging. THEORY AND METHODS: In this work, we show that the fluid suppression can actually be understood by the known concept of spillover dilution, which itself can be derived from the Bloch-McConnell equations in comparison to the heuristic approach. Therefore, we derive a novel post-processing formula that efficiently removes fluid artifact, and explains previous approaches. We demonstrate the utility of this APTw assessment in silico, in vitro, and in vivo in brain tumor patients acquired at MR scanners from different vendors. RESULTS: Our results show a reduction of the CEST signals from fluid environments while keeping the APTw-CEST signal intensity almost unchanged for semi-solid tissue structures such as the contralateral normal appearing white matter. This further allows us to use the same color bar settings as for conventional APTw imaging. CONCLUSION: Fluid suppression has considerable value in improving the readability of APTw maps in the neuro-oncological field. In this work, we derive a novel post-processing formula from the underlying Bloch-McConnell equations that efficiently removes fluid artifact, and explains previous approaches which justify the derivation of this metric from a theoretical point of view, to reassure the scientific and medical field about its use.

Improving magnetic resonance spectroscopy in the brainstem periaqueductal gray using spectral registration.

PURPOSE: Functional understanding of the periaqueductal gray (PAG), a clinically relevant brainstem region, can be advanced using 1 H-MRS. However, the PAG's small size and high levels of physiological noise are methodologically challenging. This study aimed to (1) improve 1 H-MRS quality in the PAG using spectral registration for frequency and phase error correction; (2) investigate whether spectral registration is particularly useful in cases of greater head motion; and (3) examine metabolite quantification using literature-based or individual-based water relaxation times. METHODS: Spectra were acquired in 33 healthy volunteers (50.1 years, SD = 17.19, 18 females) on a 3 T Philipps MR system using a point-resolved spectroscopy (PRESS) sequence optimized with very selective saturation pulses (OVERPRESS) and voxel-based flip angle calibration (effective volume of interest size: 8.8 × 10.2 × 12.2 mm3 ). Spectra were fitted using LCModel and SNR, NAA peak linewidths and Cramér-Rao lower bounds (CRLBs) were measured after spectral registration and after minimal frequency alignment. RESULTS: Spectral registration improved SNR by 5% (p = 0.026, median value post-correction: 18.0) and spectral linewidth by 23% (p < 0.001, 4.3 Hz), and reduced the metabolites' CRLBs by 1% to 15% (p < 0.026). Correlational analyses revealed smaller SNR improvements with greater head motion (p = 0.010) recorded using a markerless motion tracking system. Higher metabolite concentrations were detected using individual-based compared to literature-based water relaxation times (p < 0.001). CONCLUSION: This study demonstrates high-quality 1 H-MRS acquisition in the PAG using spectral registration. This shows promise for future 1 H-MRS studies in the PAG and possibly other clinically relevant brain regions with similar methodological challenges.

Reproducibility of 3D pH-weighted chemical exchange saturation transfer contrast in the healthy cervical spinal cord.

Spinal cord ischemia and hypoxia can be caused by compression, injury, and vascular alterations. Measuring ischemia and hypoxia directly in the spinal cord noninvasively remains challenging. Ischemia and hypoxia alter tissue pH, providing a physiologic parameter that may be more directly related to tissue viability. Chemical exchange saturation transfer (CEST) is an MRI contrast mechanism that can be made sensitive to pH. More specifically, amine/amide concentration independent detection (AACID) is a recently developed endogenous CEST contrast that has demonstrated sensitivity to intracellular pH at 9.4 T. The goal of this study was to evaluate the reproducibility of AACID CEST measurements at different levels of the healthy cervical spinal cord at 3.0 T incorporating B1 correction. Using a 3.0 T MRI scanner, two 3D CEST scans (saturation pulse train followed by a 3D snapshot gradient-echo readout) were performed on 12 healthy subjects approximately 10 days apart, with the CEST volume centered at the C4 level for all subjects. Scan-rescan reproducibility was evaluated by examining between and within-subject coefficients of variation (CVs) and absolute AACID value differences. The C4 level of the spinal cord demonstrated the lowest within-subject CVs (4.1%-4.3%), between-subject CVs (5.6%-6.3%), and absolute AACID percent difference (5.8-6.1%). The B1 correction scheme significantly improved reproducibility (adjusted p-value = 0.002) compared with the noncorrected data, suggesting that implementing B1 corrections in the spinal cord is beneficial. It was concluded that pH-weighted AACID measurements, incorporating B1-inhomogeneity correction, were reproducible within subjects along the healthy cervical spinal cord and that optimal image quality was achieved at the center of the 3D CEST volume.

Fully Automated Valve Segmentation for Blood Flow Assessment From 4D Flow MRI Including Automated Cardiac Valve Tracking and Transvalvular Velocity Mapping.

BACKGROUND: Automated 4D flow MRI valvular flow quantification without time-consuming manual segmentation might improve workflow. PURPOSE: Compare automated valve segmentation (AS) to manual (MS), and manually corrected automated segmentation (AMS), in corrected atrioventricular septum defect (c-AVSD) patients and healthy volunteers, for assessing net forward volume (NFV) and regurgitation fraction (RF). STUDY TYPE: Retrospective. POPULATION: 27 c-AVSD patients (median, 23 years; interquartile range, 16-31 years) and 24 healthy volunteers (25 years; 12.5-36.5 years). FIELD STRENGTH/SEQUENCE: Whole-heart 4D flow MRI and cine steady-state free precession at 3T. ASSESSMENT: After automatic valve tracking, valve annuli were segmented on time-resolved reformatted trans-valvular velocity images by AS, MS, and AMS. NFV was calculated for all valves, and RF for right and left atrioventricular valves (RAVV and LAVV). NFV variation (standard deviation divided by mean NFV) and NFV differences (NFV difference of a valve vs. mean NFV of other valves) expressed internal NFV consistency. STATISTICAL TESTS: Comparisons between methods were assessed by Wilcoxon signed-rank tests, and intra/interobserver variability by intraclass correlation coefficients (ICCs). P < 0.05 was considered statistically significant, with multiple testing correction. RESULTS: AMS mean analysis time was significantly shorter compared with MS (5.3 ± 1.6 minutes vs. 9.1 ± 2.5 minutes). MS NFV variation (6.0%) was significantly smaller compared with AMS (6.3%), and AS (8.2%). Median NFV difference of RAVV, LAVV, PV, and AoV between segmentation methods ranged from -0.7-1.0 mL, -0.5-2.8 mL, -1.1-3.6 mL, and - 3.1--2.1 mL, respectively. Median RAVV and LAVV RF, between 7.1%-7.5% and 3.8%-4.3%, respectively, were not significantly different between methods. Intraobserver/interobserver agreement for AMS and MS was strong-to-excellent for NFV and RF (ICC ≥0.88). DATA CONCLUSION: MS demonstrates strongest internal consistency, followed closely by AMS, and AS. Automated segmentation, with or without manual correction, can be considered for 4D flow MRI valvular flow quantification. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Morphometric magnetic resonance imaging (MRI) postprocessing in MRI-negative patients with first unprovoked seizure.

OBJECTIVE: The aim of the study was to evaluate the benefits of morphometric magnetic resonance imaging (MRI) postprocessing in patients presenting with a first seizure and negative MRI results and to investigate these findings in the context of the clinical and electroencephalographic data, seizure recurrence rates, and epilepsy diagnosis in these patients. METHODS: We retrospectively reviewed 97 MRI scans of patients with first unprovoked epileptic seizure and no evidence of epileptogenic lesion on clinical routine MRI. Morphometric Analysis Program (MAP; v2018), automated postprocessing software, was used to identify subtle, potentially epileptogenic lesions in the three-dimensional T1-weighted MRI data. The resulting probability maps were examined together with the conventional MRI images by a reviewer who remained blinded to the patients' clinical and electroencephalographical data. Clinical data were prospectively collected between February 2018 and May 2023. RESULTS: Among the apparently MRI-negative patients, a total of 18 of 97 (18.6%) showed cortical changes suggestive of focal cortical dysplasia. Within the population with positive MAP findings (MAP+), seizure recurrence rates were 61.1% and 66.7% at 1 and 2 years after the first unprovoked seizure, respectively. Conversely, patients with negative MAP findings (MAP-) had lower seizure recurrence rates of 27.8% and 34.2% at 1 and 2 years after the first unprovoked seizure, respectively. Patients with MAP+ findings were significantly more likely to be diagnosed with epilepsy than those patients with MAP- findings (χ2 [1, n = 97] = 14.820, p < .001, odds ratio = 21.371, 95% CI = 2.710-168.531) during a mean follow-up time of 22.51 months (SD = 16.7 months, range = 1-61 months). SIGNIFICANCE: MRI postprocessing can be a valuable tool for detecting subtle epileptogenic lesions in patients with a first seizure and negative MRI results. Patients with first seizure and MAP+ findings had high seizure recurrence rates, meeting the criteria for beginning epilepsy.

A Comparative Study of AI-Based Automated and Manual Postprocessing of Head and Neck CT Angiography: An Independent External Validation with Multi-Vendor and Multi-Center Data.

PURPOSE: To externally validate the performance of automated postprocessing (AP) on head and neck CT Angiography (CTA) and compare it with manual postprocessing (MP). METHODS: This retrospective study included head and neck CTA-exams of patients from three tertiary hospitals acquired on CT scanners from five manufacturers. AP was performed by CerebralDoc. The image quality was assessed using Likert scales, and the qualitative and quantitative diagnostic performance of arterial stenosis and aneurysm, postprocessing time, and scanning radiation dose were also evaluated. RESULTS: A total of 250 patients were included. Among these, 55 patients exhibited significant stenosis (≥ 50%), and 33 patients had aneurysms, diagnosed using original CTA datasets and corresponding multiplanar reconstructions as the reference. While the scores of the V4 segment and the edge of the M1 segment on volume rendering (VR), as well as the C4 segment on maximum intensity projection (MIP), were significantly lower with AP compared to MP across vendors (all P < 0.05), most scores in AP demonstrated image quality that was either superior to or comparable with that of MP. Furthermore, the diagnostic performance of AP was either superior to or comparable with that of MP. Moreover, AP also exhibited advantages in terms of postprocessing time and radiation dose when compared to MP (P < 0.001). CONCLUSION: The AP of CerebralDoc presents clear advantages over MP and holds significant clinical value. However, further optimization is required in the image quality of the V4 and M1 segments on VR as well as the C4 segment on MIP.

In silico and in vitro metabolism studies of the new synthetic opiate AP-237 (bucinnazine) using bioinformatics tools.

The recent emergence of new synthetic opioids (NSOs) compounds in the illicit market is increasingly related to fatal cases. Identification and medical care of NSO intoxication cases are challenging, particularly due to high frequency of new products and extensive metabolism. As the study of NSO metabolism is crucial for the identification of these drugs in cases of intoxication, we aimed to investigate the metabolism of the piperazine NSO AP-237 (= bucinnazine). Two complementary approaches (in silico and in vitro) were used to identify putative AP-237 metabolites which could be used as consumption markers. In silico metabolism studies were realized by combining four open access softwares (MetaTrans, SyGMa, Glory X, Biotransformer 3.0). In vitro experiments were performed by incubating AP-237 (20 µM) in differentiated HepaRG cells during 0 h, 8 h, 24 h or 48 h. Cell supernatant were extracted and analyzed by liquid chromatography coupled to high-resolution mass spectrometry and data were reprocessed using three strategies (MetGem, GNPS or Compound Discoverer®). A total of 28 phase I and six phase II metabolites was predicted in silico. Molecular networking identified seven putative phase I metabolites (m/z 203.154, m/z 247.180, m/z 271.180, two m/z 289.191 isomers, m/z 305.186, m/z 329.222), including four previously unknown metabolites. Overall, this cross-disciplinary approach with molecular networking on data acquired in vitro and in silico prediction enabled to propose relevant candidate as AP-237 consumption markers that could be added to mass spectrometry libraries to help diagnose intoxication.

Fast, high-quality, and unshielded 0.2 T low-field mobile MRI using minimal hardware resources.

OBJECTIVE: To propose a deep learning-based low-field mobile MRI strategy for fast, high-quality, unshielded imaging using minimal hardware resources. METHODS: Firstly, we analyze the correlation of EMI signals between the sensing coil and the MRI coil to preliminarily verify the feasibility of active EMI shielding using a single sensing coil. Then, a powerful deep learning EMI elimination model is proposed, which can accurately predict the EMI components in the MRI coil signals using EMI signals from at least one sensing coil. Further, deep learning models with different task objectives (super-resolution and denoising) are strategically stacked for multi-level post-processing to enable fast and high-quality low-field MRI. Finally, extensive phantom and brain experiments were conducted on a home-built 0.2 T mobile brain scanner for the evaluation of the proposed strategy. RESULTS: 20 healthy volunteers were recruited to participate in the experiment. The results show that the proposed strategy enables the 0.2 T scanner to generate images with sufficient anatomical information and diagnostic value under unshielded conditions using a single sensing coil. In particular, the EMI elimination outperforms the state-of-the-art deep learning methods and numerical computation methods. In addition, 2 × super-resolution (DDSRNet) and denoising (SwinIR) techniques enable further improvements in imaging speed and quality. DISCUSSION: The proposed strategy enables low-field mobile MRI scanners to achieve fast, high-quality imaging under unshielded conditions using minimal hardware resources, which has great significance for the widespread deployment of low-field mobile MRI scanners.

Improving the operational forecasts of outdoor Universal Thermal Climate Index with post-processing.

The Universal Thermal Climate Index (UTCI) is a thermal comfort index that describes how the human body experiences ambient conditions. It has units of temperature and considers physiological aspects of the human body. It takes into account the effect of air temperature, humidity, wind, radiation, and clothes. It is increasingly used in many countries as a measure of thermal comfort for outdoor conditions, and its value is calculated as part of the operational meteorological forecast. At the same time, forecasts of outdoor UTCI tend to have a relatively large error caused by the error of meteorological forecasts. In Slovenia, there is a relatively dense network of meteorological stations. Crucially, at these stations, global solar radiation measurements are performed continuously, which makes estimating the actual value of the UTCI more accurate compared to the situation where no radiation measurements are available. We used seven years of measurements in hourly resolution from 42 stations to first verify the operational UTCI forecast for the first forecast day and, secondly, to try to improve the forecast via post-processing. We used two machine-learning methods, linear regression, and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both methods reduced the daily mean error from about 2.6 ∘ C to almost zero, while the daily mean absolute error decreased from 5 ∘ C to 3 ∘ C for the neural network and 3.5 ∘ C for linear regression. Both methods, especially the neural network, also substantially reduced the dependence of the error on the time of the day.

Postprocessing Workflow for Laboratory Diffraction Contrast Tomography: A Case Study on Chromite Geomaterials.

Texture stands as a fundamental descriptor in the realms of geology and earth and planetary science. Beyond offering insights into the geological processes underlying mineral formation, its characterization plays a pivotal role in advancing engineering applications, notably in mining, mineral processing, and metal extraction, by providing quantitative data for predictive modeling. Laboratory diffraction contrast tomography (LabDCT), a recently developed 3D characterization technique, offers nondestructive measurement of grain phases including their morphology, distribution, and crystal orientation. It has recently shown its potential to assess 3D textures in complex natural rock samples. This study looks at improving on previous work by examining the artifacts and presents a novel postprocessing workflow designed to correct them. The workflow is developed to rectify inaccurate grain boundaries and interpolate partially reconstructed grains to provide more accurate results and is illustrated using multi-scan examples on chromite sands and natural chromitite from the Upper Group 2 Reef layer in South Africa. The postcorrected LabDCT results were validated through qualitative and quantitative assessment using 2D electron back-scattered diffraction on polished sample surfaces. The successful implementation of this postprocessing workflow underscores its substantial potential in achieving precise textural characterization and will provide valuable insights for both earth science and engineering applications.

Characterization of kilovoltage x-ray image guidance system with a novel post-processing algorithm on a new slip ring-mounted radiotherapy system.

PURPOSE: This study evaluates the performance of a kilovoltage x-ray image-guidance system equipped with a novel post-processing optimization algorithm on the newly introduced TAICHI linear accelerator (Linac). METHODS: A comparative study involving image quality tests and radiation dose measurements was conducted across six scanning protocols of the kV-cone beam computed tomography (CBCT) system on the TAICHI Linac. The performance assessment utilized the conventional Feldkamp-Davis-Kress (FDK) algorithm and a novel Non-Local Means denoising and adaptive scattering correction (NLM-ASC) algorithm. Image quality metrics, including spatial resolution, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR), were evaluated using a Catphan 604 phantom. Radiation doses for low-dose and standard protocols were measured using a computed tomography dose index (CTDI) phantom, with comparative measurements from the Halcyon Linac's iterative CBCT (iCBCT). RESULTS: The NLM-ASC algorithm significantly improved image quality, achieving a 300%-1000% increase in CNR and SNR over the FDK-only images and it also showed a 100%-200% improvement over the iCBCT images from Halcyon's head protocol. The optimized low-dose protocols yielded higher image quality than the standard FDK protocols, indicating potential for reduced radiation exposure. Clinical implementation confirmed the TAICHI system's utility for precise and adaptive radiotherapy. CONCLUSION: The kV-IGRT system on the TAICHI Linac, with its novel post-processing algorithm, demonstrated superior image quality suitable for routine clinical use, effectively reducing image noise without compromising other quality metrics.

Automatic image quality evaluation in digital radiography using for-processing and for-presentation images.

PURPOSE: To investigate the impact of digital image post-processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions. METHODS: A custom-made phantom constructed according to the instructions given in the IAEA Human Health Series No.39 publication was used, along with the respective software that automatically calculates various IQ metrics. Images with various exposure parameters were acquired with a digital radiography unit, which for each acquisition produces two images: one for-processing (raw) and one for-presentation (clinical). Various examination protocols were used, which incorporate diverse post-processing algorithms. The IQ metrics' values (IQ-scores) obtained were analyzed to investigate the effects of increasing incident air kerma (IAK) on the image receptor, tube potential (kVp), additional filtration, and examination protocol on image quality, and the differences between image type (raw or clinical). RESULTS: The IQ-scores were consistent for repeated identical exposures for both raw and clinical images. The effect that changes in exposure parameters and examination protocol had on IQ-scores were different depending on the IQ metric and image type. The expected positive effect that increasing IAK and decreasing tube potential should have on IQ was clearly exhibited in two IQ metrics only, the signal difference-to-noise-ratio (SDNR) and the detectability index (d'), for both image types. No effect of additional filtration on any of the IQ metrics was detected on images of either type. An interesting finding of the study was that for all different image acquisition selections the d' scores were larger in raw images, whereas the other IQ metrics were larger in clinical images for most of the cases. CONCLUSIONS: Since IQ-scores of raw and their respective clinical images may be largely different, the same type of image should be consistently used for monitoring IQ constancy and when results from different X-ray systems are compared.

Research on Three-Dimensional Reconstruction of Ribs Based on Point Cloud Adaptive Smoothing Denoising.

The traditional methods for 3D reconstruction mainly involve using image processing techniques or deep learning segmentation models for rib extraction. After post-processing, voxel-based rib reconstruction is achieved. However, these methods suffer from limited reconstruction accuracy and low computational efficiency. To overcome these limitations, this paper proposes a 3D rib reconstruction method based on point cloud adaptive smoothing and denoising. We converted voxel data from CT images to multi-attribute point cloud data. Then, we applied point cloud adaptive smoothing and denoising methods to eliminate noise and non-rib points in the point cloud. Additionally, efficient 3D reconstruction and post-processing techniques were employed to achieve high-accuracy and comprehensive 3D rib reconstruction results. Experimental calculations demonstrated that compared to voxel-based 3D rib reconstruction methods, the 3D rib models generated by the proposed method achieved a 40% improvement in reconstruction accuracy and were twice as efficient as the former.

Exploring the Impact of the NULL Class on In-the-Wild Human Activity Recognition.

Monitoring activities of daily living (ADLs) plays an important role in measuring and responding to a person's ability to manage their basic physical needs. Effective recognition systems for monitoring ADLs must successfully recognize naturalistic activities that also realistically occur at infrequent intervals. However, existing systems primarily focus on either recognizing more separable, controlled activity types or are trained on balanced datasets where activities occur more frequently. In our work, we investigate the challenges associated with applying machine learning to an imbalanced dataset collected from a fully in-the-wild environment. This analysis shows that the combination of preprocessing techniques to increase recall and postprocessing techniques to increase precision can result in more desirable models for tasks such as ADL monitoring. In a user-independent evaluation using in-the-wild data, these techniques resulted in a model that achieved an event-based F1-score of over 0.9 for brushing teeth, combing hair, walking, and washing hands. This work tackles fundamental challenges in machine learning that will need to be addressed in order for these systems to be deployed and reliably work in the real world.

Pseudo-Spectral Spatial Feature Extraction and Enhanced Fusion Image for Efficient Meter-Sized Lunar Impact Crater Automatic Detection in Digital Orthophoto Map.

Impact craters are crucial for our understanding of planetary resources, geological ages, and the history of evolution. We designed a novel pseudo-spectral spatial feature extraction and enhanced fusion (PSEF) method with the YOLO network to address the problems encountered during the detection of the numerous and densely distributed meter-sized impact craters on the lunar surface. The illumination incidence edge features, isotropic edge features, and eigen frequency features are extracted by Sobel filtering, LoG filtering, and frequency domain bandpass filtering, respectively. Then, the PSEF images are created by pseudo-spectral spatial techniques to preserve additional details from the original DOM data. Moreover, we conducted experiments using the DES method to optimize the post-processing parameters of the models, thereby determining the parameter ranges for practical deployment. Compared with the Basal model, the PSEF model exhibited superior performance, as indicated by multiple measurement metrics, including the precision, recall, F1-score, mAP, and robustness, etc. Additionally, a statistical analysis of the error metrics of the predicted bounding boxes shows that the PSEF model performance is excellent in predicting the size, shape, and location of impact craters. These advancements offer a more accurate and consistent method to detect the meter-sized craters on planetary surfaces, providing crucial support for the exploration and study of celestial bodies in our solar system.

Cryo-EM Map Anisotropy Can Be Attenuated by Map Post-Processing and a New Method for Its Estimation.

One of the most important challenges in cryogenic electron microscopy (cryo-EM) is the substantial number of samples that exhibit preferred orientations, which leads to an uneven coverage of the projection sphere. As a result, the overall quality of the reconstructed maps can be severely affected, as manifested by the presence of anisotropy in the map resolution. Several methods have been proposed to measure the directional resolution of maps in tandem with experimental protocols to address the problem of preferential orientations in cryo-EM. Following these works, in this manuscript we identified one potential limitation that may affect most of the existing methods and we proposed an alternative approach to evaluate the presence of preferential orientations in cryo-EM reconstructions. In addition, we also showed that some of the most recently proposed cryo-EM map post-processing algorithms can attenuate map anisotropy, thus offering alternative visualization opportunities for cases affected by moderate levels of preferential orientations.

Fit comparison of interim crowns manufactured with open and proprietary 3D printing modes versus milling technology: An in vitro study.

OBJECTIVES: This study aimed to assess the fit of interim crowns produced using DLP-based 3D printing with different manufacturing workflows-open and proprietary-versus milling technology. METHODS: A total of 120 crowns were evaluated using the replica technique. The control group (Mill, n = 30) was manufactured via subtractive technology. Experimental groups were printed using a DLP printer (SprintRay Pro95). In the proprietary mode (SR100, n = 30), manufacturer resin was used with a 100-µm layer thickness (LT) and a splashing cleaning postprocessing. In the open mode, validated resin was used. Group B100 (n = 30) had a 100-µm LT, and group B50 (n = 30) had a 50-µm followed by postprocessing in an ultrasonic bath with full immersion in isopropyl alcohol. Kruskal-Wallis tests with Bonferroni correction was applied after normal analysis (α = 0.05). RESULTS: Group B50 exhibited the best overall fit (123.87 ± 67.42 µm), which was comparable to the gold standard Milling group, which demonstrated the lowest marginal fit (p = 0.760). SR100 showed significantly poorer performance compared to Mill, B50, and B100 (p < 0.001). CONCLUSIONS: 3D printed and milled interim crowns generally demonstrated clinically acceptable fit, with the exception of the SR100 group. Postprocessing notably influenced crown fit, with the open mode with total immersion in isopropyl alcohol being superior. CLINICAL SIGNIFICANCE: The present study demonstrates that the selection of an optimal manufacturing and postprocessing workflow results in superior fit for interim crowns. This enables dental professionals to evaluate protocols and ensure reliable outcomes with improved clinical outcomes in interim crown fabrication.

From bytes to bites: Advancing the food industry with three-dimensional food printing.

The rapid advancement of three-dimensional (3D) printing (i.e., a type of additive manufacturing) technology has brought about significant advances in various industries, including the food industry. Among its many potential benefits, 3D food printing offers a promising solution to deliver products meeting the unique nutritional needs of diverse populations while also promoting sustainability within the food system. However, this is an emerging field, and there are several aspects to consider when planning for use of 3D food printing for large-scale food production. This comprehensive review explores the importance of food safety when using 3D printing to produce food products, including pathogens of concern, machine hygiene, and cleanability, as well as the role of macronutrients and storage conditions in microbial risks. Furthermore, postprocessing factors such as packaging, transportation, and dispensing of 3D-printed foods are discussed. Finally, this review delves into barriers of implementation of 3D food printers and presents both the limitations and opportunities of 3D food printing technology.

Digital postprocessing analysis of prostatic perfusion in neutered dogs.

B-mode ultrasound is routinely performed to evaluate the prostate gland in neutered dogs, although, the detection of malignancies may be challenging. Contrast-enhanced ultrasound (CEUS) has shown to be useful for the assessment of prostatic perfusion in normal and diseased dogs, although the interpretation of contrast ultrasonographic features may still be subjective. A quantitative tool for evaluating prostatic perfusion might improve the reliability of the results in terms of early detection of prostate neoplasia in neutered dogs. The present study aimed to evaluate the applicability of a postprocessing analysis tool to CEUS of the prostate in healthy neutered dogs, to provide quantitative measurements, and to study the influence of individual characteristics on prostatic regression. Twenty-three neutered dogs underwent a B-mode and CEUS examination of the prostate to acquire data about prostatic morphology and microcirculation. The prostate was imaged using a 5-7.5 MHz linear transducer and contrast was administered intravenously. Videoclips were analyzed by using Qontrast software and a postprocessing digital analysis tool (ImageJ) to measure perfusion peak intensity, time to peak, and vascularization ratio at the moment of the peak, which were then related to body weight, age, and time elapsed since orchiectomy. Correlation tests revealed higher vascularization in younger compared with older dogs (P < .05) and in smaller compared with larger dogs (P < .05). Time elapsed since orchiectomy (P > .05) did not affect prostatic perfusion. Contrast-enhanced ultrasound and the postprocessing analysis tool ImageJ allowed analysis of vascular perfusion in all dogs and have the potential to improve the diagnostic possibilities for andrological examination.

Image processing setting adaptions according to image dose and radiologist preference can improve image quality in computed radiography of the equine distal limb: A cadaveric study.

Image processing (IP) in digital radiography has been steadily refined to improve image quality. Adaptable settings enable users to adjust systems to their specific requirements. This prospective, analytical study aimed to investigate the influence of different IP settings and dose reductions on image quality. Included were 20 cadaveric equine limb specimens distal to the metacarpophalangeal and metatarsophalangeal joints. Images were processed with the Dynamic Visualization II system (Fujifilm) using five different IP settings including multiobjective frequency processing, flexible noise control (FNC), and virtual grid processing (VGP). Seven criteria were assessed by three veterinary radiology Diplomates and one veterinary radiology resident in a blinded study using a scoring system. Algorithm comparison was performed using an absolute visual grading analysis. The rating of bone structures was improved by VGP at full dose (P < .05; AUCVGC  = 0.45). Überschwinger artifact perception was enhanced by VGP (P < .001; AUCVGC  = 0.66), whereas image noise perception was suppressed by FNC (P < .001; AUCVGC  = 0.29). The ratings of bone structures were improved by FNC at 50% dose (P < .05; AUCVGC  = 0.44), and 25% dose (P < .001; AUCVGC  = 0.32), and clinically acceptable image quality was maintained at 50% dose (mean rating 2.16; 95.8% ratings sufficient or better). The favored IP setting varied among observers, with higher agreement at lower dose levels. These findings supported using individualized IP settings based on the radiologist's preferences and situational image requirements, rather than using default settings.