Scan Time Research Articles

Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study

Objectives: The aim of this study was to evaluate Cu-64 PET phantom image quality using Bayesian Penalized Likelihood (BPL) and Ordered Subset Expectation Maximum with point-spread function modeling (OSEM-PSF) reconstruction algorithms. In the BPL, the regularization parameter β was varied to identify the optimum value for image quality. In the OSEM-PSF, the effect of acquisition time was evaluated to assess the feasibility of shortened scan duration. Methods: A NEMA IEC PET body phantom was filled with known activities of water soluble Cu-64. The phantom was imaged on a PET/CT scanner and was reconstructed using BPL and OSEM-PSF algorithms. For the BPL reconstruction, various β values (150, 250, 350, 450, and 550) were evaluated. For the OSEM-PSF algorithm, reconstructions were performed using list-mode data intervals ranging from 7.5 to 240 s. Image quality was assessed by evaluating the signal to noise ratio (SNR), contrast to noise ratio (CNR), and background variability (BV). Results: The SNR and CNR were higher in images reconstructed with BPL compared to OSEM-PSF. Both the SNR and CNR increased with increasing β, peaking at β = 550. The CNR for all β, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images with β = 550 demonstrated the highest improvement in image quality. For OSEM-PSF reconstructed images with list-mode data duration ≥ 120 s, the noise level and CNR were not significantly different from the baseline 240 s list-mode data duration. Conclusions: BPL reconstruction improved Cu-64 PET phantom image quality by increasing SNR and CNR relative to OSEM-PSF reconstruction. Additionally, this study demonstrated scan time can be reduced from 240 to 120 s when using OSEM-PSF reconstruction while maintaining similar image quality. This study provides baseline data that may guide future studies aimed to improve clinical Cu-64 imaging.

Clinical implementation of first trimester screening for congenital heart defects.

To examine the feasibility and performance of implementing a standardized fetal cardiac scan at the time of a routine first-trimester ultrasound scan. A retrospective, single-center study in an unselected population between March 2021 and July 2022. A standardized cardiac scan protocol consisting of a four-chamber and 3-vessel trachea view with color Doppler was implemented as part of the routine first-trimester scan. Sonographers were asked to categorize the fetal heart anatomy. Data were stratified into two groups based on the possibility of evaluating the fetal heart. The influence of maternal and fetal characteristics and the detection of major congenital heart disease were investigated. A total of 5083 fetuses were included. The fetal heart evaluation was completed in 84.9%. The proportion of successful scans increased throughout the study period from 76% in the first month to 92% in the last month. High maternal body mass index and early gestational age at scan significantly decreased the feasibility. The first-trimester detection of major congenital heart defects was 7/16, of which four cases were identified by the cardiac scan protocol with no false-positive cases. First-trimester evaluation of the fetal heart by a standardized scan protocol is feasible to implement in daily practice. It can contribute to the earlier detection of congenital heart defects at a very low false positive rate.

Improved MRI methods to quantify retinal and choroidal blood flow applied to a model of glaucoma.

Blood flow (BF) of the retinal and choroidal vasculatures can be quantitatively imaged using MRI. This study sought to improve methods of data acquisition and analysis for MRI of layer-specific retinal and choroidal BF and then applied this approach to detect reduced ocular BF in a well-established mouse model of glaucoma from both eyes. Quantitative BF magnetic resonance imaging (MRI) was performed on glaucomatous DBA/2J and normal C57BL/6J mice. Arterial spin labeling MRI was applied to image retinal and choroidal BF using custom-made dual eye coils that could image both eyes during the same scan. Statistics using data from a single eye or two eyes were compared. BF values were calculated using two approaches. The BF rate per quantity of tissue was calculated as commonly done, and the peak BF values of the retinal and choroidal vasculatures were taken. Additionally, the BF rate per retinal surface area was calculated using a new analysis approach to attempt to reduce partial volume and variability by integrating BF over the retinal and choroidal depths. Ocular BF of both eyes could be imaged using the dual coil setup without effecting scan time. Intraocular pressure was significantly elevated in DBA/2J mice compared to C57BL/6J mice (P<0.01). Both retinal and choroidal BF were significantly decreased in DBA/2J mice in comparison to the age-matched normal C57BL/6J mice across all measurements (P < 0.01). From simulations, the values from the integrated BF analysis method had less partial volume effect, and from in vivo scans, this analysis approach also improved power. The dual eye coil setup allows bilateral eye data acquisition, increasing the amount of data acquired without increasing acquisition times in vivo. The reduced ocular BF found using the improved acquisition and analysis approaches replicated the results of previous studies on DBA/2J mice. The ocular hypertensive stress-induced BF reduction found within these mice may represent changes associated with glaucomatous progression.

IoT-Enhanced local attention dual networks for pathological image restoration in healthcare

High-resolution pathological images play a pivotal role in accurate disease diagnosis and are important in precision medicine. However, obtaining real-time high-resolution images faces challenges due to hardware limitations and scanning time constraints. Conventional image super-resolution restoration algorithms struggle to provide satisfactory results for pathological images, resulting in blurred and unrealistic restorations. In response to this challenge, this research proposes a pioneering approach by integrating Internet of Things (IoT) technology with Local Attention Dual Network (IOT-LAT) for super-resolution restoration of pathological images. The enhanced IOT-LATincorporates IoT-based data acquisition and processing, IoT-Enhanced non-local attention mechanisms, Gaussian constraint, and parameter-sharing strategies in up-sampling and down-sampling branches. This integration enables real-time super-resolution restoration of pathological images with improved accuracy. The reconstructed images exhibit a structural similarity of 0.914 and a peak signal-to-noise ratio of 30.84 dB. These results validate the effectiveness of the suggested approach in precisely reconstructing high-frequency details and improving modelling efficiency via a lightweight non-local attention mechanism enhanced by the Internet of Things. This work discusses IoT and non-local attention dual networks to improve pathological image super-resolution restoration. This allows for faster and more accurate disease diagnosis and treatment in precision medicine.

Seasonal variation in D2/3 dopamine receptor availability in the human brain

PurposeBrain functional and physiological plasticity is essential to combat dynamic environmental challenges. The rhythmic dopamine signaling pathway, which regulates emotion, reward and learning, shows seasonal patterns with higher capacity of dopamine synthesis and lower number of dopamine transporters during dark seasons. However, seasonal variation of the dopamine receptor signaling remains to be characterized.MethodsBased on a historical database of healthy human brain [11C]raclopride PET scans (n = 291, 224 males and 67 females), we investigated the seasonal patterns of D2/3 dopamine receptor signaling. Daylength at the time of scanning was used as a predictor for brain regional non-displaceable binding of the radiotracer, while controlling for age and sex.ResultsDaylength was negatively correlated with availability of D2/3 dopamine receptors in the striatum. The largest effect was found in the left caudate, and based on the primary sample, every 4.26 h (i.e., one standard deviation) increase of daylength was associated with a mean 2.8% drop (95% CI -0.042 to -0.014) of the receptor availability.ConclusionsSeasonally varying D2/3 receptor signaling may also underlie the seasonality of mood, feeding, and motivational processes. Our finding suggests that in future studies of brain dopamine signaling, especially in high-latitude regions, the effect of seasonality should be considered.

Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking.

To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework. A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach. In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time. The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.

Potential predictive value of IVIM MR for xerostomia in nasopharyngeal carcinoma

Background and PurposeXerostomia, caused by radiation-induced parotid damage, is the most commonly reported radiotherapy (RT) complication for nasopharyngeal carcinoma (NPC). The purpose of this study was to evaluate the value of intravoxel incoherent motion (IVIM) MR in monitoring radiation-induced parotid gland damage and predicting the risk of xerostomia. MethodsFifty-four NPC patients were enrolled and underwent at least three IVIM MR scans: before (pre-RT), after 5 fractions of (5th-RT), halfway through (mid-RT), and after RT (post-RT). The degree of xerostomia patients was assessed before each MR examination. Furthermore, the time when patients first reported xerostomia symptoms was recorded. The changes in IVIM parameters throughout RT, as well as the relationships between IVIM parameters and xerostomia, were analysed. ResultAll IVIM parameters increased significantly from pre-RT to post-RT (p < 0.001). The rates of D, D* and f increase increased significantly from pre-RT to mid-RT (p < 0.001), indicating that cell necrosis mainly occurs in the first half of RT. In multivariate analysis, N3 (p = 0.014), pre-D (p = 0.007) and pre-D* (p = 0.003) were independent factors influencing xerostomia. D and f were significantly higher at 5th-RT than at pre-RT (both p < 0.05). IVIM detected parotid gland injury at 5th-RT at an average scanning time of 6.18 ± 1.07 days, earlier than the 11.94 ± 2.61 days when the patient first complained of xerostomia according to the RTOG scale (p < 0.001). ConclusionsIVIM MR can dynamically monitor radiation-induced parotid gland damage and assess it earlier and more objectively than RTOG toxicity. Moreover, IVIM can screen people at risk of more severe xerostomia early.

Single-shot diffusion trace spectroscopic imaging using radial echo planar trajectories.

Demonstrate the feasibility and evaluate the performance of single-shot diffusion trace-weighted radial echo planar spectroscopic imaging (Trace DW-REPSI) for quantifying the trace ADC in phantom and in vivo using a 3T clinical scanner. Trace DW-REPSI datasets were acquired in 10 phantom and 10 healthy volunteers, with a maximum b-value of 1601 s/mm2 and diffusion time of 10.75 ms. The self-navigation properties of radial acquisitions were used for corrections of shot-to-shot phase and frequency shift fluctuations of the raw data. In vivo trace ADCs of total NAA (tNAA), total creatine (tCr), and total choline (tCho) extrapolated to pure gray and white matter fractions were compared, as well as trace ADCs estimated in voxels within white or gray matter-dominant regions. Trace ADCs in phantom show excellent agreement with reported values, and in vivo ADCs agree well with the expected differences between gray and white matter. For tNAA, tCr, and tCho, the trace ADCs extrapolated to pure gray and white matter ranged from 0.18-0.27 and 0.26-0.38 μm2/ms, respectively. In sets of gray and white matter-dominant voxels, the values ranged from 0.21 to 0.27 and 0.24 to 0.31 μm2/ms, respectively. The overestimated trace ADCs from this sequence can be attributed to the short diffusion time. This study presents the first demonstration of the single-shot diffusion trace-weighted spectroscopic imaging sequence using radial echo planar trajectories. The Trace DW-REPSI sequence could provide an estimate of the trace ADC in a much shorter scan time compared to conventional approaches that require three separate measurements.

Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction.

A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical-pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0.

Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index.

For effective optimization of MR fingerprinting (MRF) pulse sequences, estimating and minimizing errors from actual scan conditions are crucial. Although virtual-scan simulations offer an approximation to these errors, their computational demands become expensive for high-dimensional MRF frameworks, where interactions between more than two tissue properties are considered. This complexity makes sequence optimization impractical. We introduce a new mathematical model, the systematic error index (SEI), to address the scalability challenges for high-dimensional MRF sequence design. By eliminating the need to perform dictionary matching, the SEI model approximates quantification errors with low computational costs. The SEI model was validated in comparison with virtual-scan simulations. The SEI model was further applied to optimize three high-dimensional MRF sequences that quantify two to four tissue properties. The optimized scans were examined in simulations and healthy subjects. The proposed SEI model closely approximated the virtual-scan simulation outcomes while achieving hundred- to thousand-times acceleration in the computational speed. In both simulation and in vivo experiments, the optimized MRF sequences yield higher measurement accuracy with fewer undersampling artifacts at shorter scan times than the heuristically designed sequences. We developed an efficient method for estimating real-world errors in MRF scans with high computational efficiency. Our results illustrate that the SEI model could approximate errors both qualitatively and quantitatively. We also proved the practicality of the SEI model of optimizing sequences for high-dimensional MRF frameworks with manageable computational power. The optimized high-dimensional MRF scans exhibited enhanced robustness against undersampling and system imperfections with faster scan times.

Quantification of water exchange across the blood-brain barrier using noncontrast MR fingerprinting.

A method is proposed to quantify cerebral blood volume ( ) and intravascular water residence time ( ) using MR fingerprinting (MRF), applied using a spoiled gradient echo sequence without the need for contrast agent. An in silico study optimized an acquisition protocol to maximize the sensitivity of the measurement to and changes. Its accuracy in the presence of variations in , , and was evaluated. The optimized protocol (scan time of 19 min) was then tested in a exploratory healthy volunteer study (10 volunteers, mean age 24 3, six males) at 3 T with a repeat scan taken after repositioning to allow estimation of repeatability. Simulations show that assuming literature values for and , no variation in , while fitting only and , leads to large errors in quantification of and , regardless of noise levels. However, simulations also show that matching , , , and , simultaneously is feasible at clinically achievable noise levels. Across the healthy volunteers, all parameter quantifications fell within the expected literature range. In addition, the maps show good agreement between hemispheres suggesting physiologically relevant information is being extracted. Expected differences between white and gray matter (p < 0.0001) and (p < 0.0001) are observed, and show no significant differences, p = 0.4 and p = 0.6, respectively. Moderate to excellent repeatability was seen between repeat scans: mean intra-class correlation coefficient of , , , and . We demonstrate that regional simultaneous quantification of , , , and using MRF is feasible in vivo.

Through their eyes: Multi-subject brain decoding with simple alignment techniques

Abstract To-date, brain decoding literature has focused on single-subject studies, that is, reconstructing stimuli presented to a subject under fMRI acquisition from the fMRI activity of the same subject. The objective of this study is to introduce a generalization technique that enables the decoding of a subject’s brain based on fMRI activity of another subject, that is, cross-subject brain decoding. To this end, we also explore cross-subject data alignment techniques. Data alignment is the attempt to register different subjects in a common anatomical or functional space for further and more general analysis. We utilized the Natural Scenes Dataset, a comprehensive 7T fMRI experiment focused on vision of natural images. The dataset contains fMRI data from multiple subjects exposed to 9,841 images, where 982 images have been viewed by all subjects. Our method involved training a decoding model on one subject’s data, aligning new data from other subjects to this space, and testing the decoding on the second subject based on information aligned to the first subject. We also compared different techniques for fMRI data alignment, specifically ridge regression, hyper alignment, and anatomical alignment. We found that cross-subject brain decoding is possible, even with a small subset of the dataset, specifically, using the common data, which are around 10% of the total data, namely 982 images, with performances in decoding comparable to the ones achieved by single-subject decoding. Cross-subject decoding is still feasible using half or a quarter of this number of images with slightly lower performances. Ridge regression emerged as the best method for functional alignment in fine-grained information decoding, outperforming all other techniques. By aligning multiple subjects, we achieved high-quality brain decoding and a potential reduction in scan time by 90%. This substantial decrease in scan time could open up unprecedented opportunities for more efficient experiment execution and further advancements in the field, which commonly requires prohibitive (20 hours) scan time per subject.

Two for One-Combined Morphologic and Quantitative Knee Joint MRI Using a Versatile Turbo Spin-Echo Platform.

Quantitative MRI techniques such as T2 and T1ρ mapping are beneficial in evaluating knee joint pathologies; however, long acquisition times limit their clinical adoption. MIXTURE (Multi-Interleaved X-prepared Turbo Spin-Echo with IntUitive RElaxometry) provides a versatile turbo spin-echo (TSE) platform for simultaneous morphologic and quantitative joint imaging. Two MIXTURE sequences were designed along clinical requirements: "MIX1", combining proton density (PD)-weighted fat-saturated (FS) images and T2 mapping (acquisition time: 4:59 min), and "MIX2", combining T1-weighted images and T1ρ mapping (6:38 min). MIXTURE sequences and their reference 2D and 3D TSE counterparts were acquired from ten human cadaveric knee joints at 3.0 T. Contrast, contrast-to-noise ratios, and coefficients of variation were comparatively evaluated using parametric tests. Clinical radiologists (n = 3) assessed diagnostic quality as a function of sequence and anatomic structure using five-point Likert scales and ordinal regression, with a significance level of α = 0.01. MIX1 and MIX2 had at least equal diagnostic quality compared to reference sequences of the same image weighting. Contrast, contrast-to-noise ratios, and coefficients of variation were largely similar for the PD-weighted FS and T1-weighted images. In clinically feasible scan times, MIXTURE sequences yield morphologic, TSE-based images of diagnostic quality and quantitative parameter maps with additional insights on soft tissue composition and ultrastructure.

Modelling lower-limb peripheral arterial disease using clinically available datasets: impact of inflow boundary conditions on hemodynamic indices for restenosis prediction

Background and ObjectivesThe integration of hemodynamic markers as risk factors in restenosis prediction models for lower-limb peripheral arteries is hindered by fragmented clinical datasets. Computed tomography (CT) scans enable vessel geometry reconstruction and can be obtained at different times than the Doppler ultrasound (DUS) images, which provide information on blood flow velocity. Computational fluid dynamics (CFD) simulations allow the computation of near-wall hemodynamic indices, whose accuracy depends on the prescribed inlet boundary condition (BC), derived from the DUS images. This study aims to: (i) investigate the impact of different DUS-derived velocity waveforms on CFD results; (ii) test whether the same vessel areas, subjected to altered hemodynamics, can be detected independently of the applied inlet BC; (iii) suggest suitable DUS images to obtain reliable CFD results. MethodsCFD simulations were conducted on three patients treated with bypass surgery, using patient-specific DUS-derived inlet BCs recorded at either the same or different time points than the CT scan. The impact of the chosen inflow condition on bypass hemodynamics was assessed in terms of wall shear stress (WSS)-derived quantities. Patient-specific critical thresholds for the hemodynamic indices were applied to identify critical luminal areas and compare the results with a reference obtained with a DUS image acquired in close temporal proximity to the CT scan. ResultsThe main findings indicate that: (i) DUS-derived inlet velocity waveforms acquired at different time points than the CT scan led to statistically significantly different CFD results (p<0.001); (ii) the same luminal surface areas, exposed to low time-averaged WSS, could be identified independently of the applied inlet BCs; (iii) similar outcomes were observed for the other hemodynamic indices if the prescribed inlet velocity waveform had the same shape and comparable systolic acceleration time to the one recorded in close temporal proximity to the CT scan. ConclusionsDespite a lack of standardised data collection for diseased lower-limb peripheral arteries, an accurate estimation of luminal areas subjected to altered near-wall hemodynamics is possible independently of the applied inlet BC. This holds if the applied inlet waveform shares some characteristics – derivable from the DUS report – as one matching the acquisition time of the CT scan.

Fast magnetization transfer saturation imaging of the brain using MP2RAGE T1 mapping.

Magnetization transfer saturation (MTsat) mapping is commonly used to examine the macromolecular content of brain tissue. This study compared variable flip angle (VFA) T1 mapping against compressed-sensing MP2RAGE (csMP2RAGE) T1 mapping for accelerating MTsat imaging. VFA, MP2RAGE, and csMP2RAGE were compared against inversion-recovery T1 in an aqueous phantom at 3 T. The same 1-mm VFA, MP2RAGE, and csMP2RAGE protocols were acquired in 4 healthy subjects to compare T1 and MTsat. Bloch-McConnell simulations were used to investigate differences between the phantom and in vivo T1 results. Ten healthy controls were imaged twice with the csMP2RAGE MTsat protocol to quantify repeatability. The MP2RAGE and csMP2RAGE protocols were 13.7% and 32.4% faster than the VFA protocol, respectively. At these scan times, all approaches provided strong repeatability and accurate T1 times (< 5% difference) in the phantom, but T1 accuracy was more impacted by T2 for VFA than for MP2RAGE. In vivo, VFA estimated longer T1 times than MP2RAGE and csMP2RAGE. Simulations suggest that the differences in the T1 measured using VFA, MP2RAGE, and inversion recovery could be explained by the magnetization-transfer effects. In the test-retest experiment, we found that the csMP2RAGE has a minimum detectable change of 2.3% for T1 mapping and 7.8% for MTsat imaging. We demonstrated that MP2RAGE can be used in place of VFA T1 mapping in an MTsat protocol. Furthermore, a shorter scan time and high repeatability can be achieved using the csMP2RAGE sequence.

Detection of Dural Defect Localization Using 4-Dimensional Dynamic Computed Tomography Myelography for Patients with Superficial Siderosis

There are cases of superficial siderosis (SS) with spinal ventral fluid-filled collection in the spinal canal. In our previous study, the balanced steady-state free precession sequence magnetic resonance imaging is useful in identifying the location of dural defects. However, because of its narrow scan area and long scan time, it cannot easily detect the defect location in some patients with small dural defect. In this study, we applied 4-dimensional (4D) dynamic computed tomography (CT) imaging, including time-axis imaging, to myelography using the latest CT imaging equipment, which can perform short-time continuous imaging, to identify the dural defect site. Twenty SS patients with ventral fluid-filled collection in the spinal canal (9 males, 11 females; mean age 61.6years) underwent 4D dynamic CT myelography. A 192-row helical CT (SOMATOM Force, SIEMENS, Munich, Germany) with high-speed scanning capability was used to obtain 9-11 scans per minute at low dose while passing contrast medium into the subarachnoid space. Then, contrast leakage sites were identified. The contrast leakage sites could be identified in all 20 cases: C7/Th1, 2 cases; Th1/2, 5 cases; Th2/3, 9 cases; Th3/4, 1 case; Th5/6, 1 case; Th7/8, 1 case; and Th8/9, 1 case. Eighteen cases underwent surgical operation, and actual dural defects were confirmed at the contrast leakage sites. The mean±standard deviation of leakage time from contrast agent injection was 19.0±9.2s. The 4D dynamic CT myelography can be used to reliably identify the location of spinal fluid leakage. In SS cases, dural defects could be visualized in an average of 19seconds.

Comparison between R2'-based and R2*-based χ-separation methods: A clinical evaluation in individuals with multiple sclerosis.

Susceptibility source separation, or χ-separation, estimates diamagnetic (χdia) and paramagnetic susceptibility (χpara) signals in the brain using local field and R2' (= R2* - R2) maps. Recently proposed R2*-based χ-separation methods allow for χ-separation using only multi-echo gradient echo (ME-GRE) data, eliminating the need for additional data acquisition for R2 mapping. Although this approach reduces scan time and enhances clinical utility, the impact of missing R2 information remains a subject of exploration. In this study, we evaluate the viability of two previously proposed R2*-based χ-separation methods as alternatives to their R2'-based counterparts: model-based R2*-χ-separation versus χ-separation and deep learning-based χ-sepnet-R2* versus χ-sepnet-R2'. Their performances are assessed in individuals with multiple sclerosis (MS), comparing them with their corresponding R2'-based counterparts (i.e., R2*-χ-separation vs. χ-separation and χ-sepnet-R2* vs. χ-sepnet-R2'). The evaluations encompass qualitative visual assessments by experienced neuroradiologists and quantitative analyses, including region of interest analyses and linear regression analyses. Qualitatively, R2*-χ-separation tends to report higher χpara and χdia values compared with χ-separation, leading to less distinct lesion contrasts, while χ-sepnet-R2* closely aligns with χ-sepnet-R2'. Quantitative analysis reveals a robust correlation between both R2*-based methods and their R2'-based counterparts (r ≥ 0.88). Specifically, in the whole-brain voxels, χ-sepnet-R2* exhibits higher correlation and better linearity than R2*-χ-separation (χdia/χpara from R2*-χ-separation: r = 0.88/0.90, slope = 0.79/0.86; χdia/χpara from χ-sepnet-R2*: r = 0.90/0.92, slope = 0.99/0.97). In MS lesions, both R2*-based methods display comparable correlation and linearity (χdia/χpara from R2*-χ-separation: r = 0.90/0.91, slope = 0.98/0.91; χdia/χpara from χ-sepnet-R2*: r = 0.88/0.88, slope = 0.91/0.95). Notably, χ-sepnet-R2* demonstrates negligible offsets, whereas R2*-χ-separation exhibits relatively large offsets (0.02 ppm in the whole brain and 0.01 ppm in the MS lesions), potentially indicating the false presence of myelin or iron in MS lesions. Overall, both R2*-based χ-separation methods demonstrated their viability as alternatives to their R2'-based counterparts. χ-sepnet-R2* showed better alignment with its R2'-based counterpart with minimal susceptibility offsets, compared with R2*-χ-separation that reported higher χpara and χdia values compared with R2'-based χ-separation.

Impact of Breathing Control Training Program on sonographic quantification of abdominal vasculature.

To compare vascular scanning parameters (vessel diameter, peak systolic velocity, end-diastolic velocity, and resistive index) and scanning time before and after breathing control training program for selected abdominal vessels. This study was pre and post quasi-experimental. The researchers designed a breathing training program that gives participants instructions through a video describing breathing maneuvers. Data were collected at the ultrasound laboratory/College of Health and Rehabilitation Sciences in Princess Nourah bint Abdul Rahman University, Riyadh, Saudi Arabia from January 2023 to November 2023. About 49 volunteers at the university participated in the study. Scanning was performed two times for the right renal artery, upper abdominal aorta, inferior vena cava, and superior mesenteric artery. Scanning time was measured before and after the program as well. A paired sample t-test was used to compare the parameters means and time before and after the program. The program had a significant effect on the following parameters: right renal artery peak systolic velocity (p=0.042), upper abdominal aortic peak systolic velocity, and resistive index (p=0.014, p=0.014 respectively), superior mesenteric artery and inferior vena cava diameters (p=0.010 and p=0.020). The scanning time was reduced significantly (p<0.001). The breathing training program saves time and improves ultrasound measurement quality. Hospitals and health centers should consider the importance of breathing control training programs before abdominal scanning.

Evaluation of the accuracy of seven intraoral scanners for the full dentate and partially edentulous complete-arch mandibular casts: An in vitro comparison

Statement of problemIntraoral scanners (IOSs) are widely used in dentistry, providing high accuracy in short-range scanning. Nevertheless, when scanning the full dental arch, it remains a challenge. Furthermore, there is a lack of studies reporting the differences in scan accuracy between dental arches with large-span mucosal areas and fully dentate casts or optimal IOS selection for different dental statuses. PurposeThis study aimed to evaluate the accuracy and scanning time of different IOSs for full dentate (FD) and partially edentulous (PE) casts with missing teeth in the #34–#44 range and to determine the IOSs with the optimal clinical adaptability and scanning accuracy for different complete-arch casts. Material and methodsReference scans of two complete-arch (FD and PE) casts were obtained using a laboratory scanner (Ceramill Map 600). Subsequently, the same casts were scanned ten times each by seven IOSs (3Shape Trios 3, CS3600, Planmeca Emerald, iTero Element 5D, Medit i500, BAMBOO B1, and Shining Aoralscan 3), and the scanning time was recorded. The test data were superimposed on the reference scans for the selected areas, and three-dimensional deviations between the reference and test casts (trueness), and between test casts (precision) were determined using reverse engineering software (Geomagic Wrap). The dataset was analyzed using a two-factor analysis of variance with post-hoc Bonferroni tests. ResultsTwo-factor analysis of variance revealed significant differences in accuracy and scanning time for different casts (P < 0.001) and IOSs (P < 0.001). For the FD cast, the i500 (0.35 ± 0.11 mm trueness) and CS3600 (0.23 ± 0.12 mm precision) performed worse than the remaining scanners. For the PE cast, the BAMBOO B1(0.89 ± 0.58 mm trueness; 0.88 ± 0.48 mm precision) performed worse than the remaining scanners. There were no differences in the accuracy of scanning between the Element 5D and Emerald for both cast types. However, the scanning time differed significantly between the different IOSs (P < 0.001). Regardless of the cast type, the fastest and slowest scans were performed by the Trios3 and CS3600 scanners respectively. ConclusionsThe accuracy and scanning time differed between the different IOSs and types of complete-arch casts.

Silicon-based optical phased array with a reconfigurable aperture for “gaze” scanning of LiDAR

Light detection and ranging (LiDAR) serves as one of the key components in the fields of autonomous driving, surveying mapping, and environment detection. Conventionally, dense points clouds are pursued by LiDAR systems to provide high-definition 3D images. However, the LiDAR is typically used to produce abundant yet redundant data for scanning the homogeneous background of scenes, resulting in power waste and excessive processing time. Hence, it is highly desirable for a LiDAR system to “gaze” at the target of interest by dense scanning and rough sparse scans on the uninteresting areas. Here, we propose a LiDAR structure based on an optical phased array (OPA) with reconfigurable apertures to achieve such a gaze scanning function. By virtue of the cascaded optical switch integrated on the OPA chip, a 64-, 128-, 192-, or 256-channel antenna can be selected discretionarily to construct an aperture with variable size. The corresponding divergence angles for the far-field beam are 0.32°, 0.15°, 0.10°, and 0.08°, respectively. The reconfigurable-aperture OPA enables the LiDAR system to perform rough scans via the large beam spots prior to fine scans of the target by using the tiny beam spots. In this way, the OPA-based LiDAR can perform the “gaze” function and achieve full-range scanning efficiently. The scanning time and power consumption can be reduced by 1/4 while precise details of the target are maintained. Finally, we embed the OPA into a frequency-modulated continuous-wave (FMCW) system to demonstrate the “gaze” function in beam scanning. Experiment results show that the number of precise scanning points can be reduced by 2/3 yet can obtain the reasonable outline of the target. The reconfigurable-aperture OPA (RA-OPA) can be a promising candidate for the applications of rapid recognition, like car navigation and robot vision.