Reduced Scan Time Research Articles

Standardization and advancements efforts in breast diffusion-weighted imaging.

Recent advancements in breast magnetic resonance imaging (MRI) have significantly enhanced breast cancer detection and characterization. Breast MRI offers superior sensitivity, particularly valuable for high-risk screening and assessing disease extent. Abbreviated protocols have emerged, providing efficient cancer detection while reducing scan time and cost. Diffusion-weighted imaging (DWI), a non-contrast technique, has shown promise in differentiating malignant from benign lesions. It offers shorter scanning times and eliminates contrast agent risks. Apparent diffusion coefficient (ADC) values provide quantitative measures for lesion characterization, potentially reducing unnecessary biopsies. Studies have revealed some correlations between ADC values and hormone receptor status in breast cancers, although substantial variability exists among studies. However, standardization remains challenging. Initiatives such as European Society of Breast Imaging (EUSOBI), Diffusion-Weighted Imaging Screening Trial (DWIST), Quantitative Imaging Biomarkers Alliance (QIBA) have proposed guidelines to ensure consistency in imaging protocols and equipment specifications, addressing variability in ADC measurements across different sites and vendors. Advanced techniques like Intravoxel incoherent motion (IVIM) and non-Gaussian DWI offer insights into tissue microvasculature and microstructure. Despite ongoing challenges, the integration of these advanced MRI techniques shows great promise for improving breast cancer diagnosis, characterization, and treatment planning. Continued research and standardization efforts are crucial for maximizing the potential of breast DWI in enhancing patient care and outcomes.

Efficient File Security Scanning: Combining Hash Histories and Bloom Filters to Minimize Redundancy

As file repositories expand in size, traditional full-file security scanning becomes computationally expensive and redundant. This paper introduces a novel hybrid framework that leverages hash histories embedded in file metadata and Bloom filters for efficient security scanning. The approach ensures that only modified or newly added files are scanned, reducing overhead while maintaining robust security coverage. By augmenting file metadata with hash histories, the system provides decentralized tracking of file state changes. Bloom filters further optimize the process by efficiently determining whether a file requires scanning. The tradeoff between increased file sizes due to metadata augmentation and the improved scanning performance is thoroughly analyzed. Evaluations demonstrate significant reductions in scan time and resource usage, making the framework highly suitable for large-scale file repositories. Keywords File security, Hash histories, Bloom filters, Metadata augmentation, Incremental scanning, Security optimization

Use of Multiparametric and Biparametric Magnetic Resonance Imaging in Bladder Cancer Staging: Prospective Observational Study and Analysis of Radiologist Learning Curve.

Background: Nowadays, thanks to the introduction of the VI-RADS scoring system, mpMRI has shown promising results in pre-TURBT assessment of muscular invasiveness of BCa, even if its application in everyday practice is still limited. This might be due to a lack in the literature about the learning curve of radiologists and about the characteristics of the exam. With the aim to reduce scan time and patient discomfort while maintaining diagnostic accuracy, bpMRI has been introduced as a possible alternative to mpMRI in this group of patients. This study reports a single-center experience using mpMRI and the VI-RADS scoring system to differentiate NMIBC from MIBC. The primary aim of the study is to assess diagnostic accuracy of mpMRI using the VI-RADS scoring system. The secondary aim is to evaluate the learning curve of an experienced mpMRI radiologist. Additionally, we perform a retrospective assessment of the same group of patients evaluating only DWIs and T2-weighted images, as they underwent bpMRI, to compare the performance of mpMRI and bpMRI. Materials and Methods: From 11/2021 to 11/2023, patients with suspected newly diagnosed BCa were enrolled in this prospective study. All patients underwent mpMRI prior to TURBT in a highly specialized radiology center for MRI. According to VI-RADS, a cutoff of ≥3 was assumed to define MIBC. Histological TURBT reports were compared with preoperative VI-RADS scores to assess the accuracy of mpMRI in discriminating between NMIBC and MIBC. Furthermore, to assess the learning curve of the reading radiologist we analyzed the rate of patients correctly classified as MIBC at MRI. Finally, we evaluated the performance of a hypothetic biparametric MRI in classifying our cohort according to VI-RADS score and compared it with mpMRI performance by using DeLong's test. Data analysis was performed using Jamovi software v.2.3 and R software v.4.2.1. Results: A total of 133 patients were enrolled. mpMRI showed sensitivity and specificity of 86% (95% confidence interval [CI]: 64-97) and 95% (95% CI: 89-98), respectively. The learning curve analysis of the reading radiologist showed that the rate of patients correctly classified as MIBC rapidly increases reaching its plateau after 40 cases. The hypothetic bpMRI showed a sensitivity of 76% (95% CI: 53-92) and a specificity of 93% (95% CI: 86-97), with no significant difference with mpMRI performance (p = 0.10). Conclusions: Our study confirms the effectiveness of MRI, particularly with the VI-RADS scoring system, in differentiating NMIBC from MIBC. The learning curve analysis underscores the importance of radiologist training in optimizing diagnostic accuracy. Future research should focus on enhancing the sensitivity of bpMRI and further validating these findings in larger and multicentric studies.

Optimization of MR-ARFI for Human Transcranial Focused Ultrasound.

Magnetic resonance acoustic radiation force imaging (MR-ARFI) is an exceptionally promising technique to non-invasively confirm targeting accuracy and estimate exposure of low-intensity transcranial focused ultrasound stimulation. MR-ARFI uses magnetic field motion encoding gradients to visualize the MR phase changes generated by microscopic displacements at the ultrasound focus. Implementing MR-ARFI in the human central nervous system has been hindered by 1) phase distortion caused by subject motion, and 2) insufficient signal-to-noise ratio at low (<1.0 MPa) ultrasound pressures. The purpose of this study was to optimize human MR-ARFI to allow reduced ultrasound exposure while at the same time being robust to bulk and physiological motion. We demonstrate that a time series of single-shot spiral acquisitions, while triggering ultrasound on and off in blocks, provides ARFI maps that with correction are largely immune to bulk and pulsatile brain motion. Furthermore, the time series approach allows for a reduction in ultrasound exposure per slice while improving motion robustness with reduced scan time. The focused ultrasound beam can be visualized in an 80 second scan with our protocol, enabling iteration for image-guided targeting. We demonstrated robust ARFI signals at the expected target in 4 participants. Our results provide persuasive proof-of-principle that MR-ARFI can be used as a tool to guide ultrasound-based precision neural circuit therapeutics.

Comparison of different acceleration factors of artificial intelligence-compressed sensing for brachial plexus MRI imaging: scanning time and image quality

Background3D brachial plexus MRI scanning is prone to examination failure due to the lengthy scan times, which can lead to patient discomfort and motion artifacts. Our purpose is to investigate the efficacy of artificial intelligence-assisted compressed sensing (ACS) in improving the acceleration efficiency and maintaining or enhancing the image quality of brachial plexus MR imaging.MethodsA total of 30 volunteers underwent 3D sampling perfection with application-optimized contrast using different flip angle evolution short time inversion recovery using a 3.0T MR scanner. The imaging protocol included parallel imaging (PI) and ACS employing acceleration factors of 4.37, 6.22, and 9.03. Radiologists evaluated the neural detail display, fat suppression effectiveness, presence of image artifacts, and overall image quality. Signal intensity and standard deviation of specific anatomical sites within the brachial plexus and background tissues were measured, with signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) subsequently calculated. Cohen's weighted kappa (κ), One-way ANOVA, Kruskal–Wallis and pairwise comparisons with Bonferroni-adjusted significance level. P < 0.05 was considered statistically significant.ResultsACS significantly reduced scanning times compared to PI. Evaluations revealed differences in subjective scores and SNR across the sequences (P < 0.05), with no marked differences in CNR (P > 0.05). For subjective scores, ACS 9.03 were lower than the other three sequences in neural details display, image artifacts and overall image quality. There was no significant difference in fat suppression. For objective quantitative evaluation, SNR of right C6 root in ACS 6.22 and ACS 9.03 was higher than that in PI; SNR of left C6 root in ACS 4.37, ACS 6.22 and ACS 9.03 was higher than that in PI; SNR of medial cord in ACS 6.22, ACS 9.03 was higher than that in PI.ConclusionCompared with PI, ACS can shorten scanning time while ensuring good image quality.

Performance of Abbreviated Breast MRI in High-Risk Patients in a Tertiary Care Academic Medical Center.

The development of abbreviated breast MRI (AB-MRI) protocols reduce scan times. This paper reports the performance of AB-MRI at a tertiary care public academic medical center in comparison with established literature. This HIPAA-compliant IRB-approved retrospective study reviewed 413 AB-MRI screenings in high-risk patients from June 2020 to March 2023. Data were collected from 3 databases (MagView, Cerner PowerChart, and Prism Primordial). Demographics and overall BI-RADS assessment were recorded. For all positive (BI-RADS 0, 3, 4, 5) examinations, manual review of each case was performed. Performance metrics (sensitivity, specificity, cancer detection rate [CDR], recall rate, positive predictive value [PPV] 3 and negative predictive value [NPV]) were calculated. PubMed and Google Scholar were used to review similar AB-MRI studies to compare performance metrics. There were 413 AB-MRI examinations from 413 unique patients. The majority of cases were audit-negative BI-RADS 1 or 2 (83.8%, 346/413). There were 67 (16.2%, 67/413) audit-positive cases with 3.6% (15/413) BI-RADS 3, 10.9% (45/413) BI-RADS 4, 0.7% (3/413) BI-RADS 5, and 1.0% (4/413) BI-RADS 0. Performance metrics showed a sensitivity of 100.0% (95% CI, 63.1%-100.0%) and a specificity of 85.7% (95% CI, 81.9%-88.9%). The PPV3 was 14.3% (95% CI, 5.1%-23.5%), and the NPV was 100.0% (95% CI, 99.0%-100.0%). The CDR was 19.4 per 1000 screenings. The results are comparable to prior literature and benchmark data. This study demonstrates high sensitivity (100.0%) and NPV (100.0%) of AB-MRI with comparable specificity (85.7%) and CDR (19.4/1000) to the literature, adding support to the use of AB-MRI. Further research is needed to optimize AB-MRI protocols.

Simultaneous Multislice Cardiac Multimapping based on Locally Low-Rank and Sparsity Constraints

Although quantitative myocardial T1 and T2 mappings are clinically used to evaluate myocardial diseases, their application needs a minimum of 6 breath-holds to cover 3 short-axis slices. The purpose of this work is to simultaneously quantify multi-slice myocardial T1 and T2 across 3 short-axis slices in one breath-hold by combining simultaneous multi-slice (SMS) with Multimapping. An SMS-Multimapping sequence with multi-band RF excitations and Cartesian FLASH readouts was developed for data acquisition. When 3 slices are simultaneously acquired, the acceleration rate is around 12-fold, causing a highly ill-conditioned reconstruction problem. To mitigate image artifacts and noise caused by the ill-conditioning, a reconstruction algorithm based on Locally Low-Rank and Sparsity (LLRS) was developed. Validation was performed in phantoms and in vivo imaging, with 20 healthy subjects and 4 patients, regarding regional mean, precision, and scan-rescan reproducibility. The phantom imaging shows that SMS-Multimapping with LLRS accurately reconstructed multi-slice T1 and T2 maps despite a 6-fold acceleration of scan time. Healthy subject imaging shows that the proposed LLRS algorithm substantially improved image quality relative to split slice-GRAPPA. Compared with MOLLI, SMS-Multimapping exhibited higher T1 mean (1118±43ms vs 1190±49ms, P<0.01), lower precision (67±17ms vs 90±17ms, P<0.01), and acceptable scan-rescan reproducibility measured by two scans 10-minute apart (bias=1.4ms for MOLLI and 9.0ms for SMS-Multimapping). Compared with bSSFP T2 mapping, SMS-Multimapping exhibited similar T2 mean (43.5±3.3ms vs 43.0±3.5ms, P=0.64), similar precision (4.9±2.1ms vs 5.1±1.0ms, P=0.93), and acceptable scan-rescan reproducibility (bias=0.13ms for bSSFP T2 mapping and 0.55ms for SMS-Multimapping). In patients, SMS-Multimapping clearly showed the abnormality in a similar fashion as the reference methods despite using only one breath-hold. SMS-Multimapping with the proposed LLRS reconstruction can measure multi-slice T1 and T2 maps in one breath-hold with good accuracy, reasonable precision, and acceptable reproducibility, achieving a 6-fold reduction of scan time and an improvement of patient comfort.

The Role of 2, 4, and 5-dimensional Cardiac Flow MRI for Evaluation of Valvulopathies: A Literature Review.

Two-dimensional phase-contrast magnetic resonance imaging (2D flow MRI) and its multidimensional alternatives, 4D and 5D flow MRI, measure blood flow in the heart and great vessels. While 2D flow MRI is the standard technique, it has limitations regarding need for precise image plane prescribing and long scan time. In contrast, 4D and 5D flow MRI acquire 3D volumes, enabling retrospective assessment of all vessels. This review evaluates these three techniques for quantification of blood flow of the aortic and pulmonary valves in congenital heart disease. A systematic literature search was conducted in August 2024 using the PUBMED database, including articles comparing 2D, 4D, and 5D flow MRI. Fifteen articles comparing 2D and 4D, one comparing 2D and 5D and three articles comparing 4D and 5D flow MRI were included. No study compared all three techniques. 2D, 4D and 5D flow MRI demonstrated a good agreement for flow quantification. 4D flow MRI, however, tends to present a better accuracy and internal consistency than 2D flow MRI for determination of peak velocities and flow in stenotic lesions, particularly when comparing velocities to echocardiography. 4D and 5D flow MRI are associated with shorter scan times than 2D flow MRI. 4D and 5D flow MRI appear to offer promising alternatives to 2D flow MRI with the advantage of reduced scan times. Larger and prospective studies including echocardiography are needed to evaluate the potential of 4D and 5D to replace 2D flow MRI for flow quantification and peak velocity determination.

Comparison of trueness, time, and number of images among different denture digitization protocols

AbstractPurposeTo compare trueness, time, and number of images of different denture digitization protocols.Materials and MethodsMaxillary and mandibular complete prostheses (n = 10) were fabricated and attached with four fiducial markers. Reference scans were obtained using a laboratory scanner. Test scans were obtained using three different protocols: intraoral scanner (IOS) with manufacturer's scanning pattern (MA), IOS with rolling scanning pattern (RO), and IOS‐ polyvinylsiloxane technique (IOS‐PVS). The scan time and number of images taken were recorded for analysis. Using 3‐dimensional (3D) inspection software (Geomagic control X), corresponding test scans were superimposed to the reference scan using overall best fit. For trueness analysis, the root mean square (RMS) value of the overall best‐fit superimposition was calculated. One‐way ANOVA followed by Games‐Howell and Tukey post‐hoc tests were applied to analyze trueness, scan time, and number of images. Qualitative analysis of trueness was performed using 3D color mapping.ResultsThe lowest RMS value was in the mandibular RO protocol (0.10 ±0.01 mm). The highest RMS value was mandibular scans of the IOS‐PVS protocol (1.46 ± 0.09 mm). The longest digitization time was recorded in the maxillary MA group (3.34 ± 0.70 min), while the shortest was in the mandibular RO protocol (2.48 ± 0.56 min). Qualitative analysis revealed that deviation in IOS‐PVS protocol occurred around the border area of the prosthesis.ConclusionThe denture digitization protocols tested significantly affected trueness, total scanning time, and number of images. Digitizing dentures using the RO protocol improved trueness and reduced scanning time and the number of images.

Enhanced strain mapping Unveils internal deformation dynamics in Silicon-based lithium-ion batteries during electrochemical cycling

Silicon-based anodes have emerged as a promising advancement in lithium-ion battery technology, offering significantly higher lithium storage capacities than traditional graphite. However, the volumetric expansion of silicon-anodes can swell by up to 300 % during lithiation-presents serious challenges to their structural integrity and electrochemical stability. This study investigates the internal structural dynamics of silicon anodes during lithiation and delithiation cycles. A novel cell design for a 18,650 cylindrical cell featuring micro-sized internal speckles within the silicon anode is presented. This design improves the simulation of electrochemical conditions and allows for precise displacement tracking, mitigating impacts on capacity and cycle performance while enhancing Digital Volume Correlation (DVC) analysis. The research prioritizes reducing scan time and radiation exposure in micro-CT assessments of Li-ion cells, and improves the accuracy of internal strain mapping via DVC. Displacement fields over three charging and discharging cycles are documented. Notably, uneven volumetric changes are observed, with local displacements reaching up to 35 µm in areas smaller than 2.5 mm in radius, which contracted during charging and expanded during discharging. This protocol offers insights into the relationship between electrode mechanics and cell performance, promoting non-destructive evaluations of internal structures in commercial cells.

Accelerated cardiac cine with spatio-coil regularized deep learning reconstruction.

To develop an iterative deep learning (DL) reconstruction with spatio-coil regularization and multichannel k-space data consistency for accelerated cine imaging. This study proposes a Spatio-Coil Regularized DL (SCR-DL) approach for iterative deep learning reconstruction incorporating multicoil information in data consistency and regularizer. SCR-DL uses shift-invariant convolutional kernels to interpolate missing k-space lines and reconstruct individual coil images, followed by a regularizer that operates simultaneously across spatial and coil dimensions using learned image priors. At 8-fold acceleration, SCR-DL was compared with Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA), sensitivity encoding (SENSE)-based DL and spatio-temporal regularized (STR)-DL reconstruction. In the retrospective undersampled cine, images were quantitatively evaluated using normalized mean square error (NMSE) and structural similarity index measure (SSIM). Additionally, agreement for left-ventricular ejection fraction and left-ventricular mass were assessed using prospectively accelerated cine images at 2-fold and 8-fold accelerations. The SCR-DL algorithm successfully reconstructed highly accelerated cine images. SCR-DL had significant improvements in NMSE (0.03 ± 0.02) and SSIM (91.4% ± 2.7%) compared with GRAPPA (NMSE: 0.09 ± 0.04, SSIM: 69.9% ± 11.1%; p < 0.001), SENSE-DL (NMSE: 0.07 ± 0.04, SSIM: 86.9% ± 3.2%; p < 0.001), and STR-DL (NMSE: 0.04 ± 0.03, SSIM: 90.0% ± 2.5%; p < 0.001) with retrospective undersampled cine. Despite the 3-fold reduction in scan time, there was no difference between left-ventricular ejection fraction (59.8 ± 4.5 vs. 60.8 ± 4.8, p = 0.46) or left-ventricular mass (73.6 ± 19.4 g vs. 73.2 ± 19.7 g, p = 0.95) between R = 2 and R = 8 prospectively accelerated cine images. SCR-DL enabled highly accelerated cardiac cine imaging, significantly reducing breath-hold time. Compared with GRAPPA or SENSE-DL, images reconstructed with SCR-DL showed superior NMSE and SSIM.

Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings

BackgroundConventional MR acceleration techniques, such as compressed sensing, parallel imaging, and half Fourier often face limitations, including noise amplification, reduced signal-to-noise ratio (SNR) and increased susceptibility to artifacts, which can compromise image quality, especially in high-speed acquisitions. Artificial intelligence (AI)-assisted compressed sensing (ACS) has emerged as a novel approach that combines the conventional techniques with advanced AI algorithms. The objective of this study was to examine the imaging quality of the ACS approach by qualitative and quantitative analysis for brain, spine, kidney, liver, and knee MR imaging, as well as compare the performance of this method with conventional (non-ACS) MR imaging.MethodsThis study included 50 subjects. Three radiologists independently assessed the quality of MR images based on artefacts, image sharpness, overall image quality and diagnostic efficacy. SNR, contrast-to-noise ratio (CNR), edge content (EC), enhancement measure (EME), scanning time were used for quantitative evaluation. The Cohen’s kappa correlation coefficient (k) was employed to measure radiologists’ inter-observer agreement, and the Mann Whitney U-test used for comparison between non-ACS and ACS.ResultsThe qualitative analysis of three radiologists demonstrated that ACS images showed superior clinical information than non-ACS images with a mean k of ~ 0.70. The images acquired with ACS approach showed statistically higher values (p < 0.05) for SNR, CNR, EC, and EME compared to the non-ACS images. Furthermore, the study’s findings indicated that ACS-enabled images reduced scan time by more than 50% while maintaining high imaging quality.ConclusionIntegrating ACS technology into routine clinical settings has the potential to speed up image acquisition, improve image quality, and enhance diagnostic procedures and patient throughput.

Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data.

Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagnosis of ischemic stroke. Achieving accurate and rapid APT imaging is crucial for this application. However, conventional APT quantification methods either lack accuracy or are time-consuming. Machine learning (ML) has recently been recognized as a potential solution to improve APT quantification. In this paper, we applied an ML model trained on a new type of partially synthetic data, along with an optimization approach utilizing recursive feature elimination, to predict APT imaging in an animal stroke model. This partially synthetic datum is not a simple blend of measured and simulated chemical exchange saturation transfer (CEST) signals. Rather, it integrates the underlying components including all CEST, direct water saturation, and magnetization transfer effects partly derived from measurements and simulations to reconstruct the CEST signals using an inverse summation relationship. Training with partially synthetic data requires less in vivo data compared to training entirely with fully synthetic or in vivo data, making it a more practical approach. Since this type of data closely resembles real tissue, it leads to more accurate predictions than ML models trained on fully synthetic data. Results indicate that an ML model trained on this partially synthetic data can successfully predict the APT effect with enhanced accuracy, providing significant contrast between stroke lesions and normal tissues, thus clearly delineating lesions. In contrast, conventional quantification methods such as the asymmetric analysis method, three-point method, and multiple-pool model Lorentzian fit showed inadequate accuracy in quantifying the APT effect. Moreover, ML methods trained using in vivo data and fully synthetic data exhibited poor predictive performance due to insufficient training data and inaccurate simulation pool settings or parameter ranges, respectively. Following optimization, only 13 frequency offsets were selected from the initial 69, resulting in significantly reduced scan time.

Enhancing bone scan image quality: an improved self-supervised denoising approach.

Bone scans play an important role in skeletal lesion assessment, but gamma cameras exhibit challenges with low sensitivity and high noise levels. Deep learning (DL) has emerged as a promising solution to enhance image quality without increasing radiation exposure or scan time. However, existing self-supervised denoising methods, such as Noise2Noise (N2N), may introduce deviations from the clinical standard in bone scans. This study proposes an improved self-supervised denoising technique to minimize discrepancies between DL-based denoising and full scan images. &#xD;Retrospective analysis of 351 whole-body bone scan data sets was conducted. In this study, we used N2N and Noise2FullCount (N2F) denoising models, along with an interpolated version of N2N (iN2N). Denoising networks were separately trained for each reduced scan time from 5 to 50%, and also trained for mixed training datasets, which include all shortened scans. We performed quantitative analysis and clinical evaluation by nuclear medicine experts.&#xD;The denoising networks effectively generated images resembling full scans, with N2F revealing distinctive patterns for different scan times, N2N producing smooth textures with slight blurring, and iN2N closely mirroring full scan patterns. Quantitative analysis showed that denoising improved with longer input times and mixed count training outperformed fixed count training. Traditional denoising methods lagged behind DL-based denoising. N2N demonstrated limitations in long-scan images. Clinical evaluation favored N2N and iN2N in resolution, noise, blurriness, and findings, showcasing their potential for enhanced diagnostic performance in quarter-time scans.&#xD;The improved self-supervised denoising technique presented in this study offers a viable solution to enhance bone scan image quality, minimizing deviations from clinical standards. The method's effectiveness was demonstrated quantitatively and clinically, showing promise for quarter-time scans without compromising diagnostic performance. This approach holds potential for improving bone scan interpretations, aiding in more accurate clinical diagnoses.

Simultaneous multinuclear MRI via a single RF channel

Magnetic resonance imaging (MRI) stands as one of the most powerful noninvasive and non-destructive imaging techniques, finding extensive utility in medical and industrial applications. Its ability to acquire signals from multiple nuclei grants it additional levels of strength by providing multi-dimensional datasets of the object under test. However, this typically requires dedicated hardware to detect each nucleus. In this paper, we report on the use of a digital lock-in amplifier to perform simultaneous multi-nuclear MRI with a single physical radio frequency (RF) channel. While we showcase this concept by demonstrating the results of fully parallel (TX and RX) 1H and 19F MRI images, we emphasize that it is not limited to two nuclei but can accommodate more nuclei with no extra cost on the hardware or scan time. The scalability is virtually unlimited, constrained only by the processing speed of the digital unit. Furthermore, we demonstrate that the quality of parallel imaging with SNR of 54 is comparable to the commercial single channel with SNR of 43. Thus with no reduction in imaging quality, the proposed concept promises a tremendous reduction in scan time, system complexity, and hardware costs.

Accelerating brain three-dimensional T2 fluid-attenuated inversion recovery using artificial intelligence-assisted compressed sensing: a comparison study with parallel imaging.

Shortening the acquisition time of brain three-dimensional T2 fluid-attenuated inversion recovery (3D T2 FLAIR) by using acceleration techniques has the potential to reduce motion artifacts in images and facilitate clinical application. This study aimed to assess the image quality of brain 3D T2 FLAIR accelerated by artificial intelligence-assisted compressed sensing (ACS) in comparison to 3D T2 FLAIR accelerated by parallel imaging (PI). In this prospective cohort study, 102 consecutive participants, including both healthy individuals and those with suspected brain diseases, were recruited and underwent both ACS- and PI-3D T2 FLAIR scans with a 3.0-Tesla magnetic resonance imaging system from February 2023 to October 2023 in Beijing Tiantan Hospital, Capital Medical University. Quantitative assessment involved white matter (WM) and gray matter (GM) signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), whole-image sharpness, and tumor volume. Qualitative assessment included the scoring of overall image quality, GM-WM border sharpness, and diagnostic confidence in lesion detection. ACS-3D T2 FLAIR exhibited a shorter acquisition time compared to PI-3D T2 FLAIR (105 vs. 320 seconds). ACS-3D T2 FLAIR, compared to PI-3D T2 FLAIR, demonstrated a significantly higher mean SNRWM (25.922±6.811 vs. 22.544±5.853; P<0.001), SNRGM (18.324±7.137 vs. 17.102±6.659; P=0.049), CNRWM/GM (4.613±1.547 vs. 4.160±1.552; P<0.001), and sharpness (0.413±0.049 vs. 0.396±0.034; P<0.001), while no significant differences were found for the overall image quality ratings (P=0.063) or GM-WM border sharpness ratings (P=0.125). A good agreement on tumor volume was achieved between ACS-3D T2 FLAIR and PI-3D T2 FLAIR images (intraclass correlation coefficient =0.999; 0.998-1.000; P<0.001). Images acquired with ACS demonstrated nearly equivalent diagnostic confidence to those obtained with PI (P>0.05). The ACS technique offers a substantial reduction in scanning time for brain 3D T2 FLAIR compared to PI while maintaining good image quality and equivalent diagnostic confidence.

Robot path planning for metrology in hybrid manufacturing

The objective of the wire arc additive manufacturing (WAAM) hybrid cell is to significantly reduce lead time associated with large scale parts. The WAAM process utilizes a 6 degree-of-freedom (DOF) robot manipulator in addition to a 2 DOF part positioner. After printing, the part is translated into position for scanning to inform the subtractive manufacturing process. Scanning in a manual setting can be a long and arduous process to generate scans of sufficient quality for the basis of informed machining. Effective path planning for scanning with the intent to reduce scanning time, increase quality of scans, and move toward a full automation of the hybrid manufacturing cell is investigated in this paper. Simulation software is used to create and verify path plans prior to importing and implementing on a 6 DOF robotic manipulator to which a GOM ATOS Q 3D scanner is mounted. The quality, amount of time necessary to produce, and number of scans required to produce a sufficient representation for machining are compared using three methods. The first method is a manual scanning configuration, the second is using a generalized path plan, and the third is using a geometry-based path plan.

Clinical Validation Study of Deep Learning-Generated Magnetic Resonance Images

This research utilizes a deep learning-based image generation algorithm to generate pseudo-sagittal STIR sequences from sagittal T1WI and T2WI MR images. The evaluations include both subjective assessments by two physicians and objective analyses, measuring image quality through SNR and CNR in ROIs of five different tissues. Further analyses, including MAE, PSNR, SSIM, and COR, establish a strong correlation between the generated STIR sequences and the gold standard, with Bland-Altman analysis indicating pixel consistency. The findings indicate that the deep learning-generated STIR sequences not only align with but potentially surpass the gold standard in terms of image quality and clinical diagnostic capabilities. Moreover, the approach demonstrates promise for clinical implementation, offering reduced scan time and enhanced imaging efficiency.

Single-shot multi-b-value (SSMb) diffusion-weighted MRI using spin echo and stimulated echoes with variable flip angles.

Conventional diffusion-weighted imaging (DWI) sequences employing a spin echo or stimulated echo sensitize diffusion with a specific b-value at a fixed diffusion direction and diffusion time (Δ). To compute apparent diffusion coefficient (ADC) and other diffusion parameters, the sequence needs to be repeated multiple times by varying the b-value and/or gradient direction. In this study, we developed a single-shot multi-b-value (SSMb) diffusion MRI technique, which combines a spin echo and a train of stimulated echoes produced with variable flip angles. The method involves a pair of 90° radio frequency (RF) pulses that straddle a diffusion gradient lobe (GD), to rephase the magnetization in the transverse plane, producing a diffusion-weighted spin echo acquired by the first echo-planar imaging (EPI) readout train. The magnetization stored along the longitudinal axis is successively re-excited by a series of n variable-flip-angle pulses, each followed by a diffusion gradient lobe GD and a subsequent EPI readout train to sample n stimulated-echo signals. As such, (n + 1) diffusion-weighted images, each with a distinct b-value, are acquired in a single shot. The SSMb sequence was demonstrated on a diffusion phantom and healthy human brain to produce diffusion-weighted images, which werequantitative analyzed using a mono-exponential model. In the phantom experiment, SSMb provided similar ADC values to those from a commercial spin-echo EPI (SE-EPI) sequence (r = 0.999). In the human brain experiment, SSMb enabled a fourfold scan time reduction and yielded slightly lower ADC values (0.83 ± 0.26 μm2/ms) than SE-EPI (0.88 ± 0.29 μm2/ms) in all voxels excluding cerebrospinal fluid, likely due to the influence of varying diffusion times. The feasibility of using SSMb to acquire multiple images in a single shot for intravoxel incoherent motion (IVIM) analysis was also demonstrated. In conclusion, despite a relatively lowsignal-to-noise ratio, the proposed SSMb technique can substantially increase the data acquisition efficiency in DWI studies.

Fourier Convolution Block with global receptive field for MRI reconstruction

Reconstructing images from under-sampled Magnetic Resonance Imaging (MRI) signals significantly reduces scan time and improves clinical practice. However, Convolutional Neural Network (CNN)-based methods, while demonstrating great performance in MRI reconstruction, may face limitations due to their restricted receptive field (RF), hindering the capture of global features. This is particularly crucial for reconstruction, as aliasing artifacts are distributed globally. Recent advancements in Vision Transformers have further emphasized the significance of a large RF. In this study, we proposed a novel global Fourier Convolution Block (FCB) with whole image RF and low computational complexity by transforming the regular spatial domain convolutions into frequency domain. Visualizations of the effective RF and trained kernels demonstrated that FCB improves the RF of reconstruction models in practice. The proposed FCB was evaluated on four popular CNN architectures using brain and knee MRI datasets. Models with FCB achieved superior PSNR and SSIM than baseline models and exhibited more details and texture recovery. The code is publicly available at https://github.com/Haozhoong/FCB.