Reduce Acquisition Time Research Articles

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.

Low-rank reconstruction for simultaneous double half-echo 23Na and undersampled 23Na multi-quantum coherences MRI.

To develop a new sequence to simultaneously acquire Cartesian sodium (23Na) MRI and accelerated Cartesian single (SQ) and triple quantum (TQ) sodium MRI of in vivo human brain at 7 T by leveraging two dedicated low-rank reconstruction frameworks. The Double Half-Echo technique enables short echo time Cartesian 23Na MRI and acquires two k-space halves, reconstructed by a low-rank coupling constraint. Additionally, three-dimensional (3D) 23Na Multi-Quantum Coherences (MQC) MRI requires multi-echo sampling paired with phase-cycling, exhibiting a redundant multidimensional space. Simultaneous Autocalibrating and k-Space Estimation (SAKE) were used to reconstruct highly undersampled 23Na MQC MRI. Reconstruction performance was assessed against five-dimensional (5D) CS, evaluating structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR), and quantification of tissue sodium concentration and TQ/SQ ratio in silico, in vitro, and in vivo. The proposed sequence enabled the simultaneous acquisition of fully sampled 23Na MRI while leveraging prospective undersampling for 23Na MQC MRI. SAKE improved TQ image reconstruction regarding SSIM by 6% and reduced RMSE by 35% compared to 5D CS in vivo. Thanks to prospective undersampling, the spatial resolution of 23Na MQC MRI was enhanced from mm3 to mm3 while reducing acquisition time from min to min. The proposed sequence, coupled with low-rank reconstructions, provides an efficient framework for comprehensive whole-brain sodium MRI, combining TSC, T2*, and TQ/SQ ratio estimations. Additionally, low-rank matrix completion enables the reconstruction of highly undersampled 23Na MQC MRI, allowing for accelerated acquisition or enhanced spatial resolution.

AI-Accelerated prostate MRI: a systematic review.

Prostate cancer ranks among the most prevalent cancers affecting men globally. While conventional MRI serves as a diagnostic tool, its extended acquisition time, associated costs, and strain on healthcare systems, underscore the necessity for more efficient methods. The emergence of AI-acceleration in prostate MRI offers promise to mitigate these challenges. A systematic review of studies looking at AI-accelerated prostate MRI was conducted, with a focus on acquisition time along with various qualitative and quantitative measurements. Two primary findings were observed. Firstly, all studies indicated that AI-acceleration in MRI achieved notable reductions in acquisition times without compromising image quality. This efficiency offers potential clinical advantages, including reduced scan durations, improved scheduling, diminished patient discomfort, and economic benefits. Secondly, AI demonstrated a beneficial effect in reducing or maintaining artifact levels in T2-weighted images despite this accelerated acquisition time. Inconsistent results were found in all other domains, which were likely influenced by factors such as heterogeneity in methodologies, variability in AI models, and diverse radiologist profiles. These variances underscore the need for larger, more robust studies, standardisation, and diverse training datasets for AI models. The integration of AI-acceleration in prostate MRI thus far shows some promising results for efficient and enhanced scanning. These advancements may fill current gaps in early detection and prognosis. However, careful navigation and collaborative efforts are essential to overcome challenges and maximize the potential of this innovative and evolving field. This paper reveals overall significant reductions in acquisition time without compromised image quality in AI-accelerated prostate MRI, highlighting potential clinical and diagnostic advantages.

Abstract PO3-07-11: Concordance between abbreviated and complete magnetic resonance protocols for screening woman at moderate and high risk for breast cancer

Abstract Introduction: Patients at high risk for developing breast cancer are potential beneficiaries of personalized screening strategies. Breast magnetic resonance imaging (MRI) has shown high sensitivity, but its broader use remains constrained by issues such as high cost and long examination time. The abbreviated magnetic resonance protocol is presented as an alternative to overcome these challenges. Our primary outcome was to evaluate the concordance of the final report between full and abbreviated protocols. Secondary outcomes included the acquisition and reading time of images from the abbreviated protocol. Methods: A cross-sectional study was conducted on asymptomatic women at moderate and high risk for breast cancer who underwent the full MRI screening protocol in private health institutions in the city of Belo Horizonte, Brazil. The abbreviated protocol was derived from the full protocol by the computer through selection of specific sequences. Each image was independently blind read, and assigned a category from the Breast Imaging - Reporting and Data System (BI-RADS) by at least two examiners. The results were then compared using the Cohen's kappa and Intraclass Correlation Coefficients. Results: The study included 170 women, and a concordance rate of 96,9% was found for the reported BI-RADS category between protocols in this population. A 21-minute reduction was observed in examination time for the abbreviated protocol. Conclusion: The significant concordance between protocols indicates that despite the reduction in acquisition time, number of acquired images, and method's complexity, the abbreviated MRI protocol meets most screening demands. It is, therefore, a viable alternative for cost reduction and improving patients tolerability for the studied population. Table 1. Clinical characteristics of the study population Absolute and percentage data about age, personal risk for breast cancer, family history, and previous genetic testing in the study population. Table 2. Concordance analysis between abbreviated and complete MRI protocols based on the ACR-BIRADS classification - Coen Kappa and Intraclass Correlation Coefficient The Kappa value of 0.9687 (95% CI 0.9426 - 0.9949, p < 0.01) indicates almost perfect concordance between the two protocols. The intraclass correlation coefficient of 0.982 (95% CI 0.97 - 0.987, p < 0.01), also indicates excellent concordance using the two-way random effect models criteria. Citation Format: Mariana Mitraud Ottoni Guedes, Agnaldo Lopes da Silva Filho, Thais Paiva Moraes, Adriene Moraes Campos, Ivie Braga de Paula, Felipe Marcondes de Oliveira Coelho, Barbara Silveira Santana, Bertha Andrade Coelho, Maria Fernanda Sperotto Valadares Gontijo, Leticia Martins De Araujo Campos Linhares, Anna Dias Salvador, Waldeir José de Almeida Junior, José Tadeu Campos de Avelar. Concordance between abbreviated and complete magnetic resonance protocols for screening woman at moderate and high risk for breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-11.

Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications.

Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa. In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy. A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05). DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

Amide proton transfer-weighted imaging with a short acquisition time based on a self B0 correction using the turbo spin echo-Dixon method: A phantom study

PurposeConventional amide proton transfer (APT)-weighted imaging requires a chemical exchange saturation transfer (CEST) sequence with multiple saturation frequency offsets and a B0 correction sequence, plus a long acquisition time that can be reduced by applying the conventional method using CEST images with seven radiation pulses (i.e., the seven-points method). For a further reduction of acquisition times, we propose fast two-dimensional (2D) APT-weighted imaging based on a self B0 correction using the turbo spin echo (TSE)-Dixon method. We conducted a phantom study to investigate the accuracy of TSE-Dixon APT-weighted imaging. MethodsWe prepared two types of phantoms with six samples for a concentrationdependent evaluation and a pH-dependent evaluation. APT-weighted images were acquired by the conventional, seven-points, and TSE-Dixon methods. Linear regression analyses assessed the dependence between each method's APT signal intensities (SIs) and the concentration or pH. We performed a one-way analysis of variance with Tukey's honestly significant difference post hoc test to compare the APT SIs among the three methods. The agreement of the APT SIs between the conventional and seven-points or TSE-Dixon methods was assessed by a Bland- Altman plot analysis. ResultsThe APT SIs of all three acquisition methods showed positive concentration dependence and pH dependence. No significant differences were observed in the APT SIs between the conventional and TSE-Dixon methods at each concentration. The Bland-Altman plot analyses showed that the APT SIs measured with the seven-points method resulted in 0.42% bias and narrow 95% limits of agreement (LOA) (0.93%–0.09%) compared to the conventional method. The APT SIs measured using the TSE-Dixon method showed 0.14% bias and similar 95% LOA (−0.33% to 0.61%) compared with the seven-points method. The APT SIs of all three methods showed positive pH dependence. At each pH, no significant differences in the APT SIs were observed among the methods. Bland-Altman plot analyses showed that the APT SIs measured with the seven-points method resulted in low bias (0.03%) and narrow 95% LOA (−0.30% to 0.36%) compared to the conventional method. The APT SIs measured by the TSE-Dixon method showed slightly larger bias (0.29%) and similar 95% LOA (from −0.15% to 0.72%) compared to those measured by the seven-points method. ConclusionThese results demonstrated that our proposed method has the same concentration dependence and pH dependence as the conventional method and the seven-points method. We thus expect that APT-weighted imaging with less influence of motion can be obtained in clinical examinations.

Multi-contrast echo-planar imaging sequence (Echo-planar imaging mix) in clinical situations demanding faster MRI-brain scans.

The excellent resolution offered by magnetic resonance imaging (MRI) has a trade-off in the form of scan duration. The purpose of the present study was to assess the clinical utility of echo-planar imaging mix (EPIMix), an echo-planar imaging-based MRI sequence for the brain with a short acquisition time. This was a retrospective observational study of 50 patients, who could benefit from faster MRI brain scans. The T1, T2, fluid attenuated inversion recovery, diffusion-weighted imaging (DWI), and T2*/susceptibility-weighted imaging sequences were acquired, conventionally and with EPIMix. Conventional and EPIMix images were assessed by two radiologists for overall quality, motion, and susceptibility artifacts and scored on a Likert scale. The scores given for conventional and EPIMix images were compared. The diagnostic performance of EPIMix was also assessed by the ability to detect clinically relevant findings. The acquisition time for conventional MRI was 11 min and 45 s and for EPIMix 1 min and 15 s. All EPIMix images were sufficient for diagnostic use. On assessment of the diagnostic performance, it was excellent for ischemic and hemorrhagic strokes. Smaller lesions, lesions adjacent to bone, and post-operative tumors were difficult to identify. Moderate to perfect agreement (Kappa values 0.41-1) was seen between radiologists for all categories except skull base, calvarial, and orbital lesions. Image quality, artifact assessment showed excellent interobserver agreement (>90%) for the scores. All EPIMix images showed reduced motion artifacts. The EPIMix-DWI was comparable to conventional-DWI in terms of quality and artifacts. The remaining sequences showed reduced quality and increased susceptibility. The EPIMix has a significantly reduced acquisition time than conventional MRI and could be used instead of conventional MRI in situations demanding faster scans such as suspected acute ischemic or hemorrhagic stroke. In other clinical scenarios, it could help tailor the MRI examination for each patient.

Deep learning reconstruction for turbo spin echo to prospectively accelerate ankle MRI: A multi-reader study

PurposeTo evaluate a deep learning reconstruction for turbo spin echo (DLR-TSE) sequence of ankle magnetic resonance imaging (MRI) in terms of acquisition time, image quality, and lesion detectability by comparing with conventional TSE. MethodsBetween March 2023 and May 2023, patients with an indication for ankle MRI were prospectively enrolled. Each patient underwent a conventional TSE protocol and a prospectively undersampled DLR-TSE protocol. Four experienced radiologists independently assessed image quality using a 5-point scale and reviewed structural abnormalities. Image quality assessment included overall image quality, differentiation of anatomic details, diagnostic confidence, artifacts, and noise. Interchangeability analysis was performed to evaluate the equivalence of DLR-TSE relative to conventional TSE for detection of structural pathologies. ResultsIn total, 56 patients were included (mean age, 32.6 ± 10.6 years; 35 men). The DLR-TSE (233 s) protocol enabled a 57.4 % reduction in total acquisition time, compared with the conventional TSE protocol (547 s). DLR-TSE images had superior overall image quality, fewer artifacts, and less noise (all P < 0.05), compared with conventional TSE images, according to mean ratings by the four readers. Differentiation of anatomic details, diagnostic confidence, and assessments of structural abnormalities showed no differences between the two techniques (P > 0.05). Furthermore, DLR-TSE demonstrated diagnostic equivalence with conventional TSE, based on interchangeability analysis involving all analyzed structural abnormalities. ConclusionDLR can prospectively accelerate conventional TSE to a level comparable with a 4-minute comprehensive examination of the ankle, while providing superior image quality and similar lesion detectability in clinical practice.

Fast System Matrix Generation Based on Single Angle Calibration in Open-Sided Field Free Line Magnetic Particle Imaging.

Open-sided field-free line magnetic particle imaging (OS FFL MPI) is a novel medical imaging system configuration that has received significant attention in recent years. However, the measurement-based system matrix (SM) image reconstruction for OS FFL MPI typically requires multiple angle calibration (MAC), which is time-consuming in practice. To address this issue, we propose a fast 2D SM generation method that requires only a single angle calibration (SAC). The SAC method exploits the rotational invariance of the system function. Based on the measured single angle system function, the system function is rotated to generate system functions at other angles, and then the SM for image reconstruction is constructed. Then, we conducted various simulation experiments and built an OS FFL MPI scanner to evaluate the proposed SAC method. The experiments demonstrating the effectiveness of SAC in reducing calibration workload, requiring fewer scanning numbers while maintaining a similar image reconstruction quality compared to MAC method. Furthermore, the SM generated by SAC produces consistent imaging results with the SM generated by MAC, regardless of the interpolation algorithms, the number of rotation angles, or the signal-to-noise ratios employed in phantom imaging experiments. SAC has been experimentally verified to reduce acquisition time while maintaining accurate and robust reconstruction performance. The significance of SAC lies in its contribution to improving calibration efficiency in OS FFL MPI, potentially facilitating the implementation of MPI in a wider range of applications.

Fetal Cardiac MRI Using Doppler US Gating: Emerging Technology and Clinical Implications.

Fetal cardiac MRI using Doppler US gating is an emerging technique to support prenatal diagnosis of congenital heart disease and other cardiovascular abnormalities. Analogous to postnatal electrocardiographically gated cardiac MRI, this technique enables directly gated MRI of the fetal heart throughout the cardiac cycle, allowing for immediate data reconstruction and review of image quality. This review outlines the technical principles and challenges of cardiac MRI with Doppler US gating, such as loss of gating signal due to fetal movement. A practical workflow of patient preparation for the use of Doppler US-gated fetal cardiac MRI in clinical routine is provided. Currently applied MRI sequences (ie, cine or four-dimensional flow imaging), with special consideration of technical adaptations to the fetal heart, are summarized. The authors provide a literature review on the clinical benefits of Doppler US-gated fetal cardiac MRI for gaining additional diagnostic information on cardiovascular malformations and fetal hemodynamics. Finally, future perspectives of Doppler US-gated fetal cardiac MRI and further technical developments to reduce acquisition times and eliminate sources of artifacts are discussed. Keywords: MR Fetal, Ultrasound Doppler, Cardiac, Heart, Congenital, Obstetrics, Fetus Supplemental material is available for this article. © RSNA, 2024.

Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study.

Multi-shell diffusion Magnetic Resonance Imaging (dMRI) data has been widely used to characterise white matter microstructure in several neurodegenerative diseases. The lack of standardised dMRI protocols often implies the acquisition of redundant measurements, resulting in prolonged acquisition times. In this study, we investigate the impact of the number of gradient directions on Diffusion Tensor Imaging (DTI) and on Neurite Orientation Dispersion and Density Imaging (NODDI) metrics. Data from 124 healthy controls collected in three different longitudinal studies were included. Using an in-house algorithm, we reduced the number of gradient directions in each data shell. We estimated DTI and NODDI measures on six white matter bundles clinically relevant for neurodegenerative diseases. Fractional Anisotropy (FA) measures on bundles where data were sampled at the 30% rate, showed a median L1 distance of up to 3.92% and a 95% CI of (1.74, 8.97)% when compared to those obtained at reference sampling. Mean Diffusivity (MD) reached up to 4.31% and a 95% CI of (1.60, 16.98)% on the same premises. At a sampling rate of 50%, we obtained a median of 3.90% and a 95% CI of (1.99, 16.65)% in FA, and 5.49% with a 95% CI of (2.14, 21.68)% in MD. The Intra-Cellular volume fraction (ICvf) median L1 distance was up to 2.83% with a 95% CI of (1.98, 4.82)% at a 30% sampling rate and 3.95% with a 95% CI of (2.39, 7.81)% at a 50% sampling rate. The volume difference of the reconstructed white matter at reference and 50% sampling reached a maximum of (2.09 ± 0.81)%. In conclusion, DTI and NODDI measures reported at reference sampling were comparable to those obtained when the number of dMRI volumes was reduced by up to 30%. Close to reference DTI and NODDI metrics were estimated with a significant reduction in acquisition time using three shells, respectively with: 4 directions at a b value of 700 s/mm2, 14 at 1000 s/mm2, and 32 at 2000 s/mm2. The study revealed aspects that can be important for large-scale clinical studies on bundle-specific diffusion MRI.

Performance of the Iterative OSEM and HYPER Algorithm for Total-body PET at SUVmax with a Low 18F-FDG Activity, a Short Acquisition Time and Small Lesions.

The primary objective of this comparative investigation was to examine the qualitative attributes of image reconstructions utilizing two distinct algorithms, namely OSEM and HYPER Iterative, in total-body 18F- FDG PET/CT under various acquisition durations and injection activities. An initial assessment was executed using a NEMA phantom to compare image quality engendered by OSEM and HYPER Iterative algorithms. Parameters such as BV, COV, and CRC were meticulously evaluated. Subsequently, a prospective cohort study was conducted on 50 patients, employing both reconstruction algorithms. The study was compartmentalized into distinct acquisition time and dosage groups. Lesions were further categorized into three size-based groups. Quantifiable metrics including SD of noise, SUVmax, SNR, and TBR were computed. Additionally, the differences in values, namely &#916;SUVmax, &#916;TBR, %&#916;SUVmax, %&#916;SD, and %&#916;SNR, between OSEM and HYPER Iterative algorithms were also calculated. The HYPER Iterative algorithm showed reduced BV and COV compared to OSEM in the phantom study, with constant acquisition time. In the clinical study, lesion SUVmax, TBR, and SNR were significantly elevated in images reconstructed using the HYPER Iterative algorithm in comparison to those generated by OSEM (p &#60; 0.001). Furthermore, an amplified increase in SUVmax was predominantly discernible in lesions with dimensions less than 10 mm. Metrics such as %&#916;SNR and %&#916;SD in HYPER Iterative exhibited improvements correlating with reduced acquisition times and dosages, wherein a more pronounced degree of enhancement was observable in both &#916;SUVmax and &#916;TBR. The HYPER Iterative algorithm significantly improves SUVmax and reduces noise level, with particular efficacy in lesions measuring ≤ 10 mm and under conditions of abbreviated acquisition times and lower dosages.

Comparative analysis of image quality and interchangeability between standard and deep learning-reconstructed T2-weighted spine MRI

Rationale and objectivesMRI reconstruction of undersampled data using a deep learning (DL) network has been recently performed as part of accelerated imaging. Herein, we compared DL-reconstructed T2-weighted image (T2-WI) to conventional T2-WI regarding image quality and degenerative lesion detection. Materials and methodsSixty-two patients underwent C-spine (n = 27) or L-spine (n = 35) MRIs, including conventional and DL-reconstructed T2-WI. Image quality was assessed with non-uniformity measurement and 4-scale grading of structural visibility. Three readers (R1, R2, R3) independently assessed the presence and types of degenerative lesions. Student t-test was used to compare non-uniformity measurements. Interprotocol and interobserver agreement of structural visibility was analyzed with Wilcoxon signed-rank test and weighted-κ values, respectively. The diagnostic equivalence of degenerative lesion detection between two protocols was assessed with interchangeability test. ResultsThe acquisition time of DL-reconstructed images was reduced to about 21–58% compared to conventional images. Non-uniformity measurement was insignificantly different between the two images (p-value = 0.17). All readers rated DL-reconstructed images as showing the same or superior structural visibility compared to conventional images. Significantly improved visibility was observed at disk margin of C-spine (R1, p < 0.001; R2, p = 0.04) and dorsal root ganglia (R1, p = 0.03; R3, p = 0.02) and facet joint (R1, p = 0.04; R2, p < 0.001; R3, p = 0.03) of L-spine. Interobserver agreements of image quality were variable in each structure. Clinical interchangeability between two protocols for degenerative lesion detection was verified showing <5% in the upper bounds of 95% confidence intervals of agreement rate differences. ConclusionsDL-reconstructed T2-WI demonstrates comparable image quality and diagnostic performance with conventional T2-WI in spine imaging, with reduced acquisition time.

Low Observable Uncrewed Aerial Vehicle Wind Tunnel Model Design, Manufacturing, and Aerodynamic Characterization

Developing wind tunnel models is time consuming, labor intensive, and expensive. Rapid prototyping for wind tunnel tests is an effective, faster, and cheaper method to obtain aerodynamic performance results while considerably reducing acquisition time and cost for the models. Generally, the rapid prototyping models suffer from insufficient stiffness or strength to withstand the loads generated during a wind tunnel test. In the present study, a rapid prototype model reinforced with metallic inserts was produced to experimentally investigate the aerodynamic characteristics of an uncrewed aerial vehicle with various wingtip deflections. The fused deposition modeling process was used to make the outer mold, whereas the metallic parts were produced using laser cutting and the computer numerical control machining process. Then, the model was evaluated both experimentally and numerically. The test campaign presented in this work was conducted in the de Havilland low-speed wind tunnel facility at the University of Glasgow. For better characterization of flow patterns dominated by leading edge vortices, numerical simulations were run using OpenFOAM 8.0 and validated with experimental data. The experimental data obtained from the hybrid rapid-prototyped model agreed well with the numerical results. This demonstrates the efficacy of hybrid rapid-prototyped models in providing reliable results for initial baseline aerodynamic database development within a short period and at a reduced cost for wind tunnel tests.

Total-Body PET/CT Applications in Cardiovascular Diseases: A Perspective Document of the SNMMI Cardiovascular Council.

Digital PET/CT systems with a long axial field of view have become available and are emerging as the current state of the art. These new camera systems provide wider anatomic coverage, leading to major increases in system sensitivity. Preliminary results have demonstrated improvements in image quality and quantification, as well as substantial advantages in tracer kinetic modeling from dynamic imaging. These systems also potentially allow for low-dose examinations and major reductions in acquisition time. Thereby, they hold great promise to improve PET-based interrogation of cardiac physiology and biology. Additionally, the whole-body coverage enables simultaneous assessment of multiple organs and the large vascular structures of the body, opening new opportunities for imaging systemic mechanisms, disorders, or treatments and their interactions with the cardiovascular system as a whole. The aim of this perspective document is to debate the potential applications, challenges, opportunities, and remaining challenges of applying PET/CT with a long axial field of view to the field of cardiovascular disease.

Deep learning-accelerated image reconstruction in back pain-MRI imaging: reduction of acquisition time and improvement of image quality

IntroductionLow back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intelligence showed promise in improving image quality and simultaneously reducing scan time. This study evaluates the performance of deep learning (DL)-based T2 turbo spin-echo (TSE, T2DLR) and T1 TSE (T1DLR) in lumbar spine imaging regarding acquisition time, image quality, artifact resistance, and diagnostic confidence.Material and methodsThis retrospective monocentric study included 60 patients with lower back pain who underwent lumbar spinal MRI between February and April 2023. MRI parameters and DL reconstruction (DLR) techniques were utilized to acquire images. Two neuroradiologists independently evaluated image datasets based on various parameters using a 4-point Likert scale.ResultsAccelerated imaging showed significantly less image noise and artifacts, as well as better image sharpness, compared to standard imaging. Overall image quality and diagnostic confidence were higher in accelerated imaging. Relevant disk herniations and spinal fractures were detected in both DLR and conventional images. Both readers favored accelerated imaging in the majority of examinations. The lumbar spine examination time was cut by 61% in accelerated imaging compared to standard imaging.ConclusionIn conclusion, the utilization of deep learning-based image reconstruction techniques in lumbar spinal imaging resulted in significant time savings of up to 61% compared to standard imaging, while also improving image quality and diagnostic confidence. These findings highlight the potential of these techniques to enhance efficiency and accuracy in clinical practice for patients with lower back pain.

Parallel computational ghost imaging with modulation patterns multiplexing and permutation inspired by compound eyes

Real-time computational ghost imaging (CGI) has received significant attention in recent years to overcome the trade-off between long acquisition time and high reconstructed image quality of CGI. Inspired by compound eyes, we propose a parallel computational ghost imaging with modulation patterns multiplexing and permutation to achieve a faster and high-resolution CGI. With modulation patterns multiplexing and permutation, several small overlapping fields-of-view can be obtained; meanwhile, the difficulty in alignment of illumination light field and multiple detectors can be well resolved. The method combining compound eyes with multi-detectors to capture light intensity can resolve the issue of a gap between detector units in the array detector. Parallel computation facilitates significantly reduced acquisition time, while maintaining reconstructed quality without compromising the sampling ratio. Experiments indicate that using m × m detectors reduce modulation pattern count, projector storage, and projection time to around 1/m2 of typical CGI methods, while increasing image resolution to m2 times. This work greatly promotes the practicability of parallel computational ghost imaging and provides optional solution for real-time computational ghost imaging.

Dynamic parallel imaging at 9.4 T using reconfigurable receive coaxial dipoles.

Parallel imaging is one of the key MRI technologies that allow reduction of image acquisition time. However, the parallel imaging reconstruction commonly leads to a signal-to-noise ratio (SNR) drop evaluated using a so-called geometrical factor (g-factor). The g-factor is minimized by increasing the number of array elements and their spatial diversity. At the same time, increasing the element count requires a decrease in their size. This may lead to insufficient coil loading, an increase in the relative noise contribution from the RF coil itself, and hence SNR reduction. Previously, instead of increasing the channel number, we introduced the concept of electronically switchable time-varying sensitivities, which was shown to improve parallel imaging performance. In this approach, each reconfigurable receive element supports two spatially distinct sensitivity profiles. In this work, we developed and evaluated a novel eight-element human head receive-only reconfigurable coaxial dipole array for human head imaging at 9.4 T. In contrast to the previously reported reconfigurable dipole array, the new design does not include direct current (DC) control wires connected directly to the dipoles. The coaxial cable itself is used to deliver DC voltage to the PIN diodes located at the ends of the antennas. Thus, the novel reconfigurable coaxial dipole design opens a way to scale the dynamic parallel imaging up to a realistic number of channels, that is, 32 and above. The novel array was optimized and tested experimentally, including in vivo studies. It was found that dynamic sensitivity switching provided an 8% lower mean and 33% lower maximum g-factor (for Ry × Rz = 2 × 2 acceleration) compared with conventional static sensitivities.

Abstract 128: Multi-Direction Diffusion Weighted Imaging on Portable, Low-Field Magnetic Resonance Imaging

Background and aims: Portable, low-field (LF) MRI has the potential to improve access to expeditious, definitive brain imaging and facilitate diagnosis of acute stroke. Currently available diffusion-weighted imaging (DWI) protocols at LF are limited to a single diffusion direction due to acquisition duration. However, single-direction diffusion has reduced sensitivity for detecting acute ischemic infarcts, particularly small lesions residing in white matter tracts. The purpose of this study was to establish the feasibility of acquiring multi-direction DWI compared with single-direction counterparts on LF-MRI. Methods: Patients presenting with a diagnosis of acute ischemic stroke between July and September 2023 were eligible. Consented patients underwent DWI acquisition on a 0.064T LF-MRI (Mk1.9; Hyperfine Research Inc). Three diffusion directions (x, y, and z) were acquired with a b weighting of 900 s/mm 2 and a single acquisition with a b 0 s/mm 2 . The b 900 images were co-registered to the b 0, trace and apparent diffusion coefficient (ADC) maps calculated, and the final images interpolated at 1 mm 3 . Results: Ten patients presenting to the Massachusetts General Hospital with acute ischemic stroke were consented and imaged within 72 hours of last known well. The total acquisition time was 14 minutes, with all subjects able to tolerate the scan duration. Ischemic lesions as small as 0.1 mL were detectable on the LF-MRI (17.5 +/- 18.2 mL). An example of each diffusion direction individually, the combined trace, and corresponding ADC maps are shown in Figure 1, compared with conventional high-field (HF) diffusion images acquired within 30 minutes of the LF acquisition. Conclusion: Multi-direction DWI imaging is feasible on a 0.064T LF-MRI scanner. Our experience suggests further modifications to the pulse sequence and scanner configuration may facilitate a reduction in acquisition time, improve resolution, or both.

Advances in multimodal mass spectrometry for single-cell analysis and imaging enhancement.

Multimodal mass spectrometry (MMS) incorporates an imaging modality with probe-based mass spectrometry (MS) to enable precise, targeted data acquisition and provide additional biological and chemical data not available by MS alone. Two categories of MMS are covered; in the first, an imaging modality guides the MS probe to target individual cells and to reduce acquisition time by automatically defining regions of interest. In the second category, imaging and MS data are coupled in the data analysis pipeline to increase the effective spatial resolution using a higher resolution imaging method, correct for tissue deformation, and incorporate fine morphological features in an MS imaging dataset. Recent methodological and computational developments are covered along with their application to single-cell and imaging analyses.