Reduced Scan Time Research Articles

Investigating the Impact of Compressed Sensing Techniques and IoT in Medical Imaging

This research paper examines compressed sensing's impact on medical imaging. Math and signal processing inspired compressed sensing. Future picture-capturing will be radically different. The paper focuses on adaptive random sampling (ARS), iterative shrinkage-thresholding algorithms (ISTA), and temporal compressed sensing (TCS). These approaches were rigorously tested using MRIs, X-rays, and dynamic imaging patterns. Low scan times, picture quality, and dynamic imaging were the main test criteria. The technologies considerably reduced scan time, demonstrating their potential to speed up imaging procedures. The reconstructed photos had higher SNRs and SSIs than those obtained using normal techniques, indicating greater accuracy. The TCS algorithm's dynamic imaging skills, especially evident in heart and musculoskeletal imaging, eliminated motion defects while exhibiting real-time physiological changes. The study was expanded to incorporate customized treatment, and the recommended procedures have proven amazing adaptability to each patient's demands. This adaptability fits current medical treatments, making unique imaging technologies viable.

Manifold-based denoising for Ferumoxytol-enhanced 3D cardiac cine MRI.

The two-dimensional (2D) cine cardiovascular magnetic resonance (CMR) technique is the reference standard for assessing cardiac function. However, one challenge with 2D cine is that the acquisition time for the whole cine stack is long and requires multiple breath holds, which may not be feasible for pediatric or ill patients. Though single breath-hold multi-slice cine may address the issue, it can only acquire low-resolution images, and hence, affect the accuracy of cardiac function assessment. To address these challenges, a Ferumoxytol-enhanced, free breathing, isotropic high-resolution 3D cine technique was developed. The method produces high-contrast cine images with short acquisition times by using compressed sensing together with a manifold-based method for image denoising. This study included fifteen patients (9.1 $ \pm $ 5.6 yrs.) who were referred for clinical cardiovascular magnetic resonance imaging (MRI) with Ferumoxytol contrast and were prescribed the 3D cine sequence. The data was acquired on a 1.5T scanner. Statistical analysis shows that the manifold-based denoised 3D cine can accurately measure ventricular function with no significant differences when compared to the conventional 2D breath-hold (BH) cine. The multiplanar reconstructed images of the proposed 3D cine method are visually comparable to the golden standard 2D BH cine method in terms of clarity, contrast, and anatomical precision. The proposed method eliminated the need for breath holds, reduced scan times, enabled multiplanar reconstruction within an isotropic data set, and has the potential to be used as an effective tool to access cardiovascular conditions.

Advanced Abdominal MRI Techniques and Problem-Solving Strategies.

MRI plays an important role in abdominal imaging because of its ability to detect and characterize focal lesions. However, MRI examinations have several challenges, such as comparatively long scan times and motion management through breath-holding maneuvers. Techniques for reducing scan time with acceptable image quality, such as parallel imaging, compressed sensing, and cutting-edge deep learning techniques, have been developed to enable problem-solving strategies. Additionally, free-breathing techniques for dynamic contrast-enhanced imaging, such as extra-dimensional-volumetric interpolated breath-hold examination, golden-angle radial sparse parallel, and liver acceleration volume acquisition Star, can help patients with severe dyspnea or those under sedation to undergo abdominal MRI. We aimed to present various advanced abdominal MRI techniques for reducing the scan time while maintaining image quality and free-breathing techniques for dynamic imaging and illustrate cases using the techniques mentioned above. A review of these advanced techniques can assist in the appropriate interpretation of sequences.

Evaluation of a deep learning magnetic resonance imaging reconstruction method for synthetic computed tomography generation in prostate radiotherapy

Background and PurposeIn magnetic resonance imaging (MRI) only radiotherapy computed tomography (CT) is excluded. The method relies entirely on synthetic CT images generated from MRI. This study evaluates the compatibility of a commercial synthetic CT (sCT) with an accelerated commercial deep learning reconstruction (DLR) in MRI-only prostate radiotherapy. Materials and MethodsFor a group of 24 patients (cohort 1) the effects of DLR were studied in isolation. MRI data were reconstructed conventionally and with DLR from identical k-space data, and sCTs were generated for both reconstructions. The sCT quality, Hounsfield Unit (HU) and dosimetric impact were investigated. In another group of 15 patients (cohort 2) effects on sCT generation using accelerated MRI acquisition (40 % time reduction) reconstructed with DLR were investigated. ResultssCT images from both cohorts, generated from DLR MRI data, were of clinically expected image quality. The mean dose differences for targets and organs at risks in cohort 1 were <0.06 Gy, corresponding to a 0.1 % prescribed dose difference. Similar dose differences were observed in cohort 2. Gamma pass rates for cohort 1 were 100 % for criteria 3 %/3mm, 2 %/2mm and 1 %/1mm for all dose levels. Mean error and mean absolute error inside the body, between sCTs, averaged over all cohort 1 subjects, were −1.1 ± 0.6 [−2.4 0.2] and 2.9 ± 0.4 [2.3 3.9] HU, respectively. ConclusionsDLR was suitable for sCT generation with clinically negligible differences in HU and calculated dose compared to the conventional MRI reconstruction method. For sCT generation DLR enables scan time reduction, without compromised sCT quality.

Improved test-retest reliability of and susceptibility quantification using multishot multi-echo 3D EPI.

This study aimed to evaluate the potential of 3D EPI for improving the reliability of -weighted data and quantification of decay rate and susceptibility (χ) compared with conventional gradient-echo (GRE)-based acquisition. Eight healthy subjects in a wide age range were recruited. Each subject received repeated scans for both GRE and EPI acquisitions with an isotropic 1 mm resolution at 3 T. Maps of and χ were quantified, and their interscan differences were used to evaluate the test-retest reliability. Interprotocol differences of and χ between GRE and EPI were also measured voxel by voxel and in selected regions of interest to test the consistency between the two acquisition methods. The quantifications of and χ using EPI protocols showed increased test-retest reliability with higher EPI factors up to 5 as performed in the experiment and were consistent with those based on GRE. The result suggests that multishot multi-echo 3D EPI can be a useful alternative acquisition method for -weighted MRI and quantification of and χ with reduced scan time, improved test-retest reliability, and similar accuracy compared with commonly used 3D GRE.

Current MR imaging of cartilage in the context of knee osteoarthritis (part1) : Principles and sequences

Magnetic resonance imaging (MRI) is the clinical method of choice for cartilage imaging in the context of degenerative and nondegenerative joint diseases. The MRI-based definitions of osteoarthritis rely on the detection of osteophytes, cartilage pathologies, bone marrow edema and meniscal lesions but currently ascientific consensus is lacking. In the clinical routine proton density-weighted, fat-suppressed 2D turbo spin echo sequences with echo times of 30-40 ms are predominantly used, which are sufficiently sensitive and specific for the assessment of cartilage. The additionally acquired T1-weighted sequences are primarily used for evaluating other intra-articular and periarticular structures. Diagnostically relevant artifacts include magic angle and chemical shift artifacts, which can lead to artificial signal enhancement in cartilage or incorrect representations of the subchondral lamina and its thickness. Although scientifically validated, high-resolution 3D gradient echo sequences (for cartilage segmentation) and compositional MR sequences (for quantification of physical tissue parameters) are currently reserved for scientific research questions. The future integration of artificial intelligence techniques in areas such as image reconstruction (to reduce scan times while maintaining image quality), image analysis (for automated identification of cartilage defects), and image postprocessing (for automated segmentation of cartilage in terms of volume and thickness) will significantly improve the diagnostic workflow and advance the field further.

An ultra‐fast MRI protocol in dementia enabled by Wave‐CAIPI

AbstractBackgroundStructural brain imaging is essential for the diagnostic workup in dementia and for safety monitoring of disease modifying therapies. MRI has several advantages over other imaging techniques, but its use is limited by cost and availability. Reducing acquisition time holds the potential for its democratisation. We developed a prototype ultra‐fast MRI protocol based on an optimised 3D parallel imaging approach. Here we present the finalised version, developed using a qualitative and quantitative approach to optimisation.MethodIn this ongoing real‐world study, participants are sequentially recruited from the cognitive disorders' clinics of a tertiary centre. Patients due to have a standard 3T MRI scan as part of their diagnostic pathway are eligible. The standard‐of‐care protocol includes T1w, T2w, FLAIR and T2*/SWI sequences [scan time ∼18mins]. The prototype rapid protocol utilises Siemens work‐in‐progress 3D Wave‐CAIPI (Controlled Aliasing in Parallel Imaging) sequences, enabling a reduction in scan time of more than 60% (table 1). For qualitative analysis, scans (of 9 individuals) were reviewed by six raters and assessed side‐by‐side against the gold‐standard scan for diagnostic utility. For a quantitative analysis, volumes were calculated from T1w scans using the default cortical reconstruction and volumetric segmentation pipeline in FreeSurfer 7.3.2. Key volumes (e.g., deep grey matter, cortical grey matter, white matter) for each participant were compared between the two protocols. Independent visual QC was performed on T1w datasets by an experienced clinical trials team.ResultSide‐by‐side visual assessment results of 9 participants is shown in figure 1. For all sequences bar FLAIR, raters scored the ultra‐fast sequences of comparable image quality in at least 67% of cases. For quantitative analysis, 10 T1w sequence pairs were reviewed, with 1 clinical scan failing QC due to motion. Bland‐Altman analyses (figure 2.) comparing volumes between the clinical and ultra‐fast sequences show little bias.ConclusionOur preliminary results demonstrate that for most contrasts the ultra‐fast sequences are diagnostically non‐inferior and quantitatively equivalent to the clinical protocol both for diagnosis and for safety monitoring.

Recent Developments in Speeding up Prostate MRI.

The increasing incidence of prostate cancer cases worldwide has led to a tremendous demand for multiparametric MRI (mpMRI). In order to relieve the pressure on healthcare, reducing mpMRI scan time is necessary. This review focuses on recent techniques proposed for faster mpMRI acquisition, specifically shortening T2W and DWI sequences while adhering to the PI-RADS (Prostate Imaging Reporting and Data System) guidelines. Speeding up techniques in the reviewed studies rely on more efficient sampling of data, ranging from the acquisition of fewer averages or b-values to adjustment of the pulse sequence. Novel acquisition methods based on undersampling techniques are often followed by suitable reconstruction methods typically incorporating synthetic priori information. These reconstruction methods often use artificial intelligence for various tasks such as denoising, artifact correction, improvement of image quality, and in the case of DWI, for the generation of synthetic high b-value images or apparent diffusion coefficient maps. Reduction of mpMRI scan time is possible, but it is crucial to maintain diagnostic quality, confirmed through radiological evaluation, to integrate the proposed methods into the standard mpMRI protocol. Additionally, before clinical integration, prospective studies are recommended to validate undersampling techniques to avoid potentially inaccurate results demonstrated by retrospective analysis. This review provides an overview of recently proposed techniques, discussing their implementation, advantages, disadvantages, and diagnostic performance according to PI-RADS guidelines compared to conventional methods. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI.

For time-consuming diffusion-weighted imaging (DWI) of the breast, deep learning-based imaging acceleration appears particularly promising. To investigate a combined k-space-to-image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI. Retrospective. 133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI. 3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2). DWI data were retrospectively processed using deep learning-based k-space-to-image reconstruction (DL-DWI) and an additional super-resolution algorithm (SRDL-DWI). In addition to signal-to-noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL- and SRDL-DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven-point rating scale. Friedman's rank-based analysis of variance with Bonferroni-corrected pairwise post-hoc tests. P < 0.05 was considered significant. Both DL- and SRDL-DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL-DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818-0.848). Irrespective of b-value, both standard and DL-DWI produced superior SNR compared to SRDL-DWI. ADC values were slightly higher in SRDL-DWI (+0.5%) and DL-DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL-/SRDL-DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel-wise error. Deep learning-based k-space-to-image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super-resolution interpolation allows for substantial improvement of subjective image quality. 4 TECHNICAL EFFICACY: Stage 1.

Design and characterization of a hyperbolic-elliptical lens pair in a rapid beam steering system for single-pixel terahertz spectral imaging of the cornea.

Terahertz (THz) time-domain spectroscopy has been investigated for assessment of the hydration levels in the cornea, intraocular pressure, and changes in corneal topography. Previous efforts at THz imaging of the cornea have employed off-axis parabolic mirrors to achieve normal incidence along the spherical surface. However, this comes at the cost of an asymmetric field-of-view (FOV) and a long scan time because it requires raster-scanning of the collimated beam across the large mirror diameter. This paper proposes a solution by designing a pair of aspheric lenses that can provide a larger symmetric spherical FOV (9.6 mm) and reduce the scan time by two orders of magnitude using a novel beam-steering approach. A hyperbolic-elliptical lens was designed and optimized to achieve normal incidence and phase-front matching between the focused THz beam and the target curvature. The lenses were machined from a slab of high-density polyethylene and characterized in comparison to ray-tracing simulations by imaging several targets of similar sizes to the cornea. Our experimental results showed excellent agreement in the increased symmetric FOV and confirmed the reduction in scan time to about 3-4 seconds. In the future, this lens design process can be extended for imaging the sclera of the eye and other curved biological surfaces, such as the nose and fingers.

Abstract 13323: Impact of an Institutional Process Change Adopting End-Systolic Coronary CTA Acquisition on Patient Throughput and Image Quality

Introduction: Prospective ECG-triggered end-systolic coronary computed tomographic angiography (CCTA) acquisition is less prone to motion artifacts with arrhythmias and high heart rates, and with increasing utilization, has the potential to improve scan and CT laboratory efficiency by reducing the need for IV/oral beta-blockers (BB) prior to the scan. Methods: We implemented an institutional process change in CCTA performance effective January 2023, transitioning from prospective ECG-triggered mid-diastolic acquisitions supervised by a physician at the scanner to an algorithmic approach with prospective end-systolic acquisition (200-400 ms after R peak) without BB administration, all on 3 rd -generation 192 slice dual-source scanners. We reviewed 100 consecutive CCTAs done pre- and post-process change in Jan 2022 and Jan 2023. Two experienced CCTA readers assessed segmental image quality by consensus using an 18-segment SCCT model on a 5-point Likert scale (1=non-interpretable; 2=poor; 3=satisfactory; 4=good; 5=excellent). Results: Post-process change, median overall patient appointment [94 (78 - 127) min vs. 69 (53 - 86) min; P &lt; 0.001] and scan times [12 (10 - 16) min vs. 7 (6 - 9) min; P &lt;.001] were significantly lower, and patient throughput doubled [2 (2 - 3)/day vs. 4 (3 - 6)/day; P =.024]. Median segmental image quality score decreased from excellent to good [5 (4 - 5) vs. 4 (4 - 5); P &lt;.001], with a decrease in signal-noise ratio [23.5 (18 - 28.8) vs. 11.8 (8.7 - 14.7); P &lt;.001], and no significant difference in poor or non-interpretable segments in the end-systolic cohort. More scans were repeated with end-systolic scans (10% vs. 18%), with no significant change in radiation dose (Table 1). Conclusion: Utilizing prospective ECG-triggered end-systolic acquisition avoided the use of BB, significantly reduced scan times, doubled patient throughput, at similar radiation doses, and maintained diagnostic performance with only a mild compromise in image quality.

Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.

Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data. Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme. The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations. Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.

Deep learning-based noise reduction preserves quantitative MRI biomarkers in patients with brain tumors

The use of relaxometry and Diffusion-Tensor Imaging sequences for brain tumor assessment is limited by their long acquisition time. We aim to test the effect of a denoising algorithm based on a Deep Learning Reconstruction (DLR) technique on quantitative MRI parameters while reducing scan time. In 22 consecutive patients with brain tumors, DLR applied to fast and noisy MR sequences preserves the mean values of quantitative parameters (fractional anisotropy, mean diffusivity, T1 and T2-relaxation time) and produces maps with higher structural similarity compared to long duration sequences. This could promote wider use of these biomarkers in clinical setting.

Highly accelerated compressed sensing 4D flow MRI in congenital and acquired heart disease: comparison of aorta and main pulmonary artery flow parameters with conventional 4D flow MRI in children and young adults.

Four-dimensional flow (4D flow) MRI has become a clinically utilized cardiovascular flow assessment tool. However, scans can be lengthy and may require anesthesia in younger children. Adding compressed sensing can decrease scan time, but its impact on hemodynamic data accuracy needs additional assessment. To compare 4D flow hemodynamics acquired with and without compressed sensing. Twenty-seven patients (median age: 13 [IQR: 9.5] years) underwent conventional and compressed sensing cardiovascular 4D flow following informed consent. Conventional 4D flow was performed using parallel imaging and an acceleration factor of 2. Compressed sensing 4D flow was performed with an acceleration factor of 7.7. Regions of interest were placed to compare flow parameters in the ascending aorta and main pulmonary artery. Paired Student's t-tests, Wilcoxon signed-rank tests, Bland-Altman plots, and intraclass correlation coefficients were conducted. A P-value of < 0.05 was considered statistically significant. Mean scan acquisition time was reduced by 59% using compressed sensing (3.4 vs. 8.2min, P < 0.001). Flow quantification was similar for compressed sensing and conventional 4D flow for the ascending aorta net flow: 47 vs. 49ml/beat (P = 0.28); forward flow: 49 vs. 50ml/beat (P = 0.07), and main pulmonary artery net flow: 49 vs. 51ml/beat (P = 0.18); forward flow: 50 vs. 55ml/beat (P = 0.07). Peak systolic velocity was significantly underestimated by compressed sensing 4D flow in the ascending aorta: 114 vs. 128cm/s (P < 0.001) and main pulmonary artery: 106 vs. 112cm/s (P = 0.02). For both the aorta and main pulmonary artery, compressed sensing 4D flow provided equivalent net and forward flow values compared to conventional 4D flow but underestimated peak systolic velocity. By reducing scan time, compressed sensing 4D flow may decrease the need for anesthesia and increase scanner output without significantly compromising data integrity.

Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers

BackgroundTo investigate the potential of combining compressed sensing (CS) and deep learning (DL) for accelerated two-dimensional (2D) and three-dimensional (3D) magnetic resonance imaging (MRI) of the shoulder.MethodsTwenty healthy volunteers were examined using at 3-T scanner with a fat-saturated, coronal, 2D proton density-weighted sequence with four acceleration levels (2.3, 4, 6, and 8) and a 3D sequence with three acceleration levels (8, 10, and 13), all accelerated with CS and reconstructed using the conventional algorithm and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using 6 criteria on a 5-point Likert scale (overall impression, artifacts, and delineation of the subscapularis tendon, bone, acromioclavicular joint, and glenoid labrum). Objective image quality was measured by calculating signal-to-noise-ratio, contrast-to-noise-ratio, and a structural similarity index measure. All reconstructions were compared to the clinical standard (CS 2D acceleration factor 2.3; CS 3D acceleration factor 8). Additionally, subjective and objective image quality were compared between CS and CS-AI with the same acceleration levels.ResultsBoth 2D and 3D sequences reconstructed with CS-AI achieved on average significantly better subjective and objective image quality compared to sequences reconstructed with CS with the same acceleration factor (p ≤ 0.011). Comparing CS-AI to the reference sequences showed that 4-fold acceleration for 2D sequences and 13-fold acceleration for 3D sequences without significant loss of quality (p ≥ 0.058).ConclusionsFor MRI of the shoulder at 3 T, a DL-based algorithm allowed additional acceleration of acquisition times compared to the conventional approach.Relevance statementThe combination of deep-learning and compressed sensing hold the potential for further scan time reduction in 2D and 3D imaging of the shoulder while providing overall better objective and subjective image quality compared to the conventional approach.Trial registrationDRKS00024156.Key points• Combination of compressed sensing and deep learning improved image quality and allows for significant acceleration of shoulder MRI.• Deep learning-based algorithm achieved better subjective and objective image quality than conventional compressed sensing.• For shoulder MRI at 3 T, 40% faster image acquisition for 2D sequences and 38% faster image acquisition for 3D sequences may be possible.Graphical

Simultaneous quantification of perfusion, permeability, and leakage effects in brain gliomas using dynamic spin-and-gradient-echo echoplanar imaging MRI

ObjectiveTo determine the feasibility and biologic correlations of dynamic susceptibility contrast (DSC), dynamic contrast enhanced (DCE), and quantitative maps derived from contrast leakage effects obtained simultaneously in gliomas using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) during a single contrast injection.Materials and methodsThirty-eight patients with enhancing brain gliomas were prospectively imaged with dynamic SAGE-EPI, which was processed to compute traditional DSC metrics (normalized relative cerebral blood flow [nrCBV], percentage of signal recovery [PSR]), DCE metrics (volume transfer constant [Ktrans], extravascular compartment [ve]), and leakage effect metrics: ΔR2,ss* (reflecting T2*-leakage effects), ΔR1,ss (reflecting T1-leakage effects), and the transverse relaxivity at tracer equilibrium (TRATE, reflecting the balance between ΔR2,ss* and ΔR1,ss). These metrics were compared between patient subgroups (treatment-naïve [TN] vs recurrent [R]) and biological features (IDH status, Ki67 expression).ResultsIn IDH wild-type gliomas (IDHwt—i.e., glioblastomas), previous exposure to treatment determined lower TRATE (p = 0.002), as well as higher PSR (p = 0.006), Ktrans (p = 0.17), ΔR1,ss (p = 0.035), ve (p = 0.006), and ADC (p = 0.016). In IDH-mutant gliomas (IDHm), previous treatment determined higher Ktrans and ΔR1,ss (p = 0.026). In TN-gliomas, dynamic SAGE-EPI metrics tended to be influenced by IDH status (p ranging 0.09–0.14). TRATE values above 142 mM−1s−1 were exclusively seen in TN-IDHwt, and, in TN-gliomas, this cutoff had 89% sensitivity and 80% specificity as a predictor of Ki67 > 10%.ConclusionsDynamic SAGE-EPI enables simultaneous quantification of brain tumor perfusion and permeability, as well as mapping of novel metrics related to cytoarchitecture (TRATE) and blood–brain barrier disruption (ΔR1,ss), with a single contrast injection.Clinical relevance statementSimultaneous DSC and DCE analysis with dynamic SAGE-EPI reduces scanning time and contrast dose, respectively alleviating concerns about imaging protocol length and gadolinium adverse effects and accumulation, while providing novel leakage effect metrics reflecting blood–brain barrier disruption and tumor tissue cytoarchitecture.Key Points• Traditionally, perfusion and permeability imaging for brain tumors requires two separate contrast injections and acquisitions.• Dynamic spin-and-gradient-echo echoplanar imaging enables simultaneous perfusion and permeability imaging.• Dynamic spin-and-gradient-echo echoplanar imaging provides new image contrasts reflecting blood–brain barrier disruption and cytoarchitecture characteristics.Graphical

Evaluasi Perbandingan Informasi Citra MRI Brain Sekuen Diffusion Weighted Imaging Irisan Axial Pada Variasi Nilai SENSE di Instalasi Radiologi RSUD Provinsi NTB

Magnetic Resonance is a radiodiagnostic examination tool in the field of radiology, especially in the medical world. Diffusion Weighted Imaging is a method to assess molecular function and microarchitecture of the human body. DWI signal contrast can be measured with visible diffusion coefficient maps and serves as a tool for treatment response evaluation and disease progression assessment. Parallel imaging techniques involve phased array coils to process signals from a slice, then combine these slices to form an image, reducing scan time by reducing the acquired K-space lines. The formation of Sensitivity Encoding (SENSE) can shorten the insertion of K-space. This study aims to test variations of SENSE values 2.0 and 2.5 in MRI brain examinations with axial DWI sequences that are capable of visualizing anatomical information with fast scan times without compromising image quality, resulting in optimal images. This research is quantitative with an experimental approach. The sample consists of 10 patients using voluntary sampling of MRI Brain with axial DWI sequence and varying SENSE values from July to August 2023. Data were collected through questionnaires filled out by three radiology specialists at the Radiology Installation of the Provincial Hospital of West Nusa Tenggara (RSUD Provinsi NTB). The results of the Wilcoxon test showed a significant difference in anatomical information in MRI brain with axial DWI sequences between SENSE values of 2 and 2.5, with a p-value of 0.012. The mean rank value results indicated a sum of ranks of 280.00 for the SENSE 2 value, demonstrating greater optimality compared to SENSE 2.5. This study concludes that there is a significant difference in anatomical information in MRI brain with axial DWI sequences between SENSE values of 2 and 2.5. SENSE 2 tends to provide more optimal information.

Highly accelerated multi-shot intravoxel incoherent motion diffusion-weighted imaging in brain enabled by parametric POCS-based multiplexed sensitivity encoding.

Recently, intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has also been demonstrated as an imaging tool for applications in neurological and neurovascular diseases. However, the use of single-shot diffusion-weighted echo-planar imaging for IVIM DWI acquisition leads to suboptimal data quality: for instance, geometric distortion and deteriorated image quality at high spatial resolution. Although the recently commercialized multi-shot acquisition methods, such as multiplexed sensitivity encoding (MUSE), can attain high-resolution and high-quality DWI with signal-to-noise ratio (SNR) performance superior to that of the conventional parallel imaging method, the prolonged scan time associated with multi-shot acquisition is impractical for routine IVIM DWI. This study proposes an acquisition and reconstruction framework based on parametric-POCSMUSE to accelerate the four-shot IVIM DWI with 70% reduction of total scan time (13 min 8 s versus 4 min 8 s). First, the four-shot IVIM DWI scan with 17 b values was accelerated by acquiring only one segment per b value except for b values of 0 and 600 s/mm2 . Second, an IVIM-estimation scheme was integrated into the parametric-POCSMUSE to enable joint reconstruction of multi-b images from under-sampled four-shot IVIM DWI data. In vivo experiments on both healthy subjects and patients show that the proposed framework successfully produced multi-b DW images with significantly higher SNRs and lower reconstruction errors than did the conventional acceleration method based on parallel imaging. In addition, the IVIM quantitative maps estimated from the data produced by the proposed framework showed quality comparable to that of fully sampled MUSE-reconstructed images, suggesting that the proposed framework can enable highly accelerated multi-shot IVIM DWI without sacrificing data quality. In summary, the proposed framework can make multi-shot IVIM DWI feasible in a routine MRI examination, with reasonable scan time and improved geometric fidelity.

Research Progress in Diffusion Spectrum Imaging.

Studies have demonstrated that many regions in the human brain include multidirectional fiber tracts, in which the diffusion of water molecules within image voxels does not follow a Gaussian distribution. Therefore, the conventional diffusion tensor imaging (DTI) that hypothesizes a single fiber orientation within a voxel is intrinsically incapable of revealing the complex microstructures of brain tissues. Diffusion spectrum imaging (DSI) employs a pulse sequence with different b-values along multiple gradient directions to sample the diffusion information of water molecules in the entire q-space and then quantitatively estimates the diffusion profile using a probability density function with a high angular resolution. Studies have suggested that DSI can reliably observe the multidirectional fibers within each voxel and allow fiber tracking along different directions, which can improve fiber reconstruction reflecting the true but complicated brain structures that were not observed in the previous DTI studies. Moreover, with increasing angular resolution, DSI is able to reveal new neuroimaging biomarkers used for disease diagnosis and the prediction of disorder progression. However, so far, this method has not been used widely in clinical studies, due to its overly long scanning time and difficult post-processing. Within this context, the current paper aims to conduct a comprehensive review of DSI research, including the fundamental principles, methodology, and application progress of DSI tractography. By summarizing the DSI studies in recent years, we propose potential solutions towards the existing problem in the methodology and applications of DSI technology as follows: (1) using compressed sensing to undersample data and to reconstruct the diffusion signal may be an efficient and promising method for reducing scanning time; (2) the probability density function includes more information than the orientation distribution function, and it should be extended in application studies; and (3) large-sample study is encouraged to confirm the reliability and reproducibility of findings in clinical diseases. These findings may help deepen the understanding of the DSI method and promote its development in clinical applications.

Transformer-based deep learning denoising of single and multi-delay 3D arterial spin labeling.

To present a Swin Transformer-based deep learning (DL) model (SwinIR) for denoising single-delay and multi-delay 3D arterial spin labeling (ASL) and compare its performance with convolutional neural network (CNN) and other Transformer-based methods. SwinIR and CNN-based spatial denoising models were developed for single-delay ASL. The models were trained on 66 subjects (119 scans) and tested on 39 subjects (44 scans) from three different vendors. Spatiotemporal denoising models were developed using another dataset (6 subjects, 10 scans) of multi-delay ASL. A range of input conditions was tested for denoising single and multi-delay ASL, respectively. The performance was evaluated using similarity metrics, spatial SNR and quantification accuracy of cerebral blood flow (CBF), and arterial transit time (ATT). SwinIR outperformed CNN and other Transformer-based networks, whereas pseudo-3D models performed better than 2D models for denoising single-delay ASL. The similarity metrics and image quality (SNR) improved with more slices in pseudo-3D models and further improved when using M0 as input, but introduced greater biases for CBF quantification. Pseudo-3D models with three slices achieved optimal balance between SNR and accuracy, which can be generalized to different vendors. For multi-delay ASL, spatiotemporal denoising models had better performance than spatial-only models with reduced biases in fitted CBF and ATT maps. SwinIR provided better performance than CNN and other Transformer-based methods for denoising both single and multi-delay 3D ASL data. The proposed model offers flexibility to improve image quality and/or reduce scan time for 3D ASL to facilitate its clinical use.