Increase In Scan Time Research Articles

G.O.O.S.E.: Introducing a novel five‐stage structured framework for the early phases of teaching and learning ultrasound

AbstractThe sonography profession faces a critical global workforce shortage, particularly pronounced in Australia, due to insufficient clinical training opportunities and an ageing workforce. The financial burden and increased scan times associated with training exacerbate this issue. Innovative approaches, such as intensive ultrasound courses and pair scanning protocols, have shown promise in addressing these challenges. The GOOSE framework (Get the window, Optimise the view, Optimise the image, Select the image, Explain the findings) offers a structured, step‐by‐step approach to practical sonography training. This framework deconstructs complex ultrasound skills into manageable tasks, optimises cognitive load management, and provides explicit guidance for novice learners. The GOOSE framework is designed to enhance the efficiency of teaching and learning by simplifying complex skills into clear, actionable steps, supported by checklists that guide both instructors and students through the training process. The GOOSE framework offers a systematisation of knowledge transfer that, combined with deliberate practice and structured feedback, ensures learners progressively build their skills with focused, goal‐directed activities. While this model is yet to be validated, initial implementations have shown promising results in improving trainee performance, reducing supervision workload, and standardising instructional methods. This article outlines the principles of the GOOSE framework, its development, and its application in a specialised training facility, proposing it as a standardised model to enhance practical ultrasound education and improve teaching and learning efficiency.

Whole-brain T2 mapping with radial sampling and retrospective motion correction at 3T.

Several barriers prevent the use of whole-brain T2 mapping in routine use despite increasing interest in this parameter. One of the main barriers is the long scan time resulting in patient discomfort and motion corrupted data. To address this challenge, a method for accurate whole-brain T2 mapping with a limited acquisition time and motion correction capabilities is investigated. A 3D radial multi-echo spin-echo sequence was implemented with optimized sampling trajectory enabling the estimation of intra-scan motion, subsequently used to correct the raw data. Motion corrected echo images are then reconstructed with linear subspace constrained reconstruction. Experiments were carried out on phantom and volunteers at 3T to evaluate the accuracy of the T2 estimation, the sensitivity to lesions and the efficiency of the correction on motion corrupted data. Whole-brain T2 mapping acquired in less than 7 min enabled the depiction of lesions in the white matter with longer T2. Data retrospectively corrupted with typical motion traces of pediatric patients highly benefited from the motion correction by reducing the error in T2 estimates within the lesions. All datasets acquired on seven volunteers, with deliberate motion, also showed that motion corrupted T2 maps could be improved with the retrospective motion correction both at the voxel level and the structure level. A whole-brain T2 mapping sequence with retrospective intra-scan motion correction and reasonable acquisition time is proposed. The method necessitates advanced iterative reconstruction strategies but no additional navigator, external device, or increased scan time is required.

Fat-free noncontrast whole-heart CMR with fast and power-optimized off-resonant water excitation pulses

BackgroundCardiovascular MRI (CMR) faces challenges due to the interference of bright fat signals in visualizing structures like coronary arteries. Effective fat suppression is crucial, especially when using whole-heart CMR techniques. Conventional methods often fall short due to rapid fat signal recovery, leading to residual fat content hindering visualization. Water-selective off-resonant radiofrequency (RF) pulses have been proposed but come with tradeoffs between pulse duration, which increases scan time, and increased RF energy deposit, which limits their applicability due to specific absorption rate (SAR) constraints. The study introduces a lipid-insensitive binomial off-resonant (LIBOR) RF pulse, which addresses concerns about SAR and scan time, and aims to provide a comprehensive quantitative comparison with published off-resonant RF pulses for CMR at 3T. MethodsA short (1ms) LIBOR pulse, with reduced RF power requirements, was developed and implemented in a free-breathing respiratory-self-navigated 3D radial whole-heart CMR sequence at 3T. A binomial off-resonant rectangular (BORR) pulse with matched duration, as well as previously published lipid-insensitive binomial off-resonant excitation (LIBRE) pulses (1ms and 2.2ms), were implemented and optimized for fat suppression in numerical simulations and validated in volunteers (n=3). Whole-heart CMR was performed in volunteers(n=10) with all four pulses. The signal-to-noise ratio (SNR) of ventricular blood, skeletal muscle, myocardium, and subcutaneous fat and the coronary vessel detection rates and sharpness were compared. ResultsExperimental results validated numerical findings and near homogeneous fat suppression was achieved with all four pulses. Comparing the short RF pulses (1ms), LIBOR reduced the RF power nearly two-fold compared with LIBRE, and three-fold compared with BORR, and LIBOR significantly decreased overall fat SNR from cardiac scans, compared to LIBRE and BORR. The reduction in RF pulse duration (from 2.2ms to 1ms) shortened the whole-heart acquisition from 8.5min to 7min. No significant differences in coronary arteries detection and sharpness were found when comparing all four pulses. ConclusionLIBOR pulses enabled whole-heart CMR under 7minutes at 3T, with large volume fat signal suppression, while reducing RF power compared with LIBRE and BORR pulses. LIBOR is an excellent candidate to address SAR problems encountered in CMR sequences where fat suppression remains challenging and short RF pulses are required. AVAILABILITY OF DATA AND MATERIALSAn online repository containing the anonymized human MRI raw data, as well as RF pulse shapes used in this study is publicly available at:https://zenodo.org/records/8338079(PART 1: KNEE V1-V3, HEART V1-V5)https://zenodo.org/records/10715769 (PART 2: HEART V6-V10)Matlab code to 1) simulate the different RF pulses within a GRE sequence and 2) to read and display the anonymized raw data is available from: https://github.com/QIS-MRI/LIBOR_LIBRE_BORR_SimulationCodeThe compiled research sequence can be requested through the Teamplay platform of Siemens Healthineers.

Reducing thermal noise in high-resolution quantitative magnetic resonance imaging rotating frame relaxation mapping of the human brain at 3 T.

Quantitative maps of rotating frame relaxation (RFR) time constants are sensitive and useful magnetic resonance imaging tools with which to evaluate tissue integrity in vivo. However, to date, only moderate image resolutions of 1.6 x 1.6 x 3.6 mm3 have been used for whole-brain coverage RFR mapping in humans at 3 T. For more precise morphometrical examinations, higher spatial resolutions are desirable. Towards achieving the long-term goal of increasing the spatial resolution of RFR mapping without increasing scan times, we explore the use of the recently introduced Transform domain NOise Reduction with DIstribution Corrected principal component analysis (T-NORDIC) algorithm for thermal noise reduction. RFR acquisitions at 3 T were obtained from eight healthy participants (seven males and one female) aged 52 ± 20 years, including adiabatic T1ρ, T2ρ, and nonadiabatic Relaxation Along a Fictitious Field (RAFF) in the rotating frame of rank n = 4 (RAFF4) with both 1.6 x 1.6 x 3.6 mm3 and 1.25 x 1.25 x 2 mm3 image resolutions. We compared RFR values and their confidence intervals (CIs) obtained from fitting the denoised versus nondenoised images, at both voxel and regional levels separately for each resolution and RFR metric. The comparison of metrics obtained from denoised versus nondenoised images was performed with a two-sample paired t-test and statistical significance was set at pless than 0.05 after Bonferroni correction for multiple comparisons. The use of T-NORDIC on the RFR images prior to the fitting procedure decreases the uncertainty of parameter estimation (lower CIs) at both spatial resolutions. The effect was particularly prominent at high-spatial resolution for RAFF4. Moreover, T-NORDIC did not degrade map quality, and it had minimal impact on the RFR values. Denoising RFR images with T-NORDIC improves parameter estimation while preserving the image quality and accuracy of all RFR maps, ultimately enabling high-resolution RFR mapping in scan times that are suitable for clinical settings.

Unfolding coil localized errors from an imperfect slice profile using a structured autocalibration matrix: An application to reduce outflow effects in cine bSSFP imaging.

Balanced steady-state free precession (bSSFP) imaging is susceptible to outflow effects where excited spins leaving the slice as part of the blood stream are misprojected back onto the imaging plane. Previous work proposed using slice-encoding steps to localize these outflow effects from corrupting the target slice, at the expense of prolonged scan time. This present study extends this idea by proposing a means of significantly reducing most of the outflowing signal from the imaged slice using a coil localization method that acquires a slice-encoded calibration scan in addition to the 2D data, without being nearly as time-demanding as our previous method. This coil localization method is titled UNfolding Coil Localized Errors from an imperfect slice profile using a Structured Autocalibration Matrix (UNCLE SAM). Retrospective and prospective evaluations were carried out. Both featured a 2D acquisition and a separate slice-encoded calibration of the center in-plane -space lines across all desired slice-encoding steps. Retrospective results featured a slice-by-slice comparison of the slice-encoded images with UNCLE SAM. UNCLE SAM's subtraction from the slice-encoded image was compared with a subtraction from the flow-corrupted 2D image, to demonstrate UNCLE SAM's capability to unfold outflowing spins. UNCLE SAM's comparison with slice encoding showed that UNCLE SAM was able to unfold up to 74% of what slice encoding achieved. Prospective results showed significant reduction in outflow effects with only a marginal increase in scan time from the 2D acquisition. We developed a method that effectively unfolds most outflowing spins from corrupting the target slice and does not require the explicit use of slice-encoding gradients. This development offers a method to reduce most outflow effects from the target slice within a clinically feasible scan duration compared with the fully sampled slice-encoding technique.

Prospective motion correction for brain MRI using spherical navigators.

To demonstrate for the first time the feasibility of performing prospective motion correction using spherical navigators (SNAVs). SNAVs were interleaved in a 3D FLASH sequence with an additional short baseline scan (6.8 s) for fast rotation estimation. Assessment of SNAV-based prospective motion correction was performed in six volunteers. Participant motion was guided using randomly generated stepwise prompts as well as prompts derived from real motion cases. Experiments were performed on a 3 T MRI scanner using a 32-channel head coil. When optimized for real-time application, SNAV-based motion estimates were computed in 25.8 ± 1.3 ms. Phantom-based quantification of rotation and translation accuracy indicated mean absolute errors of 0.10 ± 0.09° and 0.25 ± 0.14 mm, respectively. Implementing SNAV-based motion estimates for prospective motion correction led to a clear improvement in image quality with minimal increase in scan time (<5%). Optimization of SNAV processing for real-time application enables prospective motion correction with low latency and minimal scan time requirements.

Full Mass Range ΦSDM Orbitrap Mass Spectrometry for DIA Proteome Analysis

Optimizing data-independent acquisition methods for proteomics applications often requires balancing spectral resolution and acquisition speed. Here, we describe a real-time full mass range implementation of the phase-constrained spectrum deconvolution method (ΦSDM) for Orbitrap mass spectrometry that increases mass resolving power without increasing scan time. Comparing its performance to the standard enhanced Fourier transformation signal processing revealed that the increased resolving power of ΦSDM is beneficial in areas of high peptide density and comes with a greater ability to resolve low-abundance signals. In a standard 2 h analysis of a 200ng HeLa digest, this resulted in an increase of 16% in the number of quantified peptides. As the acquisition speed becomes even more important when using fast chromatographic gradients, we further applied ΦSDM methods to a range of shorter gradient lengths (21, 12, and 5min). While ΦSDM improved identification rates and spectral quality in all tested gradients, it proved particularly advantageous for the 5min gradient. Here, the number of identified protein groups and peptides increased by >15% in comparison to enhanced Fourier transformation processing. In conclusion, ΦSDM is an alternative signal processing algorithm for processing Orbitrap data that can improve spectral quality and benefit quantitative accuracy in typical proteomics experiments, especially when using short gradients.

Convolutional network denoising for acceleration of multi-shot diffusion MRI

Multi-shot echo planar imaging is a promising technique to reduce geometric distortions and increase spatial resolution in diffusion-weighted MRI (DWI), at the expense of increased scan time. Moreover, performing DWI in the body requires multiple repetitions to obtain sufficient signal-to-noise ratio, which further increases the scan time. This work proposes to reduce the number of repetitions and perform denoising of high b-value images using a convolutional network denoising trained on single-shot DWI to accelerate the acquisition of multi-shot DWI. Convolutional network denoising is demonstrated to accelerate the acquisition of 2-shot DWI by a factor of 4 compared to the clinical standard on patients with rectal cancer. Image quality was evaluated using qualitative scores from expert body radiologists between accelerated and non-accelerated acquisition. Additionally, the effect of convolutional network denoising on each image quality score was analyzed using a Wilcoxon signed-rank test. Convolutional network denoising would enable to increase the number of shots without increasing scan time for significant geometric artifact reduction and spatial resolution increase.

Inking cell blocks improves scanner detection for diagnosis in pathology.

Cell blocks may be hard to be totally automatically detected by the scanner (ADS), generating incomplete whole slide images (WSIs), with areas that are not scanned, leading to possible false negative diagnosis. The aim of this study is to test if inking the cell blocks helps increasing ADS. Test 1: 15 cell blocks were sectioned, one half inked black (1HB) and the other inked green (1HG). Each of the halves was individually processed to generate a WSI stained by the H&E. 1HBs and 1HGs had similar scanning time (median 59 s vs. 65 s, p = .126) and file sizes (median 382 Mb vs. 381 Mb, p = .567). The black ink interfered less in the observation (2.2% vs. 44.4%; p < .001) than in the green one. Test 2: 15 cell blocks were sectioned, one half inked black (2HB) and the other left unstained/null (2HN). Each of the halves was individually processed to generate three WSIs-one HE, one periodic-acid Schiff (PAS), and one immunostained by cytokeratin AE1&AE3 (CKAE1AE3). HE and PAS WSIs from both 2HN and 2HB groups were all totally ADS and had similar scanning times and file sizes. Concerning immunostaining with CKAE1AE3: ADS (46.7% vs. 93.3%; p = .014), median time for scanning (57 s vs. 83 s; p < .001) and file size (178 Mb vs. 338 Mb; p < .001) were reduced significantly in the 2HN group in comparison with the 2HB. Although increasing scanning time and file size, inking the cell blocks helps increasing ADS after immunostaining, improving the safety and efficiency of the digital pathology workflow.

Deep learning-based reconstruction can improve canine thoracolumbar magnetic resonance image quality and reduce slice thickness.

In veterinary practice, thin-sliced thoracolumbar MRI is useful in detecting small lesions, especially in small-breed dogs. However, it is challenging due to the partial volume averaging effect and increase in scan time. Currently, deep learning-based reconstruction (DLR), a part of artificial intelligence, has been applied in diagnostic imaging. We hypothesized that the diagnostic performance of thin-slice thoracolumbar MRI with DLR would be superior to conventional MRI. This prospective, method comparison study aimed to determine the adequate slice thickness of a deep learning model for thin-slice thoracolumbar MRI. Sagittal and transverse T2-weighted MRI at the thoracolumbar region were performed on 12 clinically healthy beagle dogs; the images obtained were categorized into five groups according to slice thickness: conventional thickness of 3mm (3 CON) and thicknesses of 3, 2, 1.5, and 1mm with DLR (3 DLR, 2 DLR, 1.5 DLR, and 1 DLR, respectively). Quantitative analysis was performed using signal-to-noise ratio (SNR) and contrast-to-noise ratio. Qualitative analysis involved the evaluation of perceived SNR, structural visibility, and overall image quality using a four-point scale. Moreover, nerve root visibility was evaluated using transverse images. Quantitative and qualitative values were compared among the five groups. Compared with the 3 CON group, the 3 DLR, 2 DLR, and 1.5 DLR groups exhibited significantly higher quantitative and qualitative values. Nerve root visibility was significantly higher in 2 DLR, 1.5 DLR, and 1 DLR images than in 3 DLR and 3 CON images. Compared with conventional MRI, DLR reduced the slice thickness by up to one-half and improved image quality in this sample of clinically healthy beagles.

FMRI with whole-brain coverage, 75-ms temporal resolution and high SNR by combining HiHi reshuffling and multiband imaging

Increasing the temporal resolution of the blood‑oxygen level-dependent (BOLD) response is usually accompanied by a decrease in repetition time and therefore also a reduction of the magnetic resonance (MR) signal due to incomplete T1 relaxation and thus a loss of signal-to-noise ratio (SNR). A previous data reordering method can achieve higher temporal sampling rate without the loss of SNR but at the cost of increased scan time. In this proof-of-principle work, we show that combining HiHi reshuffling with multiband acceleration allows us to measure the in vivo BOLD response with a 75-ms sampling rate that is decoupled from the acquisition repetition time (here 1.5 s and hence higher SNR) while covering the entire forebrain with 60 2-mm slices in a ~ 35-min scan. We provide single-voxel time-courses of the BOLD responses in the primary visual and primary motor cortices in three fMRI experiments on a 7 T scanner - 1 male (scanned twice on different days for test-retest reproducibility) and 1 female participant.

Dose reduction and image enhancement in micro-CT using deep learning.

In preclinical settings, micro-computed tomography (CT) provides a powerful tool to acquire high resolution anatomical images of rodents and offers the advantage to in vivo non-invasively assess disease progression and therapy efficacy. Much higher resolutions are needed to achieve scale-equivalent discriminatory capabilities in rodents as those in humans. High resolution imaging however comes at the expense of increased scan times and higher doses. Specifically, with preclinical longitudinal imaging, there are concerns that dose accumulation may affect experimental outcomes of animal models. Dose reduction efforts under the ALARA (as low as reasonably achievable) principles are thus a key point of attention. However, low dose CT acquisitions inherently induce higher noise levels which deteriorate image quality and negatively impact diagnostic performance. Many denoising techniques already exist, and deep learning (DL) has become increasingly popular for image denoising, but research has mostly focused on clinical CT with limited studies conducted on preclinical CT imaging. We investigate the potential of convolutional neural networks (CNN) for restoring high quality micro-CT images from low dose (noisy) images. The novelty of the CNN denoising frameworks presented in this work consists of utilizingimage pairs with realistic CT noise present in the input as well as thetarget image used for the model training; a noisier image acquired with a low dose protocol is matched to a less noisy image acquired with a higher dose scan of the same mouse. Low and high dose ex vivo micro-CT scans of 38 mice were acquired. Two CNN models, based on a 2D and 3D four-layer U-Net, were trained with mean absolute error (30 training, 4 validation and 4 test sets). To assess denoising performance, ex vivo mice and phantom data were used. Both CNN approaches were compared to existing methods, like spatial filtering (Gaussian, Median, Wiener) and iterative total variation image reconstruction algorithm. Image quality metrics were derived from the phantom images. A first observer study (n=23) was set-up to rank overall quality of differently denoised images. A second observer study (n=18) estimated the dose reduction factor of the investigated 2D CNN method. Visual and quantitative results show that both CNN algorithms exhibit superior performance in terms of noise suppression, structural preservation and contrast enhancement over comparator methods. The quality scoring by 23 medical imaging experts also indicates that the investigated 2D CNN approach is consistently evaluated as the best performing denoising method. Results from the second observer study and quantitative measurements suggest that CNN-based denoising could offer a 2-4× dose reduction, with an estimated dose reduction factor of about 3.2 for the considered 2D network. Our results demonstrate the potential of DL in micro-CT for higher quality imaging at low dose acquisition settings. In the context of preclinical research, this offers promising future prospects for managing the cumulative severity effects of radiation in longitudinal studies.

Kz-accelerated variable-density stack-of-stars MRI

This work aimed to develop a modified stack-of-stars golden-angle radial sampling scheme with variable-density acceleration along the slice (kz) dimension (referred to as VD-stack-of-stars) and to test this new sampling trajectory with multi-coil compressed sensing reconstruction for rapid motion-robust 3D liver MRI. VD-stack-of-stars sampling implements additional variable-density undersampling along the kz dimension, so that slice resolution (or volumetric coverage) can be increased without prolonging scan time. The new sampling trajectory (with increased slice resolution) was compared with standard stack-of-stars sampling with fully sampled kz (with standard slice resolution) in both non-contrast-enhanced free-breathing liver MRI and dynamic contrast-enhanced MRI (DCE-MRI) of the liver in volunteers. For both sampling trajectories, respiratory motion was extracted from the acquired radial data, and images were reconstructed using motion-compensated (respiratory-resolved or respiratory-weighted) dynamic radial compressed sensing reconstruction techniques. Qualitative image quality assessment (visual assessment by experienced radiologists) and quantitative analysis (as a metric of image sharpness) were performed to compare images acquired using the new and standard stack-of-stars sampling trajectories. Compared to standard stack-of-stars sampling, both non-contrast-enhanced and DCE liver MR images acquired with VD-stack-of-stars sampling presented improved overall image quality, sharper liver edges and increased hepatic vessel clarity in all image planes. The results have suggested that the proposed VD-stack-of-stars sampling scheme can achieve improved performance (increased slice resolution or volumetric coverage with better image quality) over standard stack-of-stars sampling in free-breathing DCE-MRI without increasing scan time. The reformatted coronal and sagittal images with better slice resolution may provide added clinical value.

Role of radial k –space sampling technique in non-cooperative patients for the compensation of motion artifact in magnetic resonance imaging (MRI).

Objective: To access the role of radial K-space sampling technique for the compensation of motion artifact in magnetic resonance imaging (MRI). Study Design: Systematic Literature Search. Setting: Department of Radiology, Shalimar Hospital, Lahore. Period: March 2019 to Oct 2019. Material &amp; Methods: Was conducted with the help the of following search engines: Google scholar, PubMed, NCBI, Medline and Medscape databases from 1999 up to 2019 for names or acronyms of Radial K-space sampling, propeller sequence, management of motion artifact in MRI, RADAR sequence for motion compensation in MRI, k-space in clinic and multivane. Only those studies were included in this review study which shows the role of radial K-space sampling Technique for compensation of motion artifact. Total 104 studies were selected and after evaluation only 42 studies were included. Results: According to literature radial k-space sampling improved the quality of image and provided the scans with reduced motion artifact. Whereas in case of specificity (N= 16) the mean calculated value was 80.87 and Std. Deviation was 7.022. In case of radial k-space sampling technique (N=42) calculated mean was .26 and std. deviation was .701. The main disadvantage of radial k-space sampling is increased scan time. Conclusion: After reviewing literature of 42 studies, it was assessed that the use of radial K-space sampling technique or propeller sequence adjacent to the standard MRI sequences can reduce the motion artifact and will increase the image quality.

Modification of [18F]-FDG PET/CT imaging protocols in obese oncology patients: A nationwide survey

IntroductionThe use of medical imaging for diagnosis, staging and follow-up in Oncology context is incredibly important, being the use of [18F]-FDG PET/CT particularly advantageous in specific contexts like the case of obese patients. However, imaging the latter can be challenging sometimes, since their own body size may affect overall image quality and adds technical difficulties for the operator(s) performing the examination. MethodsThis research project was developed with the aim of analysing the current personal practices of Portuguese Nuclear Medicine Technologists (NMTs) in the adaptation of 18F-FDG PET/CT oncological protocols for obese patients and comparing the results with parameters referenced in literature. A non-experimental research study was conducted using a survey delivered online to NMTs through social media platforms (Facebook® and LinkedIn®) and by sending the link directly to contacts within the research team professional and personal networks. ResultsAnswers from a total of 26 participants were obtained, with 88.5% of participants admitting modifying technical protocols in examinations for obese patients. Changes in PET protocols included an increase in the administered activity (60.9%), an increase in scan time per individual bed position (69.6%) and the use of Time-of-Flight (TOF) technology whenever available. Protocol changes in CT included increasing the mA (82.6%), raising the KVp (47.8%), the application of iterative reconstruction (69.6%) and the use of automatic exposure control (AEC) (52.2%). The remaining parameters (pitch, algorithm, slice thickness, display FOV, gantry rotation time and energy acceptance window) were claimed not to be modified by around 90% of professionals. ConclusionPortuguese NMTs tend to change the [18F]-FDG PET/CT protocols for obese patients. However, while some of the parameters appear to be contradictory or redundant, others require further optimisation, especially in the CT component. Implications for practiceEfforts should be made to optimize acquisition protocols used in [18F]-FDG PET/CT scans for obese patients.

Multiple B-Value Model-Based Residual Network (MORN) for Accelerated High-Resolution Diffusion-Weighted Imaging.

Single-Shot Echo Planar Imaging (SSEPI) based Diffusion Weighted Imaging (DWI) has shortcomings such as low resolution and severe distortions. In contrast, Multi-Shot EPI (MSEPI) provides optimal spatial resolution but increases scan time. This study proposed a Multiple b-value mOdel-based Residual Network (MORN) model to reconstruct multiple b-value high-resolution DWI from undersampled k-space data simultaneously. We incorporated Parallel Imaging (PI) into a residual U-net to reconstruct multiple b-value multi-coil data with the supervision of MUltiplexed Sensitivity-Encoding (MUSE) reconstructed Multi-Shot DWI (MSDWI). Moreover, asymmetric concatenations among different b-values and the combined loss to back propagate helped the feature transfer. After training and validation of the MORN in a dataset of 32 healthy cases, additional assessments were performed on 6 patients with different tumor types. The experimental results demonstrated that the MORN model outperformed conventional PI reconstruction (i.e. SENSE) and two state-of-the-art deep learning methods (SENSE-GAN and VSNet) in terms of PSNR (Peak Signal-to-Noise Ratio), SSIM (Structual SIMilarity) and apparent diffusion coefficient maps. In addition, using the pre-trained model under DWI, the MORN achieved consistent fractional anisotrophy and mean diffusivity reconstructed from multiple diffusion directions. Hence, the proposed method shows potential in clinical application according to the observations on tumor patients as well as images of multiple diffusion directions.

Motion corrected silent ZTE neuroimaging.

PurposeTo develop self‐navigated motion correction for 3D silent zero echo time (ZTE) based neuroimaging and characterize its performance for different types of head motion.MethodsThe proposed method termed MERLIN (Motion Estimation & Retrospective correction Leveraging Interleaved Navigators) achieves self‐navigation by using interleaved 3D phyllotaxis k‐space sampling. Low resolution navigator images are reconstructed continuously throughout the ZTE acquisition using a sliding window and co‐registered in image space relative to a fixed reference position. Rigid body motion corrections are then applied retrospectively to the k‐space trajectory and raw data and reconstructed into a final, high‐resolution ZTE image.ResultsMERLIN demonstrated successful and consistent motion correction for magnetization prepared ZTE images for a range of different instructed motion paradigms. The acoustic noise response of the self‐navigated phyllotaxis trajectory was found to be only slightly above ambient noise levels (<4 dBA).ConclusionSilent ZTE imaging combined with MERLIN addresses two major challenges intrinsic to MRI (i.e., subject motion and acoustic noise) in a synergistic and integrated manner without increase in scan time and thereby forms a versatile and powerful framework for clinical and research MR neuroimaging applications.

Coronary Computed Tomography Angiography-Based Calcium Scoring: In Vitro and In Vivo Validation of a Novel Virtual Noniodine Reconstruction Algorithm on a Clinical, First-Generation Dual-Source Photon Counting-Detector System.

The aim of this study was to evaluate coronary computed tomography angiography (CCTA)-based in vitro and in vivo coronary artery calcium scoring (CACS) using a novel virtual noniodine reconstruction (PureCalcium) on a clinical first-generation photon-counting detector-computed tomography system compared with virtual noncontrast (VNC) reconstructions and true noncontrast (TNC) acquisitions. Although CACS and CCTA are well-established techniques for the assessment of coronary artery disease, they are complementary acquisitions, translating into increased scan time and patient radiation dose. Hence, accurate CACS derived from a single CCTA acquisition would be highly desirable. In this study, CACS based on PureCalcium, VNC, and TNC, reconstructions was evaluated in a CACS phantom and in 67 patients (70 [59/80] years, 58.2% male) undergoing CCTA on a first-generation photon counting detector-computed tomography system. Coronary artery calcium scores were quantified for the 3 reconstructions and compared using Wilcoxon test. Agreement was evaluated by Pearson and Spearman correlation and Bland-Altman analysis. Classification of coronary artery calcium score categories (0, 1-10, 11-100, 101-400, and >400) was compared using Cohen κ . Phantom studies demonstrated strong agreement between CACS PureCalcium and CACS TNC (60.7 ± 90.6 vs 67.3 ± 88.3, P = 0.01, r = 0.98, intraclass correlation [ICC] = 0.98; mean bias, 6.6; limits of agreement [LoA], -39.8/26.6), whereas CACS VNC showed a significant underestimation (42.4 ± 75.3 vs 67.3 ± 88.3, P < 0.001, r = 0.94, ICC = 0.89; mean bias, 24.9; LoA, -87.1/37.2). In vivo comparison confirmed a high correlation but revealed an underestimation of CACS PureCalcium (169.3 [0.7/969.4] vs 232.2 [26.5/1112.2], P < 0.001, r = 0.97, ICC = 0.98; mean bias, -113.5; LoA, -470.2/243.2). In comparison, CACS VNC showed a similarly high correlation, but a substantially larger underestimation (24.3 [0/272.3] vs 232.2 [26.5/1112.2], P < 0.001, r = 0.97, ICC = 0.54; mean bias, -551.6; LoA, -2037.5/934.4). CACS PureCalcium showed superior agreement of CACS classification ( κ = 0.88) than CACS VNC ( κ = 0.60). The accuracy of CACS quantification and classification based on PureCalcium reconstructions of CCTA outperforms CACS derived from VNC reconstructions.

Higher-order multi-shell diffusion measures complement tensor metrics and volume in gray matter when predicting age and cognition

Recent advances in diffusion-weighted imaging have enabled us to probe the microstructure of even gray matter non-invasively. However, these advanced multi-shell protocols are often not included in large-scale studies as they significantly increase scan time. In this study, we investigated whether one set of multi-shell diffusion metrics commonly used in gray matter (as derived from Neurite Orientation Dispersion and Density Imaging, NODDI) provide enough additional information over typical tensor and volume metrics to justify the increased acquisition time, using the cognitive aging framework in the human hippocampus as a testbed. We first demonstrated that NODDI metrics are robust and reliable by replicating previous findings from our lab in a larger population of 79 younger (20.41 ± 1.89 years, 46 females) and 75 older (73.56 ± 6.26 years, 45 females) adults, showing that these metrics in the hippocampal subfields are sensitive to age and memory performance. We then asked how these subfield specific hippocampal NODDI metrics compared with standard tensor metrics and volume in predicting age and memory ability. We discovered that both NODDI and tensor measures separately predicted age and cognition in comparable capacities. However, integrating these modalities together considerably increased the predictive power of our logistic models, indicating that NODDI and tensor measures may be capturing independent microstructural information. We use these findings to encourage neuroimaging data collection consortiums to include a multi-shell diffusion sequence in their protocols since existing NODDI measures (and potential future multi-shell measures) may be able to capture microstructural variance that is missed by traditional approaches, even in studies exclusively examining gray matter.

A diagnostic index based on quantitative susceptibility mapping and voxel-based morphometry may improve early diagnosis of Alzheimer's disease.

Voxel-based morphometry (VBM) is widely used to quantify the progression of Alzheimer's disease (AD), but improvement is still needed for accurate early diagnosis. We evaluated the feasibility of a novel diagnosis index for early diagnosis of AD based on quantitative susceptibility mapping (QSM) and VBM. Thirty-seven patients with AD, 24 patients with mild cognitive impairment (MCI) due to AD, and 36 cognitively normal (NC) subjects from four centers were included. A hybrid sequence was performed by using 3-T MRI with a 3D multi-echo GRE sequence to obtain both a T1-weighted image for VBM and phase images for QSM. The index was calculated from specific voxels in QSM and VBM images by using a linear support vector machine. The method of voxel extraction was optimized to maximize diagnostic accuracy, and the optimized index was compared with the conventional VBM-based index using receiver operating characteristic analysis. The index was optimal when voxels were extracted as increased susceptibility (AD > NC) in the parietal lobe and decreased gray matter volume (AD < NC) in the limbic system. The optimized proposed index showed excellent performance for discrimination between AD and NC (AUC = 0.94, p = 1.1 × 10-10) and good performance for MCI and NC (AUC = 0.87, p = 1.8 × 10-6), but poor performance for AD and MCI (AUC = 0.68, p = 0.018). Compared with the conventional index, AUCs were improved for all cases, especially for MCI and NC (p < 0.05). In this preliminary study, the proposed index based on QSM and VBM improved the diagnostic performance between MCI and NC groups compared with the VBM-based index. • We developed a novel diagnostic index for Alzheimer's disease based on quantitative susceptibility mapping (QSM) and voxel-based morphometry (VBM). • QSM and VBM images can be acquired simultaneously in a single sequence with little increasing scan time. • In this preliminary study, the proposed diagnostic index improved the discriminative performance between mild cognitive impairment and normal control groups compared with the conventional VBM-based index.