Fast carotid artery MR angiography with compressed sensing based three-dimensional time-of-flight sequence

ObjectiveIn this study, we sought to investigate the feasibility of fast carotid artery MR angiography (MRA) by combining three-dimensional time-of-flight (3D TOF) with compressed sensing method (CS-3D TOF).Materials and methodsA pseudo-sequential phase encoding order was developed for CS-3D TOF to generate hyper-intense vessel and suppress background tissues in under-sampled 3D k-space. Seven healthy volunteers and one patient with carotid artery stenosis were recruited for this study. Five sequential CS-3D TOF scans were implemented at 1, 2, 3, 4 and 5-fold acceleration factors for carotid artery MRA. Blood signal-to-tissue ratio (BTR) values for fully-sampled and under-sampled acquisitions were calculated and compared in seven subjects. Blood area (BA) was measured and compared between fully sampled acquisition and each under-sampled one.ResultsThere were no significant differences between the fully-sampled dataset and each under-sampled in BTR comparisons (P > 0.05 for all comparisons). The carotid vessel BAs measured from the images of CS-3D TOF sequences with 2, 3, 4 and 5-fold acceleration scans were all highly correlated with that of the fully-sampled acquisition. The contrast between blood vessels and background tissues of the images at 2 to 5-fold acceleration is comparable to that of fully sampled images. The images at 2 × to 5 × exhibit the comparable lumen definition to the corresponding images at 1 ×.ConclusionBy combining the pseudo-sequential phase encoding order, CS reconstruction, and 3D TOF sequence, this technique provides excellent visualizations for carotid vessel and calcifications in a short scan time. It has the potential to be integrated into current multiple blood contrast imaging protocol.     

Improved acceleration of phase-contrast flow imaging with magnitude difference regularization

PurposeTo develop a regularized image reconstruction algorithm for improved scan acceleration of phase-contrast (PC) flow MRI.MethodsBased on the magnitude similarity between bipolar-encoded k-space data, magnitude-difference regularization was incorporated into the conventional compressed sensing (CS) reconstruction. The gradient of the magnitude regularization was derived so the reconstruction problem can be solved using non-linear conjugate gradient with backtracking line search. Phase contrast flow data obtained in the peripheral arteries of healthy and patient subjects were retrospectively undersampled for testing the proposed reconstruction method. Three-dimensional velocity-encoded PC flow MRI was performed with prospective 4-fold undersampling for measuring arotic flow velocity in a healthy volunteer.ResultsIn the femoral arteries of healthy volunteers, the root-mean-square (RMS) errors of mean velocities were 0.56 ± 0.09 cm/s with CS-only reconstruction and 0.46 ± 0.08 cm/s with addition of magnitude regularization for three-fold acceleration; 1.34 ± 0.17 cm/s (CS only) and 1.08 ± 0.15 cm/s (magnitude regularized) for four-fold acceleration. In the iliac arteries of the patient, the RMS errors of mean velocities were 0.72 ± 0.12 cm/s and 0.56 ± 0.10 for three-fold acceleration, and 1.75 ± 0.21 and 1.24 ± 0.19 cm/s for four-fold acceleration (in the order of CS-only and magnitude regularized reconstructions). In the popliteal arteries, the RMS errors were 0.61 ± 0.10 cm/s and 0.42 ± 0.11 for three-fold acceleration, and 1.41 ± 0.19 and 1.12 ± 0.17 cm/s for four-fold acceleration. The maximum through-plane mean flow velocities were measured as 63.2 cm/s and 84.5 cm/s in ascending and descending aortas, respectively.ConclusionThe addition of magnitude-difference regularization into conventional CS reconstruction improves the accuracy of image reconstruction using highly undersampled phase-contrast flow MR data.     

Compressed sensing based simultaneous black- and gray-blood carotid vessel wall MR imaging

ObjectiveIn this study, we sought to demonstrate the blood suppression performance, image quality and morphological measurements for compressed sensing (CS) based simultaneous 3D black- and gray-blood imaging sequence (CS-siBLAG) in carotid vessel wall MR imaging.Materials and methodsSeven healthy volunteers and five patients were recruited. Healthy subjects underwent five CS-siBLAG scans with 1, 2, 3, 4 and 5-fold accelerations. Signal-to-tissue ratio (STR) and contrast-to-tissue ratio (CTR) were computed as the measures of flowing signal suppression performance and the image quality for black-blood imaging of the technique. Vessel lumen area (LA) and wall area (WA) were compared between fully sampled acquisition and each accelerated acquisition. Patients underwent three CS-siBLAG scans with 1, 3 and 5-fold accelerations as well as a 3D time of flight (3D TOF) scan. Two radiologists reviewed the under-sampled black- and gray-blood image quality.ResultsSTR and CTR values obtained with 2 to 5-fold accelerations were not significantly different from those with full acquisition. LA and WA measured at 2 ×, 3 ×, 4 × and 5 × were all highly correlated to the corresponding values at 1 ×. For patients imaging, two radiologists both found that the dual-contrast images at 3 × acceleration exhibited comparable image quality to that of the fully sampled acquisition, and that the images at 5 × exhibited slightly blurred vessel wall and outer vessel wall boundaries.ConclusionBy combining the CS under-sampling pattern and reconstruction, pseudo-centric phase encoding order and dual blood contrast sequences, this technique provides spatially registered black- and gray-blood images and excellent visualization for vessel wall imaging and gray-blood imaging in a short scan time.     

Accelerated free-breathing 3D whole-heart magnetic resonance angiography with a radial phyllotaxis trajectory,compressed sensing,and curvelet transform

PurposeTo develop and validate an accelerated free-breathing 3D whole-heart magnetic resonance angiography (MRA) technique using a radial k-space trajectory with compressed sensing and curvelet transform.MethodA 3D radial phyllotaxis trajectory was implemented to traverse the centerline of k-space immediately before the segmented whole-heart MRA data acquisition at each cardiac cycle. The k-space centerlines were used to correct the respiratory-induced heart motion in the acquired MRA data. The corrected MRA data were then reconstructed by a novel compressed sensing algorithm using curvelets as the sparsifying domain. The proposed 3D whole-heart MRA technique (radial CS curvelet) was then prospectively validated against compressed sensing with a conventional wavelet transform (radial CS wavelet) and a standard Cartesian acquisition in terms of scan time and border sharpness.ResultsFifteen patients (females 10, median age 34-year-old) underwent 3D whole-heart MRA imaging using a standard Cartesian trajectory and our proposed radial phyllotaxis trajectory. Scan time for radial phyllotaxis was significantly shorter than Cartesian (4.88 ± 0.86 min. vs. 6.84 ± 1.79 min., P-value = 0.004). Radial CS curvelet border sharpness was slightly lower than Cartesian and, for the majority of vessels, was significantly better than radial CS wavelet (P-value < 0.050).ConclusionThe proposed technique of 3D whole-heart MRA acquisition with a radial CS curvelet has a shorter scan time and slightly lower vessel sharpness compared to the Cartesian acquisition with radial profile ordering, and has slightly better sharpness than radial CS wavelet. Future work on this technique includes additional clinical trials and extending this technique to 3D cine imaging.     

Towards accelerated quantitative sodium MRI at 7 T in the skeletal muscle: Comparison of anisotropic acquisition- and compressed sensing techniques

PurposeTo compare three anisotropic acquisition schemes and three compressed sensing (CS) approaches for accelerated tissue sodium concentration (TSC) quantification using ²³Na MRI at 7 T.Materials and methodsThree anisotropic 3D-radial acquisition sequences were evaluated using simulations, phantom- and in vivo TSC measurements: An anisotropic density-adapted 3D-radial sequence (3DPR-C), a 3D acquisition-weighted density-adapted stack-of-stars sampling scheme (SOS) and a SOS approach with golden-ratio rotation (SOS-GR). Eight healthy volunteers were examined at a 7 Tesla MRI system. TSC measurements of the calf were conducted with a nominal spatial resolution of Δx = (3.0 × 3.0 × 15.0) mm³ and a field of view of (156.0 × 156.0 × 240.0) mm³ for multiple undersampling factors (USF). Three CS reconstructions were evaluated: Total variation CS (TV-CS), 3D dictionary-learning compressed sensing (3D-DLCS) and TV-CS with a block matching prior (TV-BL-CS). Results of the simulations and measurements were compared to a simulated ground truth (GT) or a fully sampled reference measurement (FS), respectively. The deviation of the mean TSC evaluated in multiple ROI (mE_GT/FS) and the normalized root-mean-squared error (NRMSE) for simulations were evaluated for CS and NUFFT reconstructions.ResultsIn simulations, the SOS-GR yielded the lowest NRMSE and mE_GT (< 4%) with NUFFT for an acquisition time (TA) of less than 2 min. CS further improved the results. In simulations and measurements, the best TSC quantification results were obtained with 3D-DLCS and SOS-GR (lowest NRMSE, mE_GT < 2.6% in simulations, mE_GT < 10.7% for phantom measurements and mE_FS < 6% in vivo) with an USF = 4.1 (TA < 2 min). TV-CS showed no or only slight improvements to NUFFT. The results of TV-BL-CS were similar to 3D-DLCS.DiscussionThe TA for TSC measurements could be reduced to less than 2 min by using adapted sequences such as SOS-GR and CS reconstruction approaches such as 3D-DLCS or TV-BL-CS, while the quantitative accuracy stays comparable to a fully sampled NUFFT reconstruction (approx. 8 min TA). In future, the lower TA could improve clinical applicability of TSC measurements.     

Rapid acquisition of magnetic resonance imaging of the shoulder using three-dimensional fast spin echo sequence with compressed sensing

PurposeTo evaluate the feasibility of 3D fast spin-echo (FSE) imaging with compressed sensing (CS) for the assessment of shoulder.Materials and methodsTwenty-nine patients who underwent shoulder MRI including image sets of axial 3D-FSE sequence without CS and with CS, using an acceleration factor of 1.5, were included. Quantitative assessment was performed by calculating the root mean square error (RMSE) and structural similarity index (SSIM). Two musculoskeletal radiologists compared image quality of 3D-FSE sequences without CS and with CS, and scored the qualitative agreement between sequences, using a five-point scale. Diagnostic agreement for pathologic shoulder lesions between the two sequences was evaluated.ResultsThe acquisition time of 3D-FSE MRI was reduced using CS (3 min 23 s vs. 2 min 22 s). Quantitative evaluations showed a significant correlation between the two sequences (r = 0.872–0.993, p < 0.05) and SSIM was in an acceptable range (0.940–0.993; mean ± standard deviation, 0.968 ± 0.018). Qualitative image quality showed good to excellent agreement between 3D-FSE images without CS and with CS. Diagnostic agreement for pathologic shoulder lesions between the two sequences was very good (κ = 0.915–1).ConclusionsThe 3D-FSE sequence with CS is feasible in evaluating the shoulder joint with reduced scan time compared to 3D-FSE without CS.     

Application of compressed sensing to in vivo 3D ¹⁹F CSI

This study shows how applying compressed sensing (CS) to (19)F chemical shift imaging (CSI) makes highly accurate and reproducible reconstructions from undersampled datasets possible. The missing background signal in (19)F CSI provides the required sparsity needed for application of CS. Simulations were performed to test the influence of different CS-related parameters on reconstruction quality. To test the proposed method on a realistic signal distribution, the simulation results were validated by ex vivo experiments. Additionally, undersampled in vivo 3D CSI mouse datasets were successfully reconstructed using CS. The study results suggest that CS can be used to accurately and reproducibly reconstruct undersampled (19)F spectroscopic datasets. Thus, the scanning time of in vivo(19)F CSI experiments can be significantly reduced while preserving the ability to distinguish between different (19)F markers. The gain in scan time provides high flexibility in adjusting measurement parameters. These features make this technique a useful tool for multiple biological and medical applications.     

Accelerated 3D Coronary Vessel Wall MR Imaging Based on Compressed Sensing with a Block-Weighted Total Variation Regularization

Coronary vessel wall magnetic resonance (MR) imaging is important for heart disease diagnosis but often suffers long scan time. Compressed sensing (CS) has been previously used to accelerate MR imaging by reconstructing an MR image from undersampled k-space data using a regularization framework. However, the widely used regularizations in the current CS methods often lead to smoothing effects and thus are unable to reconstruct the coronary vessel walls with sufficient resolution. To address this issue, a novel block-weighted total variation regularization is presented to accelerate the coronary vessel wall MR imaging. The proposed regularization divides the image into two parts: a region-of-interest (ROI) which contains the coronary vessel wall, and the other region with less concerned features. Different penalty weights are given to the two regions. As a result, the small details within ROI do not suffer from over-smoothing while the noise outside the ROI can be significantly suppressed. Results with both numerical simulations and in vivo experiments demonstrated that the proposed method can reconstruct the coronary vessel wall from undersampled k-space data with higher qualities than the conventional CS with the total variation or the edge-preserved total variation.     

Three dimension double inversion recovery gray matter imaging using compressed sensing

The double inversion recovery (DIR) imaging technique has various applications such as black blood magnetic resonance imaging and gray/white matter imaging. Recent clinical studies show the promise of DIR for high resolution three dimensional (3D) gray matter imaging. One drawback in this case however is the long data acquisition time needed to obtain the fully sampled 3D spatial frequency domain (k-space) data. In this paper, we propose a method to solve this problem using the compressed sensing (CS) algorithm with contourlet transform. The contourlet transform is an effective sparsifying transform especially for images with smooth contours. Therefore, we applied this algorithm to undersampled DIR images and compared with a CS algorithm using wavelet transform by evaluating the reconstruction performance of each algorithm for undersampled k-space data. The results show that the proposed CS algorithm achieves a more accurate reconstruction in terms of the mean structural similarity index and root mean square error than the CS algorithm using wavelet transform.     

IKWI-net: A cross-domain convolutional neural network for undersampled magnetic resonance image reconstruction

Magnetic resonance imaging (MRI) is widely used to get the information of anatomical structure and physiological function with the advantages of high resolution and non-invasive scanning. But the long acquisition time limits its application. To reduce the time consumption of MRI, compressed sensing (CS) theory has been proposed to reconstruct MRI images from undersampled k-space data. But conventional CS methods mostly use iterative methods that take lots of time. Recently, deep learning methods are proposed to achieve faster reconstruction, but most of them only pay attention to a single domain, such as the image domain or k-space. To take advantage of the feature representation in different domains, we propose a cross-domain method based on deep learning, which first uses convolutional neural networks (CNNs) in the image domain, k-space and wavelet domain simultaneously. The combined order of the three domains is also first studied in this work, which has a significant effect on reconstruction. The proposed IKWI-net achieves the best performance in various combinations, which utilizes CNNs in the image domain, k-space, wavelet domain and image domain sequentially. Compared with several deep learning methods, experiments show it also achieves mean improvements of 0.91 dB in peak signal-to-noise ratio (PSNR) and 0.005 in structural similarity (SSIM).     

O-space with high resolution readouts outperforms radial imaging

PurposeWhile O-Space imaging is well known to accelerate image acquisition beyond traditional Cartesian sampling, its advantages compared to undersampled radial imaging, the linear trajectory most akin to O-Space imaging, have not been detailed. In addition, previous studies have focused on ultrafast imaging with very high acceleration factors and relatively low resolution. The purpose of this work is to directly compare O-Space and radial imaging in their potential to deliver highly undersampled images of high resolution and minimal artifacts, as needed for diagnostic applications. We report that the greatest advantages to O-Space imaging are observed with extended data acquisition readouts.Theory and methodsA sampling strategy that uses high resolution readouts is presented and applied to compare the potential of radial and O-Space sequences to generate high resolution images at high undersampling factors. Simulations and phantom studies were performed to investigate whether use of extended readout windows in O-Space imaging would increase k-space sampling and improve image quality, compared to radial imaging.ResultsExperimental O-Space images acquired with high resolution readouts show fewer artifacts and greater sharpness than radial imaging with equivalent scan parameters. Radial images taken with longer readouts show stronger undersampling artifacts, which can cause small or subtle image features to disappear. These features are preserved in a comparable O-Space image.ConclusionsHigh resolution O-Space imaging yields highly undersampled images of high resolution and minimal artifacts. The additional nonlinear gradient field improves image quality beyond conventional radial imaging.     

Intracranial vessel wall imaging framework – Data acquisition,processing, and visualization

ObjectiveAssessment of vessel walls is an integral part in diagnosis and disease monitoring of vascular diseases such as vasculitis. Vessel wall imaging (VWI), in particular of intracranial arteries, is the domain of Magnetic Resonance Imaging (MRI) – but still remains a challenge. The tortuous anatomy of intracranial arteries and the need for high resolution within clinically acceptable scan times require special technical conditions regarding the hardware and software environments.Materials and methodsIn this work a dedicated framework for intracranial VWI is presented offering an optimized, black-blood 3D T1-weighted post-contrast Compressed Sensing (CS)-accelerated MRI sequence prototype combined with dedicated 3D-GUI supported post-processing tool for the CPR visualization of tortuous arbitrary vessel structures.ResultsUsing CS accelerated MRI sequence, the scanning time for high-resolution 3D black-blood CS-space data could be reduced to under 10 min. These data are adequate for a further processing to extract straightened visualizations (curved planar reformats – CPR). First patient data sets could be acquired in clinical environment.ConclusionA highly versatile framework for VWI visualization was demonstrated utilizing a post-processing tool to extract CPR reformats from high-resolution 3D black-blood CS-SPACE data, enabling simplified and optimized assessment of intracranial arteries in intracranial vascular disorders, especially in suspected intracranial vasculitis, by stretching their tortuous course. The processing time from about 15–20 min per patient (data acquisition and further processing) allows the integration into clinical routine.     

Dosimetric comparison of water phantoms,ion chambers,and data acquisition modes for LINAC characterization

PurposeIn this study a dosimetric comparison utilizing continuous data acquisition and discrete data acquisition is examined using IBA Blue Phantom (IBA Dosimetry, Schwarzenbruck, Germany) and PTW (PTW, Freiberg, Germany) MP3-M water tanks. The tanks were compared according to several factors including set up time, ease of use, and data acquisition times. A tertiary objective is to study the response of several ionization chambers in the two tanks examined.MethodsMeasurements made using a Varian 23EX LINAC (Varian Medical Systems, Palo Alto, CA) include PDDs and beam profiles for various field sizes with IBA CC13, PTW Semiflex 31010, PTW Pinpoint N31016, and PTW 31013 ion chambers for photons (6, 18 MV) and electrons (6, 9, 12, 15, and 18 MeV). Radial and transverse profile scans were done at depths of maximum dose, 5 cm, 10 cm, and 20 cm using the same set of tanks and detectors for the photon beams. Radial and transverse profile scans were done at depth of maximum dose for the electron beams on the same tanks and chambers. Data processing and analysis was performed using PTW's MEPHYSTO Navigator software and IBA's OmniPro Accept version 6.6 for the respective water tank systems.ResultsPDD values agree to within 1% and dmax to within 1 mm for the PTW MP3-M tank using PTW 31010 and Blue Phantom using IBA CC13 chamber, respectively and larger discrepancy with the PTW PinPoint N31016 chamber at 6 MV. With respect to setup time the PTW MP3-M and IBA Blue phantom tank took about 20 and 40 min, respectively. Scan times were longer by 5–15 min per field size in the PTW MP3-M tank for the square field sizes from 1 cm to 40 cm as compared to the IBA Blue phantom. However, data processing times were higher by 7 min per field size with the IBA system.ConclusionsTank measurements showed little deviation with the higher energy photons as compared to the lower energy photons with regards to the PDD measurements. Chamber construction as well as tank set up may be causing the slight deviation in data. It is important to identify the exact source of the potential errors to ensure that proper tank usage is performed when making such measurements to ensure that patient safety is in compliance. Beam profiles done with different chambers and tanks showed little to no deviation from one to another. With regards to continuous versus discrete data measurements the main difference was in the data processing technique used. Discrete data obtained required less data processing as compared to the continuous data acquired.     

Multispectral 3D phase-encoded turbo spin-echo for imaging near metal: Limitations and possibilities demonstrated by simulations and phantom experiments

To see improvements in the imaging performance near biomaterial implants we assessed a multispectral fully phase-encoded turbo spin-echo (ms3D-PE-TSE) sequence for artifact reduction capabilities and scan time efficiency in simulation and phantom experiments.For this purpose, ms3D-PE-TSE and ms3D-TSE sequences were implemented to obtain multispectral images (± 20 kHz) of a cobalt-chromium (CoCr) knee implant embedded in agarose. In addition, a knee implant computer model and the acquired ms3D-PE-TSE images were used to investigate the possibilities for scan time acceleration using field-of-view (FOV) reduction for off-resonance frequency bins and compressed sensing reconstructions of undersampled data. Both acceleration methods were combined to acquire a + 10 kHz frequency bin in a second experiment.The obtained ms3D-PE-TSE images showed no susceptibility related artifacts, while ms3D-TSE images suffered from hyper-intensity artifacts. The limitations of ms3D-TSE were apparent in the far off-resonance regions (±[10–20] kHz) located close to the implant. The scan time calculations showed that ms3D-PE-TSE can be applied in a clinically relevant timeframe (~ 12 min), when omitting the three central frequency bins. The feasibility of CS acceleration for ms3D-PE-TSE was demonstrated using retrospective reconstructions before combining CS and rFOV imaging to decrease the scan time for the + 10 kHz frequency bin from ~ 10.9 min to ~ 3.5 min, while also increasing the spatial resolution fourfold. The temporally resolved signal of ms3D-PE-TSE proved to be useful to decrease the intensity ripples after sum-of-squares reconstructions and increase the signal-to-noise ratio.The presented results suggest that the scan time limitations of ms3D-PE-TSE can be sufficiently addressed when focusing on signal acquisitions in the direct vicinity of metal implants. Because these regions cannot be measured with existing multispectral methods, the presented ms3D-PE-TSE method may enable the detection of inflammation or (pseudo-)tumors in locations close to the implant.     

High-performance rapid MR parameter mapping using model-based deep adversarial learning

PurposeTo develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping.MethodsThe proposed method provides an image reconstruction framework by combining the end-to-end convolutional neural network (CNN) mapping, adversarial learning, and MR physical models. The CNN performs direct image-to-parameter mapping by transforming a series of undersampled images directly into MR parameter maps. Adversarial learning is used to improve image sharpness and enable better texture restoration during the image-to-parameter conversion. An additional pathway concerning the MR signal model is added between the estimated parameter maps and undersampled k-space data to ensure the data consistency during network training. The proposed framework was evaluated on T₂ mapping of the brain and the knee at an acceleration rate R = 8 and was compared with other state-of-the-art reconstruction methods. Global and regional quantitative assessments were performed to demonstrate the reconstruction performance of the proposed method.ResultsThe proposed adversarial learning approach achieved accurate T₂ mapping up to R = 8 in brain and knee joint image datasets. Compared to conventional reconstruction approaches that exploit image sparsity and low-rankness, the proposed method yielded lower errors and higher similarity to the reference and better image sharpness in the T₂ estimation. The quantitative metrics were normalized root mean square error of 3.6% for brain and 7.3% for knee, structural similarity index of 85.1% for brain and 83.2% for knee, and tenengrad measures of 9.2% for brain and 10.1% for the knee. The adversarial approach also achieved better performance for maintaining greater image texture and sharpness in comparison to the CNN approach without adversarial learning.ConclusionThe proposed framework by incorporating the efficient end-to-end CNN mapping, adversarial learning, and physical model enforced data consistency is a promising approach for rapid and efficient reconstruction of quantitative MR parameters.     

Free-running cardiac magnetic resonance fingerprinting: Joint T1/T2 map and Cine imaging

PurposeTo develop and evaluate a novel non-ECG triggered 2D magnetic resonance fingerprinting (MRF) sequence allowing for simultaneous myocardial T₁ and T₂ mapping and cardiac Cine imaging.MethodsCardiac MRF (cMRF) has been recently proposed to provide joint T₁/T₂ myocardial mapping by triggering the acquisition to mid-diastole and relying on a subject-dependent dictionary of MR signal evolutions to generate the maps. In this work, we propose a novel “free-running” (non-ECG triggered) cMRF framework for simultaneous myocardial T₁ and T₂ mapping and cardiac Cine imaging in a single scan. Free-running cMRF is based on a transient state bSSFP acquisition with tiny golden angle radial readouts, varying flip angle and multiple adiabatic inversion pulses. The acquired data is retrospectively gated into several cardiac phases, which are reconstructed with an approach that combines parallel imaging, low rank modelling and patch-based high-order tensor regularization. Free-running cMRF was evaluated in a standardized phantom and ten healthy subjects. Comparison with reference spin-echo, MOLLI, SASHA, T₂-GRASE and Cine was performed.ResultsT₁ and T₂ values obtained with the proposed approach were in good agreement with reference phantom values (ICC(A,1) > 0.99). Reported values for myocardium septum T₁ were 1043 ± 48 ms, 1150 ± 100 ms and 1160 ± 79 ms for MOLLI, SASHA and free-running cMRF respectively and for T₂ of 51.7 ± 4.1 ms and 44.6 ± 4.1 ms for T₂-GRASE and free-running cMRF respectively. Good agreement was observed between free-running cMRF and conventional Cine 2D ejection fraction (bias = −0.83%).ConclusionThe proposed free-running cardiac MRF approach allows for simultaneous assessment of myocardial T₁ and T₂ and Cine imaging in a single scan.     

A fast screening protocol for carotid plaques imaging using 3D multi-contrast MRI without contrast agent

PurposeTo implement a fast (~ 15 min) MRI protocol for carotid plaque screening using 3D multi-contrast MRI sequences without contrast agent on a 3 Tesla MRI scanner.Materials and methods7 healthy volunteers and 25 patients with clinically confirmed transient ischemic attack or suspected cerebrovascular ischemia were included in this study. The proposed protocol, including 3D T1-weighted and T2-weighted SPACE (variable-flip-angle 3D turbo spin echo), and T1-weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) was performed first and was followed by 2D T1-weighted and T2-weighted turbo spin echo, and post-contrast T1-weighted SPACE sequences. Image quality, number of plaques, and vessel wall thicknesses measured at the intersection of the plaques were evaluated and compared between sequences.ResultsAverage examination time of the proposed protocol was 14.6 min. The average image quality scores of 3D T1-weighted, T2-weighted SPACE, and T1-weighted magnetization prepared rapid acquisition gradient echo were 3.69, 3.75, and 3.48, respectively. There was no significant difference in detecting the number of plaques and vulnerable plaques using pre-contrast 3D images with or without post-contrast T1-weighted SPACE. The 3D SPACE and 2D turbo spin echo sequences had excellent agreement (R = 0.96 for T1-weighted and 0.98 for T2-weighted, p < 0.001) regarding vessel wall thickness measurements.ConclusionThe proposed protocol demonstrated the feasibility of attaining carotid plaque screening within a 15-minute scan, which provided sufficient anatomical coverage and critical diagnostic information. This protocol offers the potential for rapid and reliable screening for carotid plaques without contrast agent.     

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions

PurposeTo assess a radiomic machine learning (ML) model in classifying solid adrenal lesions (ALs) without fat signal drop on chemical shift (CS) as benign or malignant.Method55 indeterminate ALs (21 lipid poor adenomas, 15 benign pheocromocytomas, 1 oncocytoma, 12 metastases, 6 primary tumors) showing no fat signal drop on CS were retrospectively included. Manual 3D segmentation on T2-weighted and CS images was performed for subsequent radiomic feature extraction. After feature stability testing and an 80–20% train-test split, the train set was balanced via oversampling. Following a multi-step feature selection, an Extra Trees model was tuned with 5-fold stratified cross-validation in the train set and then tested on the hold-out test set.ResultsA total of 3396 features were extracted from each AL, of which 133 resulted unstable while none had low variance (< 0.01). Highly correlated (r > 0.8) features were also excluded, leaving 440 parameters. Among these, Support Vector Machine 5-fold stratified cross-validated recursive feature elimination selected a subset of 6 features. ML obtained a cross-validation accuracy of 0.94 on the train and 0.91 on the test sets. Precision, recall and F1 score were respectively 0.92, 0.91 and 0.91.ConclusionsOur MRI handcrafted radiomics and ML pipeline proved useful to characterize benign and malignant solid indeterminate adrenal lesions.     

Magnetic resonance neurography of the lumbosacral plexus at 3 Tesla – CSF-suppressed imaging with submillimeter resolution by a three-dimensional turbo spin echo sequence

PurposeTo investigate magnetic resonance neurography (MRN) of the lumbosacral plexus (LSP) with cerebrospinal fluid (CSF) suppression by using submillimeter resolution for three-dimensional (3D) turbo spin echo (TSE) imaging.Materials and methodsUsing extended phase graph (EPG) analysis, the signal response of CSF was simulated considering dephasing from coherent motion for frequency-encoding voxel sizes ranging from 0.3 to 1.3 mm and for CSF velocities ranging from 0 to 4 cm/s. In-vivo MRN included 3D TSE data with frequency encoding parallel to the feet/head axis from 15 healthy adults (mean age: 28.5 ± 3.8 years, 5 females; acquisition voxel size: 2 × 2 × 2 mm³) and 16 pediatric patients (mean age: 6.7 ± 4.1 years, 7 females; acquisition voxel size: 0.7 × 0.7 × 1.4 mm³) acquired at 3 Tesla. Five of the adults were scanned repetitively with changing acquisition voxel sizes (1 × 2 × 2 mm³, 0.7 × 2× 2 mm³, and 0.5 × 2 × 2 mm³). Measurements of the bilateral ganglion of the L5 nerve root, averaged between sides, as well as the CSF in the thecal sac were obtained for all included subjects and compared between adults and pediatric patients and between voxel sizes, using a CSF-to-nerve signal ratio (CSFNR).ResultsAccording to simulations, the CSF signal is reduced along the echo train for moving spins. Specifically, it can be reduced by over 90% compared to the maximum simulated signal for flow velocities above 2 cm/s, and could be most effectively suppressed by considering a frequency-encoding voxel size of 0.8 mm or less. For in-vivo measurements, mean CSFNR was 1.52 ± 0.22 for adults and 0.10 ± 0.03 for pediatric patients (p < .0001). Differences in CSFNR were significant between measurements using a voxel size of 2 × 2 × 2 mm³ and measurements in data with reduced voxel sizes (p ≤ .0012), with submillimeter resolution (particularly 0.5 × 2 × 2 mm³) providing highest CSF suppression.ConclusionsApplying frequency-encoding voxel sizes in submillimeter range for 3D TSE imaging with frequency encoding parallel to the feet/head axis may considerably improve MRN of LSP pathology in adults in the future because of favorable CSF suppression.     

Free breathing whole-heart 3D CINE MRI with self-gated Cartesian trajectory

PurposeTo present a method that uses a novel free-running self-gated acquisition to achieve isotropic resolution in whole heart 3D Cartesian cardiac CINE MRI.Material and methods3D cardiac CINE MRI using navigator gating results in long acquisition times. Recently, several frameworks based on self-gated non-Cartesian trajectories have been proposed to accelerate this acquisition. However, non-Cartesian reconstructions are computationally expensive due to gridding, particularly in 3D. In this work, we propose a novel highly efficient self-gated Cartesian approach for 3D cardiac CINE MRI. Acquisition is performed using CArtesian trajectory with Spiral PRofile ordering and Tiny golden angle step for eddy current reduction (so called here CASPR-Tiger). Data is acquired continuously under free breathing (retrospective ECG gating, no preparation pulses interruption) for 4–5 min and 4D whole-heart volumes (3D + cardiac phases) with isotropic spatial resolution are reconstructed from all available data using a soft gating technique combined with temporal total variation (TV) constrained iterative SENSE reconstruction.ResultsFor data acquired on eight healthy subjects and three patients, the reconstructed images using the proposed method had good contrast and spatio-temporal variations, correctly recovering diastolic and systolic cardiac phases. Non-significant differences (P > 0.05) were observed in cardiac functional measurements obtained with proposed 3D approach and gold standard 2D multi-slice breath-hold acquisition.ConclusionThe proposed approach enables isotropic 3D whole heart Cartesian cardiac CINE MRI in 4 to 5 min free breathing acquisition.