Fat suppression for ultrashort echo time imaging using a novel soft-hard composite radiofrequency pulse.

PurposeTo design a soft-hard composite pulse for fat suppression and water excitation in ultrashort echo time (UTE) imaging with minimal short T2 signal attenuation.MethodsThe composite pulse contains a narrow bandwidth soft pulse centered on the fat peak with a small negative flip angle (-α) and a short rectangular pulse with a small positive flip angle (α). The fat magnetization experiences both tipping-down and -back with an identical flip angle and thus returns to the equilibrium state, leaving only the excited water magnetization. Bloch simulations, as well as knee, tibia, and ankle UTE imaging studies, were performed to investigate the effectiveness of fat suppression and corresponding water signal attenuation. A conventional fat saturation (FatSat) module was used for comparison. Signal suppression ratio (SSR), defined as the ratio of signal difference between non-fat-suppression and fat-suppression images over the non-fat-suppression signal, was introduced to evaluate the efficiency of the composite pulse.ResultsNumerical simulations demonstrate that the soft-hard pulse has little saturation effect on short T2 water signals. Knee, tibia, and ankle UTE imaging results suggest that comparable fat suppression can be achieved with the soft-hard pulse and the FatSat module. However, much less water saturation is induced by the soft-hard pulse, especially for short T2 tissues, with SSRs reduced from 71.8 ± 6.9% to 5.8 ± 4.4% for meniscus, from 68.7 ± 5.5% to 7.7 ± 7.6% for bone, and from 62.9 ± 12.0% to 4.8 ± 3.2% for the Achilles tendon.ConclusionThe soft-hard composite pulse can suppress fat signals in UTE imaging with little signal attenuation on short T2 tissues

Evaluation of cortical bone perfusion using dynamic contrast enhanced ultrashort echo time imaging: a feasibility study.

Background:Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has been used to study perfusion in a wide variety of soft tissues including the bone marrow. Study of perfusion in hard tissues such as cortical bone has been much more limited because of the lack of detectable MR signal from them using conventional pulse sequences. However, two-dimensional (2D) ultrashort echo time (UTE) sequences detect signal from cortical bone and allow fast imaging of this tissue. In addition, adiabatic 2D inversion recovery UTE (IR-UTE) sequences can provide excellent signal suppression of soft tissues, such as muscle and marrow, and allow cortical bone to be seen with high contrast and reduced artefacts. We aimed to assess the feasibility of using 2D UTE and 2D IR-UTE sequences to perform DCE-MRI in the cortical bone of rabbits and human volunteers. Methods:Cortical bone perfusion was studied in rabbits (n=12) and human volunteers (n=3) using 2D UTE and 2D IR-UTE sequences on a clinical 3T scanner. Dynamic data with an in-plane resolution of ~0.5×0.5 mm2, single slice thickness of 3 mm for rabbits and 10 mm for human volunteers, and temporal resolution of 23 s for 2D UTE imaging of rabbits, 28 s for 2D UTE imaging of human volunteers, and 60 s for 2D IR-UTE imaging of both the rabbits and human volunteers were acquired before and after the injection of a Gd contrast agent (Gd-BOPTA: Multihance; Bracco Imaging SpA, Milan, Italy). The dose was 0.06 mmol/kg for rabbits and 0.2 mmol/kg for human subjects. Kinetic analyses based on the Brix model, as well as simple calculations of maximum enhancement (ME) and enhancement slope (ES), were performed. Results:The 12 rabbits showed a mean Ktrans of 0.36±0.07 min-1, Kep of 8.42±3.17 min-1, ME of 28.30±6.83, ES of 0.35±0.18 for the femur with the 2D UTE sequence, and a mean Ktrans of 0.45±0.10 min-1, Kep of 9.80±0.50 min-1, ME of 48.84±12.12, and ES of 0.69±0.27 for the femur with the 2D IR-UTE sequence. Lower ME and ES values were observed in the tibial midshaft of healthy human volunteers compared to rabbits. Conclusions:These results show that 2D UTE and 2D IR-UTE sequences are capable of detecting dynamic contrast enhancement in cortical bone in both rabbits and healthy human volunteers. Clinical studies with these techniques are likely to be feasible

Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U-Net with transfer learning.

PurposeTo develop a deep learning-based method for knee menisci segmentation in 3D ultrashort echo time (UTE) cones MR imaging, and to automatically determine MR relaxation times, namely the T1, T1ρ , and T2∗ parameters, which can be used to assess knee osteoarthritis (OA).MethodsWhole knee joint imaging was performed using 3D UTE cones sequences to collect data from 61 human subjects. Regions of interest (ROIs) were outlined by 2 experienced radiologists based on subtracted T1ρ -weighted MR images. Transfer learning was applied to develop 2D attention U-Net convolutional neural networks for the menisci segmentation based on each radiologist's ROIs separately. Dice scores were calculated to assess segmentation performance. Next, the T1, T1ρ , T2∗ relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared.ResultsThe models developed using ROIs provided by 2 radiologists achieved high Dice scores of 0.860 and 0.833, while the radiologists' manual segmentations achieved a Dice score of 0.820. Linear correlation coefficients for the T1, T1ρ , and T2∗ relaxations calculated using the automatic and manual segmentations ranged between 0.90 and 0.97, and there were no associated differences between the estimated average meniscal relaxation parameters. The deep learning models achieved segmentation performance equivalent to the inter-observer variability of 2 radiologists.ConclusionThe proposed deep learning-based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA

Solving Continual Combinatorial Selection via Deep Reinforcement Learning

We consider the Markov Decision Process (MDP) of selecting a subset of items at each step, termed the Select-MDP (S-MDP). The large state and action spaces of S-MDPs make them intractable to solve with typical reinforcement learning (RL) algorithms especially when the number of items is huge. In this paper, we present a deep RL algorithm to solve this issue by adopting the following key ideas. First, we convert the original S-MDP into an Iterative Select-MDP (IS-MDP), which is equivalent to the S-MDP in terms of optimal actions. IS-MDP decomposes a joint action of selecting K items simultaneously into K iterative selections resulting in the decrease of actions at the expense of an exponential increase of states. Second, we overcome this state space explo-sion by exploiting a special symmetry in IS-MDPs with novel weight shared Q-networks, which prov-ably maintain sufficient expressive power. Various experiments demonstrate that our approach works well even when the item space is large and that it scales to environments with item spaces different from those used in training.Comment: Accepted to IJCAI 2019,14 pages,8 figure

Advanced magnetic resonance imaging of cartilage components in haemophilic joints reveals that cartilage hemosiderin correlates with joint deterioration.

IntroductionEvidence suggests that toxic iron is involved in haemophilic joint destruction.AimTo determine whether joint iron deposition is linked to clinical and imaging outcomes in order to optimize management of haemophilic joint disease.MethodsAdults with haemophilia A or haemophilia B (n = 23, ≥ age 21) of all severities were recruited prospectively to undergo assessment with Hemophilia Joint Health Scores (HJHS), pain scores (visual analogue scale [VAS]) and magnetic resonance imaging (MRI) at 3T using conventional MRI protocols and 4-echo 3D-UTE-Cones sequences for one affected arthropathic joint. MRI was scored blinded by two musculoskeletal radiologists using the International Prophylaxis Study Group (IPSG) MRI scale. Additionally, UTE-T2* values of cartilage were quantified. Correlations between parameters were performed using Spearman rank correlation. Two patients subsequently underwent knee arthroplasty, which permitted linking of histological findings (including Perl's reaction) with MRI results.ResultsMRI scores did not correlate with pain scores or HJHS. Sixteen joints had sufficient cartilage for UTE-T2* analysis. T2* values for cartilage correlated inversely with HJHS (rs  = -0.81, P < 0.001) and MRI scores (rs  = -0.52, P = 0.037). This was unexpected since UTE-T2* values decrease with better joint status in patients with osteoarthritis, suggesting that iron was present and responsible for the effects. Histological analysis of cartilage confirmed iron deposition within chondrocytes, associated with low UTE-T2* values.ConclusionsIron accumulation can occur in cartilage (not only in synovium) and shows a clear association with joint health. Cartilage iron is a novel biomarker which, if quantifiable with innovative joint-specific MRI T2* sequences, may guide treatment optimization

MRI-based porosity index (PI) and suppression ratio (SR) in the tibial cortex show significant differences between normal, osteopenic, and osteoporotic female subjects

IntroductionUltrashort echo time (UTE) MRI enables quantitative assessment of cortical bone. The signal ratio in dual-echo UTE imaging, known as porosity index (PI), as well as the signal ratio between UTE and inversion recovery UTE (IR-UTE) imaging, known as the suppression ratio (SR), are two rapid UTE-based bone evaluation techniques developed to reduce the time demand and cost in future clinical studies. The goal of this study was to investigate the performance of PI and SR in detecting bone quality differences between subjects with osteoporosis (OPo), osteopenia (OPe), and normal bone (Normal).MethodsTibial midshaft of fourteen OPe (72 ± 6 years old), thirty-one OPo (72 ± 6 years old), and thirty-seven Normal (36 ± 19 years old) subjects were scanned using dual-echo UTE and IR-UTE sequences on a clinical 3T scanner. Measured PI, SR, and bone thickness were compared between OPo, OPe, and normal bone (Normal) subjects using the Kruskal–Wallis test by ranks. Spearman’s rank correlation coefficients were calculated between dual-energy x-ray absorptiometry (DEXA) T-score and UTE-MRI results.ResultsPI was significantly higher in the OPo group compared with the Normal (24.1%) and OPe (16.3%) groups. SR was significantly higher in the OPo group compared with the Normal (41.5%) and OPe (21.8%) groups. SR differences between the OPe and Normal groups were also statistically significant (16.2%). Cortical bone was significantly thinner in the OPo group compared with the Normal (22.0%) and OPe (13.0%) groups. DEXA T-scores in subjects were significantly correlated with PI (R=-0.32), SR (R=-0.50), and bone thickness (R=0.51).DiscussionPI and SR, as rapid UTE-MRI-based techniques, may be useful tools to detect and monitor bone quality changes, in addition to bone morphology, in individuals affected by osteoporosis