Purpose
To 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).
Methods
Whole 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.
Results
The 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.
Conclusion
The 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.
Purpose
To develop a 3D adiabatic T1ρ prepared ultrashort echo time cones (3D AdiabT1ρ UTE‐Cones) sequence for whole knee imaging on a clinical 3T scanner.
Methods
A train of adiabatic full passage ...pulses were used for spin locking, followed by time‐efficient multispoke UTE acquisition to detect signals from both short and long T2 tissues in the whole knee joint. A modified signal model was proposed for multispoke UTE data fitting. The feasibility of this 3D AdiabT1ρ UTE‐Cones technique was demonstrated through numerical simulation, phantom, and ex vivo knee sample studies. The 3D AdiabT1ρ UTE‐Cones technique was then applied to 6 in vivo knee joints of healthy volunteers to measure T1ρ values of quadriceps tendon, patellar tendon, anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), meniscus, patellar cartilage, and muscle.
Results
Numerical simulation, phantom and ex vivo knee sample studies demonstrated the feasibility of whole knee imaging using the proposed multispoke 3D AdiabT1ρ UTE‐Cones sequence. The healthy volunteer knee study demonstrated an averaged T1ρ of 13.9 ± 0.7 ms for the quadriceps tendon, 9.7 ± 0.8 ms for the patellar tendon, 34.9 ± 2.8 ms for the ACL, 21.6 ± 1.4 ms for the PCL, 22.5 ± 1.9 ms for the meniscus, 44.5 ± 2.4 ms for the patellar cartilage, and 43.2 ± 1.1 ms for the muscle.
Conclusion
The 3D AdiabT1ρ UTE‐Cones sequence allows volumetric T1ρ assessment of both short and long T2 tissues in the knee joint on a clinical 3T scanner.
Purpose
To measure T1 relaxations for the major tissues in whole knee joints on a clinical 3T scanner.
Methods
The 3D UTE‐Cones actual flip angle imaging (AFI) method was used to map the transmission ...radiofrequency field (B1) in both short and long T2 tissues, which was then used to correct the 3D UTE‐Cones variable flip angle (VFA) fitting to generate accurate T1 maps. Numerical simulation was carried out to investigate the accuracy of T1 measurement for a range of T2 values, excitation pulse durations, and B1 errors. Then, the 3D UTE‐Cones AFI‐VFA method was applied to healthy volunteers (N = 16) to quantify the T1 of knee tissues including cartilage, meniscus, quadriceps tendon, patellar tendon, anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), marrow, and muscles at 3T.
Results
Numerical simulation showed that the 3D UTE‐Cones AFI‐VFA technique can provide accurate T1 measurements (error <1%) when the tissue T2 is longer than 1 ms and a 150 μs excitation RF pulse is used and therefore is suitable for most knee joint tissues. The proposed 3D UTE‐Cones AFI‐VFA method showed an average T1 of 1098 ± 67 ms for cartilage, 833 ± 47 ms for meniscus, 800 ± 66 ms for quadriceps tendon, 656 ± 43 ms for patellar tendon, 873 ± 38 ms for ACL, 832 ± 49 ms for PCL, 379 ± 18 ms for marrow, and 1393 ± 46 ms for muscles.
Conclusion
The 3D UTE‐Cones AFI‐VFA method allows volumetric T1 measurement of the major tissues in whole knee joints on a clinical 3T scanner.
Evidence suggests that fasting exerts extensive antitumor effects in various cancers, including colorectal cancer (CRC). However, the mechanism behind this response is unclear. We investigate the ...effect of fasting on glucose metabolism and malignancy in CRC. We find that fasting upregulates the expression of a cholesterogenic gene, Farnesyl-Diphosphate Farnesyltransferase 1 (FDFT1), during the inhibition of CRC cell aerobic glycolysis and proliferation. In addition, the downregulation of FDFT1 is correlated with malignant progression and poor prognosis in CRC. Moreover, FDFT1 acts as a critical tumor suppressor in CRC. Mechanistically, FDFT1 performs its tumor-inhibitory function by negatively regulating AKT/mTOR/HIF1α signaling. Furthermore, mTOR inhibitor can synergize with fasting in inhibiting the proliferation of CRC. These results indicate that FDFT1 is a key downstream target of the fasting response and may be involved in CRC cell glucose metabolism. Our results suggest therapeutic implications in CRC and potential crosstalk between a cholesterogenic gene and glycolysis.
Purpose
To investigate direct imaging of trabecular bone using a 3D adiabatic inversion recovery prepared ultrashort TE cones (3D IR‐UTE‐Cones) sequence.
Methods
The proposed 3D IR‐UTE‐Cones sequence ...used a broadband adiabatic inversion pulse together with a short TR/TI combination to suppress signals from long T2 tissues such as muscle and marrow fat, followed by multispoke UTE acquisition to detect signal from short T2 water components in trabecular bone. The feasibility of this technique for robust suppression of long T2 tissues was first demonstrated through numerical simulations. The proposed IR‐UTE‐Cones sequence was applied to a hip agarose bone phantom and to 6 healthy volunteers for morphologic and quantitative T2∗ and proton density mapping of trabecular bone.
Results
Numeric simulation suggests that the IR technique with a short TR/TI combination provides sufficient suppression of long T2 tissues with a wide range of T1s. High contrast imaging of trabecular bone can be achieved ex vivo and in vivo, with fitted T2∗ values of 0.3–0.45 ms and proton densities of 5–9 mol/L.
Conclusion
The 3D IR‐UTE‐Cones sequence with a short TR/TI combination provides robust suppression of long T2 tissues and allows both selective imaging and quantitative (T2∗ and proton density) assessment of short T2 water components in trabecular bone in vivo.
Purpose
To investigate the effect of fat saturation (FatSat) on quantitative UTE imaging of variable knee tissues on a 3T scanner.
Methods
Three quantitative UTE imaging techniques, including the UTE ...multi‐echo sequence for T2∗ measurement, the adiabatic T1ρ prepared UTE sequence for T1ρ measurement, and the magnetization transfer (MT)‐prepared UTE sequence for MT ratio (MTR) and macromolecular proton fraction (MMF) measurements were used in this study. Twelve samples of cartilage and twelve samples of meniscus, as well as six whole knee cadaveric specimens, were imaged with the three above‐mentioned UTE sequences with and without FatSat. The difference, correlation, and agreement between the UTE measurements with and without FatSat were calculated to investigate the effects of FatSat on quantification.
Results
Fat was well‐suppressed using all three UTE sequences when FatSat was deployed. For the small sample study, the quantification difference ratio (QDR) values of all the measured biomarkers ranged from 0.7% to 12.6%, whereas for the whole knee joint specimen study, the QDR values ranged from 0.2% to 12.0%. Except for T1ρ in muscle and MMF in meniscus (p > 0.05), most of the measurements showed statistical differences for T1ρ, MTR, and MMF (p < 0.05) between FatSat and non‐FatSat scans. Most of the measurements for T2∗ showed no significant differences (p > 0.05). Strong correlations were found for all the biomarkers between measurements with and without FatSat.
Conclusion
The UTE biomarkers showed good correlation and agreement with some slight differences between the scans with and without FatSat.
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