Search

Purpose: The goals of this study include: (a) generating tailored magnetic resonance fingerprinting (TMRF) based non‐synthetic imaging; (b) assessing the repeatability of TMRF and deep learning‐based mapping of in vitro ISMRM/NIST phantom and in vivo brain data of healthy human subjects. Methods: We have acquired qualitative images obtained from the vendor‐supplied gold standard (GS), MRF (synthetic), and TMRF (non‐synthetic) on one representative healthy human brain. We also acquired 30 datasets on the ISMRM/NIST phantom for the in vitro repeatability study on a GE Discovery 3T MR750w scanner using the TMRF sequence. We compared T1 and T2 maps generated from 30 ISMRM/NIST phantom datasets to the spin‐echo (SE) based GS method as part of the in vitro repeatability study. R‐squared coefficient of determination in a simple linear regression and Bland–Altman analysis were computed for 30 datasets of ISMRM/NIST phantom to assess the accuracy of in vitro quantitative TMRF data. The repeatability of T1 and T2 estimates by TMRF was evaluated by calculating the standard deviation (SD) divided by the average of 30 datasets for each sphere, respectively. We acquired 10 volunteers for the in vivo repeatability study on the same scanner using the same TMRF sequence. These volunteers were imaged five times with two runs per repetition, resulting in 100 in vivo datasets. Five contrasts, T1 and T2 maps of 10 human volunteers acquired over five repetitions, were evaluated in the in vivo repeatability study. We computed the intraclass correlation coefficient (ICC) of the signal‐to‐noise ratio (SNR), signal intensities, T1 and T2 relaxation times in white matter (WM), and gray matter (GM). Results: The synthetic images generated from MRF show partial volume and flow artifacts compared to non‐synthetic images obtained from TMRF images and the GS. In vitro studies show that TMRF estimates have less than 5% variations except sphere 14 in the T2 array (6.36%). TMRF and SE relaxometry measurements were strongly correlated; R2 values were 0.9958 and 0.9789 for T1 and T2 estimates, respectively. Based on the ICC values, SNR, mean intensity values, and relaxation times of WM and GM for the in vivo studies were consistent. T1 and T2 values of WM and GM were similar to previously published values. The mean ± SD of T1 and T2 for WM for ten subjects and five repeats are 992 ± 41 ms and 99 ± 6 ms, while the corresponding values for T1 and T2 for GM are 1598 ± 73 ms and 152 ± 14 ms. Conclusion: TMRF and deep learning‐based reconstruction produce repeatable, non‐synthetic multi‐contrast images, and parametric maps simultaneously. [ABSTRACT FROM AUTHOR]

Purpose: To compare the bias and inherent reliability of the quantitative (T1 and T2) imaging metrics generated from the magnetic resonance fingerprinting (MRF) technique using the ISMRM/NIST system phantom in an international multicenter setting. Method: ISMRM/NIST MRI system phantom provides standard reference T1 and T2 relaxation values (vendor‐provided) for each of the 14 vials in T1 and T2 arrays. MRF‐SSFP scans repeated over 30 days on GE 1.5 and 3.0 T scanners at three collaborative centers. MRF estimated T1, and T2 values averaged over 30 days were compared with the phantom vendor‐provided and spin‐echo (SE) based convention gold standard (GS) method. Repeatability and reproducibility were characterized by the within‐case coefficient of variation (wCV) of the MRF data acquired over 30 days, along with the biases. Result: For the wide ranges of MRF estimated T1 values, vials #1‐8 (T1 relaxation time between 2033 and 184 ms) exhibited a wCV less than 3% and 4%, respectively, on 3.0 and 1.5 T scanners. T2 values in vials #1‐8 (T2 relaxation, 1044‐45 ms) have shown wCV to be <7% on both 3.0 and 1.5 T scanners. A stronger linear correlation overall for T1 (R2 = 0.9960 and 0.9963 at center‐1 and center‐2 on 3.0 T scanner, and R2 = 0.9951 and 0.9988 at center‐1 and center‐3 on 1.5 T scanner) compared to T2 (R2 = 0.9971 and 0.9972 at center‐1 and center‐2 on 3.0 T scanner, and R2 = 0.9815 and 0.9754 at center‐1 and center‐3 on 1.5 T scanner). Bland–Altman (BA) analysis showed MRF based T1 and T2 values were within the limit of agreement (LOA) except for one data point. The mean difference or bias and 95% lower bound (LB) and upper bound (UB) LOA are reported in the format; mean bias: 95% LB LOA: 95% UB LOA. The biases for T1 values were 21.34: −50.00: 92.69, 21.32: −47.29: 89.94 ms, and for T2 values were −19.88: −42.37: 2.61, −19.06: −43.58: 5.45 ms on 3.0 T scanner at center‐1 and center‐2, respectively. Similarly, on 1.5 T scanner biases for T1 values were 26.54: −53.41: 106.50, 9.997: −51.94: 71.94 ms, and for T2 values were −23.84: −135.40: 87.76, −37.30: 134.30: 59.73 ms at center‐1 and center‐3, respectively. Additionally, the correlation between the SE based GS and MRF estimated T1 and T2 values (R2 = 0.9969 and 0.9977) showed a similar trend as we observed between vendor‐provided and MRF estimated T1 and T2 values (R2 = 0.9963 and 0.9972). In addition to correlation, BA analysis showed that all the vials are within the LOA between the GS and vendor‐provided for the T1 values and except one vial for T2. All the vials are within the LOA between GS and MRF except one vial in T1 and T2 array. The wCV for reproducibility was <3% for both T1 and T2 values in vials #1‐8, for all the 14 vials, wCV calculated for reproducibility was <4% for T1 values and <5% for T2. Conclusion: This study shows that MRF is highly repeatable (wCV <4% for T1 and <7% for T2) and reproducible (wCV < 3% for both T1 and T2) in certain vials (vials #1‐8). [ABSTRACT FROM AUTHOR]

Purpose: Precise segmentation of clinical target volume (CTV) is the key to stomach cancer radiotherapy. We proposed a novel stochastic width‐deep neural network (SW‐DNN) for better automatically contouring stomach CTV. Methods: Stochastic width‐deep neural network was an end‐to‐end approach, of which the core component was a novel SW mechanism that employed shortcut connections between the encoder and decoder in a random manner, and thus the width of the SW‐DNN was stochastically adjustable to obtain improved segmentation results. In total, 150 stomach cancer patient computed tomography (CT) cases with the corresponding CTV labels were collected and used to train and evaluate the SW‐DNN. Three common quantitative measures: true positive volume fraction (TPVF), positive predictive value (PPV), and Dice similarity coefficient (DSC) were used to evaluate the segmentation accuracy. Results: Clinical target volumes calculated by SW‐DNN had significant quantitative advantages over three state‐of‐the‐art methods. The average DSC value of SW‐DNN was 2.1%, 2.8%, and 3.6% higher than that of three state‐of‐the‐art methods. The average DSC, TPVF, and PPV values of SW‐DNN were 2.1%, 4.0%, and 0.3% higher than that of the corresponding constant width DNN. Conclusions: Stochastic width‐deep neural network provided better performance for contouring stomach cancer CTV accurately and efficiently. It is a promising solution in clinical radiotherapy planning for stomach cancer. [ABSTRACT FROM AUTHOR]