Search

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time–concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue curves were created from true AIFs combined with compartment-model parameters from a random distribution. Using Bloch simulations, a dictionary was constructed for a saturation-recovery 3D radial stack-of-stars sequence, accounting for deviations such as flip angle, T2* effects, and residual longitudinal magnetization after the saturation. A preliminary simulation study established the optimal tissue curve number using a bidirectional long short-term memory (Bi-LSTM) network with just AIF loss. Further optimization of the loss function involves comparing just AIF loss, AIF with compartment-model-based parameter loss, and AIF with compartment-model tissue loss. The optimized network was examined with both simulation and hybrid data, which included in vivo 3D stack-of-star datasets for testing. The AIF peak value accuracy and k t r a n s results were assessed. Results: Increasing the number of tissue curves can be beneficial when added tissue curves can provide extra information. Using just the AIF loss outperforms the other two proposed losses, including adding either a compartment-model-based tissue loss or a compartment-model parameter loss to the AIF loss. With the simulated data, the Bi-LSTM network reduced the AIF peak error from −23.6 ± 24.4% of the AIF using the dictionary method to 0.2 ± 7.2% (AIF input only) and 0.3 ± 2.5% (AIF + ten tissue curve inputs) of the network AIF. The corresponding k t r a n s error was reduced from −13.5 ± 8.8% to −0.6 ± 6.6% and 0.3 ± 2.1%. With the hybrid data (simulated data for training; in vivo data for testing), the AIF peak error was 15.0 ± 5.3% and the corresponding k t r a n s error was 20.7 ± 11.6% for the AIF using the dictionary method. The hybrid data revealed that using the AIF + tissue inputs reduced errors, with peak error (1.3 ± 11.1%) and k t r a n s error (−2.4 ± 6.7%). Conclusions: Integrating tissue curves with AIF curves into network inputs improves the precision of AI-driven AIF corrections. This result was seen both with simulated data and with applying the network trained only on simulated data to a limited in vivo test dataset. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to design a method of myocardial T1 quantification in small laboratory animals and to investigate the effects of spatiotemporal regularization and the needed acquisition duration. Methods: We propose a compressed‐sensing approach to T1 quantification based on self‐gated inversion‐recovery radial two/three‐dimensional (2D/3D) golden‐angle stack‐of‐stars acquisition with image reconstruction performed using total‐variation spatiotemporal regularization. The method was tested on a phantom and on a healthy rat, as well as on rats in a small myocardium‐remodeling study. Results: The results showed a good match of the T1 estimates with the results obtained using the ground‐truth method on a phantom and with the literature values for rats myocardium. The proposed 2D and 3D methods showed significant differences between normal and remodeling myocardium groups for acquisition lengths down to approximately 5 and 15 min, respectively. Conclusions: A new 2D and 3D method for quantification of myocardial T1 in rats was proposed. We have shown the capability of both techniques to distinguish between normal and remodeling myocardial tissue. We have shown the effects of image‐reconstruction regularization weights and acquisition length on the T1 estimates. [ABSTRACT FROM AUTHOR]

Background: The use of a gradient echo spin echo (GESE) method to obtain rapid T2 and T2* estimation in the heart has been proposed. The effect of acquisition parameter settings on T2 and T2* bias and precision have not been investigated in depth. Purpose: To understand factors impacting the quantification of T2 and T2* values with a gradient echo spin echo (GESE) method using echo planar imaging (EPI) readouts in a reduced field of view acquisition. Methods: The GESE method is implemented with a reduced field‐of‐view using an outer volume suppression (OVS) technique to minimize the time for multi‐echo EPI readouts. The number of EPI readouts (images) for the GESE is optimized using Cramer‐Rao Lower Bound (CRLB) and Monte Carlo simulations with a nonlinear least‐square (NLLS) estimator. The SNR requirements were studied using the latter simulation method for a selected range of T2 and T2* values and T2/T2* ratios. Two healthy control subjects were imaged with the proposed GESE sequence and evaluated with the NLLS estimation method. In addition, the proposed OVS method was compared with a saturation bands OVS method in one subject. Clinical T2 and T2* mappings were used as the reference. Results: The optimal number of EPI readouts is five and the performance is slightly better when the refocusing pulse is placed between the 2nd and 3rd readouts. The SNR requirement for achieving a target bias < 1 ms and standard deviation (SD) < 5 ms is more demanding when T2/T2* ratio increases. The minimum SNR requirement in the GESE acquisition should vary from 6 to 20 depending on specific myocardial T2 and T2* values at 3T. The T2 and T2* estimates using the proposed OVS method and the saturation bands OVS method are both similar to the reference. Conclusion: The GESE sequence with five EPI readouts is a feasible and efficient technique that can estimate T2 and T2* values in the septal myocardium within a heartbeat when the SNR requirement can be satisfied. [ABSTRACT FROM AUTHOR]