BUDA-MESMERISE: Rapid acquisition and unsupervised parameter estimation for T1 , T2 , M0 , B0 , and B1 maps.

PURPOSE: Rapid acquisition scheme and parameter estimation method are proposed to acquire distortion-free spin- and stimulated-echo signals and combine the signals with a physics-driven unsupervised network to estimate T1 , T2 , and proton density (M0 ) parameter maps, along with B0 and B1 information from the acquired signals. THEORY AND METHODS: An imaging sequence with three 90° RF pulses is utilized to acquire spin- and stimulated-echo signals. We utilize blip-up/-down acquisition to eliminate geometric distortion incurred by the effects of B0 inhomogeneity on rapid EPI acquisitions. For multislice imaging, echo-shifting is applied to utilize dead time between the second and third RF pulses to encode information from additional slice positions. To estimate parameter maps from the spin- and stimulated-echo signals with high fidelity, 2 estimation methods, analytic fitting and a novel unsupervised deep neural network method, are developed. RESULTS: The proposed acquisition provided distortion-free T1 , T2 , relative proton density (M0), B0 , and B1 maps with high fidelity both in phantom and in vivo brain experiments. From the rapidly acquired spin- and stimulated-echo signals, analytic fitting and the network-based method were able to estimate T1 , T2 , M0 , B0 , and B1 maps with high accuracy. Network estimates demonstrated noise robustness owing to the fact that the convolutional layers take information into account from spatially adjacent voxels. CONCLUSION: The proposed acquisition/reconstruction technique enabled whole-brain acquisition of coregistered, distortion-free, T1 , T2 , M0 , B0 , and B1 maps at 1 × 1 × 5 mm3 resolution in 50 s. The proposed unsupervised neural network provided noise-robust parameter estimates from this rapid acquisition.

A locally segmented reconstruction method for parallel imaging.

PURPOSE: A locally segmented parallel imaging reconstruction method is proposed that efficiently utilizes sensitivity distribution of multichannel receiver coil. THEORY AND METHODS: A method of locally segmenting a MR signal is introduced to maximize the differences in sensitivity between receiver channels. A 1D Fourier transformation of the undersampled k-space data is performed along the readout direction, which generates a hybrid 2D space. The hybrid space is partitioned into localized segments along the readout direction. In every localized segment, kernels representing relation between adjacent signals are estimated from autocalibration signals, and data at unsampled points are estimated using the kernels. Then, the images are reconstructed from full k-space data that consists of the sampled data and the estimated data at unsampled points. RESULTS: In a computer simulation and in vivo experiments, the locally segmented reconstruction method produced fewer residual artifacts compared to the conventional parallel imaging reconstruction methods with the same kernel geometry. The performance gain of the proposed method comes from maximizing encoding capability of receiver channels, thus resulting in the accurately estimated kernel weights that reflect the relation between adjacent signals. CONCLUSION: The proposed spatial segmentation method maximally utilizes differences in the sensitivity of receiver channels to reconstruct images with reduced artifacts.

Optimization of steady-state pulsed CEST imaging for amide proton transfer at 3T MRI.

PURPOSE: To optimize a steady-state imaging sequence for maximizing the amide proton transfer effects in pulsed-CEST (pCEST) imaging. METHOD: The steady-state pCEST (SS-pCEST) sequence is a fast CEST imaging scheme that applies repetitive short RF pulses for generating CEST and acquiring MR imaging signal alternately. To maximize the obtainable amide proton transfer effects, the SS-pCEST scheme is analyzed and optimized with respect to not only the imaging parameters but also the imaging schemes of the signal acquisition part. Three imaging parameters such as the flip angle and RF power for saturation and the flip angle for imaging are selected as factors affecting the obtainable CEST effects; and 2 imaging schemes, namely, SSFP and spoiled gradient echo sequences, are analyzed and compared for numerical simulations and MRI experiments at 3 tesla. RESULTS: SS-pCEST combined with SSFP could provide higher amide proton transfer effects than that with spoiled gradient echo. Furthermore, in the proposed SS-pCEST imaging with SSFP, 3 imaging parameters can be independently optimized so that the optimization complexities can be reduced. CONCLUSION: We optimized the SS-pCEST imaging method with SSFP to maximize the amide proton transfer effects. In addition, our analysis showed the SSFP sequence was more efficient than the spoiled gradient echo sequence for SS-pCEST imaging.

A multicontrast imaging method using steady-state free precession with alternating RF flip angles.

PURPOSE: To obtain multicontrast images including fat-suppressed contrast image, a novel multicontrast imaging method using an SSFP sequence with alternating RF flip angles is proposed. METHODS: The proposed method uses the balanced SSFP sequence with 2 flip angles. In general, the conventional balanced SSFP sequence has its own unique contrast, which combines both FID signal and echo signal under a steady-state condition. By using alternating RF flip angles and RF phase cycling, various image contrasts weighted by proton density, T1 , and T2 can be obtained. The proposed method offers multicontrast images with fat suppression by using the combination of 2 images obtained just after alternating RF pulses, respectively. RESULTS: As demonstrated by simulations, phantom and in vivo experiments, the proposed method provides multicontrast knee images including fat-suppressed contrast images. CONCLUSION: The proposed method can be a useful tool for clinical diagnosis, such as the cartilage segmentation and the fast screening of lesions.

RenseNet: A Deep Learning Network Incorporating Residual and Dense Blocks with Edge Conservative Module to Improve Small-Lesion Classification and Model Interpretation.

Deep learning has become an essential tool in medical image analysis owing to its remarkable performance. Target classification and model interpretability are key applications of deep learning in medical image analysis, and hence many deep learning-based algorithms have emerged. Many existing deep learning-based algorithms include pooling operations, which are a type of subsampling used to enlarge the receptive field. However, pooling operations degrade the image details in terms of signal processing theory, which is significantly sensitive to small objects in an image. Therefore, in this study, we designed a Rense block and edge conservative module to effectively manipulate previous feature information in the feed-forward learning process. Specifically, a Rense block, an optimal design that incorporates skip connections of residual and dense blocks, was demonstrated through mathematical analysis. Furthermore, we avoid blurring of the features in the pooling operation through a compensation path in the edge conservative module. Two independent CT datasets of kidney stones and lung tumors, in which small lesions are often included in the images, were used to verify the proposed RenseNet. The results of the classification and explanation heatmaps show that the proposed RenseNet provides the best inference and interpretation compared to current state-of-the-art methods. The proposed RenseNet can significantly contribute to efficient diagnosis and treatment because it is effective for small lesions that might be misclassified or misinterpreted.