CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI.

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The 'CMRxRecon' dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.

Nerve regeneration by interferon intervention in aging brain.

Neural stem cells (NSCs) are shielded from viral infection by interferon (IFN) defense. As individuals age, activation of NSC decreases with a significant decline of stemness marker Sex-determining region Y box 2 (Sox2) while IFN signaling enhances (Kalamakis et al, 2019). Given that low-level type-I IFN under normal physiological conditions can promote dormant hematopoietic stem cell differentiation (Baldridge et al, 2010), whether there is an inner connection between IFN signaling and NSC function remains elusive. In this issue of EMBO Molecular Medicine, Carvajal Ibanez et al (2023) reveal that IFN-ß, a type-I interferon, induces cell-type-specific interferon-stimulated genes (ISGs) and regulates global protein synthesis by orchestrating mTOR1 activity and stem cell cycle that retain NSCs at the G0 phase and repress Sox2 expression. As a consequence, NSCs exit the activation state and become inclined to differentiation.

Multiple B-Value Model-Based Residual Network (MORN) for Accelerated High-Resolution Diffusion-Weighted Imaging.

Single-Shot Echo Planar Imaging (SSEPI) based Diffusion Weighted Imaging (DWI) has shortcomings such as low resolution and severe distortions. In contrast, Multi-Shot EPI (MSEPI) provides optimal spatial resolution but increases scan time. This study proposed a Multiple b-value mOdel-based Residual Network (MORN) model to reconstruct multiple b-value high-resolution DWI from undersampled k-space data simultaneously. We incorporated Parallel Imaging (PI) into a residual U-net to reconstruct multiple b-value multi-coil data with the supervision of MUltiplexed Sensitivity-Encoding (MUSE) reconstructed Multi-Shot DWI (MSDWI). Moreover, asymmetric concatenations among different b-values and the combined loss to back propagate helped the feature transfer. After training and validation of the MORN in a dataset of 32 healthy cases, additional assessments were performed on 6 patients with different tumor types. The experimental results demonstrated that the MORN model outperformed conventional PI reconstruction (i.e. SENSE) and two state-of-the-art deep learning methods (SENSE-GAN and VSNet) in terms of PSNR (Peak Signal-to-Noise Ratio), SSIM (Structual SIMilarity) and apparent diffusion coefficient maps. In addition, using the pre-trained model under DWI, the MORN achieved consistent fractional anisotrophy and mean diffusivity reconstructed from multiple diffusion directions. Hence, the proposed method shows potential in clinical application according to the observations on tumor patients as well as images of multiple diffusion directions.

EPI phase error correction with deep learning (PEC-DL) at 7 T.

PURPOSE: The phase mismatch between odd and even echoes in EPI causes Nyquist ghost artifacts. Existing ghost correction methods often suffer from severe residual artifacts and are ineffective with k-space undersampling data. This study proposed a deep learning-based method (PEC-DL) to correct phase errors for DWI at 7 Tesla. METHODS: The acquired k-space data were divided into 2 independent undersampled datasets according to their readout polarities. Then the proposed PEC-DL network reconstructed 2 ghost-free images using the undersampled data without calibration and navigator data. The network was trained with fully sampled images and applied to two- and fourfold accelerated data. Healthy volunteers and patients with Moyamoya disease were recruited to validate the efficacy of the PEC-DL method. RESULTS: The PEC-DL method was capable to mitigate the ghost artifacts in DWI in healthy volunteers as well as patients with Moyamoya disease. The fourfold accelerated results showed much less distortion in the lesions of the Moyamoya patient using high b-value DWI and the corresponding ADC maps. The ghost-to-signal ratios were significantly lower in PEC-DL images compared to conventional linear phase corrections, mini-entropy, and PEC-GRAPPA algorithms. CONCLUSION: The proposed method can effectively eliminate ghost artifacts for full sampled and up to fourfold accelerated EPI data without calibration and navigator data.

MAGnitude-Image-to-Complex K-space (MAGIC-K) Net: A Data Augmentation Network for Image Reconstruction.

Deep learning has demonstrated superior performance in image reconstruction compared to most conventional iterative algorithms. However, their effectiveness and generalization capability are highly dependent on the sample size and diversity of the training data. Deep learning-based reconstruction requires multi-coil raw k-space data, which are not collected by routine scans. On the other hand, large amounts of magnitude images are readily available in hospitals. Hence, we proposed the MAGnitude Images to Complex K-space (MAGIC-K) Net to generate multi-coil k-space data from existing magnitude images and a limited number of required raw k-space data to facilitate the reconstruction. Compared to some basic data augmentation methods applying global intensity and displacement transformations to the source images, the MAGIC-K Net can generate more realistic intensity variations and displacements from pairs of anatomical Digital Imaging and Communications in Medicine (DICOM) images. The reconstruction performance was validated in 30 healthy volunteers and 6 patients with different types of tumors. The experimental results demonstrated that the high-resolution Diffusion Weighted Image (DWI) reconstruction benefited from the proposed augmentation method. The MAGIC-K Net enabled the deep learning network to reconstruct images with superior performance in both healthy and tumor patients, qualitatively and quantitatively.

Deep learning based multiplexed sensitivity-encoding (DL-MUSE) for high-resolution multi-shot DWI.

PURPOSE: A phase correction method for high-resolution multi-shot (MSH) diffusion weighted imaging (DWI) is proposed. The efficacy and generalization capability of the method were validated on both healthy volunteers and patients. THEORY AND METHODS: Conventionally, inter-shot phase variations for MSH echo-planar imaging (EPI) DWI are corrected by model-based algorithms. However, many acquisition imperfections are hard to measure accurately for conventional model-based methods, making the phase estimation and artifacts suppression unreliable. We propose a deep learning multiplexed sensitivity-encoding (DL-MUSE) framework to improve the phase estimations based on convolutional neural network (CNN) reconstruction. Aliasing-free single-shot (SSH) DW images, which have been used routinely in clinical settings, were used for training before the aliasing correction of MSH-DWI images. A dual-channel U-net comprising multiple convolutional layers was used for the phase estimation of MSH-EPI. The network was trained on a dataset containing 30 healthy volunteers and tested on another dataset of 52 healthy subjects and 15 patients with lesions or tumors with different shot numbers (4, 6 and 8). To further validate the generalization capability of our network, we acquired a dataset with different numbers of shots, TEs, partial Fourier factors, resolutions, ETLs, FOVs, coil numbers, and image orientations from two sites. We also compared the reconstruction performance of our proposed method with that of the conventional MUSE and SSH-EPI qualitatively and quantitatively. RESULTS: Our results show that DL-MUSE is capable of correcting inter-shot phase errors with high and robust performance. Compared to conventional model-based MUSE, our method, by applying deep learning-based phase corrections, showed reduced distortion, noise level, and signal loss in high b-value DWIs. The improvements of image quality become more evident as the shot number increases from 4 to 8, especially in those central regions of the images, where g-factor artifacts are severe. Furthermore, the proposed method could provide the information about the orientation of the white matter with better consistency and achieve finer fibers delineation compared to the SSH-EPI method. Besides, the experiments on volunteers and patients from two different sites demonstrated the generalizability of our proposed method preliminarily. CONCLUSION: A deep learning-based reconstruction algorithm for MSH-EPI images, which helps improve image quality greatly, was proposed. Results from healthy volunteers and tumor patients demonstrated the feasibility and generalization performances of our method for high-resolution MSH-EPI DWI, which can be used for routine clinical applications as well as neuroimaging research.

Fusion of thymosin alpha 1 with mutant IgG1 CH3 prolongs half-life and enhances antitumor effects in vivo.

Thymosin alpha 1 (Tα1) is an immunomodulatory polypeptide secreted from the thymus. Tα1 has a wide range of biological functions, such as immunomodulation and endocrine regulation. Tα1 also displays antiviral and antitumor activities. Tα1 has been successfully used in clinical adjuvant therapy for solid tumors to improve the immune response of patients undergoing chemotherapy and radiotherapy. However, the half-life of Tα1 in the body is short, so frequent administration is required to maintain efficacy. In order to improve the pharmacokinetic profile of Tα1, we linked the mutated CH3 (mCH3) fragment of IgG1 (human) to the C-terminus of Tα1 to produce a long-acting fusion protein, Tα1-mCH3. The half-life of Tα1-mCH3 (47 h) was substantially increased compared with that of the parent molecule Tα1 (3 h). In vivo studies indicated that mCH3 fusion retained the original biological activity of Tα1, and Tα1-mCH3 showed slightly better immunomodulatory effect than Ta1. In the 4 T1 and B16F10 tumor xenograft models, Tα1-mCH3 induced a greater abundance of CD4+ and CD8+ T-cells in tumor tissues compared with Ta1. Tα1-mCH3 exhibited better effect in promoting the production of IL-2 and IFN-γ compared with Tα1. Therefore, Tα1-mCH3 more efficiently inhibited the growth of 4 T1 and B16F10 tumors than Tα1. In conclusion, fusion with mCH3 is an attractive strategy to lengthen the half-life and increase the activity of Tα1.

A cell-internalizing peptide endows tumstatin7 with enhanced antitumor properties.

Tumstatin7 (CNYYSNS) is an antitumor peptide derived from the NC1 domain of Type IV collagen that has been associated with tumor angiogenesis. In this work, we generated a peptide composed of tumstatin7 fused to TAT, a cell-internalizing peptide consisting of 11 amino acids. Tumstatin7-TAT was internalized by cells and triggered cell death. The new peptide was more potent in inducing B16F10 melanoma cell apoptosis in vitro than the shorter tumstatin7. Whereas tumstatin7-TAT significantly reduced tumor cell viability, tumstatin7 showed only weak effects even at the highest treatment concentration applied. Both tumstatin7-TAT and tumstatin7 inhibited cell migration in an in vitro wound healing model, and the former was more effective than the latter in inhibiting tumor growth in vivo. Combining the cell-internalizing property of TAT with the tumor-specific property of tumstatin7 may provide a useful adjunct to tumor therapy.

Generation of a novel long-acting thymosin alpha1-Fc fusion protein and its efficacy for the inhibition of breast cancer in vivo.

Thymosin alpha1 (Tα1) is a multifunctional polypeptide involved in immunoregulation, which universally exists in various organs. The clinical application, however, is limited because of its short half-life. The Fc domain of human IgG has functional properties, improving the affinity and serum half-life. In this study, we fused Tα1 to Fc domain of human IgG1 for generation of a novel long-acting fusion protein, termed Tα1-Fc. Tα1-Fc was expressed, purified, and identified. The results showed that Tα1-Fc was more potent than Tα1 in inhibiting the growth of 4T1 and MCF-7 breast cancer cells in vivo. Furthermore, in the 4T1 tumor model the mice treated with Tα1-Fc exhibited higher level of CD4 and CD8 cells compared with that of the mice Tα1 treated. The interferon-γ and interleukin-2 level in the Tα1-Fc-treated mice was higher than that in the Tα1-treated mice. Tα1-Fc could also alleviate immunosuppression induced by hydrocortisone. In summary, Tα1-Fc provides a novel potent strategy to recruit immune cells against tumors and enhance the antitumor activity of Tα1.

Thymosin Alpha1-Fc Modulates the Immune System and Down-regulates the Progression of Melanoma and Breast Cancer with a Prolonged Half-life.

Thymosin alpha 1 (Tα1) is a biological response modifier that has been introduced into markets for treating several diseases. Given the short serum half-life of Tα1 and the rapid development of Fc fusion proteins, we used genetic engineering method to construct the recombinant plasmid to express Tα1-Fc (Fc domain of human IgG4) fusion protein. A single-factor experiment was performed with different inducers of varying concentrations for different times to get the optimal condition of induced expression. Pure proteins higher than 90.3% were obtained by using 5 mM lactose for 4 h with a final production about 160.4 mg/L. The in vivo serum half-life of Tα1-Fc is 25 h, almost 13 times longer than Tα1 in mice models. Also, the long-acting protein has a stronger activity in repairing immune injury through increasing number of lymphocytes. Tα1-Fc displayed a more effective antitumor activity in the 4T1 and B16F10 tumor xenograft models by upregulating CD86 expression, secreting IFN-γ and IL-2, and increasing the number of tumor-infiltrating CD4+ T and CD8+ T cells. Our study on the novel modified Tα1 with the Fc segment provides valuable information for the development of new immunotherapy in cancer.

Effects on tumor growth and immunosuppression of a modified Tα1 peptide along with its circular dichroism spectroscopy data.

The data presented in this article are related to the research article entitled "Immunomodulatory and Enhanced Antitumor Activity of a Modified Thymosin α1 in Melanoma and Lung Cancer" (Wang et al., 2018). Tα1 has been evaluated as effective in cancer treatment. In order to make it capable to target tumor, a peptide iRGD was introduced to Tα1. The anti-tumor activity was accessed by constructing in vivo melanoma and human non-small-cell lung cancer models treated with Tα1-iRGD to measure the tumor volume over time and tumor weight at the last day. The concentration of IFN-γ and IL-2 in C57BL/6 mice peripheral blood was determined by ELISA. And the immunomodulatory ability of Tα1-iRGD was evaluated in vivo by thymus index and spleen index. Those functions this paper was aimed at may have relationship with its secondary structure, so the circular dichroism spectra of Tα1, iRGD and Tα1-iRGD was performed.

Effect of a C-end rule modification on antitumor activity of thymosin α1.

Thymosin α1 (Tα1), a hormone containing 28 amino acids, has been approved in several cancer therapies, but the lack of tumor-targeting hinders its full use in tumor treatment. We designed a new peptide by connecting Tα1 and RGDR, generating a product, Tα1-RGDR, where RGDR is located in the C-end with both tumor-homing and cell internalizing properties (C-end rule peptides, a consensus R/KXXR/K motif). This work aimed to study the antitumor and immunological activities of Tα1-RGDR, and its differences compared with the wild-type Tα1. The antitumor and immunological activities of Tα1-RGDR were measured using the B16F10 tumor and immunologic suppression models. Tα1-RGDR treatment led to significant inhibition of tumor growth at a dose at which Tα1 showed a slight effect in the B16F10 tumor growth model. In the immunologic suppression model, Tα1-RGDR shared almost equivalent immunomodulatory effect with Tα1. These results demonstrated the better therapeutic effects after treatment with Tα1-RGDR compared with Tα1. Moreover, both Tα1-RGDR and Tα1 shared a helical conformation in the presence of trifluoroethanol based on CD spectroscopy. Our dock information of Tα1-RGDR when combined with integrin αvß3 or neuropilin-1 further confirmed previous experimental results. All these findings suggest that Tα1-RGDR might be a useful therapy for tumors by overcoming its wild type limitation of tumor homing.

Immunomodulatory and enhanced antitumor activity of a modified thymosin α1 in melanoma and lung cancer.

Tumor-targeted therapy is an attractive strategy for cancer treatment. Peptide hormone thymosin α1 (Tα1) has been used against several diseases, including cancer, but its activity is pleiotropic. Herein, we designed a fusion protein Tα1-iRGD by introducing the tumor homing peptide iRGD to Tα1. Results show that Tα1-iRGD can promote T-cell activation and CD86 expression, thereby exerting better effect and stronger inhibitory against melanoma and lung cancer, respectively, than Tα1 in vivo. These effects are indicated by the reduced densities of tumor vessels and Tα1-iRGD accumulation in tumors. Moreover, compared with Tα1, Tα1-iRGD can attach more B16F10 and H460 cells and exhibits significantly better immunomodulatory activity in immunosuppression models induced by hydrocortisone. Circular dichroism spectroscopy and structural analysis results revealed that Tα1 and Tα1-iRGD both adopted a helical confirmation in the presence of trifluoroethanol, indicating the structural basis of their functions. These findings highlight the vital function of Tα1-iRGD in tumor-targeted therapy and suggest that Tα1-iRGD is a better antitumor drug than Tα1.