Keywords: Synthetic MR, Cancer

Motivation: Depth of myometrial invasion (DMI), lymphovascular invasion (LI) and pathological types of endometrial cancer (EC) affect decision-making on an optimal treatment plan for uterus.

Goal(s): This study aimed to investigate the feasibility of MAGIC in evaluating the DMI and predicting the pathological types of EC.

Approach: The assessment performance of MAGIC in comparison to hr-T2WI on DMI and prediction of EC types in comparison to ADC were performed.

Results: T2 and PD together had a superior predictive performance to ADC only, and sy-T2WI showed the similar assessment performance on DMI to hr-T2WI.

Impact: One more application of MAGIC-generated contrast images and quantitative parameter maps in cervical diagnosis may provide additional information on diagnosis of DMI and classification of pathological types of EC.

Keywords: Synthetic MR, Simulations

Motivation: Signal model-based synthesis of 4D flow MRI data from CFD is promising to study image acquisition, reconstruction methods and augment data for inference. However, the approach assumes idealized encoding, and particle-based Bloch simulations are needed to include complex effects.

Goal(s): Implement a particle-based Bloch simulation in CMRsim for generating synthetic multi-venc 4D flow data.

Approach: Using pulsatile, stenotic U-bend CFD data as input, simulations of moving magnetization in CMRsim generate synthetic 4D flow MRI data.

Results: 4D flow simulation is feasible in CMRsim, demonstrating tractable simulation times and good agreement with ground truth and magnetization history-based effects, namely acceleration and intra-voxel dephasing.

Impact: Numeric Bloch simulations in CMRsim permit a detailed study of imaging protocol-dependent flow-related artifacts in 4D flow MRI of complex flows and can thereby assist in improving acquisition and reconstruction approaches.

Keywords: Synthetic MR, Quantitative Imaging, nasopharyngeal carcinoma

Motivation: In nasopharyngeal carcinoma (NPC) patients, there is a significant variation in tumor response to induction chemotherapy (ICT), which directly impacts prognosis. To address this issue, we aimed to explore a novel imaging biomarker based on pre-treatment synthetic MRI to predict which patients would benefit the most from the additional ICT.

Goal(s): Evaluating the value of pre-treatment synthetic MRI quantitative parameter map histogram characteristics in predicting the efficacy of ICT for NPC.

Approach: 40 NPC patients were prospectively enrolled, and each was imaged with synthetic MRI.

Results: T2_Minimun and T2_RootMeanSquared show promise as imaging markers for predicting the response to ICT in NPC.

Impact: The utilization of synthetic MRI may serve as an effective diagnostic tool for evaluating the response to ICT in clinical practice. Identifying patients who are unlikely to respond to ICT early on, can help offer them alternative treatment options.

Keywords: Synthetic MR, Quantitative Imaging, synthetic magnetic resonance imaging, quantitative susceptibility mapping, type 2 diabetes, cognitive impairment

Motivation: Identification of high-risk dementia in T2DM is very important for early intervention.

Goal(s): To explore a feasible imaging approach in early discovering T2DM-driven cognitive impairment.

Approach: To compare ROI-based retrieved relaxation time of synthetic magnetic resonance imaging and quantitative susceptibility values between T2DM and healthy cohorts (NCs) and also correlate significant differences of all measurements with Montreal Cognitive Assessment (MoCA) scores.

Results: T2DM had higher T1 and T2 relaxation time, PD and QS values in some brain regions than NCs. Moreover, T1 value of left insula was negatively associated with MoCA.

Impact: Synthetic MRI and QSM can detect abnormal brain areas associated with in T2DM, and the former had more potential in clinically diagnosing early alteration in T2DM due to more direct visualization and measurements on the scanner console.

Keywords: Synthetic MR, Brain, Acquisition Methods, Challenges, High-Field MRI MR, Fingerprinting/Synthetic MR, MR Value, Multiple Sclerosis, Neuro, Parallel Transmit & Multiband, Relaxometry

Motivation: Redefining MP2RAGE sequence for clinical efficiency.

Goal(s): To provide a time-efficient MP2RAGE parameterization with on-demand synthetic T₁-weighted contrasts.

Approach: Sequence parameters are chosen to minimize idle time while maximizing CNR_WM/GM. Synthetic contrasts are derived from T₁ map. Experimental validation is carried for 7T brain imaging using plug-and-play parallel transmission.

Results: Time-efficient MP2RAGE reduced acquisition time by up to 40% compared to reference, while maintaining contrast quality. Multiple sclerosis patients benefited from enhanced lesion visualization with a 10min, (0.67mm)³ time-efficient protocol.  This optimization enabled shorter acquisition times or higher resolution within a given time budget, while providing an exhaustive set of brain T₁-w contrasts.

Impact: A time-efficient MP2RAGE sequence parameterization is proposed, resulting in faster, higher-resolution 3D T₁-weighted brain imaging compared to conventional settings, combined with a complete set of synthetic T₁-w contrasts generated online, with meaningful potential in clinical and research practice.

Keywords: Synthetic MR, Data Analysis, Brain Tumor Segmentation, MR Sequence Synthesis, nnU-net, GANs, Multi Contrast MR, Deep Learning

Motivation: Automated and robust segmentation of brain tumors requires multiple MRI sequences and can expedite neuro-oncological clinical trials.

Goal(s): Our goal was to develop a deep learning model for brain tumor segmentation, even when some MRI sequences are missing. 

Approach: We enhanced a GAN with attention modules to synthesize missing sequences and employed an optimized nnU-Net for segmentation using both real and synthesized sequences.

Results:  The proposed AI-based model significantly improved brain tumor segmentation, with overall Dice scores increasing from 0.688% when FLAIR is missing to 0.873% using synthesized FLAIR derived from T2, and achieving 0.901% with real FLAIR.

Impact: The developed two-stage deep learning framework, comprising synthesis and segmentation, enhances segmentation of brain tumors in MRI, especially when real sequences are unavailable. This advancement accelerates clinical trials and reduces manual segmentation time, yielding promising results.

Keywords: Synthetic MR, Precision & Accuracy

Motivation: MRI acquisition time is a critical factor for patient comfort and motion artifacts. To reduce total acquisition time, synthetic MRI is a flexible solution.

Goal(s): To optimize synthetic T₁-w (synT₁-w) MRI for increasing the accuracy (i.e. difference between synT₁-w and MPRAGE) of spinal cord cross-sectional area measurements. 

Approach: We optimized and validated synT₁-w for tracking neurodegeneration in the cervical cord after spinal cord injury and assessed the required sample size for detecting hypothetical treatment effects.

Results: Accuracy of synT₁-w improved considerably with a minor remaining bias of -0.5% compared to MPRAGE. 13.5% less participants are required when using synT₁-w instead of MPRAGE.

Impact: Synthetic MRI can help to optimize imaging protocols in clinical trials by reducing acquisition time and the number of required participants. By improving the accuracy of synthetic T₁-weighted images, better comparability with different studies using acquired MRI can be achieved.

Keywords: Synthetic MR, Prostate, Deep learning reconstruction, diffusion weighted imaging, cancer

Motivation: Synthetic-DWI (SyDWI) at high-b-value, derived from low-b-value DWI, might be beneficial for prostate cancer evaluation due to better conspicuity of lesions. Relative to conventional reconstruction (ConR), a vendor-provided deep learning reconstruction (DLR) has been reported for improving imaging quality in aspects of higher SNR and imaging sharpness.  

Goal(s): Investigate the impact of DLR on the image quality of SyDWI for prostate lesion detection.

Approach: Low-b-value DWI was reconstructed with DLR and ConR, separately. SyDWIs were generated from DWI_DLR and DWI_ConR. The image quality and lesion conspicuity were compared.  

Results: SyDWI generated from DWI_DLR showed improved image quality and enhanced prostate lesion detection.

Impact: The enhancement of prostate lesion detection would be beneficial for clinical examination.

Keywords: Synthetic MR, Cardiovascular, Late Gadolinium Enhancement, Synthetic MR

Motivation: Synthetic late gadolinium enhancement (SynLGE) has been proposed as a technique to quantify cardiac fibrosis from post-contrast T1 mapping. Current SynLGE techniques use site-specific code, limiting clinical adoption and standardization.

Goal(s): Develop a software toolbox for SynLGE image generation using retrospective T1* mapping.

Approach: An open-source software SyntheticLGE.jl was implemented in Julia and is publicly available on GitHub with two sample MOLLI datasets for software evaluation.

Results: SynLGE image generation is feasible for both SSFP and gradient-echo MOLLI imaging. 

Impact: We hope that SyntheticLGE.jl can enable standardized and reproducible synthetic LGE image generation in simple and challenging clinical scenarios.  

Keywords: Synthetic MR, Machine Learning/Artificial Intelligence

Motivation: Deep learning MRI reconstruction methods face challenges in available datasets to train models. Clinical scans can be a source for diverse data but a challenge is obtaining MRI phase.

Goal(s): We propose a method to generate multiple plausible synthetic phase images from a single magnitude-only input.

Approach: We train a multi-modal generative model enforcing consistency in the latent space during training. We evaluate the effect of latent vector dimension on diversity and quality of the synthetic images with FID score and training image reconstruction models with this synthetic data.

Results: Higher latent vector dimension resulted in more diverse and higher quality synthetic images.

Impact: This method could be used to generate multiple plausible phase images from a single scan to model effects of varying field homogeneity, RF coils,  echo time, motion, flow, and susceptibility

Keywords: Synthetic MR, Head & Neck/ENT, Differential diagnosis

Motivation: The presence of metastatic lymph nodes in HNSCC indicates a worse prognosis, biopsy is invasive and has a high incidence of false-negative results. The diagnosis value of synthetic MRI needs to be studied further.

Goal(s): To explore the value of the features derived from synthetic MRI in distinguishing between benign and malignant lymph nodes.

Approach: This study included lymph nodes of HNSCC which were confirmed by pathology, and utilized multiple methods to select meaningful features.

Results: Features derived from synthetic MRI have satisfactory discrimination efficiency.

Impact: Synthetic MRI may be able to be used as a method to help assemnet the lymph nodes in clinical practice.

Keywords: Synthetic MR, Low-Field MRI

Motivation: The necessity to enhance the quality of portable low-field MRI images, which are crucial for wider accessibility but lack high-resolution, drives this research.

Goal(s): This study aims to determine whether Synthetic 3T technology can elevate low-field image quality to that of high-field 3T standards, making it diagnostically adequate.

Approach: We employed a generative network to transform low-field images to higher quality, maintaining pixel-level accuracy and structural integrity. The study involved a paired dataset from 37-healthy subjects and an evaluation on 20-images by two neuroradiologists.

Results: Synthetic 3T demonstrated improved clarity, structure, and contrast, aligning closer to the 3T-quality than the original low-field images.

Impact: This investigation highlights the potential of Synthetic 3T to bridge the gap in portable MRI imaging, enabling broader clinical utility. Further research could pivot on its application in pathological cases, with an aim to enhance diagnostic capabilities in resource-limited settings.

Keywords: Quantitative Imaging, Quantitative Imaging, Major depressive disorder; synthetic MRI; myelin

Motivation: Major depressive disorder is a common mental illness with biological processes, such as myelin alterations, that are not yet fully understood. 

Goal(s): This study aims to explore myelin abnormalities in MDD patients utilizing Synthetic MRI (SyMRI) technique. 

Approach: Myelin content measured by SyMRI was compared between 32 MDD patients and 51 healthy controls, and the study further investigated the relationship between myelin alterations and the depressive symptoms. 

Results: Several white matter regions exhibited a significant decrease in myelin content in individuals with MDD, and a correlation was found between the depressive symptoms and these myelin deficits.

Impact: The present study highlights the potential of Synthetic MRI technique as a valuable tool for measuring myelin content, which could play a crucial role in understanding the underlying neurobiological mechanisms of Major Depressive Disorder.

Keywords: Hematology, Oncology, Quantitative Imaging, thalassemia; hypogonadotropic hypogonadism; synthetic MRI

Motivation: Pituitary T1 values and pituitary height have potential as predictive markers for hypogonadotropic hypogonadism (HH) in thalassemia major (TM) patients, yet their assessment typically needs multiple MRI scans.

Goal(s): Assess the diagnostic potential of Synthetic MRI in detection of HH, with the advantage of obtaining the required MR measurements through a single scan.

Approach: Pituitary T1, T2 and pituitary height were measured in 112 TM patients using SyMRI technique. 

Results: The AUC values for diagnosing HH with pituitary T1 values and pituitary height were 0.736 and 0.753, respectively, and the AUC value of combining these two measurements was 0.813.

Impact: Synthetic MRI technology facilitates the diagnosis of hypogonadotropic hypogonadism in TM patients, and the combination of pituitary T1 values and pituitary height yields high diagnostic accuracy for hypogonadotropic hypogonadism in TM patient.

Keywords: Myocardium, Myocardium, SASHA

Motivation: SASHA T₁ has high accuracy but low precision due to the low SNR of T₁-weighted images. Convolutional neural network has the potential to improve SASHA T₁ precision by using spatio-temporal correlations.

Goal(s): The aim of this study is to develope a convolutional neural network for improving SASHA T₁ precision.

Approach: We implemented a convolutional neural network (DeepDenoiseNet) and trained it using synthesized SASHA images from co-registered high-quality T₁, T₂, and M₀ images. Different-level noise was added to simulate low SNR SASHA images. 

Results:  DeepDenoiseNet could reduce the impaction from noise and improve SASHA T₁ precision.

Impact: The deep convolutional neural network trained with synthesized images and simulated noise could improve SASHA T₁ precision.

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, Image Synthesis, Inpainting-based

Motivation: The existing deep learning-based MRI sequence synthesis methods are prone to obscure or simulate pathology.

Goal(s): To develop an inpainting-based image synthesis network (IBSNet) for MRI sequence high-fidelity synthesis.

Approach: We designed a dual-branch structure to focus on global and local information extraction. Moreover, a joint loss is proposed to constrain the network from signal intensity, structural similarity, and edge preservation. An attention module is used to refine the intermediate feature maps from both the channel and spatial dimensions.

Results: The results show that our method outperforms other methods based on the encoder-decoder network, generative adversarial network (GAN), and diffusion model.

Impact: Our proposed inpainting-based image synthesis network can generate the target sequence from existing sequences, which can reduce the scanning time of MRIs and improve the patient experience.