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1. Evaluating the relationship between magnetic resonance image quality metrics and deep learning‐based segmentation accuracy of brain tumors

   https://doi.org/10.1002/mp.17059  

   Muthusivarajan, Rajarajeswari ; Celaya, Adrian ; Yung, Joshua P. ; Long, James P. ; Viswanath, Satish E. ; Marcus, Daniel S. ; Chung, Caroline ; Fuentes, David ( April 2024 , Medical Physics)

   Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment‐based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal‐to‐noise, contrast‐to‐noise) and segmentation accuracy.

   Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL‐based brain tumor segmentation accuracy toward developing more generalizable models for multi‐institutional data.

   We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non‐ET on MRI; with performance quantified via a 5‐fold cross‐validated Dice coefficient. MRI scans were evaluated through the open‐source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as “better” quality (BQ) or “worse” quality (WQ), via relative thresholding. Segmentation performance was re‐evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts.

   For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal‐to‐noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models.

   Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet‐based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.

    

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2. Body diffusion‐weighted imaging using magnetization prepared single‐shot fast spin echo and extended parallel imaging signal averaging

   https://doi.org/10.1002/mrm.26971  

   Gibbons, Eric K. ; Vasanawala, Shreyas S. ; Pauly, John M. ; Kerr, Adam B. ( October 2017 , Magnetic Resonance in Medicine)

   This work demonstrates a magnetization prepared diffusion‐weighted single‐shot fast spin echo (SS‐FSE) pulse sequence for the application of body imaging to improve robustness to geometric distortion. This work also proposes a scan averaging technique that is superior to magnitude averaging and is not subject to artifacts due to object phase.

   This single‐shot sequence is robust against violation of the Carr‐Purcell‐Meiboom‐Gill (CPMG) condition. This is achieved by dephasing the signal after diffusion weighting and tipping the MG component of the signal onto the longitudinal axis while the non‐MG component is spoiled. The MG signal component is then excited and captured using a traditional SS‐FSE sequence, although the echo needs to be recalled prior to each echo. Extended Parallel Imaging (ExtPI) averaging is used where coil sensitivities from the multiple acquisitions are concatenated into one large parallel imaging (PI) problem. The size of the PI problem is reduced by SVD‐based coil compression which also provides background noise suppression. This sequence and reconstruction are evaluated in simulation, phantom scans, and in vivo abdominal clinical cases.

   Simulations show that the sequence generates a stable signal throughout the echo train which leads to good image quality. This sequence is inherently low‐SNR, but much of the SNR can be regained through scan averaging and the proposed ExtPI reconstruction. In vivo results show that the proposed method is able to provide diffusion encoded images while mitigating geometric distortion artifacts compared to EPI.

   This work presents a diffusion‐prepared SS‐FSE sequence that is robust against the violation of the CPMG condition while providing diffusion contrast in clinical cases. Magn Reson Med 79:3032–3044, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

    

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3. Whole‐heart coronary MR angiography using a 3D cones phyllotaxis trajectory

   https://doi.org/10.1002/mrm.27475  

   Malavé, Mario O. ; Baron, Corey A. ; Addy, Nii Okai ; Cheng, Joseph Y. ; Yang, Phillip C. ; Hu, Bob S. ; Nishimura, Dwight G. ( October 2018 , Magnetic Resonance in Medicine)

   To develop a 3D cones steady‐state free precession sequence with improved robustness to respiratory motion while mitigating eddy current artifacts for free‐breathing whole‐heart coronary magnetic resonance angiography.

   The proposed sequence collects cone interleaves using a phyllotaxis pattern, which allows for more distributed k‐space sampling for each heartbeat compared to a typical sequential collection pattern. A Fibonacci number of segments is chosen to minimize eddy current effects with the trade‐off of an increased number of acquisition heartbeats. For verification, phyllotaxis‐cones is compared to sequential‐cones through simulations, phantom studies, andin vivocoronary scans with 8 subjects using 2D image‐based navigators for retrospective motion correction.

   Simulated point spread functions and moving phantom results show less coherent motion artifacts for phyllotaxis‐cones compared to sequential‐cones. Assessment of the right and left coronary arteries using reader scores and the image edge profile acutance vessel sharpness metric indicate superior image quality and sharpness for phyllotaxis‐cones.

   Phyllotaxis 3D cones results in improved qualitative image scores and coronary vessel sharpness for free‐breathing whole‐heart coronary magnetic resonance angiography compared to standard sequential ordering when using a steady‐state free precession sequence.

    

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4. Deblurring for spiral real‐time MRI using convolutional neural networks

   https://doi.org/10.1002/mrm.28393  

   Lim, Yongwan ; Bliesener, Yannick ; Narayanan, Shrikanth ; Nayak, Krishna S. ( July 2020 , Magnetic Resonance in Medicine)

   To develop and evaluate a fast and effective method for deblurring spiral real‐time MRI (RT‐MRI) using convolutional neural networks.

   We demonstrate a 3‐layer residual convolutional neural networks to correct image domain off‐resonance artifacts in speech production spiral RT‐MRI without the knowledge of field maps. The architecture is motivated by the traditional deblurring approaches. Spatially varying off‐resonance blur is synthetically generated by using discrete object approximation and field maps with data augmentation from a large database of 2D human speech production RT‐MRI. The effect of off‐resonance range, shift‐invariance of blur, and readout durations on deblurring performance are investigated. The proposed method is validated using synthetic and real data with longer readouts, quantitatively using image quality metrics and qualitatively via visual inspection, and with a comparison to conventional deblurring methods.

   Deblurring performance was found superior to a current autocalibrated method for in vivo data and only slightly worse than an ideal reconstruction with perfect knowledge of the field map for synthetic test data. Convolutional neural networks deblurring made it possible to visualize articulator boundaries with readouts up to 8 ms at 1.5 T, which is 3‐fold longer than the current standard practice. The computation time was 12.3 ± 2.2 ms per frame, enabling low‐latency processing for RT‐MRI applications.

   Convolutional neural networks deblurring is a practical, efficient, and field map‐free approach for the deblurring of spiral RT‐MRI. In the context of speech production imaging, this can enable 1.7‐fold improvement in scan efficiency and the use of spiral readouts at higher field strengths such as 3 T.

    

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5. Simultaneous multislice EPI prospective motion correction by real‐time receiver phase correction and coil sensitivity map interpolation

   https://doi.org/10.1002/mrm.29789  

   Li, Bo ; Li, Ningzhi ; Wang, Ze ; Balan, Radu ; Ernst, Thomas ( January 2023 , Magnetic Resonance in Medicine)

   Purpose: To improve the image reconstruction for prospective motion correction (PMC) of simultaneous multislice (SMS) EPI of the brain, an update of receiver phase and resampling of coil sensitivities are proposed and evaluated. Methods: A camera-based system was used to track head motion (3 translations and 3 rotations) and dynamically update the scan position and orientation. We derived the change in receiver phase associated with a shifted field of view (FOV) and applied it in real-time to each k-space line of the EPI readout trains. Second, for the SMS reconstruction, we adapted resampled coil sensitivity profiles reflecting the movement of slices. Single-shot gradient-echo SMS-EPI scans were performed in phantoms and human subjects for validation. Results: Brain SMS-EPI scans in the presence of motion withPMCand no phase correction for scan plane shift showed noticeable artifacts. These artifacts were visually and quantitatively attenuated when corrections were enabled. Correcting misaligned coil sensitivity maps improved the temporal SNR (tSNR) of time series by 24% (p=0.0007) for scans with large movements (up to ∼35mm and 30◦). Correcting the receiver phase improved the tSNR of a scan with minimal head movement by 50% from 50 to 75 for a United Kingdom biobank protocol. Conclusion: Reconstruction-induced motion artifacts in single-shot SMS-EPI scans acquired with PMC can be removed by dynamically adjusting the receiver phase of each line across EPI readout trains and updating coil sensitivity profiles during reconstruction. The method may be a valuable tool for SMS-EPI scans in the presence of subject motion. 

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