Low-rank reconstruction for simultaneous double half-echo 23Na and undersampled 23Na multi-quantum coherences MRI.

PURPOSE: To develop a new sequence to simultaneously acquire Cartesian sodium (23Na) MRI and accelerated Cartesian single (SQ) and triple quantum (TQ) sodium MRI of in vivo human brain at 7 T by leveraging two dedicated low-rank reconstruction frameworks. THEORY AND METHODS: The Double Half-Echo technique enables short echo time Cartesian 23Na MRI and acquires two k-space halves, reconstructed by a low-rank coupling constraint. Additionally, three-dimensional (3D) 23Na Multi-Quantum Coherences (MQC) MRI requires multi-echo sampling paired with phase-cycling, exhibiting a redundant multidimensional space. Simultaneous Autocalibrating and k-Space Estimation (SAKE) were used to reconstruct highly undersampled 23Na MQC MRI. Reconstruction performance was assessed against five-dimensional (5D) CS, evaluating structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR), and quantification of tissue sodium concentration and TQ/SQ ratio in silico, in vitro, and in vivo. RESULTS: The proposed sequence enabled the simultaneous acquisition of fully sampled 23Na MRI while leveraging prospective undersampling for 23Na MQC MRI. SAKE improved TQ image reconstruction regarding SSIM by 6% and reduced RMSE by 35% compared to 5D CS in vivo. Thanks to prospective undersampling, the spatial resolution of 23Na MQC MRI was enhanced from 8 × 8 × 15 $$ 8\times 8\times 15 $$ mm3 to 8 × 8 × 8 $$ 8\times 8\times 8 $$ mm3 while reducing acquisition time from 2 × 31 $$ 2\times 31 $$ min to 2 × 23 $$ 2\times 23 $$ min. CONCLUSION: The proposed sequence, coupled with low-rank reconstructions, provides an efficient framework for comprehensive whole-brain sodium MRI, combining TSC, T2*, and TQ/SQ ratio estimations. Additionally, low-rank matrix completion enables the reconstruction of highly undersampled 23Na MQC MRI, allowing for accelerated acquisition or enhanced spatial resolution.

Sodium Triple Quantum MR Signal Extraction Using a Single-Pulse Sequence with Single Quantum Time Efficiency.

PURPOSE: Sodium triple quantum (TQ) signal has been shown to be a valuable biomarker for cell viability. Despite its clinical potential, application of Sodium TQ signal is hindered by complex pulse sequences with long scan times. This study proposes a method to approximate the TQ signal using a single excitation pulse without phase cycling. METHODS: The proposed method is based on a single excitation pulse and a comparison of the free induction decay (FID) with the integral of the FID combined with a shifting reconstruction window. The TQ signal is calculated from this FID only. As a proof of concept, the method was also combined with a multi-echo UTE imaging sequence on a 9.4 T preclinical MRI scanner for the possibility of fast TQ MRI. RESULTS: The extracted Sodium TQ signals of single-pulse and spin echo FIDs were in close agreement with theory and TQ measurement by traditional three-pulse sequence (TQ time proportional phase increment [TQTPPI)]. For 2%, 4%, and 6% agar samples, the absolute deviations of the maximum TQ signals between SE and theoretical (time proportional phase increment TQTPPI) TQ signals were less than 1.2% (2.4%), and relative deviations were less than 4.6% (6.8%). The impact of multi-compartment systems and noise on the accuracy of the TQ signal was small for simulated data. The systematic error was <3.4% for a single quantum (SQ) SNR of 5 and at maximum <2.5% for a multi-compartment system. The method also showed the potential of fast in vivo SQ and TQ imaging. CONCLUSION: Simultaneous SQ and TQ MRI using only a single-pulse sequence and SQ time efficiency has been demonstrated. This may leverage the full potential of the Sodium TQ signal in clinical applications.

Myotubularin-related-protein-7 inhibits mutant (G12V) K-RAS by direct interaction.

Inhibition of K-RAS effectors like B-RAF or MEK1/2 is accompanied by treatment resistance in cancer patients via re-activation of PI3K and Wnt signaling. We hypothesized that myotubularin-related-protein-7 (MTMR7), which inhibits PI3K and ERK1/2 signaling downstream of RAS, directly targets RAS and thereby prevents resistance. Using cell and structural biology combined with animal studies, we show that MTMR7 binds and inhibits RAS at cellular membranes. Overexpression of MTMR7 reduced RAS GTPase activities and protein levels, ERK1/2 phosphorylation, c-FOS transcription and cancer cell proliferation in vitro. We located the RAS-inhibitory activity of MTMR7 to its charged coiled coil (CC) region and demonstrate direct interaction with the gastrointestinal cancer-relevant K-RASG12V mutant, favouring its GDP-bound state. In mouse models of gastric and intestinal cancer, a cell-permeable MTMR7-CC mimicry peptide decreased tumour growth, Ki67 proliferation index and ERK1/2 nuclear positivity. Thus, MTMR7 mimicry peptide(s) could provide a novel strategy for targeting mutant K-RAS in cancers.

A generalizable deep voxel-guided morphometry algorithm for the detection of subtle lesion dynamics in multiple sclerosis.

Introduction: Multiple sclerosis (MS) is a chronic neurological disorder characterized by the progressive loss of myelin and axonal structures in the central nervous system. Accurate detection and monitoring of MS-related changes in brain structures are crucial for disease management and treatment evaluation. We propose a deep learning algorithm for creating Voxel-Guided Morphometry (VGM) maps from longitudinal MRI brain volumes for analyzing MS disease activity. Our approach focuses on developing a generalizable model that can effectively be applied to unseen datasets. Methods: Longitudinal MS patient high-resolution 3D T1-weighted follow-up imaging from three different MRI systems were analyzed. We employed a 3D residual U-Net architecture with attention mechanisms. The U-Net serves as the backbone, enabling spatial feature extraction from MRI volumes. Attention mechanisms are integrated to enhance the model's ability to capture relevant information and highlight salient regions. Furthermore, we incorporate image normalization by histogram matching and resampling techniques to improve the networks' ability to generalize to unseen datasets from different MRI systems across imaging centers. This ensures robust performance across diverse data sources. Results: Numerous experiments were conducted using a dataset of 71 longitudinal MRI brain volumes of MS patients. Our approach demonstrated a significant improvement of 4.3% in mean absolute error (MAE) against the state-of-the-art (SOTA) method. Furthermore, the algorithm's generalizability was evaluated on two unseen datasets (n = 116) with an average improvement of 4.2% in MAE over the SOTA approach. Discussion: Results confirm that the proposed approach is fast and robust and has the potential for broader clinical applicability.

Comparison of triple quantum (TQ) TPPI and inversion recovery TQ TPPI pulse sequences at 9.4 and 21.1 T.

PURPOSE: Both sodium T1 triple quantum (TQ) signal and T1 relaxation pathways have a unique sensitivity to the sodium molecular environment. In this study an inversion recovery time proportional phase increment (IRTQTPPI) pulse sequence was investigated for simultaneous and reliable quantification of sodium TQ signal and bi-exponential T1 relaxation times. METHODS: The IRTQTPPI sequence combines inversion recovery TQ filtering and time proportional phase increment. The reliable and reproducible results were achieved by the pulse sequence optimized in three ways: (1) optimization of the nonlinear fit for the determination of both T1-TQ signal and T1 relaxation times; (2) suppression of unwanted signals by assessment of four different phase cycles; (3) nonlinear sampling during evolution time for optimal scan time without any compromises in fit accuracy. The relaxation times T1 and T2 and the TQ signals from IRTQTPPI and TQTPPI were compared between 9.4 and 21.1 T. The motional environment of the sodium nuclei was evaluated by calculation of correlation times and nuclear quadrupole interaction strengths. RESULTS: Reliable measurements of the T1-TQ signals and T1 bi-exponential relaxation times were demonstrated. The fit parameters for all four phase cycles were in good agreement with one another, with a negligible influence of unwanted signals. The agar samples yielded normalized T1-TQ signals from 3% to 16% relative to single quantum (SQ) signals at magnetic fields of both 9.4 and 21.1 T. In comparison, the normalized T2-TQ signal was in the range 15%-35%. The TQ/SQ signal ratio was decreased at 21.1 T as compared with 9.4 T for both T1 and T2 relaxation pathways. The bi-exponential T1 relaxation time separation ranged from 15 to 18 ms at 9.4 T and 15 to 21 ms at 21.1 T. The T2 relaxation time separation was larger, ranging from 28 to 35 ms at 9.4 T and 37 to 40 ms at 21.1 T. CONCLUSION: The IRTQTPPI sequence, while providing a less intensive TQ signal than TQTPPI, allows a simultaneous and reliable quantification of both the T1-TQ signal and T1 relaxation times. The unique sensitivities of the T1 and T2 relaxation pathways to different types of molecular motion provide a deeper understanding of the sodium MR environment.

Feasibility of undersampled spiral trajectories in MREPT for fast conductivity imaging.

PURPOSE: To investigate spiral-based imaging including trajectories with undersampling as a fast and robust alternative for phase-based magnetic resonance electrical properties tomography (MREPT) techniques. METHODS: Spiral trajectories with various undersampling ratios were prescribed to acquire images from an experimental phantom and a healthy volunteer at 3T. The non-Cartesian acquisitions were reconstructed using SPIRiT, and conductivity maps were derived using phase-based cr-MREPT. The resulting maps were compared between different sampling trajectories. Additionally, a conductivity map was obtained using a Cartesian balanced SSFP acquisition from the volunteer to comparatively demonstrate the robustness of the proposed method. RESULTS: The phantom and volunteer results illustrate the benefits of the spiral acquisitions. Specifically, undersampled spiral acquisitions display improved robustness against field inhomogeneity artifacts and lowered SD values with shortened readout times. Furthermore, average of conductivity values measured for the cerebrospinal fluid with the spiral acquisitions were 1.703 S/m, indicating a close agreement with the theoretical values of 1.794 S/m. CONCLUSION: A spiral-based acquisition framework for conductivity imaging with and without undersampling is presented. Overall, spiral-based acquisitions improved robustness against field inhomogeneity artifacts, while achieving whole head coverage with multiple averages in less than a minute.

Functional lung imaging of 2-year-old children after congenital diaphragmatic hernia repair using dynamic mode decomposition MRI.

OBJECTIVES: To investigate the feasibility of non-contrast-enhanced functional lung imaging in 2-year-old children after congenital diaphragmatic hernia (CDH) repair. METHODS: Fifteen patients after CDH repair were examined using non-contrast-enhanced dynamic magnetic resonance imaging (MRI). For imaging two protocols were used during free-breathing: Protocol A with high temporal resolution and Protocol B with high spatial resolution. The dynamic images were then analysed through a recently developed post-processing method called dynamic mode decomposition (DMD) to obtain ventilation and perfusion maps. The ventilation ratios (VRatio) and perfusion ratios (QRatio) of ipsilateral to contralateral lung were compared to evaluate functional differences. Lastly, DMD MRI-based perfusion results were compared with perfusion parameters obtained using dynamic contrast-enhanced (DCE) MRI to assess agreement between methods. RESULTS: Both imaging protocols successfully generated pulmonary ventilation (V) and perfusion (Q) maps in all patients. Overall, the VRatio and QRatio values were 0.84 ± 0.19 and 0.70 ± 0.24 for Protocol A, and 0.88 ± 0.18 and 0.72 ± 0.23 for Protocol B, indicating reduced ventilation ([Formula: see text]) and perfusion ([Formula: see text]) on the ipsilateral side. Moreover, there is a very strong positive correlation ([Formula: see text]) and close agreement between DMD MRI-based perfusion values and DCE MRI-based perfusion parameters. CONCLUSIONS: DMD MRI can obtain pulmonary functional information in 2-year-old CDH patients. The results obtained with DMD MRI correlate with DCE MRI, without the need for ionising radiation or exposure to contrast agents. While further studies with larger cohorts are warranted, DMD MRI is a promising option for functional lung imaging in CDH patients. CLINICAL RELEVANCE STATEMENT: We demonstrate that pulmonary ventilation and perfusion information can be obtained in 2-year-old patients after CDH repair, without the need for ionising radiation or contrast agents by utilising non-contrast-enhanced MRI acquisitions together with dynamic mode decomposition analysis. KEY POINTS: • Non-contrast-enhanced functional MR imaging is a promising option for functional lung imaging in 2-year-old children after congenital diaphragmatic hernia. • DMD MRI can generate pulmonary ventilation and perfusion maps from free-breathing dynamic acquisitions without the need for ionising radiation or contrast agents. • Lung perfusion parameters obtained with DMD MRI correlate with perfusion parameters obtained using dynamic contrast-enhanced MRI.

VirtMRI: A Tool for Teaching MRI.

Magnetic resonance image formation is not trivial and remains a difficult subject for teaching. Therefore, we saw an urgent need to facilitate teaching by developing a practical and easily accessible MR image generator. Due to the increasing interest in X-nuclei MRI, sodium image generation is also offered. The tool is implemented as a web application that is compatible with all standard desktop browsers and is open source. The user interface focuses on the parameters needed for the creation and display of the resulting images. Available MR sequences range from the standard Spin Echo and Inversion Recovery over steady-state to conventional sodium and more advanced single and triple quantum sequences. Additionally, the user interface has parameters to alter the resolution, the noise, and the k-space sampling. Our software is free to use and specifically suited for teaching purposes.

Automated prognosis of renal function decline in ADPKD patients using deep learning.

An accurate prognosis of renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD) is crucial for early intervention. Current biomarkers used are height-adjusted total kidney volume (HtTKV), estimated glomerular filtration rate (eGFR), and patient age. However, manually measuring kidney volume is time-consuming and subject to observer variability. Additionally, incorporating automatically generated features from kidney MRI images, along with conventional biomarkers, can enhance prognostic improvement. To address these issues, we developed two deep-learning algorithms. Firstly, an automated kidney volume segmentation model accurately calculates HtTKV. Secondly, we utilize segmented kidney volumes, predicted HtTKV, age, and baseline eGFR to predict chronic kidney disease (CKD) stages >=3A, >=3B, and a 30% decline in eGFR after 8 years from the baseline visit. Our approach combines a convolutional neural network (CNN) and a multi-layer perceptron (MLP). Our study included 135 subjects and the AUC scores obtained were 0.96, 0.96, and 0.95 for CKD stages >=3A, >=3B, and a 30% decline in eGFR, respectively. Furthermore, our algorithm achieved a Pearson correlation coefficient of 0.81 between predicted and measured eGFR decline. We extended our approach to predict distinct CKD stages after eight years with an AUC of 0.97. The proposed approach has the potential to enhance monitoring and facilitate prognosis in ADPKD patients, even in the early disease stages.

Deep learning-based affine medical image registration for multimodal minimal-invasive image-guided interventions - A comparative study on generalizability.

Multimodal image registration is applied in medical image analysis as it allows the integration of complementary data from multiple imaging modalities. In recent years, various neural network-based approaches for medical image registration have been presented in papers, but due to the use of different datasets, a fair comparison is not possible. In this research 20 different neural networks for an affine registration of medical images were implemented. The networks' performance and the networks' generalizability to new datasets were evaluated using two multimodal datasets - a synthetic and a real patient dataset - of three-dimensional CT and MR images of the liver. The networks were first trained semi-supervised using the synthetic dataset and then evaluated on the synthetic dataset and the unseen patient dataset. Afterwards, the networks were finetuned on the patient dataset and subsequently evaluated on the patient dataset. The networks were compared using our own developed CNN as benchmark and a conventional affine registration with SimpleElastix as baseline. Six networks improved the pre-registration Dice coefficient of the synthetic dataset significantly (p-value < 0.05) and nine networks improved the pre-registration Dice coefficient of the patient dataset significantly and are therefore able to generalize to the new datasets used in our experiments. Many different machine learning-based methods have been proposed for affine multimodal medical image registration, but few are generalizable to new data and applications. It is therefore necessary to conduct further research in order to develop medical image registration techniques that can be applied more widely.

Dynamic mode decomposition of dynamic MRI for assessment of pulmonary ventilation and perfusion.

PURPOSE: To introduce dynamic mode decomposition (DMD) as a robust alternative for the assessment of pulmonary functional information from dynamic non-contrast-enhanced acquisitions. METHODS: Pulmonary fractional ventilation and normalized perfusion maps were obtained using DMD from simulated phantoms as well as in vivo dynamic acquisitions of healthy volunteers at 1.5T. The performance of DMD was compared with conventional Fourier decomposition (FD) and matrix pencil (MP) methods in estimating functional map values. The proposed method was evaluated based on estimated signal amplitude in functional maps across varying number of measurements. RESULTS: Quantitative assessments performed on phantoms and in vivo measurements indicate that DMD is capable of successfully obtaining pulmonary functional maps. Specifically, compared to FD and MP methods, DMD is able to reduce variations in estimated amplitudes across different number of measurements. This improvement is evident in the fractional ventilation and normalized perfusion maps obtain from phantom simulations with frequency variations and noise, as well as in the maps obtained from in vivo measurements. CONCLUSIONS: A robust method for accurately estimating pulmonary ventilation and perfusion related signal changes in dynamic acquisitions is presented. The proposed method uses DMD to obtain functional maps reliably, while reducing amplitude variations caused by differences in number of measurements.

Long-term evaluation of uterine fibroid embolisation using MRI perfusion parameters and patient questionnaires: preliminary results.

BACKGROUND: Uterine fibroid embolisation (UFE) is an established treatment method for symptomatic uterine myomas. This study evaluates the efficacy of UFE using objective magnetic resonance imaging (MRI) data for size and perfusion analysis as well as patient questionnaires assessing fibroid-related symptoms. METHOD: Patients underwent MR-Angiography before UFE and 4 days, 6 and 12 months after the procedure. The images were evaluated using dedicated software. Patient questionnaires were completed before UFE and at 12 months follow-up, focussing on the embolization procedure and symptoms associated with uterine fibroids. Statistical analysis of the questionnaires was performed using paired sample t-test and Wilcoxon signed rank test, while Kruskal-Wallis test and Friedman test were applied for MRI-analysis. RESULTS: Eleven women were included. There was a significant reduction in fibroid-related symptoms. The volume reduction after 12 months was significant in both, uterus and myomas, after an initial increase in uterine volume at the first post-interventional MRI. The perfusion analysis showed that blood flow to the fibroids could be significantly reduced up to 12 months after UFE while uterine tissue was not affected. CONCLUSION: This study shows that uterine fibroid embolisation induces a significant long-term decrease in myoma size and perfusion while healthy uterine tissue remains unaffected. Fibroid-related symptoms are reduced for the sake of improved quality of life.

Synthetic Arterial Spin Labeling MRI of the Kidneys for Evaluation of Data Processing Pipeline.

Accurate quantification of perfusion is crucial for diagnosis and monitoring of kidney function. Arterial spin labeling (ASL), a completely non-invasive magnetic resonance imaging technique, is a promising method for this application. However, differences in acquisition (e.g., ASL parameters, readout) and processing (e.g., registration, segmentation) between studies impede the comparison of results. To alleviate challenges arising solely from differences in processing pipelines, synthetic data are of great value. In this work, synthetic renal ASL data were generated using body models from the XCAT phantom and perfusion was added using the general kinetic model. Our in-house developed processing pipeline was then evaluated in terms of registration, quantification, and segmentation using the synthetic data. Registration performance was evaluated qualitatively with line profiles and quantitatively with mean structural similarity index measures (MSSIMs). Perfusion values obtained from the pipeline were compared to the values assumed when generating the synthetic data. Segmentation masks obtained by semi-automated procedure of the processing pipeline were compared to the original XCAT organ masks using the Dice index. Overall, the pipeline evaluation yielded good results. After registration, line profiles were smoother and, on average, MSSIMs increased by 25%. Mean perfusion values for cortex and medulla were close to the assumed perfusion of 250 mL/100 g/min and 50 mL/100 g/min, respectively. Dice indices ranged 0.80-0.93, 0.78-0.89, and 0.64-0.84 for whole kidney, cortex, and medulla, respectively. The generation of synthetic ASL data allows flexible choice of parameters and the generated data are well suited for evaluation of processing pipelines.

Are We There Yet? The Value of Deep Learning in a Multicenter Setting for Response Prediction of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy.

This retrospective study aims to evaluate the generalizability of a promising state-of-the-art multitask deep learning (DL) model for predicting the response of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (nCRT) using a multicenter dataset. To this end, we retrained and validated a Siamese network with two U-Nets joined at multiple layers using pre- and post-therapeutic T2-weighted (T2w), diffusion-weighted (DW) images and apparent diffusion coefficient (ADC) maps of 83 LARC patients acquired under study conditions at four different medical centers. To assess the predictive performance of the model, the trained network was then applied to an external clinical routine dataset of 46 LARC patients imaged without study conditions. The training and test datasets differed significantly in terms of their composition, e.g., T-/N-staging, the time interval between initial staging/nCRT/re-staging and surgery, as well as with respect to acquisition parameters, such as resolution, echo/repetition time, flip angle and field strength. We found that even after dedicated data pre-processing, the predictive performance dropped significantly in this multicenter setting compared to a previously published single- or two-center setting. Testing the network on the external clinical routine dataset yielded an area under the receiver operating characteristic curve of 0.54 (95% confidence interval [CI]: 0.41, 0.65), when using only pre- and post-therapeutic T2w images as input, and 0.60 (95% CI: 0.48, 0.71), when using the combination of pre- and post-therapeutic T2w, DW images, and ADC maps as input. Our study highlights the importance of data quality and harmonization in clinical trials using machine learning. Only in a joint, cross-center effort, involving a multidisciplinary team can we generate large enough curated and annotated datasets and develop the necessary pre-processing pipelines for data harmonization to successfully apply DL models clinically.

Fast CBCT Reconstruction using Convolutional Neural Networks for Arbitrary Robotic C-arm Orbits.

Cone-beam CT (CBCT) with non-circular acquisition orbits has the potential to improve image quality, increase the field-of view, and facilitate minimal interference within an interventional imaging setting. Because time is of the essence in interventional imaging scenarios, rapid reconstruction methods are advantageous. Model-Based Iterative Reconstruction (MBIR) techniques implicitly handle arbitrary geometries; however, the computational burden for these approaches is particularly high. The aim of this work is to extend a previously proposed framework for fast reconstruction of non-circular CBCT trajectories. The pipeline combines a deconvolution operation on the backprojected measurements using an approximate, shift-invariant system response prior to processing with a Convolutional Neural Network (CNN). We trained and evaluated the CNN for this approach using 1800 randomized arbitrary orbits. Noisy projection data were formed from 1000 procedurally generated tetrahedral phantoms as well as anthropomorphic data in the form of 800 CT and CBCT images from the Lung Image Database Consortium Image Collection (LIDC). Using this proposed reconstruction pipeline, computation time was reduced by 90% as compared to MBIR with only minor differences in performance. Quantitative comparisons of nRMSE, FSIM and SSIM are reported. Performance was consistent for projection data simulated with acquisition orbits the network has not previously been trained on. These results suggest the potential for fast processing of arbitrary CBCT trajectory data with reconstruction times that are clinically relevant and applicable - facilitating the application of non-circular orbits in CT image-guided interventions and intraoperative imaging.

Phase-cycled balanced SSFP imaging for non-contrast-enhanced functional lung imaging.

PURPOSE: To introduce phase-cycled balanced SSFP (bSSFP) acquisition as an alternative in Fourier decomposition MRI for improved robustness against field inhomogeneities. METHODS: Series 2D dynamic lung images were acquired in 5 healthy volunteers at 1.5 T and 3 T using bSSFP sequence with multiple RF phase increments and compared with conventional single RF phase increment acquisitions. The approach was evaluated based on functional map homogeneity analysis, while ensuring image and functional map quality by means of SNR and contrast-to-noise ratio analyses. RESULTS: At both field strengths, functional maps obtained with phase-cycled acquisitions displayed improved robustness against local signal losses compared with single-phase acquisitions. The coefficient of variation (mean ± SD, across volunteers) measured in the ventilation maps resulted in 29.7 ± 2.6 at 1.5 T and 37.5 ± 3.1 at 3 T for phase-cycled acquisitions, compared with 39.9 ± 5.2 at 1.5 T and 49.5 ± 3.7 at 3 T for single-phase acquisitions, indicating a significant improvement ( p<0.05$$ p<0.05 $$ ) in ventilation map homogeneity. CONCLUSIONS: Phase-cycled bSSFP acquisitions improve robustness against field inhomogeneity artifacts and significantly improve ventilation map homogeneity at both field strengths. As such, phase-cycled bSSFP may serve as a robust alternative in lung function assessments.