A systematic review of (semi-)automatic quality control of T1-weighted MRI scans.

PURPOSE: Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks. METHODS: We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool). RESULTS: A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures. CONCLUSION: Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches.

Motion-robust and blood-suppressed M1-optimized diffusion MR imaging of the liver.

PURPOSE: To develop motion-robust, blood-suppressed diffusion-weighted imaging (DWI) of the liver with optimized diffusion encoding waveforms and evaluate the accuracy and reproducibility of quantitative apparent diffusion coefficient (ADC) measurements. METHODS: A novel approach for the design of diffusion weighting waveforms, termed M1-optimized diffusion imaging (MODI), is proposed. MODI includes an echo time-optimized motion-robust diffusion weighting gradient waveform design, with a small nonzero first-moment motion sensitivity (M1) value to enable blood signal suppression. Experiments were performed in eight healthy volunteers and five patient volunteers. In each case, DW images and ADC maps were compared between acquisitions using standard monopolar waveforms, motion moment-nulled (M1-nulled and M1-M2-nulled) waveforms, and the proposed MODI approach. RESULTS: Healthy volunteer experiments using MODI showed no significant ADC bias in the left lobe relative to the right lobe (p < .05) demonstrating robustness to cardiac motion, and no significant ADC bias with respect to monopolar-based ADC measured in the right lobe (p < .05), demonstrating blood signal suppression. In contrast, monopolar-based ADC showed significant bias in the left lobe relative to the right lobe (p < .01) due to its sensitivity to motion, and both M1-nulled and M1-M2-nulled-based ADC showed significant bias (p < .01) due to the lack of blood suppression. Preliminary patient results also suggest MODI may enable improved visualization and quantitative assessment of lesions throughout the entire liver. CONCLUSIONS: This novel method for diffusion gradient waveform design enables DWI of the liver with high robustness to motion and suppression of blood signals, overcoming the limitations of conventional monopolar waveforms and moment-nulled waveforms, respectively.

Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling.

PURPOSE: To present a novel Optimized Diffusion-weighting Gradient waveform Design (ODGD) method for the design of minimum echo time (TE), bulk motion-compensated, and concomitant gradient (CG)-nulling waveforms for diffusion MRI. METHODS: ODGD motion-compensated waveforms were designed for various moment-nullings Mn (n = 0, 1, 2), for a range of b-values, and spatial resolutions, both without (ODGD-Mn ) and with CG-nulling (ODGD-Mn -CG). Phantom and in-vivo (brain and liver) experiments were conducted with various ODGD waveforms to compare motion robustness, signal-to-noise ratio (SNR), and apparent diffusion coefficient (ADC) maps with state-of-the-art waveforms. RESULTS: ODGD-Mn and ODGD-Mn -CG waveforms reduced the TE of state-of-the-art waveforms. This TE reduction resulted in significantly higher SNR (P < 0.05) in both phantom and in-vivo experiments. ODGD-M1 improved the SNR of BIPOLAR (42.8 ± 5.3 vs. 32.9 ± 3.3) in the brain, and ODGD-M2 the SNR of motion-compensated (MOCO) and Convex Optimized Diffusion Encoding-M2 (CODE-M2 ) (12.3 ± 3.6 vs. 9.7 ± 2.9 and 10.2 ± 3.4, respectively) in the liver. Further, ODGD-M2 also showed excellent motion robustness in the liver. ODGD-Mn -CG waveforms reduced the CG-related dephasing effects of non CG-nulling waveforms in phantom and in-vivo experiments, resulting in accurate ADC maps. CONCLUSIONS: ODGD waveforms enable motion-robust diffusion MRI with reduced TEs, increased SNR, and reduced ADC bias compared to state-of-the-art waveforms in theoretical results, simulations, phantoms and in-vivo experiments.

A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data.

BACKGROUND AND OBJECTIVE: Synthetic magnetic resonance imaging (MRI) is a low cost procedure that serves as a bridge between qualitative and quantitative MRI. However, the proposed methods require very specific sequences or private protocols which have scarcely found integration in clinical scanners. We propose a learning-based approach to compute T1, T2, and PD parametric maps from only a pair of T1- and T2-weighted images customarily acquired in the clinical routine. METHODS: Our approach is based on a convolutional neural network (CNN) trained with synthetic data; specifically, a synthetic dataset with 120 volumes was constructed from the anatomical brain model of the BrainWeb tool and served as the training set. The CNN learns an end-to-end mapping function to transform the input T1- and T2-weighted images to their underlying T1, T2, and PD parametric maps. Then, conventional weighted images unseen by the network are analytically synthesized from the parametric maps. The network can be fine tuned with a small database of actual weighted images and maps for better performance. RESULTS: This approach is able to accurately compute parametric maps from synthetic brain data achieving normalized squared error values predominantly below 1%. It also yields realistic parametric maps from actual MR brain acquisitions with T1, T2, and PD values in the range of the literature and with correlation values above 0.95 compared to the T1 and T2 maps obtained from relaxometry sequences. Further, the synthesized weighted images are visually realistic; the mean square error values are always below 9% and the structural similarity index is usually above 0.90. Network fine tuning with actual maps improves performance, while training exclusively with a small database of actual maps shows a performance degradation. CONCLUSIONS: These results show that our approach is able to provide realistic parametric maps and weighted images out of a CNN that (a) is trained with a synthetic dataset and (b) needs only two inputs, which are in turn obtained from a common full-brain acquisition that takes less than 8 min of scan time. Although a fine tuning with actual maps improves performance, synthetic data is crucial to reach acceptable performance levels. Hence, we show the utility of our approach for both quantitative MRI in clinical viable times and for the synthesis of additional weighted images to those actually acquired.

Determination of optimized set of b-values for Apparent Diffusion Coefficient mapping in liver Diffusion-Weighted MRI.

In this manuscript we derive the Cramér-Rao Lower Bound (CRLB) of the monoexponential diffusion-weighted signal model under a realistic noise assumption, and propose a formulation to obtain optimized sets of b-values that maximize the noise performance of the Apparent Diffusion Coefficient (ADC) maps given a target ADC and a signal-to-noise ratio. Therefore, for various sets of parameters (S0 and ADC), signal-to-noise ratios (SNR) and noise distribution, we computed optimized sets of b-values using CRLB-based analysis in two different ways: (i) through a greedy algorithm where b-values from a pool of candidates were added iteratively to the set, and (ii) through a two b-value search algorithm were all two b-value combinations of the pool of candidates were tested. Further, optimized sets of b-values were computed from synthetic data, phantoms, and in-vivo liver diffusion-weighted imaging (DWI) experiments to validate the CRLB-based analysis. The optimized sets of b-values obtained through the proposed CRLB-based analysis showed good agreement with the optimized sets obtained experimentally from synthetic, phantoms, and in-vivo liver data. The variance of the ADC maps decreased when employing the optimized set of b-values compared to various sets of b-values proposed in the literature for in-vivo liver DWI, although differences of notable magnitude between noise models and optimization strategies were not found. In addition, the higher b-values decreased for lower SNR under the Rician noise distribution. Optimization of the set b-values is critical to maximize the noise performance (i.e., maximize the precision and minimize the variance) of the estimated ADC maps in diffusion-weighted MRI. Hence, the proposed approach may help to optimize and standardize liver diffusion-weighted MRI acquisitions by computing optimized set of b-values for a given set of parameters.

Longitudinal Connectomes as a Candidate Progression Marker for Prodromal Parkinson's Disease.

Parkinson's disease is the second most prevalent neurodegenerative disorder in the Western world. It is estimated that the neuronal loss related to Parkinson's disease precedes the clinical diagnosis by more than 10 years (prodromal phase) which leads to a subtle decline that translates into non-specific clinical signs and symptoms. By leveraging diffusion magnetic resonance imaging brain (MRI) data evaluated longitudinally, at least at two different time points, we have the opportunity of detecting and measuring brain changes early on in the neurodegenerative process, thereby allowing early detection and monitoring that can enable development and testing of disease modifying therapies. In this study, we were able to define a longitudinal degenerative Parkinson's disease progression pattern using diffusion magnetic resonance imaging connectivity information. Such pattern was discovered using a de novo early Parkinson's disease cohort (n = 21), and a cohort of Controls (n = 30). Afterward, it was tested in a cohort at high risk of being in the Parkinson's disease prodromal phase (n = 16). This progression pattern was numerically quantified with a longitudinal brain connectome progression score. This score is generated by an interpretable machine learning (ML) algorithm trained, with cross-validation, on the longitudinal connectivity information of Parkinson's disease and Control groups computed on a nigrostriatal pathway-specific parcellation atlas. Experiments indicated that the longitudinal brain connectome progression score was able to discriminate between the progression of Parkinson's disease and Control groups with an area under the receiver operating curve of 0.89 [confidence interval (CI): 0.81-0.96] and discriminate the progression of the High Risk Prodromal and Control groups with an area under the curve of 0.76 [CI: 0.66-0.92]. In these same subjects, common motor and cognitive clinical scores used in Parkinson's disease research showed little or no discriminative ability when evaluated longitudinally. Results suggest that it is possible to quantify neurodegenerative patterns of progression in the prodromal phase with longitudinal diffusion magnetic resonance imaging connectivity data and use these image-based patterns as progression markers for neurodegeneration.