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

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

Producción CientíficaPurpose: 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 versus 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 versus 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-M2-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.TEC2013-44194-PVA069U1

Experimental study of hybrid-knife endoscopic submucosal dissection (ESD) versus standard ESD in a Western country

BACKGROUND: Endoscopic submucosal dissection (ESD) is an effective but time-consuming treatment for early neoplasia that requires a high level of expertise. OBJECTIVE: The objective of this study was to assess the efficacy and learning curve of gastric ESD with a hybrid knife with high pressure water jet and to compare with standard ESD. MATERIAL AND METHODS: We performed a prospective non survival animal study comparing hybrid-knife and standard gastric ESD. Variables recorded were: Number of en-bloc ESD, number of ESD with all marks included (R0), size of specimens, time and speed of dissection and adverse events. Ten endoscopists performed a total of 50 gastric ESD (30 hybrid-knife and 20 standard). RESULTS: Forty-six (92 %) ESD were en-bloc and 25 (50 %) R0 (hybrid-knife: n = 13, 44 %; standard: n = 16, 80 %; p = 0.04). Hybrid-knife ESD was faster than standard (time: 44.6 +/- 21.4 minutes vs. 68.7 +/- 33.5 minutes; p = 0.009 and velocity: 20.8 +/- 9.2 mm(2)/min vs. 14.3 +/- 9.3 mm(2)/min (p = 0.079). Adverse events were not different. There was no change in speed with any of two techniques (hybrid-knife: From 20.33 +/- 15.68 to 28.18 +/- 20.07 mm(2)/min; p = 0.615 and standard: From 6.4 +/- 0.3 to 19.48 +/- 19.21 mm(2)/min; p = 0.607). The learning curve showed a significant improvement in R0 rate in the hybrid-knife group (from 30 % to 100 %). CONCLUSION: despite the initial performance of hybrid-knife ESD is worse than standard ESD, the learning curve with hybrid knife ESD is short and is associated with a rapid improvement. The introduction of new tools to facilitate ESD should be implemented with caution in order to avoid a negative impact on the results

Endoscopic Submucosal Dissection. Sociedad Española de Endoscopia Digestiva (SEED) clinical guideline

The reason to write this guideline is to familiarize Spanish endoscopists and gastroenterologists not only with the general indications of the procedure and possible complications but also the dedicated tools

Optimized acquisition and estimation techniques in diffusion MRI for quantitative imaging

Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is able to measure intrinsic properties of tissue structure non-invasively. By applying diffusion-weighting, DW-MRI is sensitive to microscopic water displacements, with multiple applications for tissue characterization, diagnosis and treatment monitoring. Nevertheless, the application of these long and powerful diffusion-weighting gradients results in compelling imaging challenges. Consequently, this Thesis focuses on the optimization of the Spin Echo (SE) Diffusion-Weighted Imaging (DWI) sequence to improve image quality and estimation of the diffusion-related parametric maps. As far as image quality is concerned, traditional SE DWI acquisition experiences artefacts from signal dephasing due to bulk motion, Concomitant Gradients (CGs), and Eddy Currents (ECs) which decrease image quality and complicate image interpretation. Additionally, it also suffers from severe signal attenuation due to the long Echo Time (TE) needed to achieve strong diffusion-weightings. Multiple approaches have been proposed to diminish these DWI artefacts, from synchronization, gating and complex DWI sequences such as the Twice Refocused Spin Echo (TRSE) to the application of diffusion-weighting gradients with nth-order motion-nulling and/or EC-nulling. Nevertheless, these techniques generally result in suboptimal acquisitions with long TEs. In this Thesis, we propose a versatile formulation for the design of optimized diffusion-weighting gradient waveforms that alleviates the previous drawbacks while minimizing the TE of the acquisition. The estimation of the diffusion-related parametric maps is usually affected by several confounding factors such as low accuracy and precision and lack of repeatability and reproducibility, partially caused by the previous artefacts. These confounding factors appear in both the monoexponential and the Intravoxel Incoherent Motion (IVIM) Diffusion-Weighted (DW) signal models, and hinder the establishment of their diffusion-related parametric maps as quantitative imaging biomarkers. Accuracy of the estimates, particularly of the Apparent Diffusion Coefficient (ADC) of the monoexponential DWsignal model, can be increased by using the appropriate estimator. However, the set of diffusion-weightings (i.e., set of b-values) that increases the precision of the estimated parametric maps remains unclear. In this Thesis, we derive the Cramér-Rao Lower Bound (CRLB) of both DW signal models under the assumption of DW to be affected by Rician distributed noise, and propose a formulation for the optimization of the set of b-values that maximizes the noise performance (i.e., minimizes the variance and maximizes the precision) of the estimated diffusion-related parametric maps.La resonancia magnética de difusión (DW-MRI, es sus siglas en inglés) es una modalidad de imagen médica capaz de medir las propiedades intrínsecas de la estructura de los tejidos de forma no invasiva. A través de una ponderación en difusión, la DW-MRI es sensible a los desplazamientos microscópicos de agua, lo que la dota de múltiples aplicaciones tanto para la caracterización de los tejidos como para el diagnóstico y el seguimiento de tratamientos. No obstante, la aplicación de los gradientes necesarios para realizar la ponderación de la difusión (muy potentes y duraderos) da lugar a problemas en las imágenes adquiridas. Por consiguiente, esta tesis se centra en la optimización de las secuencias de DW-MRI basadas en adquisiciones spin-echo (SE) para mejorar la calidad de las imágenes de difusión y la estimación de los mapas paramétricos relacionados con la difusión. En lo que respecta a la calidad de la imagen, la secuencia tradicional de DW-MRI basada en SE experimenta considerables artefactos de desfase de la señal debido al movimiento, a los gradientes concomitantes y a las corrientes de Foucault, lo que disminuye la calidad de las imágenes adquiridas y complica su interpretación. Además, las imágenes también sufren de una severa atenuación de la señal debido al largo tiempo necesario para lograr las fuertes ponderaciones de difusión, es decir, necesita de largos tiempos de eco (TE). Se han propuesto múltiples enfoques para disminuir los anteriores artefactos de la DW-MRI. Por ejemplo, se han utilizado desde técnicas de sincronización, y secuencias complejas de DW-MRI como la secuencia SE con doble reenfoque, hasta la aplicación de gradientes de ponderación de la difusión con anulación de movimiento de orden n-ésimo y/o anulación de las corrientes de Foucault. Sin embargo, estas técnicas generalmente dan como resultado adquisiciones subóptimas con largos TEs. En esta tesis, proponemos una formulación versátil para el diseño de formas de onda para los gradientes de ponderación de la difusión optimizadas que disminuyan los problemas anteriormente mencionados de las adquisiciones y las imágenes de DW-MRI y al mismo tiempo minimicen el TE de la adquisición. Por otro lado, la estimación de los mapas paramétricos relacionados con la difusión suele verse afectada por varios factores perjudiciales como son la baja precisión y baja exactitud, la falta de repetibilidad y falta de reproducibilidad, causados, en parte, por los artefactos descritos anteriormente. Estos factores perjudiciales aparecen tanto en los mapas de difusión estimados a partir del modelo monoexponencial de señal como en los mapas estimados a partir del modelo de movimiento incoherente intravoxel, lo que dificulta el establecimiento de biomarcadores cuantitativos a partir de los mapas paramétricos estimados. La precisión de las estimaciones, y en particular la precisión de los mapas de difusión aparente del modelo de señal monoexponencial de DW-MRI, puede aumentarse utilizando el estimador apropiado. Sin embargo, el conjunto de ponderaciones de la difusión a realizar (es decir, el conjunto de valores b) que aumentan la precisión de los mapas paramétricos estimados sigue sin estar claro. En esta Tesis se deriva la cota inferior de Cramér-Rao de ambos modelos de señal de DW-MRI bajo el supuesto de que las imágenes adquiridas de DW-MRI se ven afectadas por un ruido con distribución Rician y a su vez se propone una formulación para la optimización del conjunto de valores b que maximizan el nivel de señal (es decir, minimizan la varianza y maximizan la precisión) de los mapas paramétricos estimados relacionados con la difusión.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

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

Producción CientíficaBackground 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 minutes 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.Ministerio de Ciencia, Innovación y Universidades (grants TEC2017-82408-R and RTI2018-094569-B-I00

ISBI 2017

Mapping of the apparent diffusion coefficient (ADC), estimated from a set of diffusion-weighted (DW) images acquired with different b-values, often suffers from low SNR, which can introduce large variance in ADC maps. Unfortunately, there is no consensus on the optimal b-values to maximize the noise performance of ADC map. In this work, we determine the optimal b-values to maximize the noise performance of ADC mapping by using a Cramér-Rao Lower Bound (CRLB) approach under realistic noise assumptions. The strong agreement between the CRLB-based analysis, Monte-Carlo simulations, and ADC phantom experiment, suggests the utility of this approach to optimize DW-MRI acquisitions

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

ISMRM 2017

Diffusion-Weighted MRI (DW-MRI) often suffers from motion-related artifacts in organs that experience physiological motion. Importantly, organ motion during the application of diffusion gradients results in signal losses, which complicate image interpretation and bias quantitative measures. Motion-compensated gradient designs have been proposed, however they typically result in substantially lower b-values or severe concomitant gradient effects. In this work, we develop an approach for design of first- and second-order motion-compensated gradient waveforms based on a b-value maximization formulation including concomitant gradient nulling, and we compare it to existing techniques. The proposed design provides optimized b-values with motion compensation and concomitant gradient nullin

26TH INTERNATIONAL SOCIETY OF MAGNETIC RESONANCE IN MEDICINE ANNUAL MEETING AND EXHIBITION

Diffusion-Weighted MRI often suffers from signal attenuation due to long TE, sensitivity to physiological motion, and dephasing due to concomitant gradients (CGs). These challenges complicate image interpretation and may introduce bias in quantitative diffusion measurements. Motion moment-nulled diffusion-weighting gradients have been proposed to compensate motion, however, they frequently result in high TE and suffer from CG effects. In this work, the Optimal Diffusion-weighting Gradient waveform Design method that overcomes limitations of state-of-the-art waveforms is revisited and validated in phantom and in-vivo experiments. These diffusion-weighting gradient waveforms reduce the TE and increase the SNR of state-of-the-art waveforms without and with CG-nulling.TEC2013-44194-PVA069U1