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PURPOSE: Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. METHODS: A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. RESULTS: The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. CONCLUSIONS: The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.
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Meios de Contraste , Imageamento por Ressonância Magnética , Humanos , Meios de Contraste/farmacocinética , Reprodutibilidade dos Testes , Imageamento por Ressonância Magnética/métodos , Perfusão , Imagem de Perfusão/métodosRESUMO
PURPOSE: For reliable DCE MRI parameter estimation, k-space undersampling is essential to meet resolution, coverage, and signal-to-noise requirements. Pseudo-spiral (PS) sampling achieves this by sampling k-space on a Cartesian grid following a spiral trajectory. The goal was to optimize PS k-space sampling patterns for abdomin al DCE MRI. METHODS: The optimal PS k-space sampling pattern was determined using an anthropomorphic digital phantom. Contrast agent inflow was simulated in the liver, spleen, pancreas, and pancreatic ductal adenocarcinoma (PDAC). A total of 704 variable sampling and reconstruction approaches were created using three algorithms using different parametrizations to control sampling density, halfscan and compressed sensing regularization. The sampling patterns were evaluated based on image quality scores and the accuracy and precision of the DCE pharmacokinetic parameters. The best and worst strategies were assessed in vivo in five healthy volunteers without contrast agent administration. The best strategy was tested in a DCE scan of a PDAC patient. RESULTS: The best PS reconstruction was found to be PS-diffuse based, with quadratic distribution of readouts on a spiral, without random shuffling, halfscan factor of 0.8, and total variation regularization of 0.05 in the spatial and temporal domains. The best scoring strategy showed sharper images with less prominent artifacts in healthy volunteers compared to the worst strategy. Our suggested DCE sampling strategy also showed high quality DCE images in the PDAC patient. CONCLUSION: Using an anthropomorphic digital phantom, we identified an optimal PS sampling strategy for abdominal DCE MRI, and demonstrated feasibility in a PDAC patient.
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Abdome , Algoritmos , Meios de Contraste , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Neoplasias Pancreáticas , Imagens de Fantasmas , Humanos , Imageamento por Ressonância Magnética/métodos , Meios de Contraste/química , Abdome/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodos , Neoplasias Pancreáticas/diagnóstico por imagem , Pâncreas/diagnóstico por imagem , Fígado/diagnóstico por imagem , Razão Sinal-Ruído , Carcinoma Ductal Pancreático/diagnóstico por imagem , Adulto , Masculino , Baço/diagnóstico por imagem , Voluntários Saudáveis , Feminino , Interpretação de Imagem Assistida por Computador/métodos , Reprodutibilidade dos TestesRESUMO
PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.
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Meios de Contraste , Imageamento por Ressonância Magnética , Humanos , Reprodutibilidade dos Testes , Imageamento por Ressonância Magnética/métodos , Software , AlgoritmosRESUMO
PURPOSE: To obtain better microstructural integrity, interstitial fluid, and microvascular images from multi-b-value diffusion MRI data by using a physics-informed neural network (PINN) fitting approach. METHODS: Test-retest whole-brain inversion recovery diffusion-weighted images with multiple b-values (IVIM: intravoxel incoherent motion) were acquired on separate days for 16 patients with cerebrovascular disease on a 3.0T MRI system. The performance of the PINN three-component IVIM (3C-IVIM) model fitting approach was compared with conventional fitting approaches (i.e., non-negative least squares and two-step least squares) in terms of (1) parameter map quality, (2) test-retest repeatability, and (3) voxel-wise accuracy. Using the in vivo data, the parameter map quality was assessed by the parameter contrast-to-noise ratio (PCNR) between normal-appearing white matter and white matter hyperintensities, and test-retest repeatability was expressed by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). The voxel-wise accuracy of the 3C-IVIM parameters was determined by 10,000 computer simulations mimicking our in vivo data. Differences in PCNR and CV values obtained with the PINN approach versus conventional fitting approaches were assessed using paired Wilcoxon signed-rank tests. RESULTS: The PINN-derived 3C-IVIM parameter maps were of higher quality and more repeatable than those of conventional fitting approaches, while also achieving higher voxel-wise accuracy. CONCLUSION: Physics-informed neural networks enable robust voxel-wise estimation of three diffusion components from the diffusion-weighted signal. The repeatable and high-quality biological parameter maps generated with PINNs allow for visual evaluation of pathophysiological processes in cerebrovascular disease.
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Transtornos Cerebrovasculares , Líquido Extracelular , Humanos , Microcirculação , Imagem de Difusão por Ressonância Magnética/métodos , Redes Neurais de Computação , Movimento (Física) , Reprodutibilidade dos TestesRESUMO
Intravoxel incoherent motion (IVIM) imaging and diffusion tensor imaging (DTI) facilitate noninvasive quantification of tissue perfusion and diffusion. Both are promising biomarkers in various diseases and a combined acquisition is therefore desirable. This comes with challenges, including noisy parameter maps and long scan times, especially for the perfusion fraction f and pseudo-diffusion coefficient D*. A model-based reconstruction has the potential to overcome these challenges. As a first step, our goal was to develop a model-based reconstruction framework for IVIM and combined IVIM-DTI parameter estimation. The IVIM and IVIM-DTI models were implemented in the PyQMRI model-based reconstruction framework and validated with simulations and in vivo data. Commonly used voxel-wise nonlinear least-squares fitting was used as the reference. Simulations with the IVIM and IVIM-DTI models were performed with 100 noise realizations to assess accuracy and precision. Diffusion-weighted data were acquired for IVIM reconstruction in the liver (n = 5), as well as for IVIM-DTI in the kidneys (n = 5) and lower-leg muscles (n = 6) of healthy volunteers. The median and interquartile range (IQR) values of the IVIM and IVIM-DTI parameters were compared to assess bias and precision. With model-based reconstruction, the parameter maps exhibited less noise, which was most pronounced in the f and D* maps, both in the simulations and in vivo. The bias values in the simulations were comparable between model-based reconstruction and the reference method. The IQR was lower with model-based reconstruction compared with the reference for all parameters. In conclusion, model-based reconstruction is feasible for IVIM and IVIM-DTI and improves the precision of the parameter estimates, particularly for f and D* maps.
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Imagem de Difusão por Ressonância Magnética , Imagem de Tensor de Difusão , Humanos , Movimento (Física) , Imagem de Difusão por Ressonância Magnética/métodos , Fígado/diagnóstico por imagem , Músculo EsqueléticoRESUMO
Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs-at-risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.
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Neoplasias , Planejamento da Radioterapia Assistida por Computador , Humanos , Imageamento por Ressonância Magnética/métodos , Movimento (Física) , Neoplasias/diagnóstico por imagem , Neoplasias/radioterapia , Planejamento da Radioterapia Assistida por Computador/métodosRESUMO
BACKGROUND: Restaging of locally advanced pancreatic cancer (LAPC) after induction chemotherapy using contrast-enhanced computed tomography (CE-CT) imaging is imprecise in evaluating local tumor response. This study explored the value of 3 Tesla (3 T) contrast-enhanced (CE) and diffusion-weighted (DWI) magnetic resonance imaging (MRI) for local tumor restaging. METHODS: This is a prospective pilot study including 20 consecutive patients with LAPC with RECIST non-progressive disease on CE-CT after induction chemotherapy. Restaging CE-CT, CE-MRI, and DWI-MRI were retrospectively evaluated by two abdominal radiologists in consensus, scoring tumor size and vascular involvement. A halo sign was defined as replacement of solid perivascular (arterial and venous) tumor tissue by a zone of fatty-like signal intensity. RESULTS: Adequate MRI was obtained in 19 patients with LAPC after induction chemotherapy. Tumor diameter was non-significantly smaller on CE-MRI compared to CE-CT (26 mm vs. 30 mm; p = 0.073). An MRI-halo sign was seen on CE-MRI in 52.6% (n = 10/19), whereas a CT-halo sign was seen in 10.5% (n = 2/19) of patients (p = 0.016). An MRI-halo sign was not associated with resection rate (60.0% vs. 62.5%; p = 1.000). In the resection cohort, patients with an MRI-halo sign had a non-significant increased R0 resection rate as compared to patients without an MRI-halo sign (66.7% vs. 20.0%; p = 0.242). Positive and negative predictive values of the CE-MRI-halo sign for R0 resection were 66.7% and 66.7%, respectively. CONCLUSIONS: 3 T CE-MRI and the MRI-halo sign might be helpful to assess the effect of induction chemotherapy in patients with LAPC, but its diagnostic accuracy has to be evaluated in larger series.
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Quimioterapia de Indução , Neoplasias Pancreáticas , Humanos , Estudos Prospectivos , Projetos Piloto , Estudos Retrospectivos , Estadiamento de Neoplasias , Imageamento por Ressonância Magnética/métodos , Neoplasias Pancreáticas/diagnóstico por imagem , Neoplasias Pancreáticas/tratamento farmacológico , Neoplasias Pancreáticas/cirurgiaRESUMO
OBJECTIVE: Dysphagia or difficulty in swallowing is a potentially hazardous clinical problem that needs regular monitoring. Real-time 2D MRI of swallowing is a promising radiation-free alternative to the current clinical standard: videofluoroscopy. However, aspiration may be missed if it occurs outside this single imaged slice. We therefore aimed to image swallowing in 3D real time at 12 frames per second (fps). MATERIALS AND METHODS: At 3 T, three 3D real-time MRI acquisition approaches were compared to the 2D acquisition: an aligned stack-of-stars (SOS), and a rotated SOS with a golden-angle increment and with a tiny golden-angle increment. The optimal 3D acquisition was determined by computer simulations and phantom scans. Subsequently, five healthy volunteers were scanned and swallowing parameters were measured. RESULTS: Although the rotated SOS approaches resulted in better image quality in simulations, in practice, the aligned SOS performed best due to the limited number of slices. The four swallowing phases could be distinguished in 3D real-time MRI, even though the spatial blurring was stronger than in 2D. The swallowing parameters were similar between 2 and 3D. CONCLUSION: At a spatial resolution of 2-by-2-by-6 mm with seven slices, swallowing can be imaged in 3D real time at a frame rate of 12 fps.
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Deglutição , Imageamento Tridimensional , Simulação por Computador , Humanos , Imageamento Tridimensional/métodos , Imageamento por Ressonância Magnética/métodos , Imagens de FantasmasRESUMO
PURPOSE: The intravoxel incoherent motion (IVIM) model for DWI might provide useful biomarkers for disease management in head and neck cancer. This study compared the repeatability of three IVIM fitting methods to the conventional nonlinear least-squares regression: Bayesian probability estimation, a recently introduced neural network approach, IVIM-NET, and a version of the neural network modified to increase consistency, IVIM-NETmod . METHODS: Ten healthy volunteers underwent two imaging sessions of the neck, two weeks apart, with two DWI acquisitions per session. Model parameters (ADC, diffusion coefficient Dt , perfusion fraction fp , and pseudo-diffusion coefficient Dp ) from each fit method were determined in the tonsils and in the pterygoid muscles. Within-subject coefficients of variation (wCV) were calculated to assess repeatability. Training of the neural network was repeated 100 times with random initialization to investigate consistency, quantified by the coefficient of variance. RESULTS: The Bayesian and neural network approaches outperformed nonlinear regression in terms of wCV. Intersession wCV of Dt in the tonsils was 23.4% for nonlinear regression, 9.7% for Bayesian estimation, 9.4% for IVIM-NET, and 11.2% for IVIM-NETmod . However, results from repeated training of the neural network on the same data set showed differences in parameter estimates: The coefficient of variances over the 100 repetitions for IVIM-NET were 15% for both Dt and fp , and 94% for Dp ; for IVIM-NETmod , these values improved to 5%, 9%, and 62%, respectively. CONCLUSION: Repeatabilities from the Bayesian and neural network approaches are superior to that of nonlinear regression for estimating IVIM parameters in the head and neck.
Assuntos
Imagem de Difusão por Ressonância Magnética , Redes Neurais de Computação , Teorema de Bayes , Biomarcadores , Humanos , Movimento (Física) , Reprodutibilidade dos TestesRESUMO
PURPOSE: Earlier work showed that IVIM-NETorig , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NEToptim , and characterizes its superior performance in pancreatic cancer patients. METHOD: In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CVNET ), respectively. The best performing network, IVIM-NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NEToptim 's performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed. RESULTS: In simulations (SNR = 20), IVIM-NEToptim outperformed IVIM-NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET (D) = 0.013 vs 0.104; CVNET (f) = 0.020 vs 0.054; CVNET (D*) = 0.036 vs 0.110). IVIM-NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NEToptim detected the most individual patients with significant parameter changes compared to day-to-day variations. CONCLUSION: IVIM-NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.
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Aprendizado Profundo , Neoplasias Pancreáticas , Algoritmos , Teorema de Bayes , Imagem de Difusão por Ressonância Magnética , Humanos , Movimento (Física) , Neoplasias Pancreáticas/diagnóstico por imagem , Física , Reprodutibilidade dos TestesRESUMO
BACKGROUND: Noninvasive diagnostic methods are urgently required in disease stratification and monitoring in nonalcoholic fatty liver disease (NAFLD). Multiparametric magnetic resonance imaging (MRI) is a promising technique to assess hepatic steatosis, inflammation, and fibrosis, potentially enabling noninvasive identification of individuals with active and advanced stages of NAFLD. PURPOSE: To examine the diagnostic performance of multiparametric MRI for the assessment of disease severity along the NAFLD disease spectrum with comparison to histological scores. STUDY TYPE: Prospective, cohort. POPULATION: Thirty-seven patients with NAFLD. FIELD STRENGTH/SEQUENCE: Multiparametric MRI at 3.0 T consisted of magnetic resonance (MR) spectroscopy (MRS) with multi-echo stimulated-echo acquisition mode, magnitude-based and three-point Dixon using a two-dimensional multi-echo gradient echo, MR elastography (MRE) using a generalized multishot gradient-recalled echo sequence and intravoxel incoherent motion (IVIM) using a multislice diffusion weighted single-shot echo-planar sequence. ASSESSMENT: Histological steatosis grades were compared to proton density fat fraction measured by MRS (PDFFMRS ), magnitude-based MRI (PDFFMRI-M ), and three-point Dixon (PDFFDixon ), as well as FibroScan® controlled attenuation parameter (CAP). Fibrosis and disease activity were compared to IVIM and MRE. FibroScan® liver stiffness measurements were compared to fibrosis levels. Diagnostic performance of all imaging parameters was determined for distinction between simple steatosis and nonalcoholic steatohepatitis (NASH). STATISTICAL TESTS: Spearman's rank test, Kruskal-Wallis test, Dunn's post-hoc test with Holm-Bonferroni P-value adjustment, receiver operating characteristic curve analysis. A P-value <0.05 was considered statistically significant. RESULTS: Histological steatosis grade correlated significantly with PDFFMRS (rs = 0.66, P < 0.001), PDFFMRI-M (rs = 0.68, P < 0.001), and PDFFDixon (rs = 0.67, P < 0.001), whereas no correlation was found with CAP. MRE and IVIM diffusion and perfusion significantly correlated with disease activity (rs = 0.55, P < 0.001, rs = -0.40, P = 0.016, rs = -0.37, P = 0.027, respectively) and fibrosis (rs = 0.55, P < 0.001, rs = -0.46, P = 0.0051; rs = -0.53, P < 0.001, respectively). MRE and IVIM diffusion had the highest area-under-the-curve for distinction between simple steatosis and NASH (0.79 and 0.73, respectively). DATA CONCLUSION: Multiparametric MRI is a promising method for noninvasive, accurate, and sensitive distinction between simple hepatic steatosis and NASH, as well as for the assessment of steatosis and fibrosis severity. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: 2.
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Técnicas de Imagem por Elasticidade , Hepatopatia Gordurosa não Alcoólica , Biópsia , Humanos , Fígado/diagnóstico por imagem , Imageamento por Ressonância Magnética , Hepatopatia Gordurosa não Alcoólica/diagnóstico por imagem , Estudos ProspectivosRESUMO
PURPOSE: This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted MRI (DW-MRI) data and evaluates its performance. METHODS: In May 2011, 10 male volunteers (age range, 29-53 years; mean, 37) underwent DW-MRI of the upper abdomen on 1.5T and 3.0T MR scanners. Regions of interest in the left and right liver lobe, pancreas, spleen, renal cortex, and renal medulla were delineated independently by 2 readers. DNNs were trained for IVIM model fitting using these data; results were compared to least-squares and Bayesian approaches to IVIM fitting. Intraclass correlation coefficients (ICCs) were used to assess consistency of measurements between readers. Intersubject variability was evaluated using coefficients of variation (CVs). The fitting error was calculated based on simulated data, and the average fitting time of each method was recorded. RESULTS: DNNs were trained successfully for IVIM parameter estimation. This approach was associated with high consistency between the 2 readers (ICCs between 50% and 97%), low intersubject variability of estimated parameter values (CVs between 9.2 and 28.4), and the lowest error when compared with least-squares and Bayesian approaches. Fitting by DNNs was several orders of magnitude quicker than the other methods, but the networks may need to be retrained for different acquisition protocols or imaged anatomical regions. CONCLUSION: DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data. Suitable software is available for download.
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Aprendizado Profundo , Imagem de Difusão por Ressonância Magnética , Rim/diagnóstico por imagem , Fígado/diagnóstico por imagem , Pâncreas/diagnóstico por imagem , Baço/diagnóstico por imagem , Adulto , Algoritmos , Teorema de Bayes , Simulação por Computador , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador , Análise dos Mínimos Quadrados , Masculino , Pessoa de Meia-Idade , Movimento (Física) , Estudos Prospectivos , Reprodutibilidade dos TestesRESUMO
PURPOSE: Flow-compensated (FC) diffusion-weighted MRI (DWI) for intravoxel-incoherent motion (IVIM) modeling allows for a more detailed description of tissue microvasculature than conventional IVIM. The long acquisition time of current FC-IVIM protocols, however, has prohibited clinical application. Therefore, we developed an optimized abdominal FC-IVIM acquisition with a clinically feasible scan time. METHODS: Precision and accuracy of the FC-IVIM parameters were assessed by fitting the FC-IVIM model to signal decay curves, simulated for different acquisition schemes. Diffusion-weighted acquisitions were added subsequently to the protocol, where we chose the combination of b-value, diffusion time and gradient profile (FC or bipolar) that resulted in the largest improvement to its accuracy and precision. The resulting two optimized FC-IVIM protocols with 25 and 50 acquisitions (FC-IVIMopt25 and FC-IVIMopt50 ), together with a complementary acquisition consisting of 50 diffusion-weighting (FC-IVIMcomp ), were acquired in repeated abdominal free-breathing FC-IVIM imaging of seven healthy volunteers. Intersession and intrasession within-subject coefficient of variation of the FC-IVIM parameters were compared for the liver, spleen, and kidneys. RESULTS: Simulations showed that the performance of FC-IVIM improved in tissue with larger perfusion fraction and signal-to-noise ratio. The scan time of the FC-IVIMopt25 and FC-IVIMopt50 protocols were 8 and 16 min. The best in vivo performance was seen in FC-IVIMopt50 . The intersession within-subject coefficients of variation of FC-IVIMopt50 were 11.6%, 16.3%, 65.5%, and 36.0% for FC-IVIM model parameters diffusivity, perfusion fraction, characteristic time and blood flow velocity, respectively. CONCLUSIONS: We have optimized the FC-IVIM protocol, allowing for clinically feasible scan times (8-16 min).
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Abdome/diagnóstico por imagem , Imagem de Difusão por Ressonância Magnética , Movimento (Física) , Adulto , Algoritmos , Simulação por Computador , Feminino , Voluntários Saudáveis , Humanos , Rim/diagnóstico por imagem , Fígado/diagnóstico por imagem , Masculino , Modelos Estatísticos , Perfusão , Reprodutibilidade dos Testes , Respiração , Processamento de Sinais Assistido por Computador , Razão Sinal-Ruído , Baço/diagnóstico por imagem , Adulto JovemRESUMO
PURPOSE: There is ample evidence that systemic sympathetic neural activity contributes to the progression of chronic kidney disease, possibly by limiting renal blood flow and thereby inducing renal hypoxia. Up to now there have been no direct observations of this mechanism in humans. We studied the effects of systemic sympathetic activation elicited by a lower body negative pressure (LBNP) on renal blood flow (RBF) and renal oxygenation in healthy humans. METHODS: Eight healthy volunteers (age 19-31 years) were subjected to progressive LBNP at - 15 and - 30 mmHg, 15 min per level. Brachial artery blood pressure was monitored intermittently. RBF was measured by phase-contrast MRI in the proximal renal artery. Renal vascular resistance was calculated as the MAP divided by the RBF. Renal oxygenation (R2*) was measured for the cortex and medulla by blood oxygen level dependent (BOLD) MRI, using a monoexponential fit. RESULTS: With a LBNP of - 30 mmHg, pulse pressure decreased from 50 ± 10 to 43 ± 7 mmHg; MAP did not change. RBF decreased from 1152 ± 80 to 1038 ± 83 mL/min to 950 ± 67 mL/min at - 30 mmHg LBNP (p = 0.013). Heart rate and renal vascular resistance increased by 38 ± 15% and 23 ± 8% (p = 0.04) at - 30 mmHg LBNP, respectively. There was no change in cortical or medullary R2* (20.3 ± 1.2 s-1 vs 19.8 ± 0.43 s-1; 28.6 ± 1.1 s-1 vs 28.0 ± 1.3 s-1). CONCLUSION: The results suggest that an increase in sympathetic vasoconstrictor drive decreases kidney perfusion without a parallel reduction in oxygenation in healthy humans. This in turn indicates that sympathetic activation suppresses renal oxygen demand and supply equally, thus allowing adequate tissue oxygenation to be maintained.
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Hipóxia , Rim/irrigação sanguínea , Rim/fisiologia , Pressão Negativa da Região Corporal Inferior/métodos , Circulação Renal/fisiologia , Sistema Nervoso Simpático/fisiologia , Adulto , Feminino , Humanos , Hipóxia/diagnóstico por imagem , Hipóxia/fisiopatologia , Rim/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Masculino , Sistema Nervoso Simpático/diagnóstico por imagem , Adulto JovemRESUMO
Objective: Delineation variation of esophageal tumors remains a large source of geometric uncertainty. In the present study, we investigated the inter- and intra-observer variation in esophageal gross tumor volume (GTV) delineation and the impact of endoscopically implanted fiducial markers on these variations. Material/Methods: Ten esophageal cancer patients with at least two markers endoscopically implanted at the cranial and caudal tumor borders and visible on the planning computed tomography (pCT) were included in this study. Five dedicated gastrointestinal radiation oncologists independently delineated GTVs on the pCT without markers and with markers. The GTV was first delineated on pCTs where markers were digitally removed and next on the original pCT with markers. Both delineation series were executed twice to determine intra-observer variation. For both the inter- and intra-observer analyses, the generalized conformity index (CIgen), and the standard deviation (SD) of the distances between delineated surfaces (i.e., overall, longitudinal, and radial SDs) were calculated. Linear mixed-effect models were used to compare the without and with markers series (α = 0.05). Results: Both the inter- and intra-observer CIgen were significantly larger in the series with markers than in the series without markers (p < .001). For the series without markers vs. with markers, the inter-observer overall SD, longitudinal SD, and radial SD was 0.63 cm vs. 0.22 cm, 1.44 cm vs. 0.42 cm, and 0.26 cm vs. 0.18 cm, respectively (p < .05); moreover, the intra-observer overall SD, longitudinal SD, and radial SD was 0.45 cm vs. 0.26 cm, 1.10 cm vs. 0.41 cm, and 0.22 cm vs. 0.15 cm, respectively (p < .05). Conclusion: The presence of markers at the cranial and caudal tumor borders significantly reduced both inter- and intra-observer GTV delineation variation, especially in the longitudinal direction. Our results endorse the use of markers in GTV delineation for esophageal cancer patients.
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Adenocarcinoma/radioterapia , Carcinoma de Células Escamosas/radioterapia , Neoplasias Esofágicas/radioterapia , Marcadores Fiduciais , Variações Dependentes do Observador , Radioterapia/normas , Adenocarcinoma/metabolismo , Adenocarcinoma/patologia , Idoso , Idoso de 80 Anos ou mais , Carcinoma de Células Escamosas/metabolismo , Carcinoma de Células Escamosas/patologia , Neoplasias Esofágicas/metabolismo , Neoplasias Esofágicas/patologia , Feminino , Seguimentos , Humanos , Masculino , Pessoa de Meia-Idade , Projetos Piloto , Prognóstico , Planejamento da Radioterapia Assistida por Computador/métodos , Estudos Retrospectivos , Taxa de Sobrevida , Tomografia Computadorizada por Raios X/métodosRESUMO
PURPOSE: To assess the effect of additional magnetic resonance imaging (MRI) alongside the planning computed tomography (CT) scan on target volume delineation in pancreatic cancer patients. MATERIAL AND METHODS: Eight observers (radiation oncologists) from six institutions delineated the gross tumor volume (GTV) on 3DCT, and internal GTV (iGTV) on 4DCT of four pancreatic cancer patients, while MRI was available in a second window (CT + MRI). Variations in volume, generalized conformity index (CIgen), and overall observer variation, expressed as standard deviation (SD) of the distances between delineated surfaces, were analyzed. CIgen is a measure of overlap of the delineated iGTVs (1 = full overlap, 0 = no overlap). Results were compared with those from an earlier study that assessed the interobserver variation by the same observers on the same patients on CT without MRI (CT-only). RESULTS: The maximum ratios between delineated volumes within a patient were 6.1 and 22.4 for the GTV (3DCT) and iGTV (4DCT), respectively. The average (root-mean-square) overall observer variations were SD = 0.41 cm (GTV) and SD = 0.73 cm (iGTV). The mean CIgen was 0.36 for GTV and 0.37 for iGTV. When compared to the iGTV delineated on CT-only, the mean volumes of the iGTV on CT + MRI were significantly smaller (32%, Wilcoxon signed-rank, p < .0005). The median volumes of the iGTV on CT + MRI were included for 97% and 92% in the median volumes of the iGTV on CT. Furthermore, CT + MRI showed smaller overall observer variations (root-mean-square SD = 0.59 cm) in six out of eight delineated structures compared to CT-only (root-mean-square SD = 0.72 cm). However, large local observer variations remained close to biliary stents and pathological lymph nodes, indicating issues with instructions and instruction compliance. CONCLUSIONS: The availability of MRI images during target delineation of pancreatic cancer on 3DCT and 4DCT resulted in smaller target volumes and reduced the interobserver variation in six out of eight delineated structures.
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Tomografia Computadorizada Quadridimensional/métodos , Imageamento Tridimensional/métodos , Imageamento por Ressonância Magnética/métodos , Neoplasias Pancreáticas/patologia , Tomografia Computadorizada por Raios X/métodos , Quimiorradioterapia , Estudos de Viabilidade , Seguimentos , Humanos , Neoplasias Pancreáticas/diagnóstico por imagem , Neoplasias Pancreáticas/terapia , PrognósticoRESUMO
BACKGROUND: Intravoxel incoherent motion (IVIM)-corrected diffusion tensor imaging (DTI) potentially enhances return-to-play (RTP) prediction after hamstring injuries. However, the long scan times hamper clinical implementation. We assessed accelerated IVIM-corrected DTI approaches in acute hamstring injuries and explore the sensitivity of the perfusion fraction (f) to acute muscle damage. METHODS: Athletes with acute hamstring injury received DTI scans of both thighs < 7 days after injury and at RTP. For a subset, DTI scans were repeated with multiband (MB) acceleration. Data from standard and MB-accelerated scans were fitted with standard and accelerated IVIM-corrected DTI approach using high b-values only. Segmentations of the injury and contralateral healthy muscles were contoured. The fitting methods as well as the standard and MB-accelerated scan were compared using linear regression analysis. For sensitivity to injury, Δ(injured minus healthy) DTI parameters between the methods and the differences between injured and healthy muscles were compared (Wilcoxon signed-rank test). RESULTS: The baseline dataset consisted of 109 athletes (16 with MB acceleration); 64 of them received an RTP scan (8 with MB acceleration). Linear regression of the standard and high-b DTI fitting showed excellent agreement. With both fitting methods, standard and MB-accelerated scans were comparable. Δ(injured minus healthy) was similar between standard and accelerated methods. For all methods, all IVIM-DTI parameters except f were significantly different between injured and healthy muscles. CONCLUSIONS: High-b DTI fitting with MB acceleration reduced the scan time from 11:08 to 3:40 min:s while maintaining sensitivity to hamstring injuries; f was not different between healthy and injured muscles. RELEVANCE STATEMENT: The accelerated IVIM-corrected DTI protocol, using fewer b-values and MB acceleration, reduced the scan time to under 4 min without affecting the sensitivity of the quantitative outcome parameters to hamstring injuries. This allows for routine clinical monitoring of hamstring injuries, which could directly benefit injury treatment and monitoring. KEY POINTS: ⢠Combining high-b DTI-fitting and multiband-acceleration dramatically reduced by two thirds the scan time. ⢠The accelerated IVIM-corrected DTI approaches maintained the sensitivity to hamstring injuries. ⢠The IVIM-derived perfusion fraction was not sensitive to hamstring injuries.
Assuntos
Imagem de Difusão por Ressonância Magnética , Imagem de Tensor de Difusão , Humanos , Imagem de Difusão por Ressonância Magnética/métodos , Movimento (Física)RESUMO
Background & Aims: Individuals with obesity may develop intrapancreatic fat deposition (IPFD) and fatty pancreas disease (FPD). Whether this causes inflammation and fibrosis and leads to pancreatic dysfunction is less established than for liver damage in metabolic dysfunction-associated steatotic liver disease (MASLD). Moreover, the interrelations of FPD and MASLD are poorly understood. Therefore, we aimed to assess IPFD and fibro-inflammation in relation to pancreatic function and liver disease severity in individuals with MASLD. Methods: Seventy-six participants from the Amsterdam MASLD-MASH cohort (ANCHOR) study underwent liver biopsy and multiparametric MRI of the liver and pancreas, consisting of proton-density fat fraction sequences, T1 mapping and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). Results: The prevalence of FPD was 37.3%. There was a clear correlation between pancreatic T1 relaxation time, which indicates fibro-inflammation, and parameters of glycemic dysregulation, namely HbA1c (R = 0.59; p <0.001), fasting glucose (R = 0.51; p <0.001) and the presence of type 2 diabetes (mean 802.0 ms vs. 733.6 ms; p <0.05). In contrast, there was no relation between IPFD and hepatic fat content (R = 0.03; p = 0.80). Pancreatic IVIM diffusion (IVIM-D) was lower in advanced liver fibrosis (p <0.05) and pancreatic perfusion (IVIM-f), reflecting vessel density, inversely correlated to histological MASLD activity (p <0.05). Conclusions: Consistent relations exist between pancreatic fibro-inflammation on MRI and endocrine function in individuals with MASLD. However, despite shared dysmetabolic drivers, our study suggests IPFD is a separate pathophysiological process from MASLD. Impact and implications: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease worldwide and 68% of people with type 2 diabetes have MASLD. However, fat infiltration and inflammation in the pancreas are understudied in individuals with MASLD. In this cross-sectional MRI study, we found no relationship between fat accumulation in the pancreas and liver in a cohort of patients with MASLD. However, our results show that inflammatory and fibrotic processes in the pancreas may be interrelated to features of type 2 diabetes and to the severity of liver disease in patients with MASLD. Overall, the results suggest that pancreatic endocrine dysfunction in individuals with MASLD may be more related to glucotoxicity than to lipotoxicity. Clinical trial number: NTR7191 (Dutch Trial Register).
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Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.
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Quantitative magnetic resonance imaging (qMRI) has been shown to provide many potential advantages for personalized adaptive radiotherapy (RT). Deep learning models have proven to increase efficiency, robustness and speed for different qMRI tasks. Therefore, this article discusses the current state-of-the-art and potential future opportunities as well as challenges related to the use of deep learning in qMRI for target contouring, quantitative parameter estimation and also the generation of synthetic computerized tomography (CT) data based on MRI in personalized RT.