Magnetic Resonance Parametric Mapping Research Articles

SwinUNet: a multiscale feature learning approach to cardiovascular magnetic resonance parametric mapping for myocardial tissue characterization.

Objective.Cardiovascular magnetic resonance (CMR) can measure T1 and T2 relaxation times for myocardial tissue characterization. However, the CMR procedure for T1/T2 parametric mapping is time-consuming, making it challenging to scan heart patients routinely in clinical practice. This study aims to accelerate CMR parametric mapping with deep learning.Approach. A deep-learning model, SwinUNet, was developed to accelerate T1/T2 mapping. SwinUNet used a convolutional UNet and a Swin transformer to form a hierarchical 3D computation structure, allowing for analyzing CMR images spatially and temporally with multiscale feature learning. A comparative study was conducted between SwinUNet and an existing deep-learning model, MyoMapNet, which only used temporal analysis for parametric mapping. The T1/T2 mapping performance was evaluated globally using mean absolute error (MAE) and structural similarity index measure (SSIM). The clinical T1/T2 indices for characterizing the left-ventricle myocardial walls were also calculated and evaluated using correlation and Bland-Altman analysis.Main results. We performed accelerated T1 mapping with ≤4 heartbeats and T2 mapping with 2 heartbeats in reference to the clinical standard, which required 11 heartbeats for T1 mapping and 3 heartbeats for T2 mapping. SwinUNet performed well in all the experiments (MAE < 50 ms, SSIM > 0.8, correlation > 0.75, and Bland-Altman agreement limits < 100 ms for T1 mapping; MAE < 1 ms, SSIM > 0.9, correlation > 0.95, and Bland-Altman agreement limits < 1.5 ms for T2 mapping). When the maximal acceleration was used (2 heartbeats), SwinUNet outperformed MyoMapNet and gave measurement accuracy similar to the clinical standard.Significance. SwinUNet offers an optimal solution to CMR parametric mapping for assessing myocardial diseases quantitatively in clinical cardiology.

Misclassification of females and males in cardiovascular magnetic resonance parametric mapping: the importance of sex-specific normal ranges for diagnosis of health vs. disease.

Cardiovascular magnetic resonance parametric mapping enables non-invasive quantitative myocardial tissue characterization. Human myocardium has normal ranges of T1 and T2 values, deviation from which may indicate disease or change in physiology. Normal myocardial T1 and T2 values are affected by biological sex. Consequently, normal ranges created with insufficient numbers of each sex may result in sampling biases, misclassification of healthy values vs. disease, and even misdiagnoses. In this study, we investigated the impact of using male normal ranges for classifying female cases as normal or abnormal (and vice versa). One hundred and forty-two healthy volunteers (male and female) were scanned on two Siemens 3T MR systems, providing averaged global myocardial T1 and T2 values on a per-subject basis. The Monte Carlo method was used to generate simulated normal ranges from these values to estimate the statistical accuracy of classifying healthy female or male cases correctly as 'normal' when using sex-specific vs. mixed-sex normal ranges. The normal male and female T1- and T2-mapping values were significantly different by sex, after adjusting for age and heart rate. Using 15 healthy volunteers who are not sex specific to establish a normal range resulted in a typical misclassification of up to 36% of healthy females and 37% of healthy males as having abnormal T1 values and up to 16% of healthy females and 12% of healthy males as having abnormal T2 values. This paper highlights the potential adverse impact on diagnostic accuracy that can occur when local normal ranges contain insufficient numbers of both sexes. Sex-specific reference ranges should thus be routinely adopted in clinical practice.

How to evaluate cardiac masses by cardiovascular magnetic resonance parametric mapping?

How to evaluate cardiac masses by cardiovascular magnetic resonance parametric mapping?

Cardiovascular Magnetic Resonance Parametric Mapping Techniques for the Assessment of Chronic Coronary Syndromes.

The term chronic coronary syndromes encompasses a variety of clinical presentations of coronary artery disease (CAD), ranging from stable angina due to epicardial coronary artery disease to microvascular coronary dysfunction. Cardiac magnetic resonance (CMR) imaging has an established role in the diagnosis, prognostication and treatment planning of patients with CAD. Recent advances in parametric mapping CMR techniques have added value in the assessment of patients with chronic coronary syndromes, even without the need for gadolinium contrast administration. Furthermore, quantitative perfusion CMR techniques have enabled the non-invasive assessment of myocardial blood flow and myocardial perfusion reserve and can reliably identify multivessel coronary artery disease and microvascular dysfunction. This review summarizes the clinical applications and the prognostic value of the novel CMR parametric mapping techniques in the setting of chronic coronary syndromes and discusses their strengths, pitfalls and future directions.

Characteristic-constrained accelerating MR T1rho mapping with blockwise infimal convolution of matrix elastic-net regularization.

Magnetic resonance parameter mapping (MRPM) plays an important role in clinical applications and biomedical researches. However, the acceleration of MRPM remains a major challenge for achieving furtherimprovements. In this work, a new undersampled k-space based joint multi-contrast image reconstruction approach named CC-IC-LMEN is proposed for accelerating MR T1rhomapping. The reconstruction formulation of the proposed CC-IC-LMEN method imposes a blockwise low-rank assumption on the characteristic-image series (c-p space) and utilizes infimal convolution (IC) to exploit and balance the generalized low-rank properties in low-and high-order c-p spaces, thereby improving the accuracy. In addition, matrix elastic-net (MEN) regularization based on the nuclear and Frobenius norms is incorporated to obtain stable and exact solutions in cases with large accelerations and noisy observations. This formulation results in a minimization problem, that can be effectively solved using a numerical algorithm based on the alternating direction method of multipliers (ADMM). Finally, T1rho maps are then generated according to the reconstructed images using nonlinear least-squares (NLSQ) curve fitting with an established relaxometrymodel. The relative l2 -norm error (RLNE) and structural similarity (SSIM) in the regions of interest (ROI) show that the CC-IC-LMEN approach is more accurate than other competing methods even in situations with heavy undersampling or noisyobservation. Our proposed CC-IC-LMEN method provides accurate and robust solutions for accelerated MR T1rhomapping.

How to evaluate cardiomyopathies by cardiovascular magnetic resonance parametric mapping and late gadolinium enhancement.

How to evaluate cardiomyopathies by cardiovascular magnetic resonance parametric mapping and late gadolinium enhancement.

Cardiovascular Magnetic Resonance Parametric Mapping Techniques: Clinical Applications and Limitations.

Parametric mapping represents a significant innovation in cardiovascular magnetic resonance (CMR) tissue characterisation, allowing the quantification of myocardial changes based on changes on T1, T2 and T2* relaxation times and extracellular volume (ECV). Its clinical use is rapidly expanding, but it requires availability of dedicated equipment as well as expertise in image acquisition and analysis. This review focuses on the principles of CMR parametric mapping, its current clinical applications, important limitations, as well as future directions of this technique in cardiovascular medicine. There is increasing evidence that CMR parametric mapping techniques provide accurate diagnostic and prognostic tools that can be applied to and support the clinical management of patients with a range of cardiovascular disease. The unique capability of CMR myocardial tissue characterisation in cardiovascular diseases has further expanded by the introduction of parametric mapping. Its use in clinical practice presents opportunities but has also limitations.

Cardiac magnetic resonance parametric mapping can detect early cardiac involvement of patients with systemic sclerosis

Abstract Background Systemic sclerosis (SSc) is divided into two subtypes, diffuse and limited cutaneous SSc (dcSSc and lcSSc). The dcSSc subtype has more severe internal organ fibrosis than lcSSc. It is unclear whether cardiovascular magnetic resonance (CMR) parametric mapping can detect early cardiac involvement in these two subtypes. Purpose To detect cardiac involvement and evaluate differences between the two subtypes, we examined CMR parametric mapping. Methods 57 consecutive SSc patients (27 dcSSc and 30 lcSSc) who visited our hospital from July 2018 to February 2021 and underwent CMR at 3.0T (Philips) were included. We analyzed myocardial damage using CMR parametric mapping and compared it with clinical data. Results Mean disease durations of dcSSc and lcSSc were 4.0±6.5 years and 4.4±8.3 years, respectively. Although there were no significant differences in LVEF (56.8±8.8 vs 59.6±7.2), the left and right atrial volume index were significantly larger in dcSSc compared with lcSSc (all p&amp;lt;0.05). Focal fibrosis as evaluated by late gadolinium enhancement was found in 9 SSc patients (15.7%) and 4 patients had pulmonary hypertension. DcSSc patients had significantly higher mid-ventricular native T1 (1350.8±73.2 vs 1312.9±52.1, p=0.029) and postcontrast T1 values (640.4±59.4 vs 604.6±42.5, p=0.015) as compared to lcSSc, although there were no significant differences in ECV and T2 values. Native T1 values in mid-ventricular septum were positively correlated with E/e' ratio of echocardiography in overall SSc patients and dcSSc patients but not in lcSSc patients (r=0.320, p=0.021; r=0.505, p=0.010; r=0.195, p=0.329). Native T1 values in mid-ventricular septum were also positively correlated with plasma levels of brain natriuretic peptide (BNP) in overall SSc patients and dcSSc patients but not in lcSSc patients (r=0.353, p=0.008; r=0.484, p=0.011; r=0.113, p=0.559).The multiple regression analysis considering age and sex revealed that mid-ventricular septum native T1 was the independent predictor of E/e' in SSc patients (β=0.306, p=0.026), and dcSSc patients (β=0.553, p=0.007) but not in lcSSc patients (β=−0.282, p=0.105). The ROC curve for predicting E/e'≥14, cut-off value of native T1 in mid-ventricular septum was ≥1348.5msec (AUC 0.762; 95% CI 0.571–0.953; sensitivity 80.0%; specificity 68.1%). BNP was significantly higher in patients with native T1≥1348.5 msec compared with native T1&amp;lt;1348.5 (102.8±112.8 vs 45.7±55.7; p=0.014). The interobserver variability of CMR parametric mapping values was excellent in this study. Conclusion DcSSc patients showed higher native T1 and larger left and right atrial volume index of CMR than lcSSc patients, suggesting that DcSSc patients had more severe myocardial involvement and left ventricular diastolic dysfunction than lcSSc patients. Early detection of the high native T1 may predict the occurrence of cardiovascular events in the future. Funding Acknowledgement Type of funding sources: None. Correlation between native T1 and BNPCorrelation between native T1 and E/e'

Diagnostic value of magnetic resonance parametric mapping for non-invasive assessment of liver fibrosis in patients with primary sclerosing cholangitis

BackgroundPrimary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease, characterized by bile duct inflammation and destruction, leading to biliary fibrosis and cirrhosis. The purpose of this study was to investigate the utility of T1 and T2 mapping parameters, including extracellular volume fraction (ECV) for non-invasive assessment of fibrosis severity in patients with PSC.MethodsIn this prospective study, patients with PSC diagnosis were consecutively enrolled from January 2019 to July 2020 and underwent liver MRI. Besides morphological sequences, MR elastography (MRE), and T1 and T2 mapping were performed. ECV was calculated from T1 relaxation times. The presence of significant fibrosis (≥ F2) was defined as MRE-derived liver stiffness ≥ 3.66 kPa and used as the reference standard, against which the diagnostic performance of MRI mapping parameters was tested. Student t test, ROC analysis and Pearson correlation were used for statistical analysis.Results32 patients with PSC (age range 19–77 years) were analyzed. Both, hepatic native T1 (r = 0.66; P < 0.001) and ECV (r = 0.69; P < 0.001) correlated with MRE-derived liver stiffness. To diagnose significant fibrosis (≥ F2), ECV revealed a sensitivity of 84.2% (95% confidence interval (CI) 62.4–94.5%) and a specificity of 84.6% (CI 57.8–95.7%); hepatic native T1 revealed a sensitivity of 52.6% (CI 31.7–72.7%) and a specificity of 100.0% (CI 77.2–100.0%). Hepatic ECV (area under the curve (AUC) 0.858) and native T1 (AUC 0.711) had an equal or higher diagnostic performance for the assessment of significant fibrosis compared to serologic fibrosis scores (APRI (AUC 0.787), FIB-4 (AUC 0.588), AAR (0.570)).ConclusionsHepatic T1 and ECV can diagnose significant fibrosis in patients with PSC. Quantitative mapping has the potential to be a new non-invasive biomarker for liver fibrosis assessment and quantification in PSC patients.

Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method.

Quantitative tissue characteristics, which provide valuable diagnostic information, can be represented by magnetic resonance (MR) parameter maps using magnetic resonance imaging (MRI); however, a long scan time is necessary to acquire them, which prevents the application of quantitative MR parameter mapping to real clinical protocols. For fast MR parameter mapping, we propose a deep model-based MR parameter mapping network called DOPAMINE that combines a deep learning network with a model-based method to reconstruct MR parameter maps from undersampled multi-channel k-space data. DOPAMINE consists of two networks: 1) an MR parameter mapping network that uses a deep convolutional neural network (CNN) that estimates initial parameter maps from undersampled k-space data (CNN-based mapping), and 2) a reconstruction network that removes aliasing artifacts in the parameter maps with a deep CNN (CNN-based reconstruction) and an interleaved data consistency layer by an embedded MR model-based optimization procedure. We demonstrated the performance of DOPAMINE in brain T1 map reconstruction with a variable flip angle (VFA) model. To evaluate the performance of DOPAMINE, we compared it with conventional parallel imaging, low-rank based reconstruction, model-based reconstruction, and state-of-the-art deep-learning-based mapping methods for three different reduction factors (R = 3, 5, and 7) and two different sampling patterns (1D Cartesian and 2D Poisson-disk). Quantitative metrics indicated that DOPAMINE outperformed other methods in reconstructing T1 maps for all sampling patterns and reduction factors. DOPAMINE exhibited quantitatively and qualitatively superior performance to that of conventional methods in reconstructing MR parameter maps from undersampled multi-channel k-space data. The proposed method can thus reduce the scan time of quantitative MR parameter mapping that uses a VFA model.

Non-invasive assessment of liver fibrosis in autoimmune hepatitis: Diagnostic value of liver magnetic resonance parametric mapping including extracellular volume fraction

PurposeAutoimmune hepatitis (AIH) is an immune-mediated chronic liver disease that leads to severe fibrosis and cirrhosis. The aim of this study was to determine the diagnostic value of T1 and T2 mapping as well as extracellular volume fraction (ECV) for non-invasive assessment of liver fibrosis in AIH patients.MethodsIn this prospective study, 27 patients (age range: 19–77 years) with AIH underwent liver MRI. T1 and T2 relaxation times as well as ECV were quantified by mapping techniques. The presence of significant fibrosis (≥ F2) was defined as magnetic resonance elastography (MRE)-based liver stiffness ≥ 3.66 kPa. MRE was used as reference standard, against which the diagnostic performance of MRI-derived mapping parameters was tested. Diagnostic performance was compared by utilizing receiver-operating characteristic (ROC) analysis.ResultsMRE-based liver stiffness correlated with both, hepatic native T1 (r = 0.69; P < 0.001) as well as ECV (r = 0.80; P < 0.001). For the assessment of significant fibrosis, ECV yielded a sensitivity of 85.7% (95% confidence interval (CI): 60.1–96.0%) and a specificity of 84.6% (CI 60.1–96.0%); hepatic native T1 yielded a sensitivity of 85.7% (CI 60.1–96.0%); and a specificity of 76.9% (CI 49.7–91.8%). Diagnostic performance of hepatic ECV (area under the curve (AUC): 0.885), native hepatic T1 (AUC: 0.846) for assessment of significant fibrosis was similar compared to clinical fibrosis scores (APRI (AUC: 0.852), FIB-4 (AUC: 0.758), and AAR (0.654) (P > 0.05 for each comparison)).ConclusionQuantitative mapping parameters such as T1 and ECV can identify significant fibrosis in AIH patients. Future studies are needed to explore the value of parametric mapping for the evaluation of different disease stages.

Magnetic resonance parametric mapping of the spleen for non-invasive assessment of portal hypertension

ObjectivesIn patients with advanced liver disease, portal hypertension is an important risk factor, leading to complications such as esophageal variceal bleeding, ascites, and hepatic encephalopathy. This study aimed to determine the diagnostic value of T1 and T2 mapping and extracellular volume fraction (ECV) for the non-invasive assessment of portal hypertension.MethodsIn this prospective study, 50 participants (33 patients with indication for trans-jugular intrahepatic portosystemic shunt (TIPS) and 17 healthy volunteers) underwent MRI. The derivation and validation cohorts included 40 and 10 participants, respectively. T1 and T2 relaxation times and ECV of the liver and the spleen were assessed using quantitative mapping techniques. Direct hepatic venous pressure gradient (HVPG) and portal pressure measurements were performed during TIPS procedure. ROC analysis was performed to compare diagnostic performance.ResultsSplenic ECV correlated with portal pressure (r = 0.72; p < 0.001) and direct HVPG (r = 0.50; p = 0.003). No significant correlations were found between native splenic T1 and T2 relaxation times with portal pressure measurements (p > 0.05, respectively). In the derivation cohort, splenic ECV revealed a perfect diagnostic performance with an AUC of 1.000 for the identification of clinically significant portal hypertension (direct HVPG ≥ 10 mmHg) and outperformed other parameters: hepatic T2 (AUC, 0.731), splenic T2 (AUC, 0.736), and splenic native T1 (AUC, 0.806) (p < 0.05, respectively). The diagnostic performance of mapping parameters was comparable in the validation cohort.ConclusionSplenic ECV was associated with portal pressure measurements in patients with advanced liver disease. Future studies should explore the diagnostic value of parametric mapping accross a broader range of pressure values.Key Points• Non-invasive assessment and monitoring of portal hypertension is an area of unmet interest.• Splenic extracellular volume fraction is strongly associated with portal pressure in patients with end-stage liver disease.• Quantitative splenic and hepatic MRI-derived parameters have a potential to become a new non-invasive diagnostic parameter to assess and monitor portal pressure.

SCOPE: signal compensation for low-rank plus sparse matrix decomposition for fast parameter mapping

Magnetic resonance (MR) parameter mapping is useful for many clinical applications. However, its practical utility is limited by the long scan time. To address this problem, this paper developed a novel image reconstruction method for fast MR parameter mapping. The proposed method (SCOPE) used a low-rank plus sparse model to reconstruct the parameter-weighted images from highly undersampled acquisitions. A signal compensation strategy was introduced to promote low rankness along the parametric direction and thus improve the reconstruction accuracy. Specifically, compensation was performed by multiplying the original signal by the inversion of the mono-exponential decay at each voxel. The performance of SCOPE was evaluated via quantitative T1ρ mapping. The results of the simulation and in vivo experiments with acceleration factors from 3 to 5 are shown. The performance of SCOPE was verified via comparisons with several low-rank and sparsity-based methods. The experimental results showed that the T1ρ maps obtained using SCOPE were more accurate than those obtained using competing methods and were comparable to the reference, even when the acceleration factor reached 5. SCOPE can greatly reduce the scan time of parameter mapping while still achieving high accuracy. This technique might therefore help facilitate fast MR parameter mapping in clinical use.

MR Parametric Mapping as a Biomarker of Early Joint Degeneration.

Context:Osteoarthritis (OA) is a common, worldwide disorder. Magnetic resonance (MR) imaging can directly and noninvasively evaluate articular cartilage and has emerged as an essential tool in the study of OA.Evidence Acquisition:A PubMed search was performed using the keywords quantitative MRI and cartilage. No limits were set on the range of years searched. Articles were reviewed for relevance with an emphasis on in vivo studies performed at 3 tesla.Study Design:Clinical review.Level of Evidence:Level 4.Results:T2, T2*, T1 (particularly when measured after exogenous contrast administration, such as with the delayed gadolinium-enhanced MR imaging of cartilage [dGEMRIC] technique), and T1ρ are among the most widely utilized quantitative MR imaging techniques to evaluate cartilage and have been implemented in various patient cohorts. Existing challenges include reproducibility of results, insufficient consensus regarding optimal sequences and parameters, and interpretation of values.Conclusion:Quantitative assessment of cartilage using MR imaging techniques likely represents the best opportunity to identify early cartilage degeneration and to follow patients after treatment. Despite existing challenges, ongoing work and unique approaches have shown exciting and promising results.

Accelerated MR parameter mapping with low-rank and sparsity constraints.

To enable accurate magnetic resonance (MR) parameter mapping with accelerated data acquisition, utilizing recent advances in constrained imaging with sparse sampling. A new constrained reconstruction method based on low-rank and sparsity constraints is proposed to accelerate MR parameter mapping. More specifically, the proposed method simultaneously imposes low-rank and joint sparse structures on contrast-weighted image sequences within a unified mathematical formulation. With a pre-estimated subspace, this formulation results in a convex optimization problem, which is solved using an efficient numerical algorithm based on the alternating direction method of multipliers. To evaluate the performance of the proposed method, two application examples were considered: (i) T2 mapping of the human brain and (ii) T1 mapping of the rat brain. For each application, the proposed method was evaluated at both moderate and high acceleration levels. Additionally, the proposed method was compared with two state-of-the-art methods that only use a single low-rank or joint sparsity constraint. The results demonstrate that the proposed method can achieve accurate parameter estimation with both moderately and highly undersampled data. Although all methods performed fairly well with moderately undersampled data, the proposed method achieved much better performance (e.g., more accurate parameter values) than the other two methods with highly undersampled data. Simultaneously imposing low-rank and sparsity constraints can effectively improve the accuracy of fast MR parameter mapping with sparse sampling.

Model-Based MR Parameter Mapping With Sparsity Constraints: Parameter Estimation and Performance Bounds

Magnetic resonance parameter mapping (e.g., T1 mapping, T2 mapping, T*2 mapping) is a valuable tool for tissue characterization. However, its practical utility has been limited due to long data acquisition time. This paper addresses this problem with a new model-based parameter mapping method. The proposed method utilizes a formulation that integrates the explicit signal model with sparsity constraints on the model parameters, enabling direct estimation of the parameters of interest from highly undersampled, noisy k-space data. An efficient greedy-pursuit algorithm is described to solve the resulting constrained parameter estimation problem. Estimation-theoretic bounds are also derived to analyze the benefits of incorporating sparsity constraints and benchmark the performance of the proposed method. The theoretical properties and empirical performance of the proposed method are illustrated in a T2 mapping application example using computer simulations.

Fast MR parameter mapping using k‐t principal component analysis

Quantification of magnetic resonance parameters plays an increasingly important role in clinical applications, such as the detection and classification of neurodegenerative diseases. The major obstacle that remains for its widespread use in clinical routine is the long scanning times. Therefore, strategies that allow for significant decreases in scan time are highly desired. Recently, the k-t principal component analysis method was introduced for dynamic cardiac imaging to accelerate data acquisition. This is done by undersampling k-t space and constraining the reconstruction of the aliased data based on the k-t Broad-use Linear Acquisition Speed-up Technique (BLAST) concept and predetermined temporal basis functions. The objective of this study was to investigate whether the k-t principal component analysis concept can be adapted to parameter quantification, specifically allowing for significant acceleration of an inversion recovery fast imaging with steady state precession (TrueFISP) acquisition. We found that three basis functions and a single training data line in central k-space were sufficient to achieve up to an 8-fold acceleration of the quantification measurement. This allows for an estimation of relaxation times T(1) and T(2) and spin density in one slice with sub-millimeter in-plane resolution, in only 6 s. Our findings demonstrate that the k-t principal component analysis method is a potential candidate to bring the acquisition time for magnetic resonance parameter mapping to a clinically acceptable level.

Compressed sensing reconstruction for magnetic resonance parameter mapping

Compressed sensing (CS) holds considerable promise to accelerate the data acquisition in magnetic resonance imaging by exploiting signal sparsity. Prior knowledge about the signal can be exploited in some applications to choose an appropriate sparsifying transform. This work presents a CS reconstruction for magnetic resonance (MR) parameter mapping, which applies an overcomplete dictionary, learned from the data model to sparsify the signal. The approach is presented and evaluated in simulations and in in vivo T(1) and T(2) mapping experiments in the brain. Accurate T(1) and T(2) maps are obtained from highly reduced data. This model-based reconstruction could also be applied to other MR parameter mapping applications like diffusion and perfusion imaging.

Detection on Incipient Fungal Attack in Wood Using Magnetic Resonance Parameter Mapping Wood Studied by Liquid State NMR Measurements

Summary Magnetic resonance imaging delivers excellent, high-resolution spatial images of damp or wet wood that can supply information on internal defects of logs to optimise cutting patterns. In normal wood, the image contrast of magnetic resonance images is primarily influenced by the distribution of moisture content within the specimen. In the case of fungal attack, it is also affected by the inherent Spin-Lattice (T1) and Spin-Spin (T2) relaxation times. We investigate changes in relaxation times in beech wood (Fagus sylvatica L.) caused by fungal attack (Coniophora puteana (Schum.) Karst.) over 6 weeks. In addition, loss of compressive strength and mass loss were tested, allowing comparison to conventional methods. Magnetic resonance imaging showed superior sensitivity in detection of early stage of fungal decay in comparison to mass or strength loss assessment.