Quantitative DCE-MRI demonstrates increased blood perfusion in Hoffa’s fat pad signal abnormalities in knee osteoarthritis, but not in patellofemoral pain

Objective: Infrapatellar fat pad (IPFP) fat-suppressed T2 (T2FS) hyperintense regions on MRI are an important imaging feature of knee osteoarthritis (OA) and are thought to represent inflammation. These regions are also common in non-OA subjects, and may not always be linked to inflammation. Our aim was to evaluate quantitative blood perfusion parameters, as surrogate measure of inflammation, within T2FS-hyperintense regions in patients with OA, with patellofemoral pain (PFP) (supposed OA precursor), and control subjects. Methods: Twenty-two knee OA patients, 35 PFP patients and 43 healthy controls were included and underwent MRI, comprising T2 and DCE-MRI sequences. T2FS-hyperintense IPFP regions were delineated and a reference region was drawn in adjacent IPFP tissue with normal signal intensity. After fitting the extended Tofts pharmacokinetic model, quantitative DCE-MRI perfusion parameters were compared between the two regions within subjects in each subgroup, using a paired Wilcoxon signed-rank test. Results: T2FS-hyperintense IPFP regions were present in 16 of 22 (73%) OA patients, 13 of 35 (37%) PFP patients, and 14 of 43 (33%) controls. DCE-MRI perfusion parameters were significantly different between regions with and without a T2FS-hyperintense signal in OA patients, demonstrating higher Ktrans compared to normal IFPF tissue (0.039 min−1 versus 0.025 min−1, p = 0.017) and higher Ve (0.157 versus 0.119, p = 0.010). For PFP patients and controls no significant differences were found. Conclusions: IPFP T2FS-hyperintense regions are associated with higher perfusion in knee OA patients in contrast to identically appearing regions in PFP patients and controls, pointing towards an inflammatory pathogenesis in OA only. Key Points: • Morphologically identical appearing T2FS-hyperintense infrapatellar fat pad regions show different perfusion in healthy subjects, subjects with patellofemoral pain, and subjects with knee osteoarthritis. • Elevated DCE-MRI perfusion parameters within T2FS-hyperintense infrapatellar fat pad regions in patients with osteoarthritis suggest an inflammatory pathogenesis in osteoarthritis, but not in patellofemoral pain and healthy subjects

Autocalibrated parallel imaging reconstruction with sampling pattern optimization for GRASE: APIR4GRASE

Purpose: To reduce artifacts and scan time of GRASE imaging by selecting an optimal sampling pattern and jointly reconstructing gradient echo and spin echo images. Methods: We jointly reconstruct images for the different echo types by considering these as additional virtual coil channels in the novel Autocalibrated Parallel Imaging Reconstruction with Sampling Pattern Optimization for GRASE (APIR4GRASE) method. Besides image reconstruction, we identify optimal sampling patterns for the acquisition. The selected optimal patterns were validated on phantom and in-vivo acquisitions. Comparison to the conventional GRASE without acceleration, and to the GRAPPA reconstruction with a single echo type was also performed. Results: Using identified optimal sampling patterns, APIR4GRASE eliminated modulation artifacts in both phantom and in-vivo experiments; mean square error (MSE) was reduced by 78% and 94%, respectively, compared to the conventional GRASE with similar scan time. Both artifacts and g-factor were reduced compared to the GRAPPA reconstruction with a single echo type. Conclusion: APIR4GRASE substantially improves the speed and quality of GRASE imaging over the state-of-the-art, and is able to reconstruct both spin echo and gradient echo images

APIR4EMC: Autocalibrated parallel imaging reconstruction for extended multi-contrast imaging

Purpose: To improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC). Methods: APIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment. Results: Compared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm3 isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results. Conclusion: Compared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging

An optimal acquisition and post-processing pipeline for hybrid IVIM-DKI in head and neck

Purpose: To optimize the diffusion-weighting b values and postprocessing pipeline for hybrid intravoxel incoherent motion diffusion kurtosis imaging in the head and neck region. Methods: Optimized diffusion-weighting b value sets ranging between 5 and 30 b values were constructed by optimizing the Cramér-Rao lower bound of the hybrid intravoxel incoherent motion diffusion kurtosis imaging model. With this model, the perfusion fraction, pseudodiffusion coefficient, diffusion coefficient, and kurtosis were estimated. Sixteen volunteers were scanned with a reference b value set and 3 repeats of the optimized sets, of which 1 with volunteers swallowing on purpose. The effects of (1) b value optim

Predicting Global Cognitive Decline in the General Population Using the Disease State Index

Background: Identifying persons at risk for cognitive decline may aid in early detection of persons at risk of dementia and to select those that would benefit most from therapeutic or preventive measures for dementia. Objective: In this study we aimed to validate whether cognitive decline in the general population can be predicted with multivariate data using a previously proposed supervised classification method: Disease State Index (DSI). Methods: We included 2,542 participants, non-demented and without mild cognitive impairment at baseline, from the population-based Rotterdam Study (mean age 60.9 ± 9.1 years). Participants with significant global cognitive decline were defined as the 5% of participants with the largest cognitive decline per year. We trained DSI to predict occurrence of significant global cognitive decline using a large variety of baseline features, including magnetic resonance imaging (MRI) features, cardiovascular risk factors, APOE-ε4 allele carriership, gait features, education, and baseline cognitive function as predictors. The prediction performance was assessed as area under the receiver operating characteristic curve (AUC), using 500 repetitions of 2-fold cross-validation experiments, in which (a randomly selected) half of the data was used for training and the other half for testing. Results: A mean AUC (95% confidence interval) for DSI prediction was 0.78 (0.77–0.79) using only age as input feature. When using all available features, a mean AUC of 0.77 (0.75–0.78) was obtained. Without age, and with age-corrected features and feature selection on MRI features, a mean AUC of 0.70 (0.63–0.76) was obtained, showing the potential of other features besides age. Conclusion: The best performance in the prediction of global cognitive decline in the general population by DSI was obtained using only age as input feature. Other features showed potential, but did not improve prediction. Future studies should evaluate whether the performance could be improved by new features, e.g., longitudinal features, and other prediction methods

Fractional order vs. exponential fitting in UTE MR imaging of the patellar tendon

Purpose: Quantification of the T2 ∗ relaxation time constant is relevant in various magnetic resonance imaging applications. Mono- or bi-exponential models are typically used to determine these parameters. However, in case of complex, heterogeneous tissues these models could lead to inaccurate results. We compared a model, provided by the fractional-order extension of the Bloch equation with the conventional models. Methods: Axial 3D ultra-short echo time (UTE) scans were acquired using a 3.0 T MRI and a 16-channel surface coil. After image registration, voxel-wise T2 ∗ was quantified with mono-exponential, bi-exponential and fractional-order fitting. We evaluated all three models repeatability and the bias of their derived parameters by fitting at various noise levels. To investigate the effect of the SNR for the different models, a Monte-Carlo experiment with 1000 repeats was performed for different noise levels for one subject. For a cross-sectional investigation, we used the mean fitted values of the ROIs in five volunteers. Results: Comparing the mono-exponential and the fractional order T2 ∗ maps, the fractional order fitting method yielded enhanced contrast and an improved delineation of the different tissues. In the case of the bi-exponential method, the long T2 ∗ component map demonstrated the anatomy clearly with high contrast. Simulations showed a nonzero bias of the parameters for all three mathematical models. ROI based fitting showed that the T2 ∗ values were different depending on the applied method, and they differed most for the patellar tendon in all subjects. Conclusions: In high SNR cases, the fractional order and bi-exponential models are both performing well with low bias. However, in all observed cases, one of the bi-exponential components has high standard deviation in T2 ∗. The bi-exponential model is suitable for T2 ∗ mapping, but we recommend using the fractional order model for cases of low SNR

T1 mapping in the rat myocardium at 7 Tesla using a modified CINE inversion recovery sequence

Purpose To evaluate the reproducibility and sensitivity of the modified CINE inversion recovery (mCINE-IR) acquisition on rats for measuring the myocardial T1 at 7 Tesla. Materials and Methods The recently published mCINE-IR acquisition on humans was applied on rats for the first time, enabling the possibility of translational studies with an identical sequence. Simulations were used to study signal evolution and heart rate dependency. Gadolinium phantoms, a heart specimen and a healthy rat were used to study reproducibility. Two cryo-infarcted rats were scanned to measure late gadolinium enhancement (LGE). Results In the phantom reproducibility studies the T1 measurements had a maximum coefficient of variation (COV) of 1.3%. For the in vivo reproducibility the COV was below 5% in the anterior cardiac segments. In simulations with phantoms and specimens, a heart rate dependency of approximately 0.5 ms/bpm was present. The T1 maps of the cryo-infarcted rats showed a clear lowering of T1 in de LGE region. Conclusion The results show that mCINE-IR is highly reproducible and that the sensitivity allows detecting T1 changes in the rat myocardium

Tissue-Specific T2* Biomarkers in Patellar Tendinopathy by Subregional Quantification Using 3D Ultrashort Echo Time MRI

Background: Quantitative MRI of patellar tendinopathy (PT) can be challenging due to spatial variation of T2* relaxation times. Purpose: 1) To compare T2* quantification using a standard approach with analysis in specific tissue compartments of the patellar tendon. 2) To evaluate test–retest reliability of different methods for fitting ultrashort echo time (UTE)-relaxometry data. Study Type: Prospective. Subjects: Sixty-five athletes with PT. Field Strength/Sequence: 3D UTE scans covering the patellar tendon were acquired using a 3.0T scanner and a 16-channel surface coil. Assessment: Voxelwise median T2* was quantified with monoexponential, fractional-order, and biexponential fitting. We applied two methods for T2* analysis: first, a standard approach by analyzing all voxels covering the proximal patellar tendon. Second, within subregions of the patellar tendon, by using thresholds on biexponential fitting parameter percentage short T2* (0–30% for mostly long T2*, 30–60% for mixed T2*, and 60–100% for mostly short T2*). Statistical Tests: Average test–retest reliability was assessed in three athletes using coefficients-of-variation (CV) and coefficients-of-repeatability (CR). Results: With standard image analysis, we found a median [interquartile range, IQR] monoexponential T2* of 6.43 msec [4.32–8.55] and fractional order T2* 4.39 msec [3.06–5.78]. The percentage of short T2* components was 52.9% [35.5–69.6]. Subregional monoexponential T2* was 13.78 msec [12.11–16.46], 7.65 msec [6.49–8.61], and 3.05 msec [2.52–3.60] and fractional order T2* 11.82 msec [10.09–14.44], 5.14 msec [4.25–5.96], and 2.19 msec [1.82–2.64] for 0–30%, 30–60%, and 60–100% short T2*, respectively. Biexponential component short T2* was 1.693 msec [1.417–2.003] for tissue with mostly short T2* and long T2* of 15.79 msec [13.47–18.61] for mostly long T2*. The average CR (CV) was 2 msec (15%), 2 msec (19%) and 10% (22%) for monoexponential, fractional order and percentage short T2*, respectively. Data Conclusion: Patellar tendinopathy is characterized by regional variability in binding states of water. Quantitative multicompartment T2* analysis in PT can be facilitated using a voxel selection method based on using biexponential fitting parameters. Level of Evidence: 1. Technical Efficacy Stage: 1

Quantitative volume and dynamic contrast-enhanced MRI derived perfusion of the infrapatellar fat pad in patellofemoral pain

Background: Patellofemoral pain (PFP) is a common knee condition and possible precursor of knee osteoarthritis (OA). Inflammation, leading to an increased perfusion, or increased volume of the infrapatellar fat pad (IPFP) may induce knee pain. The aim of the study was to compare quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters, as imaging biomarkers of inflammation, and volume of the IPFP between patients with PFP and controls and between patients with and without IPFP edema or joint effusion. Methods: Patients with PFP and healthy controls were included and underwent non-fat suppressed 3D fast-spoiled gradient-echo (FSPGR) and DCE-MRI. Image registration was applied to correct for motion. The IPFP was delineated on FSPGR using Horos software. Volume was calculated and quantitative perfusion parameters were extracted by fitting extended Tofts' pharmacokinetic model. Differences in volume and DCE-MRI parameters between patients and controls were tested by linear regression analyses. IPFP edema and effusion were analyzed identically. Results: Forty-three controls and 35 PFP patients were included. Mean IPFP volume was 26.04 (4.18) mL in control subjects and 27.52 (5.37) mL in patients. Median Ktrans was 0.017 (0.016) min-1 in control subjects and 0.016 (0.020) min-1 in patients. None of the differences in volume and perfusion parameters were statistically significant. Knees with effusion showed a higher perfusion of the IPFP compared to knees without effusion in patients only. Conclusions: The IPFP has been implicated as source of knee pain, but higher DCE-MR blood perfusion, an imaging biomarker of inflammation, and larger volume are not associated with PFP. Patient's knees with effusion showed a higher perfusion, pointing towards inflammation

T2mapping of healthy knee cartilage: Multicenter multivendor reproducibility

Background: T2 mapping is increasingly used to quantify cartilage degeneration in knee osteoarthritis (OA), yet reproducibility studies in a multicenter setting are limited. The purpose of this study was to determine the longitudinal reproducibility and multicenter variation of cartilage T2 mapping, using various MRI equipment and acquisition protocols. Methods: In this prospective multicenter study, four traveling, healthy human subjects underwent T2 mapping twice at five different centers with a 6-month-interval. Centers had various MRI scanners, field strengths, and T2 mapping acquisition protocols. Mean T2 values were calculated in six cartilage regions of interest (ROIs) as well as an average value per patient. A phantom was scanned once at each center. To evaluate longitudinal reproducibility, intraclass correlation coefficients (ICC), root-mean-square coefficient of variation (RMS-CV), and a Bland-Altman plot were used. To assess the variation of in vivo and phantom T2 values across centers, ANOVA was performed. Results: ICCs of the T2 mapping measurements per ROI and the ROI's combined ranged from 0.73 to 0.91, indicating good to excellent longitudinal reproducibility. RMS-CVs ranged from 1.1% to 1.5% (per ROI) and 0.6% to 1.6% (ROIs combined) across the centers. A Bland-Altman plot did not reveal a systematic error. Evident, but consistent, discrepancies in T2values were observed across centers, both in vivo and in the phantom. Conclusions: The results of this study suggest that T2mapping can be used to longitudinal assess cartilage degeneration in multicenter studies. Given the differences in absolute cartila