Relating pre-treatment non-Gaussian intravoxel incoherent motion diffusion-weighted imaging to human papillomavirus status and response in oropharyngeal carcinoma.

Background and purpose: Diffusion-weighted imaging (DWI) is a promising technique for response assessment in head-and-neck cancer. Recently, we optimized Non-Gaussian Intravoxel Incoherent Motion Imaging (NG-IVIM), an extension of the conventional apparent diffusion coefficient (ADC) model, for the head and neck. In the current study, we describe the first application in a group of patients with human papillomavirus (HPV)-positive and HPV-negative oropharyngeal squamous cell carcinoma. The aim of this study was to relate ADC and NG-IVIM DWI parameters to HPV status and clinical treatment response. Materials and methods: Thirty-six patients (18 HPV-positive, 18 HPV-negative) were prospectively included. Presence of progressive disease was scored within one year. The mean pre-treatment ADC and NG-IVIM parameters in the gross tumor volume were compared between HPV-positive and HPV-negative patients. In HPV-negative patients, ADC and NG-IVIM parameters were compared between patients with and without progressive disease. Results: ADC, the NG-IVIM diffusion coefficient D, and perfusion fraction f were significantly higher, while pseudo-diffusion coefficient D* and kurtosis K were significantly lower in the HPV-negative compared to HPV-positive patients. In the HPV-negative group, a significantly lower D was found for patients with progressive disease compared to complete responders. No relation with ADC was observed. Conclusion: The results of our single-center study suggest that ADC is related to HPV status, but not an independent response predictor. The NG-IVIM parameter D, however, was independently associated to response in the HPV-negative group. Noteworthy in the opposite direction as previously thought based on ADC.

Eye Size and Shape in Relation to Refractive Error in Children: A Magnetic Resonance Imaging Study.

Purpose: The purpose of this study was to determine the association between eye shape and volume measured with magnetic resonance imaging (MRI) and optical biometry and with spherical equivalent (SE) in children. Methods: For this study, there were 3637 10-year-old children from a population-based birth-cohort study that underwent optical biometry (IOL-master 500) and T2-weighted MRI scanning (height, width, and volume). Cycloplegic refractive error was determined by automated refraction. The MRI images of the eyes were segmented using an automated algorithm combining atlas registration with voxel classification. Associations among optical biometry, anthropometry, MRI measurements, and RE were tested using Pearson correlation. Differences between refractive error groups were tested using ANOVA. Results: The mean volume of the posterior segment was 6350 (±680) mm3. Myopic eyes (SE ≤ -0.5 diopters [D]) had 470 mm3 (P < 0.001) and 970 mm3 (P < 0.001) larger posterior segment volume than emmetropic and hyperopic eyes (SE ≥ +2.0D), respectively. The majority of eyes (77.1%) had an oblate shape, but 47.4% of myopic eyes had a prolate shape versus 3.9% of hyperopic eyes. The correlation between SE and MRI-derived posterior segment length (r -0.51, P < 0.001) was stronger than the correlation with height (r -0.30, P < 0.001) or width of the eye (r -0.10, P < 0.001). Conclusions: In this study, eye shape at 10 years of age was predominantly oblate, even in eyes with myopia. Of all MRI measurements, posterior segment length was most prominently associated with SE. Whether eye shape predicts future myopia development or progression should be investigated in longitudinal studies.

White matter changes measured by multi-component MR Fingerprinting in multiple sclerosis.

T2-hyperintense lesions are the key imaging marker of multiple sclerosis (MS). Previous studies have shown that the white matter surrounding such lesions is often also affected by MS. Our aim was to develop a new method to visualize and quantify the extent of white matter tissue changes in MS based on relaxometry properties. We applied a fast, multi-parametric quantitative MRI approach and used a multi-component MR Fingerprinting (MC-MRF) analysis. We assessed the differences in the MRF component representing prolongedrelaxation time between patients with MS and controls and studied the relation between this component's volume and structural white matter damage identified on FLAIR MRI scans in patients with MS. A total of 48 MS patients at two different sites and 12 healthy controls were scanned with FLAIR and MRF-EPI MRI scans. MRF scans were analyzed with a joint-sparsity multi-component analysis to obtain magnetization fraction maps of different components, representing tissues such as myelin water, white matter, gray matter and cerebrospinal fluid. In the MS patients, an additional component was identified with increased transverse relaxation times compared to the white matter, likely representing changes in free water content. Patients with MS had a higher volume of the long- component in the white matter of the brain compared to healthy controls (B (95%-CI) = 0.004 (0.0006-0.008), p = 0.02). Furthermore, this MRF component had a moderate correlation (correlation coefficient R 0.47) with visible structural white matter changes on the FLAIR scans. Also, the component was found to be more extensive compared to structural white matter changes in 73% of MS patients. In conclusion, our MRF acquisition and analysis captured white matter tissue changes in MS patients compared to controls. In patients these tissue changes were more extensive compared to visually detectable white matter changes on FLAIR scans. Our method provides a novel way to quantify the extent of white matter changes in MS patients, which is underestimated using only conventional clinical MRI scans.

Systematic review of reconstruction techniques for accelerated quantitative MRI.

PURPOSE: To systematically review the techniques that address undersampling artifacts in accelerated quantitative magnetic resonance imaging (qMRI). METHODS: A literature search was conducted using the Embase, Medline, Web of Science Core Collection, Coherence Central Register of Controlled Trials, and Google Scholar databases for studies, published before July 2022 proposing reconstruction techniques for accelerated qMRI. Studies are reviewed according to inclusion criteria, and included studies are categorized based on the methodology used. RESULTS: A total of 292 studies included in the review are categorized. A technical overview of each category is provided, and the categories are described in a unified mathematical framework. The distribution of the reviewed studies over time, application domain, and parameters of interest is illustrated. CONCLUSION: An increasing trend in the number of articles that propose new techniques for accelerated qMRI reconstruction indicates the importance of acceleration in qMRI. The techniques are mostly validated for relaxometry parameters and brain scans. The categories of techniques are compared based on theoretical grounds, highlighting existing trends and potential gaps in the field.

MR Vascular Fingerprinting with Hybrid Gradient-Spin Echo Dynamic Susceptibility Contrast MRI for Characterization of Microvasculature in Gliomas.

Characterization of tumor microvasculature is important in tumor assessment and studying treatment response. This is possible by acquiring vascular biomarkers with magnetic resonance imaging (MRI) based on dynamic susceptibility contrast (DSC). We propose magnetic resonance vascular fingerprinting (MRVF) for hybrid echo planar imaging (HEPI) acquired during the first passage of the contrast agent (CA). The proposed approach was evaluated in patients with gliomas, and we simultaneously estimated vessel radius and relative cerebral blood volume. These parameters were also compared to the respective values estimated using the previously introduced vessel size imaging (VSI) technique. The results of both methods were found to be consistent. MRVF was also found to be robust to noise in the estimation of the parameters. DSC-HEPI-based MRVF provides characterization of microvasculature in gliomas with a short acquisition time and can be further improved in several ways to increase our understanding of tumor physiology.

Multi-echo gradient echo pulse sequences: which is best for PRFS MR thermometry guided hyperthermia?

PURPOSE: MR thermometry (MRT) enables noninvasive temperature monitoring during hyperthermia treatments. MRT is already clinically applied for hyperthermia treatments in the abdomen and extremities, and devices for the head are under development. In order to optimally exploit MRT in all anatomical regions, the best sequence setup and post-processing must be selected, and the accuracy needs to be demonstrated. METHODS: MRT performance of the traditionally used double-echo gradient-echo sequence (DE-GRE, 2 echoes, 2D) was compared to multi-echo sequences: a 2D fast gradient-echo (ME-FGRE, 11 echoes) and a 3D fast gradient-echo sequence (3D-ME-FGRE, 11 echoes). The different methods were assessed on a 1.5 T MR scanner (GE Healthcare) using a phantom cooling down from 59 °C to 34 °C and unheated brains of 10 volunteers. In-plane motion of volunteers was compensated by rigid body image registration. For the ME sequences, the off-resonance frequency was calculated using a multi-peak fitting tool. To correct for B0 drift, the internal body fat was selected automatically using water/fat density maps. RESULTS: The accuracy of the best performing 3D-ME-FGRE sequence was 0.20 °C in phantom (in the clinical temperature range) and 0.75 °C in volunteers, compared to DE-GRE values of 0.37 °C and 1.96 °C, respectively. CONCLUSION: For hyperthermia applications, where accuracy is more important than resolution or scan-time, the 3D-ME-FGRE sequence is deemed the most promising candidate. Beyond its convincing MRT performance, the ME nature enables automatic selection of internal body fat for B0 drift correction, an important feature for clinical application.

Multidelay pseudocontinuous arterial spin labeling to measure blood flow in the head and neck.

Perfusion MRI is promising for the assessment, prediction, and monitoring of radiation toxicity in organs at risk in head and neck cancer. Arterial spin labeling (ASL) may be an attractive alternative for conventional perfusion MRI, that does not require the administration of contrast agents. However, currently, little is known about the characteristics and performance of ASL in healthy tissues in the head and neck region. Therefore, the purpose of this study was to optimize and evaluate multidelay pseudocontinuous ASL (pCASL) for the head and neck region and to explore nominal values and measurement repeatability for the blood flow (BF), and the transit time and T1 values needed for BF quantification in healthy tissues. Twenty healthy volunteers underwent a scan session consisting of four repeats of multidelay pCASL (postlabel delays: 1000, 1632, 2479 ms). Regions of interest were defined in the parotid glands, submandibular glands, tonsils, and the cerebellum (as a reference). Nominal values of BF were calculated as the average over four repeats per volunteer. The repeatability coefficient and within-subject coefficient of repeatability (wCV) of BF were calculated. The effect of T1 (map vs. cohort average) and transit time correction on BF was investigated. The mean BF (± SE) was 55.7 ± 3.1 ml/100 g/min for the parotid glands, 41.2 ± 2.8 ml/100 g/min for the submandibular glands, and 32.3 ± 2.2 ml/100 g/min for the tonsils. The best repeatability was found in the parotid glands (wCV = 13.3%-16.1%), followed by the submandibular glands and tonsils (wCV = 20.0%-24.6%). On average, the effect of T1 and transit time correction on BF was limited, although substantial bias occurred in individual acquisitions. In conclusion, we demonstrated the feasibility of BF measurements in the head and neck region using multidelay pCASL and reported on nominal BF values, BF repeatability, the effect of T1, and transit time in various tissues in the head and neck region.

ADEPT: Accurate Diffusion Echo-Planar imaging with multi-contrast shoTs.

PURPOSE: To introduce a novel imaging and parameter estimation framework for accurate multi-shot diffusion MRI. THEORY AND METHODS: We propose a new framework called ADEPT (Accurate Diffusion Echo-Planar imaging with multi-contrast shoTs) that enables fast diffusion MRI by allowing diffusion contrast settings to change between shots in a multi-shot EPI acquisition (i.e., intra-scan modulation). The framework estimates diffusion parameter maps directly from the acquired intra-scan modulated k-space data, while simultaneously accounting for shot-to-shot phase inconsistencies. The performance of the estimation framework is evaluated using Monte Carlo simulation studies and in-vivo experiments and compared to that of reference methods that rely on parallel imaging for shot-to-shot phase correction. RESULTS: Simulation and real-data experiments show that ADEPT yields more accurate and more precise estimates of the diffusion metrics in multi-shot EPI data in comparison with the reference methods. CONCLUSION: ADEPT allows fast multi-shot EPI diffusion MRI without significantly degrading the accuracy and precision of the estimated diffusion maps.

Pre-contrast MAGiC in treated gliomas: a pilot study of quantitative MRI.

Quantitative MR imaging is becoming more feasible to be used in clinical work since new approaches have been proposed in order to substantially accelerate the acquisition and due to the possibility of synthetically deriving weighted images from the parametric maps. However, their applicability has to be thoroughly validated in order to be included in clinical practice. In this pilot study, we acquired Magnetic Resonance Image Compilation scans to obtain T1, T2 and PD maps in 14 glioma patients. Abnormal tissue was segmented based on conventional images and using a deep learning segmentation technique to define regions of interest (ROIs). The quantitative T1, T2 and PD values inside ROIs were analyzed using the mean, the standard deviation, the skewness and the kurtosis and compared to the quantitative T1, T2 and PD values found in normal white matter. We found significant differences in pre-contrast T1 and T2 values between abnormal tissue and healthy tissue, as well as between T1w-enhancing and non-enhancing regions. ROC analysis was used to evaluate the potential of quantitative T1 and T2 values for voxel-wise classification of abnormal/normal tissue (AUC = 0.95) and of T1w enhancement/non-enhancement (AUC = 0.85). A cross-validated ROC analysis found high sensitivity (73%) and specificity (73%) with AUCs up to 0.68 on the a priori distinction between abnormal tissue with and without T1w-enhancement. These results suggest that normal tissue, abnormal tissue, and tissue with T1w-enhancement are distinguishable by their pre-contrast quantitative values but further investigation is needed.

To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging.

Multi-slice (MS) super-resolution reconstruction (SRR) methods have been proposed to improve the trade-off between resolution, signal-to-noise ratio and scan time in magnetic resonance imaging. MS-SRR consists in the estimation of an isotropic high-resolution image from a series of anisotropic MS images with a low through-plane resolution, where the anisotropic low-resolution images can be acquired according to different acquisition schemes. However, it is yet unclear how these schemes compare in terms of statistical performance criteria, especially for regularized MS-SRR. In this work, the estimation performance of two commonly adopted MS-SRR acquisition schemes based on shifted and rotated MS images respectively are evaluated in a Bayesian framework. The maximum a posteriori estimator, which introduces regularization by incorporating prior knowledge in a statistically well-defined way, is put forward as the estimator of choice and its accuracy, precision, and Bayesian mean squared error (BMSE) are used as performance criteria. Analytic calculations as well as Monte Carlo simulation experiments show that the rotated scheme outperforms the shifted scheme in terms of precision, accuracy, and BMSE. Furthermore, the superior performance of the rotated scheme is confirmed in real data experiments and in retrospective simulation experiments with and without inter-image motion. Results show that the rotated scheme allows regularized MS-SRR with a higher accuracy and precision than the shifted scheme, besides being more resilient to motion.

A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine.

Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning to accelerate the imaging process. We propose the quantitative Recurrent Inference Machine (qRIM), with a unified forward model for joint reconstruction and R2*-mapping from sparse data, embedded in a Recurrent Inference Machine (RIM), an iterative inverse problem-solving network. To study the dependency of the proposed extension of the unified forward model to network architecture, we implemented and compared a quantitative End-to-End Variational Network (qE2EVN). Experiments were performed with high-resolution multi-echo gradient echo data of the brain at 7T of a cohort study covering the entire adult life span. The error in reconstructed R2* from undersampled data relative to reference data significantly decreased for the unified model compared to sequential image reconstruction and parameter fitting using the RIM. With increasing acceleration factor, an increasing reduction in the reconstruction error was observed, pointing to a larger benefit for sparser data. Qualitatively, this was following an observed reduction of image blurriness in R2*-maps. In contrast, when using the U-Net as network architecture, a negative bias in R2* in selected regions of interest was observed. Compressed Sensing rendered accurate, but less precise estimates of R2*. The qE2EVN showed slightly inferior reconstruction quality compared to the qRIM but better quality than the U-Net and Compressed Sensing. Subcortical maturation over age measured by a linearly increasing interquartile range of R2* in the striatum was preserved up to an acceleration factor of 9. With the integrated prior of the unified forward model, the proposed qRIM can exploit the redundancy among repeated measurements and shared information between tasks, facilitating relaxometry in accelerated MRI.

Model-based super-resolution reconstruction with joint motion estimation for improved quantitative MRI parameter mapping.

Quantitative Magnetic Resonance (MR) imaging provides reproducible measurements of biophysical parameters, and has become an essential tool in clinical MR studies. Unfortunately, 3D isotropic high resolution (HR) parameter mapping is hardly feasible in clinical practice due to prohibitively long acquisition times. Moreover, accurate and precise estimation of quantitative parameters is complicated by inevitable subject motion, the risk of which increases with scanning time. In this paper, we present a model-based super-resolution reconstruction (SRR) method that jointly estimates HR quantitative parameter maps and inter-image motion parameters from a set of 2D multi-slice contrast-weighted images with a low through-plane resolution. The method uses a Bayesian approach, which allows to optimally exploit prior knowledge of the tissue and noise statistics. To demonstrate its potential, the proposed SRR method is evaluated for a T1 and T2 quantitative mapping protocol. Furthermore, the method's performance in terms of precision, accuracy, and spatial resolution is evaluated using simulated as well as real brain imaging experiments. Results show that our proposed fully flexible, quantitative SRR framework with integrated motion estimation outperforms state-of-the-art SRR methods for quantitative MRI.

Time efficiency analysis for undersampled quantitative MRI acquisitions.

To realize Quantitative MRI (QMRI) with clinically acceptable scan time, acceleration factors achieved by conventional parallel imaging techniques are often inadequate. Further acceleration is possible using model-based reconstruction. We propose a theoretical metric called TEUSQA: Time Efficiency for UnderSampled QMRI Acquisitions to inform sequence design and sample pattern optimisation. TEUSQA is designed for a particular class of reconstruction techniques that directly estimate tissue parameters, possibly using prior information to regularize the estimation. TEUSQA can be used to evaluate undersampling patterns for multi-contrast QMRI sequences targeting any tissue parameter. To verify the time efficiency predicted by TEUSQA, we performed Monte Carlo simulations and an accelerated parameter mapping with two sequences (Inversion prepared fast spin echo for T1 and T2 mapping and 3D GRASE for T2 and B0 inhomogeneity mapping). Using TEUSQA, we assessed several ways to generate undersampling patterns in silico, providing insight into the relation between sample distribution and time efficiency for different acceleration factors. The time efficiency predicted by TEUSQA was within 15% of that observed in the Monte Carlo simulations and the prospective acquisition experiment. The assessment of undersampling patterns showed that a class of good patterns could be obtained by low-discrepancy sampling. We believe that TEUSQA offers a valuable instrument for developers of novel QMRI sequences pushing the boundaries of acceleration to achieve clinically feasible protocols. Finally, we applied a time-efficient undersampling pattern selected using TEUSQA for a 32-fold accelerated scan to map T1 & T2 mapping of a healthy volunteer.

Dependency of R2 and R2 * relaxation on Gd-DTPA concentration in arterial blood: Influence of hematocrit and magnetic field strength.

Dynamic susceptibility contrast (DSC) MRI is clinically used to measure brain perfusion by monitoring the dynamic passage of a bolus of contrast agent through the brain. For quantitative analysis of the DSC images, the arterial input function is required. It is known that the original assumption of a linear relation between the R2(*) relaxation and the arterial contrast agent concentration is invalid, although the exact relation is as of yet unknown. Studying this relation in vitro is time-consuming, because of the widespread variations in field strengths, MRI sequences, contrast agents, and physiological conditions. This study aims to simulate the R2(*) versus contrast concentration relation under varying physiological and technical conditions using an adapted version of an open-source simulation tool. The approach was validated with previously acquired data in human whole blood at 1.5 T by means of a gradient-echo sequence (proof-of-concept). Subsequently, the impact of hematocrit, field strength, and oxygen saturation on this relation was studied for both gradient-echo and spin-echo sequences. The results show that for both gradient-echo and spin-echo sequences, the relaxivity increases with hematocrit and field strength, while the hematocrit dependency was nonlinear for both types of MRI sequences. By contrast, oxygen saturation has only a minor effect. In conclusion, the simulation setup has proven to be an efficient method to rapidly calibrate and estimate the relation between R2(*) and gadolinium concentration in whole blood. This knowledge will be useful in future clinical work to more accurately retrieve quantitative information on brain perfusion.

Multi-contrast multi-shot EPI for accelerated diffusion MRI.

The clinical application of diffusion MRI is practically hindered by its long scan time. In this work, we introduce a novel imaging and parameter estimation framework for time-efficient diffusion MRI. To improve the scan efficiency, we propose ADEPT (Accelerated Diffusion EPI with multi-contrast shoTs), in which diffusion contrast settings are allowed to change between shots in a multi-shot EPI acquisition (i.e. intra-scan modulation). The framework simultaneously corrects for artifacts related to shot-to-shot phase inconsistencies in multi-shot imaging by iteratively estimating the phase map parameters along with the diffusion model parameters directly from the acquired intra-scan modulated k-space data. Monte Carlo simulation experiments show the effective estimation of diffusion tensor parameters in multi-shot EPI diffusion imaging.

Accuracy and repeatability of QRAPMASTER and MRF-vFA.

Our purpose is to evaluate bias and repeatability of the quantitative MRI sequences QRAPMASTER, based on steady-state imaging, and variable Flip Angle MRF (MRF-VFA), based on the transient response. Both techniques are assessed with a standardized phantom and five volunteers on 1.5 T and 3 T clinical scanners. All scans were repeated eight times in consecutive weeks. In the phantom, the mean bias±95% confidence interval for T1 values with QRAPMASTER was 10 ± 10% on 1.5 T and 4 ± 13% on 3.0 T. The mean bias for T1 values with MRF-vFA was 21 ± 17% on 1.5 T and 9 ± 9% on 3.0 T. For T2 values the mean bias with QRAPMASTER was 12 ± 3% on 1.5 T and 23 ± 1% on 3.0 T. For T2 values the mean bias with MRF-vFA was 17 ± 1% on 1.5 T and 19 ± 2% on 3.0 T. QRAPMASTER estimated lower T1 and T2 values than MRF-vFA. Repeatability was good with low coefficients of variation (CoV). Mean CoV ± 95% confidence interval for T1 were 3.2 ± 0.4% on 1.5 T and 4.5 ± 0.8% on 3.0 T with QRAPMASTER and 2.7% ± 0.2% on 1.5 T and 2.5 ± 0.2% on 3.0 T with MRF-vFA. For T2 were 3.3 ± 1.9% on 1.5 T and 3.2 ± 0.6% on 3.0 T with QRAPMASTER and 2.0 ± 0.4% on 1.5 T and 5.7 ± 1.0% on 3.0 T with MRF-vFA. The in-vivo T1 and T2 are in the range of values previously reported by other authors. The in-vivo mean CoV ± 95% confidence interval in gray matter were for T1 1.7 ± 0.2% using QRAPMASTER and 0.7 ± 0.5% using MRF-vFA and for T2 were 0.9 ± 0.4% using QRAPMASTER and 2.4 ± 0.5% using MRF-vFA. In white matter were for T1 0.9 ± 0.3% using QRAPMASTER and 1.3 ± 1.1% using MRF-vFA and for T2 were 0.7 ± 0.4% using QRAPMASTER and 2.4 ± 0.4% using MRF-vFA. A GLM analysis showed that the variations in T1 and T2 mainly depend on the field strength and the subject, but not on the follow-up repetition in different days. This confirms the high repeatability of QRAPMASTER and MRF-vFA. In summary, QRAPMASTER and MRF-vFA on both systems were highly repeatable with moderate accuracy, providing results comparable to standard references. While repeatability was similar for both methods, QRAPMASTER was more accurate. QRAPMASTER is a tested commercial product but MRF-vFA is 4.77 times faster, which would ease the inclusion of quantitative relaxometry.

From signal-based to comprehensive magnetic resonance imaging.

We present and evaluate a new insight into magnetic resonance imaging (MRI). It is based on the algebraic description of the magnetization during the transient response-including intrinsic magnetic resonance parameters such as longitudinal and transverse relaxation times (T1, T2) and proton density (PD) and experimental conditions such as radiofrequency field (B1) and constant/homogeneous magnetic field (B0) from associated scanners. We exploit the correspondence among three different elements: the signal evolution as a result of a repetitive sequence of blocks of radiofrequency excitation pulses and encoding gradients, the continuous Bloch equations and the mathematical description of a sequence as a linear system. This approach simultaneously provides, in a single measurement, all quantitative parameters of interest as well as associated system imperfections. Finally, we demonstrate the in-vivo applicability of the new concept on a clinical MRI scanner.

Association Between T2* Relaxation Times Derived From Ultrashort Echo Time MRI and Symptoms During Exercise Therapy for Patellar Tendinopathy: A Large Prospective Study.

BACKGROUND: Exercise therapy is considered preferential treatment for patellar tendinopathy (PT). However, there is conflicting evidence for structural patellar tendon adaptation in response to exercise therapy and its association with symptoms is weak. PURPOSE: To assess the association between 1) T2* relaxation times and symptom severity; 2) baseline T2* and clinical outcome; and 3) longitudinal T2* changes and clinical outcome in athletes with PT performing exercise therapy. STUDY TYPE: Randomized controlled clinical trial. SUBJECTS: Seventy-six athletes (18-35 years) with clinically diagnosed and ultrasound-confirmed PT. FIELD STRENGTH/SEQUENCE: 3D gradient echo sequence (3.0 T). ASSESSMENT: Patients were enrolled in a randomized trial of progressive tendon-loading exercises (PTLE) versus eccentric exercise therapy (EET). Symptoms were assessed using the Victorian Institute of Sports Assessment (VISA-P) questionnaire. 3D-Ultrashort echo time (UTE)-MRI was acquired at baseline, 12 and 24 weeks. Voxel-wise T2* relaxation times were quantified using mono-exponential and bi-exponential models. T2* analysis was performed in three patellar tendon tissue compartments representing: aligned collagen, degenerative tissue, and interface. STATISTICAL TESTS: Adjusted general linear, mixed-linear models, and generalized estimating equations. RESULTS: We included 76 patients with PT (58 men, mean age 24 ± 4 years); 38 in the PTLE-group and 38 in the EET-group, of which 57 subjects remained eligible for analysis. T2* relaxation times were significantly associated with VISA-P in degenerative and interface tissues of the patellar tendon. No association was found between baseline T2* and VISA-P after 24 weeks (P > 0.29). The estimated mean T2* in degenerative tissue decreased from 14 msec (95%CI: 12-16) at baseline to 13 msec (95%CI: 11-15) at 12 weeks and to 13 msec (95%CI: 10-15) at 24 weeks. The significant decrease in T2* from baseline to 24 weeks was associated with improved clinical outcome. DATA CONCLUSION: Tissue-specific T2* relaxation times, identified with 3D-UTE-MRI, decreased significantly in athletes with patellar tendinopathy performing exercise therapy and this decrease was associated with improved clinical outcome. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 4.

T2 mapping 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 T2 values were observed across centers, both in vivo and in the phantom. CONCLUSIONS: The results of this study suggest that T2 mapping can be used to longitudinal assess cartilage degeneration in multicenter studies. Given the differences in absolute cartilage T2 values across centers, absolute T2 values derived from various centers in multicenter multivendor trials should not be pooled.

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.