Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Using Radiomics.

RATIONALE AND OBJECTIVES: Distinguishing malignant from benign liver lesions based on magnetic resonance imaging (MRI) is an important but often challenging task, especially in noncirrhotic livers. We developed and externally validated a radiomics model to quantitatively assess T2-weighted MRI to distinguish the most common malignant and benign primary solid liver lesions in noncirrhotic livers. MATERIALS AND METHODS: Data sets were retrospectively collected from three tertiary referral centers (A, B, and C) between 2002 and 2018. Patients with malignant (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) and benign (hepatocellular adenoma and focal nodular hyperplasia) lesions were included. A radiomics model based on T2-weighted MRI was developed in data set A using a combination of machine learning approaches. The model was internally evaluated on data set A through cross-validation, externally validated on data sets B and C, and compared to visual scoring of two experienced abdominal radiologists on data set C. RESULTS: The overall data set included 486 patients (A: 187, B: 98, and C: 201). The radiomics model had a mean area under the curve (AUC) of 0.78 upon internal validation on data set A and a similar AUC in external validation (B: 0.74 and C: 0.76). In data set C, the two radiologists showed moderate agreement (Cohen's κ: 0.61) and achieved AUCs of 0.86 and 0.82. CONCLUSION: Our T2-weighted MRI radiomics model shows potential for distinguishing malignant from benign primary solid liver lesions. External validation indicated that the model is generalizable despite substantial MRI acquisition protocol differences. Pending further optimization and generalization, this model may aid radiologists in improving the diagnostic workup of patients with liver lesions.

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.

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.

The Value of Quantitative Musculoskeletal Imaging.

Musculoskeletal imaging is mainly based on the subjective and qualitative analysis of imaging examinations. However, integration of quantitative assessment of imaging data could increase the value of imaging in both research and clinical practice. Some imaging modalities, such as perfusion magnetic resonance imaging (MRI), diffusion MRI, or T2 mapping, are intrinsically quantitative. But conventional morphological imaging can also be analyzed through the quantification of various parameters. The quantitative data retrieved from imaging examinations can serve as biomarkers and be used to support diagnosis, determine patient prognosis, or monitor therapy.We focus on the value, or clinical utility, of quantitative imaging in the musculoskeletal field. There is currently a trend to move from volume- to value-based payments. This review contains definitions and examines the role that quantitative imaging may play in the implementation of value-based health care. The influence of artificial intelligence on the value of quantitative musculoskeletal imaging is also discussed.

Is T1ρ Mapping an Alternative to Delayed Gadolinium-enhanced MR Imaging of Cartilage in the Assessment of Sulphated Glycosaminoglycan Content in Human Osteoarthritic Knees? An in Vivo Validation Study.

PURPOSE: To determine if T1ρ mapping can be used as an alternative to delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC) in the quantification of cartilage biochemical composition in vivo in human knees with osteoarthritis. MATERIALS AND METHODS: This study was approved by the institutional review board. Written informed consent was obtained from all participants. Twelve patients with knee osteoarthritis underwent dGEMRIC and T1ρ mapping at 3.0 T before undergoing total knee replacement. Outcomes of dGEMRIC and T1ρ mapping were calculated in six cartilage regions of interest. Femoral and tibial cartilages were harvested during total knee replacement. Cartilage sulphated glycosaminoglycan (sGAG) and collagen content were assessed with dimethylmethylene blue and hydroxyproline assays, respectively. A four-dimensional multivariate mixed-effects model was used to simultaneously assess the correlation between outcomes of dGEMRIC and T1ρ mapping and the sGAG and collagen content of the articular cartilage. RESULTS: T1 relaxation times at dGEMRIC showed strong correlation with cartilage sGAG content (r = 0.73; 95% credibility interval [CI] = 0.60, 0.83) and weak correlation with cartilage collagen content (r = 0.40; 95% CI: 0.18, 0.58). T1ρ relaxation times did not correlate with cartilage sGAG content (r = 0.04; 95% CI: -0.21, 0.28) or collagen content (r = -0.05; 95% CI = -0.31, 0.20). CONCLUSION: dGEMRIC can help accurately measure cartilage sGAG content in vivo in patients with knee osteoarthritis, whereas T1ρ mapping does not appear suitable for this purpose. Although the technique is not completely sGAG specific and requires a contrast agent, dGEMRIC is a validated and robust method for quantifying cartilage sGAG content in human osteoarthritis subjects in clinical research.

Assessment of acute intestinal graft versus host disease by abdominal magnetic resonance imaging at 3 Tesla.

OBJECTIVES: After allogeneic stem cell transplantation (SCT), a reliable diagnosis of acute graft versus host disease (aGvHD) is essential for an early and successful treatment. It is the aim of this analysis to assess intestinal aGvHD by magnetic resonance imaging (MRI). METHODS: Prior to allogeneic SCT, 64 consecutive patients underwent abdominal MRI examination on a 3 T MR system, including axial and coronal T2w sequences and a three-dimensional dynamic T1w, contrast enhanced sequence. After SCT, 20 patients with suspected aGvHD received a second MRI as well as an endoscopic examination. RESULTS: Nine patients suffered from histologically proven intestinal aGvHD. In eleven patients intestinal aGvHD was excluded. In all aGvHD patients typical MRI findings with long-segment bowel wall thickening--always involving the terminal ileum--with profound submucosal oedema, were detected. The bowel wall was significantly thickened in patients with intestinal aGvHD. Bowel contrast enhancement spared the submucosa while demonstrating strong mucosal hyperemia. CONCLUSIONS: In intestinal aGvHD, a characteristic MR-appearance can be detected. This MRI pattern might facilitate an early and non-invasive diagnosis of intestinal aGvHD. MRI might thus be used as a sensitive tool to rule out or support the clinical diagnosis of aGvHD. KEY POINTS: • Acute intestinal graft versus host disease (aGvHD) can be assessed by MRI. • The aGvHD of the bowel demonstrates a characteristic MR imaging pattern. • Bowel wall shows extensive long-segment wall thickening with profound submucosal oedema. • Terminal ileum seems invariably affected; other bowel segments show variable involvement. • Colonoscopy in suspected aGvHD should include inspection of terminal ileum.

Reversible jump MCMC methods for fully automatic motion analysis in tagged MRI.

Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method for studying regional heart dynamics. It offers great potential for quantitative analysis of a variety of kine(ma)tic parameters, but its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we evaluate the performance of four frequently used concepts found in the literature (optical flow, harmonic phase (HARP) magnetic resonance imaging, active contour fitting, and non-rigid image registration) for cardiac motion analysis in 2D tMRI image sequences, using both synthetic image data (with ground truth) and real data from preclinical (small animal) and clinical (human) studies. In addition we propose a new probabilistic method for tag tracking that serves as a complementary step to existing methods. The new method is based on a Bayesian estimation framework, implemented by means of reversible jump Markov chain Monte Carlo (MCMC) methods, and combines information about the heart dynamics, the imaging process, and tag appearance. The experimental results demonstrate that the new method improves the performance of even the best of the four previous methods. Yielding higher consistency, accuracy, and intrinsic tag reliability assessment, the proposed method allows for improved analysis of cardiac motion.

Trans-dimensional MCMC methods for fully automatic motion analysis in tagged MRI.

Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method allowing quantitative analysis of regional heart dynamics. Its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we propose a novel probabilistic method for tag tracking, implemented by means of Bayesian particle filtering and a trans-dimensional Markov chain Monte Carlo (MCMC) approach, which efficiently combines information about the imaging process and tag appearance with prior knowledge about the heart dynamics obtained by means of non-rigid image registration. Experiments using synthetic image data (with ground truth) and real data (with expert manual annotation) from preclinical (small animal) and clinical (human) studies confirm that the proposed method yields higher consistency, accuracy, and intrinsic tag reliability assessment in comparison with other frequently used tag tracking methods.

Facilitating tumor functional assessment by spatially relating 3D tumor histology and in vivo MRI: image registration approach.

BACKGROUND: Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is complicated by deformations during pathological processing, and differences in scale and information content. METHODOLOGY/PRINCIPAL FINDINGS: This study proposes a methodology for establishing an accurate 3D relation between histological sections and high resolution in vivo MRI tumor data. The key features of the methodology are: 1) standardized acquisition and processing, 2) use of an intermediate ex vivo MRI, 3) use of a reference cutting plane, 4) dense histological sampling, 5) elastic registration, and 6) use of complete 3D data sets. Five rat pancreatic tumors imaged by T2*-w MRI were used to evaluate the proposed methodology. The registration accuracy was assessed by root mean squared (RMS) distances between manually annotated landmark points in both modalities. After elastic registration the average RMS distance decreased from 1.4 to 0.7 mm. The intermediate ex vivo MRI and the reference cutting plane shared by all three 3D images (in vivo MRI, ex vivo MRI, and 3D histology data) were found to be crucial for the accurate co-registration between the 3D histological data set and in vivo MRI. The MR intensity in necrotic regions, as manually annotated in 3D histology, was significantly different from other histologically confirmed regions (i.e., viable and hemorrhagic). However, the viable and the hemorrhagic regions showed a large overlap in T2(*)-w MRI signal intensity. CONCLUSIONS: The established 3D correspondence between tumor histology and in vivo MRI enables extraction of MRI characteristics for histologically confirmed regions. The proposed methodology allows the creation of a tumor database of spatially registered multi-spectral MR images and multi-stained 3D histology.

Image subtraction facilitates assessment of volume and density change in ground-glass opacities in chest CT.

OBJECTIVES: To study the impact of image subtraction of registered images on the detection of change in pulmonary ground-glass nodules identified on chest CT. MATERIALS AND METHODS: A cohort of 33 individuals (25 men, 8 women; age range 51-75 years) with 37 focal ground-glass opacities (GGO) were recruited from a lung cancer screening trial. For every participant, 1 to 3 follow-up scans were available (total number of pairs, 84). Pairs of scans of the same nodule were registered nonrigidly and then subtracted to enhance differences in size and density. Four observers rated size and density change of the GGO between pairs of scans by visual comparison alone and with additional availability of a subtraction image and indicated their confidence. An independent experienced chest radiologist served as an arbiter having all reader data, clinical data, and follow-up examinations available. Nodule pairs for which the arbiter could not establish definite progression, regression, or stability were excluded from further evaluation. This left 59 and 58 pairs for evaluation of size and density change, respectively. Weighted kappa statistics (kappa w) were used to assess interobserver agreement and agreement with the arbiter. Statistical significance was tested with a kappa z-test. RESULTS: When the subtraction image was available, the average interobserver kappa w improved from 0.52 to 0.66 for size change and from 0.47 to 0.57 for density change. Average agreement with the arbiter improved from 0.61 to 0.76 for size change and from 0.53 to 0.64 for density change. The effect was more pronounced when observer confidence without the subtraction image was low: agreement improved from 0.26 to 0.57 and from 0.19 to 0.47 in those cases. CONCLUSIONS: Image subtraction improves the evaluation of subtle changes in pulmonary ground-glass opacities and decreases interobserver variability.