Quantitative Magnetic Resonance Imaging (qMRI) of the Small Bowel in Crohn's Disease: State-of-the-Art and Future Directions.

Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract in which repeated episodes of acute inflammation may lead to long-term bowel damage. Cross-sectional imaging is used in conjunction with endoscopy to diagnose and monitor disease and detect complications. Magnetic resonance imaging (MRI) has demonstrable utility in evaluating inflammatory activity. However, subjective interpretation of conventional MR sequences is limited in its ability to fully phenotype the underlying histopathological processes in chronic disease. In particular, conventional MRI can be confounded by the presence of mural fibrosis and muscle hypertrophy, which can mask or sometimes mimic inflammation. Quantitative MRI (qMRI) methods provide a means to better differentiate mural inflammation from fibrosis and improve quantification of these processes. qMRI may also provide more objective measures of disease activity and enable better tailoring of treatment. Here, we review quantitative MRI methods for imaging the small bowel in CD and consider the path to their clinical translation. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

A simple, clinically usable whole-body MRI system of joint assessment in adolescents and young people with juvenile idiopathic arthritis.

OBJECTIVES: To introduce and evaluate a simple method for assessing joint inflammation and structural damage on whole-body MRI (WBMRI) in juvenile idiopathic arthritis (JIA), which is usable in clinical practice. METHODS: The proposed system utilises post-contrast Dixon WBMRI scans. Joints are assessed for synovitis (grade 0-2) and structural damage (present/absent) at 81 sites. The synovitis grading is based on features including above-normal intensity synovial enhancement, synovial hypertrophy, joint effusion, subarticular bone marrow oedema and peri-articular soft tissue oedema.This system was evaluated in a prospective study of 60 young people (47 patients with JIA and 13 controls with non-inflammatory musculoskeletal pain) who underwent a WBMRI. Three readers (blinded to diagnosis) independently reviewed all images and re-reviewed 20 individual scans. The intra- and inter-reader overall agreement (OA) and the intra- and inter-reader Gwet's agreement coefficients 2 (GAC2) were measured for the detection of a) participants with ≥1 joint with inflammation or structural damage and b) joint inflammation or structural damage for each joint. RESULTS: The inter-reader OA for detecting patients with ≥1 joint with inflammation, defined as grade 2 synovitis (G2), and ≥1 joint with structural damage were 80% and 73%, respectively. The intra-reader OA for readers 1-3 were 80-90% and 75-90% respectively. The inter-reader OA and GAC2 for joint inflammation (G2) at each joint were both ≥85% for all joints but were lower if grade 1 synovitis was included as positive. CONCLUSION: The intra- and inter-reader agreements of this WBMRI assessment system are adequate for assessing objective joint inflammation and damage in JIA.

Detection of inflammation by whole-body MRI in young people with juvenile idiopathic arthritis.

OBJECTIVES: To assess the frequency of joint inflammation detected by whole-body MRI (WBMRI) in young people (YP) with JIA and controls, and to determine the relationship between WBMRI-detected inflammation and clinical findings. METHODS: YP aged 14-24 years, with JIA (patients) or arthralgia without JIA (controls), recruited from one centre, underwent a WBMRI scan after formal clinical assessment. Consensus between at least two of the three independent radiologists was required to define inflammation and damage on WBMRI, according to predefined criteria. YP with JIA were deemed clinically active as per accepted definitions. The proportions of YP with positive WBMRI scans for joint inflammation (≥1 inflamed joint) as well as serum biomarkers were compared between active vs inactive JIA patients and controls. RESULTS: Forty-seven YP with JIA (25 active and 22 inactive patients) and 13 controls were included. WBMRI detected joint inflammation in 60% (28/47) patients with JIA vs 15% (2/13) controls (difference: 44%, 95% CI 20%, 68%). More active than inactive JIA patients had WBMRI-detected inflammation [76% (19/25) vs 41% (9/22), difference: 35% (95% CI 9%, 62%)], and this was associated with a specific biomarker signature. WBMRI identified inflammation in ≥ 1 clinically inactive joint in 23/47 (49%) patients (14/25 active vs 9/22 inactive JIA patients). CONCLUSIONS: WBMRI's validity in joint assessment was demonstrated by the higher frequency of inflammation in JIA patients vs controls, and in active vs inactive JIA patients. WBMRI found unsuspected joint inflammation in 49% YP with JIA, which needs further investigation of potential clinical implications.

A multiparametric alternative to short inversion-time inversion recovery for imaging inflammation: T2water and fat fraction measurement using chemical shift-encoded turbo spin-echo MRI.

PURPOSE: Short-inversion-time inversion-recovery MRI is used widely for imaging bone and soft-tissue inflammation in rheumatic inflammatory diseases, but there is no widely available quantitative equivalent of this sequence. This limits our ability to objectively assess inflammation and distinguish it from other processes. To address this, we investigate the use of the widely available Dixon turbo spin echo (TSE Dixon) sequence as a practical approach to simultaneous water-specific T2 (T2water ) and fat fraction (FF) measurement. METHODS: We use a series of TSE Dixon acquisitions with varying effective TEs (TEeff ) to quantify T2water and FF. The validity of this approach is assessed in a series of phantom and in vivo experiments, with reference values provided by Carr-Purcell-Meiboom-Gill acquisitions, MRS, and phantoms. The effect of inflammation on parameter values is evaluated in patients with spondyloarthritis. RESULTS: The T2water estimates obtained from TSE Dixon were accurate compared with the reference values from Carr-Purcell-Meiboom-Gill and spectroscopy in both fat-free environments and in the presence of fat. FF measurements with T2water correction from TSE Dixon were accurate from 0% to 60% FF and were not confounded by T2water variations. In vivo imaging produced good quality images that were artifact free, produced plausible T2 values, separating and quantifying the effect of inflammation on T2water and FF. CONCLUSION: The T2water and FF measurements based on TSE Dixon with effective TE increments are accurate over a range of T2 and FF values and could provide a widely available quantitative alternative to the short-inversion-time inversion-recovery sequence for imaging inflamed tissue.

Volume of hyperintense inflammation (VHI): A quantitative imaging biomarker of inflammation load in spondyloarthritis, enabled by human-machine cooperation.

Qualitative visual assessment of MRI scans is a key mechanism by which inflammation is assessed in clinical practice. For example, in axial spondyloarthritis (axSpA), visual assessment focuses on the identification of regions with increased signal in the bone marrow, known as bone marrow oedema (BMO), on water-sensitive images. The identification of BMO has an important role in the diagnosis, quantification and monitoring of disease in axSpA. However, BMO evaluation depends heavily on the experience and expertise of the image reader, creating substantial imprecision. Deep learning-based segmentation is a natural approach to addressing this imprecision, but purely automated solutions require large training sets that are not currently available, and deep learning solutions with limited data may not be sufficiently trustworthy for use in clinical practice. To address this, we propose a workflow for inflammation segmentation incorporating both deep learning and human input. With this 'human-machine cooperation' workflow, a preliminary segmentation is generated automatically by deep learning; a human reader then 'cleans' the segmentation by removing extraneous segmented voxels. The final cleaned segmentation defines the volume of hyperintense inflammation (VHI), which is proposed as a quantitative imaging biomarker (QIB) of inflammation load in axSpA. We implemented and evaluated the proposed human-machine workflow in a cohort of 29 patients with axSpA who had undergone prospective MRI scans before and after starting biologic therapy. The performance of the workflow was compared against purely visual assessment in terms of inter-observer/inter-method segmentation overlap, inter-observer agreement and assessment of response to biologic therapy. The human-machine workflow showed superior inter-observer segmentation overlap than purely manual segmentation (Dice score 0.84 versus 0.56). VHI measurements produced by the workflow showed similar or better inter-observer agreement than visual scoring, with similar response assessments. We conclude that the proposed human-machine workflow offers a mechanism to improve the consistency of inflammation assessment, and that VHI could be a valuable QIB of inflammation load in axSpA, as well as offering an exemplar of human-machine cooperation more broadly.

Quantitative magnetic resonance imaging (qMRI) in axial spondyloarthritis.

Imaging, and particularly MRI, plays a crucial role in the assessment of inflammation in rheumatic disease, and forms a core component of the diagnostic pathway in axial spondyloarthritis. However, conventional imaging techniques are limited by image contrast being non-specific to inflammation and a reliance on subjective, qualitative reader interpretation. Quantitative MRI methods offer scope to address these limitations and improve our ability to accurately and precisely detect and characterise inflammation, potentially facilitating a more personalised approach to management. Here, we review quantitative MRI methods and emerging quantitative imaging biomarkers for imaging inflammation in axial spondyloarthritis. We discuss the potential benefits as well as the practical considerations that must be addressed in the movement toward clinical translation of quantitative imaging biomarkers.

MAGORINO: Magnitude-only fat fraction and R* 2 estimation with Rician noise modeling.

PURPOSE: Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R 2 * $$ {R}_2^{\ast } $$ estimation where complex-based methods fail or when phase data are inaccessible or unreliable. However, traditional magnitude-based fitting algorithms do not account for Rician noise, creating a source of bias. To address these issues, we propose an algorithm for magnitude-only PDFF and R 2 * $$ {R}_2^{\ast } $$ estimation with Rician noise modeling (MAGORINO). METHODS: Simulations of multi-echo gradient-echo signal intensities are used to investigate the performance and behavior of MAGORINO over the space of clinically plausible PDFF, R 2 * $$ {R}_2^{\ast } $$ , and SNR values. Fitting performance is assessed through detailed simulation, including likelihood function visualization, and in a multisite, multivendor, and multi-field-strength phantom data set and in vivo. RESULTS: Simulations show that Rician noise-based magnitude fitting outperforms existing Gaussian noise-based fitting and reveals two key mechanisms underpinning the observed improvement. First, the likelihood functions exhibit two local optima; Rician noise modeling increases the chance that the global optimum corresponds to the ground truth. Second, when the global optimum corresponds to ground truth for both noise models, the optimum from Rician noise modeling is closer to ground truth. Multisite phantom experiments show good agreement of MAGORINO PDFF with reference values, and in vivo experiments replicate the performance benefits observed in simulation. CONCLUSION: The MAGORINO algorithm reduces Rician noise-related bias in PDFF and R 2 * $$ {R}_2^{\ast } $$ estimation, thus addressing a key limitation of existing magnitude-only fitting methods. Our results offer insight into the importance of the noise model for selecting the correct optimum when multiple plausible optima exist.

Case Report: Gnathostomiasis Acquired in Costa Rica in a Returning Traveler to the United Kingdom.

Gnathostomiasis, caused by infection with nematode parasites in the genus Gnathostoma, is endemic in tropical and temperate zones, and is classically associated with East and Southeast Asia and, more recently, Latin America and Africa. We report a case of gnathostomiasis acquired in Costa Rica, which has not previously been considered an endemic country. The patient had eosinophilia with migratory myalgia, and the diagnosis was made after serological testing. Full resolution of symptoms and eosinophilia followed treatment with ivermectin and albendazole. The diagnosis can be challenging to make because of variability in presentation, lack of access to diagnostics, and emerging knowledge of endemic areas. Increased awareness of this disease among clinicians is vital for faster diagnosis and better outcomes in afflicted patients.

Task-driven assessment of experimental designs in diffusion MRI: A computational framework.

This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental tasks, such as clinical classification, where outcome does not depend on parameter-estimation accuracy or precision alone. Current assessment metrics evaluate experiments' ability to faithfully recover microstructural parameters rather than their task performance. The method we propose addresses this shortcoming. For a given MRI experimental design (protocol, parameter-estimation method, model, etc.), experiments are simulated start-to-finish and task performance is computed from receiver operating characteristic (ROC) curves and associated summary metrics (e.g. area under the curve (AUC)). Two experiments were performed: first, a validation of the pipeline's task performance predictions against clinical results, comparing in-silico predictions to real-world ROC/AUC; and second, a demonstration of the pipeline's advantages over traditional CED approaches, using two simulated clinical classification tasks. Comparison with clinical datasets validates our method's predictions of (a) the qualitative form of ROC curves, (b) the relative task performance of different experimental designs, and (c) the absolute performance (AUC) of each experimental design. Furthermore, we show that our method outperforms traditional task-agnostic assessment methods, enabling improved, more useful experimental design. Our pipeline produces accurate, quantitative predictions of real-world task performance. Compared to current approaches, such task-driven assessment is more likely to identify experimental designs that perform well in practice. Our method is not limited to diffusion MRI; the pipeline generalises to any task-based quantitative MRI application, and provides the foundation for developing future task-driven end-to end CED frameworks.

An information-based comparison of diffusion attenuation models in normal and inflamed bone marrow.

Diffusion-weighted imaging has received attention as a method for characterizing inflammatory exudates in bone marrow in immune-mediated inflammatory diseases and reveals an increase in diffusivity in regions of bone marrow oedema. Various models of diffusion attenuation have been investigated but the model providing the best description of tissue pathophysiology in regions of marrow oedema is unknown. Determining the most appropriate model is an important step towards protocol optimization and the development of a robust and clinically useful method. We aimed to determine which of three candidate models of diffusion attenuation most accurately describes the acquired signal from normal and inflamed bone marrow. 11 subjects with spondyloarthritis and evidence of active inflammation (ie bone marrow oedema) on MRI and 17 patients with no evidence of active inflammation underwent diffusion-weighted imaging of the sacroiliac joints (b-values 0, 50, 100, 300 and 600 s/mm2 ). Monoexponential, intravoxel incoherent motion (IVIM) and kurtosis models were fitted to the acquired signal from regions of interest in areas of bone marrow oedema and normal marrow. The three models were compared in terms of sum of squared error and information content (corrected Akaike information criterion). Model parameters were compared between regions of bone marrow oedema and regions of normal marrow. f the three models investigated, the IVIM model provided the best description of the signal over the 0-600 s/mm2 range across normal and inflamed bone marrow. There was a particular advantage of the IVIM model in normal marrow, where it was best able to capture the pronounced fast diffusion component observed in several cases. However, IVIM and kurtosis effects both became smaller and the signal behaviour became closer to monoexponential in the presence of bone marrow oedema. Our data suggest that increases in Dtissue (in the IVIM framework) might account for the reduced deviation from monoexponential behaviour in oedematous bone.

Histographic analysis of oedema and fat in inflamed bone marrow based on quantitative MRI.

OBJECTIVE: To demonstrate proof-of-concept for a quantitative MRI method using histographic analysis to assess bone marrow oedema and fat metaplasia in the sacroiliac joints. MATERIALS AND METHODS: Fifty-three adolescents aged 12-23 with known or suspected sacroiliitis were prospectively recruited and underwent quantitative MRI (qMRI) scans, consisting of chemical shift-encoded (at 3 T) and diffusion-weighted imaging (at 1.5 T), plus conventional MRI (at 1.5 T) and clinical assessment. qMRI scans produced proton-density fat fraction (PDFF) and apparent diffusion coefficient (ADC) maps of the sacroiliac joints (SIJs), which were analysed using an in-house software tool enabling partially automated ROI definition and histographic analysis. Logistic regression and receiver operating characteristic (ROC) analyses assessed the predictive performance of ADC- and PDFF-based parameters in identifying active inflammation (oedema) and structural damage (fat metaplasia). RESULTS: ADC-based parameters were associated with increased odds of oedema (all p < 0.05); ROC-AUC was higher for histographic parameters representing the upper end of the ADC distribution than for simple averages. Similarly, PDFF-based parameters were associated with increased odds of fat metaplasia (all p < 0.05); ROC area-under-the-curve was higher for histographic parameters representing the upper end of the PDFF distribution than for simple averages. Both ADC- and PDFF-based histographic parameters demonstrated excellent inter- and intra-observer agreement (ICC > 0.9). CONCLUSIONS: ADC-based parameters can differentiate patients with bone marrow oedema from those without, whilst PDFF-based parameters can differentiate patients with fat metaplasia from those without. Histographic analysis might improve performance compared with simple averages such as the mean and median and offers excellent agreement within and between observers. KEY POINTS: • Quantitative MRI with histographic analysis can identify bone marrow oedema (an active inflammatory lesion) and fat metaplasia (a 'chronic' inflammatory lesion) in patients with spondyloarthritis. • The use of histographic analysis might improve the performance of quantitative MRI for detecting bone marrow oedema and fat metaplasia compared with simple averages such as the mean and median. • Bone marrow oedema and fat metaplasia are known to be of diagnostic and prognostic significance, and the proposed method could support clinical decisions around biologic (and other) therapies in spondyloarthritis.

Practical Approaches to Bone Marrow Fat Fraction Quantification Across Magnetic Resonance Imaging Platforms.

BACKGROUND: Quantification of fat by proton density fat fraction (PDFF) measurements may be valuable for the quantification and follow-up of pathology in multicenter clinical trials and routine practice. However, many centers do not have access to specialist methods (such as chemical shift imaging) for PDFF measurement. This is a barrier to more widespread trial implementation. PURPOSE/HYPOTHESIS: To determine the agreement between fat fraction (FF) measurements derived from 1) basic vendor-supplied sequences, 2) basic sequences with offline correction, and 3) specialist vendor-supplied methods. STUDY TYPE: Prospective. POPULATION: Two substudies with ten and five healthy volunteers. FIELD STRENGTH/SEQUENCE: Site A: mDixon Quant (Philips 3T Ingenia); Site B: IDEAL and FLEX (GE 1.5T Optima MR450W); Site C: DIXON, with additional 5-echo gradient echo acquisition for offline correction (Siemens 3T Skyra); Site D: DIXON, with additional VIBE acquisitions for offline correction (Siemens 1.5T Avanto). The specialist method at site A was used as a standard to compare to the basic methods at sites B, C, and D. ASSESSMENT: Regions of interest were placed on areas of subchondral bone on FF maps from the various methods in each volunteer. STATISTICAL TESTS: Relationships between FF measurements from the various sites and Dixon methods were assessed using Bland-Altman analysis and linear regression. RESULTS: Basic methods consisting of IDEAL, LAVA FLEX, and DIXON produced FF values that were linearly related to reference FF values (P < 0.0001), but produced mean biases of up to 10%. Offline correction produced a significant reduction in bias in both substudies (P < 0.001). DATA CONCLUSION: FF measurements derived using basic vendor-supplied methods are strongly linearly related with those derived using specialist methods but produce a bias of up to 10%. A simple offline correction that is accessible even when the scanner has only basic sequence options can significantly reduce bias. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;52:298-306.

Whole body MRI in multiple myeloma: Optimising image acquisition and read times.

OBJECTIVE: To identify the whole-body MRI (WB-MRI) image type(s) with the highest value for assessment of multiple myeloma, in order to optimise acquisition protocols and read times. METHODS: Thirty patients with clinically-suspected MM underwent WB-MRI at 3 Tesla. Unenhanced Dixon images [fat-only (FO) and water-only (WO)], post contrast Dixon [fat-only plus contrast (FOC) and water-only plus contrast (WOC)] and diffusion weighted images (DWI) of the pelvis from all 30 patients were randomised and read by three experienced readers. For each image type, each reader identified and labelled all visible myeloma lesions. Each identified lesion was compared with a composite reference standard achieved by review of a complete imaging dataset by a further experienced consultant radiologist to determine truly positive lesions. Lesion count, true positives, sensitivity, and positive predictive value were determined. Time to read each scan set was recorded. Confidence for a diagnosis of myeloma was scored using a Likert scale. Conspicuity of focal lesions was assessed in terms of percent contrast and contrast to noise ratio (CNR). RESULTS: Lesion count, true positives, sensitivity and confidence scores were significantly higher when compared to other image types for DWI (P<0.0001 to 0.003), followed by WOC (significant for sensitivity (P<0.0001 to 0.004), true positives (P = 0.003 to 0.049) and positive predictive value (P< 0.0001 to 0.006)). There was no statistically significant difference in these metrics between FO and FOC. Percent contrast was highest for WOC (P = 0.001 to 0.005) and contrast to noise ratio (CNR) was highest for DWI (P = 0.03 to 0.05). Reading times were fastest for DWI across all observers (P< 0.0001 to 0.014). DISCUSSION: Observers detected more myeloma lesions on DWI images and WOC images when compared to other image types. We suggest that these image types should be read preferentially by radiologists to improve diagnostic accuracy and reporting efficiency.

Emerging quantitative MR imaging biomarkers in inflammatory arthritides.

PURPOSE: To review quantitative magnetic resonance imaging (qMRI) methods for imaging inflammation in connective tissues and the skeleton in inflammatory arthritis. This review is designed for a broad audience including radiologists, imaging technologists, rheumatologists and other healthcare professionals. METHODS: We discuss the use of qMRI for imaging skeletal inflammation from both technical and clinical perspectives. We consider how qMRI can be targeted to specific aspects of the pathological process in synovium, cartilage, bone, tendons and entheses. Evidence for the various techniques from studies of both adults and children with inflammatory arthritis is reviewed and critically appraised. RESULTS: qMRI has the potential to objectively identify, characterize and quantify inflammation of the connective tissues and skeleton in both adult and pediatric patients. Measurements of tissue properties derived using qMRI methods can serve as imaging biomarkers, which are potentially more reproducible and informative than conventional MRI methods. Several qMRI methods are nearing transition into clinical practice and may inform diagnosis and treatment decisions, with the potential to improve patient outcomes. CONCLUSIONS: qMRI enables specific assessment of inflammation in synovium, cartilage, bone, tendons and entheses, and can facilitate a more consistent, personalized approach to diagnosis, characterisation and monitoring of disease.

Recommendations for acquisition and interpretation of MRI of the spine and sacroiliac joints in the diagnosis of axial spondyloarthritis in the UK.

OBJECTIVES: To develop evidence-based recommendations on the use of MRI in the diagnosis of axial SpA (axSpA). METHODS: A working group comprising nine rheumatologists and nine musculoskeletal radiologists with an interest in axSpA was established, with support from the British Society of Spondyloarthritis (BRITSpA). Two meetings were held. In the first meeting, research questions were formulated. In the second meeting, the results of a systematic literature review designed to inform the recommendations were reviewed. An anonymized Delphi process was used to formulate the final set of recommendations. For each recommendation, the level of evidence and strength of recommendation was determined. The level of agreement was assessed using a 0-10 numerical rating scale. RESULTS: Two overarching principles were formulated, as follows: The diagnosis of axSpA is based on clinical, laboratory and imaging features (overarching principle 1), and patients with axSpA can have isolated inflammation of either the sacroiliac joints or the spine (overarching principle 2). Seven recommendations addressing the use of MRI in the assessment of patients with suspected axSpA were formulated, covering topics including recommended sequences, anatomical coverage, acquisition parameters and interpretation of active and structural MRI lesions. The level of agreement for each recommendation was very high (range 8.8-9.8). CONCLUSION: A joint rheumatology and radiology consensus on the acquisition and interpretation of MRI in axSpA diagnosis was achieved, and a research agenda formulated. This consensus should help standardize practice around MRI and ensure a more informed, consistent approach to the diagnosis of axSpA.