Novel multiparametric MRI detects improved future liver remnant quality post-dual vein embolization.

BACKGROUND: Optimisation of the future liver remnant (FLR) is crucial to outcomes of extended liver resections. This study aimed to assess the quality of the FLR before and after dual vein embolization (DVE) by quantitative multiparametric MRI. METHODS: Of 100 patients with liver metastases recruited in a clinical trial (Precision1:NCT04597710), ten consecutive patients with insufficient FLR underwent quantitative multiparametric MRI pre- and post-DVE (right portal and hepatic vein). FLR volume, liver fibro-inflammation (corrected T1) scores and fat percentage (proton density fat fraction, PDFF) were determined. Patient metrics were compared by Wilcoxon signed-rank test and statistical analysis done using R software. RESULTS: All patients underwent uncomplicated DVE with improvement in liver remnant health, median 37 days after DVE: cT1 scores reduced from median (interquartile range) 790 ms (753-833 ms) to 741 ms (708-760 ms) p = 0.014 [healthy range <795 ms], as did PDFF from 11% (4-21%), to 3% (2-12%) p = 0.017 [healthy range <5.6%]. There was a significant increase in median (interquartile range) FLR volume from 33% (30-37%)% to 49% (44-52%), p = 0.002. CONCLUSION: This non-invasive and reproducible MRI technique showed improvement in volume and quality of the FLR after DVE. This is a significant advance in our understanding of how to prevent liver failure in patients undergoing major liver surgery.

Repeatability and reproducibility of deep-learning-based liver volume and Couinaud segment volume measurement tool.

PURPOSE: Volumetric and health assessment of the liver is crucial to avoid poor post-operative outcomes following liver resection surgery. No current methods allow for concurrent and accurate measurement of both Couinaud segmental volumes for future liver remnant estimation and liver health using non-invasive imaging. In this study, we demonstrate the accuracy and precision of segmental volume measurements using new medical software, Hepatica™. METHODS: MRI scans from 48 volunteers from three previous studies were used in this analysis. Measurements obtained from Hepatica™ were compared with OsiriX. Time required per case with each software was also compared. The performance of technicians and experienced radiologists as well as the repeatability and reproducibility were compared using Bland-Altman plots and limits of agreement. RESULTS: High levels of agreement and lower inter-operator variability for liver volume measurements were shown between Hepatica™ and existing methods for liver volumetry (mean Dice score 0.947 ± 0.010). A high consistency between technicians and experienced radiologists using the device for volumetry was shown (± 3.5% of total liver volume) as well as low inter-observer and intra-observer variability. Tight limits of agreement were shown between repeated Couinaud segment volume (+ 3.4% of whole liver), segmental liver fibroinflammation and segmental liver fat measurements in the same participant on the same scanner and between different scanners. An underestimation of whole-liver volume was observed between three non-reference scanners. CONCLUSION: Hepatica™ produces accurate and precise whole-liver and Couinaud segment volume and liver tissue characteristic measurements. Measurements are consistent between trained technicians and experienced radiologists.

A Quantitative Magnetic Resonance Cholangiopancreatography Metric of Intrahepatic Biliary Dilatation Severity Detects High-Risk Primary Sclerosing Cholangitis.

Magnetic resonance imaging with magnetic resonance cholangiopancreatography (MRI-MRCP) in primary sclerosing cholangitis (PSC) is currently based on qualitative assessment and has high interobserver variability. We investigated the utility and performance of quantitative metrics derived from a three-dimensional biliary analysis tool in adult patients with PSC. MRI-MRCP, blood-based biomarkers, and FibroScan were prospectively performed in 80 participants with large-duct PSC and 20 healthy participants. Quantitative analysis was performed using MRCP+ (Perspectum Ltd., United Kingdom), and qualitative reads were performed by radiologists. Inter-reader agreements were compared. Patients were classified into high risk or low risk for disease progression, using Mayo risk score (MRS), Amsterdam-Oxford model (AOM), upper limit of normal (ULN) alkaline phosphatase (ALP), disease distribution, and presence of dominant stricture. Performance of noninvasive tools was assessed using binomial logistic regressions and receiver operating characteristic curve analyses. Quantitative biliary metrics performed well to distinguish abnormal from normal bile ducts (P < 0.0001). Interobserver agreements for MRCP+ dilatation metrics (intraclass correlation coefficient, 0.90-0.96) were superior to modified Amsterdam intrahepatic stricture severity score (κ = 0.74) and Anali score (κ = 0.38). MRCP+ intrahepatic dilatation severity showed excellent performance to classify patients into high-risk and low-risk groups, using predictors of disease severity as the reference (MRS, P < 0.0001; AOM, P = 0.0017; 2.2 × ULN ALP, P = 0.0007; 1.5 × ULN ALP, P = 0.0225; extrahepatic disease, P = 0.0331; dominant stricture, P = 0.0019). MRCP+ intrahepatic dilatation severity was an independent predictor of MRS >0 (odds ratio, 31.3; P = 0.035) in the multivariate analysis. Conclusion: Intrahepatic biliary dilatation severity calculated using MRCP+ is elevated in patients with high-risk PSC and may be used as an adjunct for risk stratification in PSC. This exploratory study has provided the groundwork for examining the utility of novel quantitative biliary metrics in multicenter studies.

Quantitative magnetic resonance imaging predicts individual future liver performance after liver resection for cancer.

The risk of poor post-operative outcome and the benefits of surgical resection as a curative therapy require careful assessment by the clinical care team for patients with primary and secondary liver cancer. Advances in surgical techniques have improved patient outcomes but identifying which individual patients are at greatest risk of poor post-operative liver performance remains a challenge. Here we report results from a multicentre observational clinical trial (ClinicalTrials.gov NCT03213314) which aimed to inform personalised pre-operative risk assessment in liver cancer surgery by evaluating liver health using quantitative multiparametric magnetic resonance imaging (MRI). We combined estimation of future liver remnant (FLR) volume with corrected T1 (cT1) of the liver parenchyma as a representation of liver health in 143 patients prior to treatment. Patients with an elevated preoperative liver cT1, indicative of fibroinflammation, had a longer post-operative hospital stay compared to those with a cT1 within the normal range (6.5 vs 5 days; p = 0.0053). A composite score combining FLR and cT1 predicted poor liver performance in the 5 days immediately following surgery (AUROC = 0.78). Furthermore, this composite score correlated with the regenerative performance of the liver in the 3 months following resection. This study highlights the utility of quantitative MRI for identifying patients at increased risk of poor post-operative liver performance and a longer stay in hospital. This approach has the potential to inform the assessment of individualised patient risk as part of the clinical decision-making process for liver cancer surgery.

Quantitative MRCP Imaging: Accuracy, Repeatability, Reproducibility, and Cohort-Derived Normative Ranges.

BACKGROUND: Magnetic resonance cholangiopancreatography (MRCP) is an important tool for noninvasive imaging of biliary disease, however, its assessment is currently subjective, resulting in the need for objective biomarkers. PURPOSE: To investigate the accuracy, scan/rescan repeatability, and cross-scanner reproducibility of a novel quantitative MRCP tool on phantoms and in vivo. Additionally, to report normative ranges derived from the healthy cohort for duct measurements and tree-level summary metrics. STUDY TYPE: Prospective. PHANTOMS/SUBJECTS: Phantoms: two bespoke designs, one with varying tube-width, curvature, and orientation, and one exhibiting a complex structure based on a real biliary tree. Subjects Twenty healthy volunteers, 10 patients with biliary disease, and 10 with nonbiliary liver disease. SEQUENCE/FIELD STRENGTH: MRCP data were acquired using heavily T2 -weighted 3D multishot fast/turbo spin echo acquisitions at 1.5T and 3T. ASSESSMENT: Digital instances of the phantoms were synthesized with varying resolution and signal-to-noise ratio. Physical 3D-printed phantoms were scanned across six scanners (two field strengths for each of three manufacturers). Human subjects were imaged on four scanners (two fieldstrengths for each of two manufacturers). STATISTICAL TESTS: Bland-Altman analysis and repeatability coefficient (RC). RESULTS: Accuracy of the diameter measurement approximated the scanning resolution, with 95% limits of agreement (LoA) from -1.1 to 1.0 mm. Excellent phantom repeatability was observed, with LoA from -0.4 to 0.4 mm. Good reproducibility was observed across the six scanners for both phantoms, with a range of LoA from -1.1 to 0.5 mm. Inter- and intraobserver agreement was high. Quantitative MRCP detected strictures and dilatations in the phantom with 76.6% and 85.9% sensitivity and 100% specificity in both. Patients and healthy volunteers exhibited significant differences in metrics including common bile duct (CBD) maximum diameter (7.6 mm vs. 5.2 mm P = 0.002), and overall biliary tree volume 12.36 mL vs. 4.61 mL, P = 0.0026). DATA CONCLUSION: The results indicate that quantitative MRCP provides accurate, repeatable, and reproducible measurements capable of objectively assessing cholangiopathic change. Evidence Level: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:807-820.

Non-local shape descriptor: a new similarity metric for deformable multi-modal registration.

Deformable registration of images obtained from different modalities remains a challenging task in medical image analysis. This paper addresses this problem and proposes a new similarity metric for multi-modal registration, the non-local shape descriptor. It aims to extract the shape of anatomical features in a non-local region. By utilizing the dense evaluation of shape descriptors, this new measure bridges the gap between intensity-based and geometric feature-based similarity criteria. Our new metric allows for accurate and reliable registration of clinical multi-modal datasets and is robust against the most considerable differences between modalities, such as non-functional intensity relations, different amounts of noise and non-uniform bias fields. The measure has been implemented in a non-rigid diffusion-regularized registration framework. It has been applied to synthetic test images and challenging clinical MRI and CT chest scans. Experimental results demonstrate its advantages over the most commonly used similarity metric - mutual information, and show improved alignment of anatomical landmarks.

Motion correction and parameter estimation in dceMRI sequences: application to colorectal cancer.

We present a novel Bayesian framework for non-rigid motion correction and pharmacokinetic parameter estimation in dceMRI sequences which incorporates a physiological image formation model into the similarity measure used for motion correction. The similarity measure is based on the maximization of the joint posterior probability of the transformations which need to be applied to each image in the dataset to bring all images into alignment, and the physiological parameters which best explain the data. The deformation framework used to deform each image is based on the diffeomorphic logDemons algorithm. We then use this method to co-register images from simulated and real dceMRI datasets and show that the method leads to an improvement in the estimation of physiological parameters as well as improved alignment of the images.

Spatio-temporal (2D+T) non-rigid registration of real-time 3D echocardiography and cardiovascular MR image sequences.

In this paper we describe a method to non-rigidly co-register a 2D slice sequence from real-time 3D echocardiography with a 2D cardiovascular MR image sequence. This is challenging because the imaging modalities have different spatial and temporal resolution. Non-rigid registration is required for accurate alignment due to imprecision of cardiac gating and natural motion variations between cardiac cycles. In our approach the deformation field between the imaging modalities is decoupled into temporal and spatial components. First, temporal alignment is performed to establish temporal correspondence between a real-time 3D echocardiography frame and a cardiovascular MR frame. Spatial alignment is then performed using an adaptive non-rigid registration algorithm based on local phase mutual information on each temporally aligned image pair. Experiments on seven volunteer datasets are reported. Evaluation of registration errors based on expert-identified landmarks shows that the spatio-temporal registration algorithm gives a mean registration error of 3.56 ± 0.49 and 3.54 ± 0.27 mm for the short and long axis sequences, respectively.

Predicting tumor location by modeling the deformation of the breast.

Breast cancer is one of the biggest killers in the western world, and early diagnosis is essential for improved prognosis. The shape of the breast varies hugely between the scenarios of magnetic resonance (MR) imaging (patient lies prone, breast hanging down under gravity), X-ray mammography (breast strongly compressed) and ultrasound or biopsy/surgery (patient lies supine), rendering image fusion an extremely difficult task. This paper is concerned with the use of the finite-element method and nonlinear elasticity to build a 3-D, patient-specific, anatomically accurate model of the breast. The model is constructed from MR images and can be deformed to simulate breast shape and predict tumor location during mammography or biopsy/surgery. Two extensions of the standard elasticity problem need to be solved: an inverse elasticity problem (arising from the fact that only a deformed, stressed, state is known initially), and the contact problem of modeling compression. The model is used for craniocaudal mediolateral oblique mammographic image matching, and a number of numerical experiments are performed.

Spatio-temporal registration of real time 3D ultrasound to cardiovascular MR sequences.

We extend our static multimodal nonrigid registration to a spatio-temporal (2D+T) co-registration of a real-time 3D ultrasound and a cardiovascular MR sequence. The motivation for our research is to assist a clinician to automatically fuse the information from multiple imaging modalities for the early diagnosis and therapy of cardiac disease. The deformation field between both sequences is decoupled into spatial and temporal components. Temporal alignment is firstly performed to re-slice both sequences using a differential registration method. Spatial alignment is then carried out between the frames corresponding to the same temporal position. The spatial deformation is modeled by the polyaffine transformation whose anchor points (or control points) are automatically detected and refined by calculating a local mis-match measure based on phase mutual information. The spatial alignment is built in an adaptive multi-scale framework to maximize the phase-based similarity measure by optimizing the parameters of the polyaffine transformation. Results demonstrate that this novel method can yield an accurate registration to particular cardiac regions.

Non-linear modelling of breast tissue.

Previous approaches to modelling the large deformation of breast tissue, as occurs, e.g. in imaging using magnetic resonance imaging or mammography, include using linear elasticity and pseudo-non-linear elasticity, in which case the non-linear deformation is approximated by a series of small linear isotropic deformations, with the (constant) Young's modulus of each linear deformation an exponential function of the total non-linear strain. In this paper, these two approaches are compared to the solution of the full non-linear elastic problem for tissue with an exponential relationship between stress and strain. Having formulated each model and related the coefficients between the models, numerical simulations are performed on a block of incompressible material. These demonstrate that the simpler models may not be appropriate even in the case of modelling deformations of the human breast under gravity.

Quiescence as a mechanism for cyclical hypoxia and acidosis.

Tumour tissue characteristically experiences fluctuations in substrate supply. This unstable microenvironment drives constitutive metabolic changes within cellular populations and, ultimately, leads to a more aggressive phenotype. Previously, variations in substrate levels were assumed to occur through oscillations in the haemodynamics of nearby and distant blood vessels. In this paper we examine an alternative hypothesis, that cycles of metabolite concentrations are also driven by cycles of cellular quiescence and proliferation. Using a mathematical modelling approach, we show that the interdependence between cell cycle and the microenvironment will induce typical cycles with the period of order hours in tumour acidity and oxygenation. As a corollary, this means that the standard assumption of metabolites entering diffusive equilibrium around the tumour is not valid; instead temporal dynamics must be considered.

Advances in functional and structural MR image analysis and implementation as FSL.

The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).

Fully Bayesian spatio-temporal modeling of FMRI data.

We present a fully Bayesian approach to modeling in functional magnetic resonance imaging (FMRI), incorporating spatio-temporal noise modeling and haemodynamic response function (HRF) modeling. A fully Bayesian approach allows for the uncertainties in the noise and signal modeling to be incorporated together to provide full posterior distributions of the HRF parameters. The noise modeling is achieved via a nonseparable space-time vector autoregressive process. Previous FMRI noise models have either been purely temporal, separable or modeling deterministic trends. The specific form of the noise process is determined using model selection techniques. Notably, this results in the need for a spatially nonstationary and temporally stationary spatial component. Within the same full model, we also investigate the variation of the HRF in different areas of the activation, and for different experimental stimuli. We propose a novel HRF model made up of half-cosines, which allows distinct combinations of parameters to represent characteristics of interest. In addition, to adaptively avoid over-fitting we propose the use of automatic relevance determination priors to force certain parameters in the model to zero with high precision if there is no evidence to support them in the data. We apply the model to three datasets and observe matter-type dependence of the spatial and temporal noise, and a negative correlation between activation height and HRF time to main peak (although we suggest that this apparent correlation may be due to a number of different effects).

Nonrigid registration of 3-D free-hand ultrasound images of the breast.

Three-dimensional (3-D) ultrasound imaging of the breast enables better assessment of diseases than conventional two-dimensional (2-D) imaging. Free-hand techniques are often used for generating 3-D data from a sequence of 2-D slice images. However, the breast deforms substantially during scanning because it is composed primarily of soft tissue. This often causes tissue mis-registration in spatial compounding of multiple scan sweeps. To overcome this problem, in this paper, instead of introducing additional constraints on scanning conditions, we use image processing techniques. We present a fully automatic algorithm for 3-D nonlinear registration of free-hand ultrasound data. It uses a block matching scheme and local statistics to estimate local tissue deformation. A Bayesian regularization method is applied to the sample displacement field. The final deformation field is obtained by fitting a B-spline approximating mesh to the sample displacement field. Registration accuracy is evaluated using phantom data and similar registration errors are achieved with (0.19 mm) and without (0.16 mm) gaps in the data. Experimental results show that registration is crucial in spatial compounding of different sweeps. The execution time of the method on moderate hardware is sufficiently fast for fairly large research studies.