First-in-human in-vivo depiction of paraganglioma metabolism by hyperpolarised 13C-magnetic resonance.

Phaeochromocytomas (PCC) and paragangliomas (PGL), cumulatively referred to as PPGLs, are neuroendocrine tumours arising from neural crest-derived cells in the sympathetic and parasympathetic nervous systems. Predicting future tumour behaviour and the likelihood of metastatic disease remains problematic as genotype-phenotype correlations are limited, the disease has variable penetrance and, to date, no reliable molecular, cellular or histological markers have emerged. Tumour metabolism quantification can be considered as a method to delineating tumour aggressiveness by utilising hyperpolarised 13 C-MR (HP-MR). The technique may provide an opportunity to non-invasively characterise disease behaviour. Here, we present the first instance of the analysis of PPGL metabolism via HP-MR in a single case.

Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition.

PURPOSE: VERDICT (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) MRI is a multi b-value, variable diffusion time DWI sequence that allows generation of ADC maps from different b-value and diffusion time combinations. The aim was to assess precision of prostate ADC measurements from varying b-value combinations using VERDICT and determine which protocol provides the most repeatable ADC. MATERIALS AND METHODS: Forty-one men (median age: 67.7 years) from a prior prospective VERDICT study (April 2016-October 2017) were analysed retrospectively. Men who were suspected of prostate cancer and scanned twice using VERDICT were included. ADC maps were formed using 5b-value combinations and the within-subject standard deviations (wSD) were calculated per ADC map. Three anatomical locations were analysed per subject: normal TZ (transition zone), normal PZ (peripheral zone), and index lesions. Repeated measures ANOVAs showed which b-value range had the lowest wSD, Spearman correlation and generalized linear model regression analysis determined whether wSD was related to ADC magnitude and ROI size. RESULTS: The mean lesion ADC for b0b1500 had the lowest wSD in most zones (0.18-0.58x10-4 mm2/s). The wSD was unaffected by ADC magnitude (Lesion: p = 0.064, TZ: p = 0.368, PZ: p = 0.072) and lesion Likert score (p = 0.95). wSD showed a decrease with ROI size pooled over zones (p = 0.019, adjusted regression coefficient = -1.6x10-3, larger ROIs for TZ versus PZ versus lesions). ADC maps formed with a maximum b-value of 500 s/mm2 had the largest wSDs (1.90-10.24x10-4 mm2/s). CONCLUSION: ADC maps generated from b0b1500 have better repeatability in normal TZ, normal PZ, and index lesions.

Comparison of models of diffusion in Wilms' tumours and normal contralateral renal tissue.

OBJECTIVE: ADC (Apparent Diffusion Coefficient) derived from Diffusion-Weighted Imaging (DWI) has shown promise as a non-invasive quantitative imaging biomarker in Wilms' tumours. However, many non-Gaussian models could be applied to DWI. This study aimed to compare the suitability of four diffusion models (mono exponential, IVIM [Intravoxel Incoherent Motion], stretched exponential, and kurtosis) in Wilms' tumours and the unaffected contralateral kidneys. MATERIALS AND METHODS: DWI data were retrospectively reviewed (110 Wilms' tumours and 75 normal kidney datasets). The goodness of fit for each model was measured voxel-wise using Akaike Information Criteria (AIC). Mean AIC was calculated for each tumour volume (or contralateral normal kidney tissue). One-way ANOVAs with Greenhouse-Geisser correction and post hoc tests using the Bonferroni correction evaluated significant differences between AIC values; the lowest AIC indicating the optimum model. RESULTS: IVIM and stretched exponential provided the best fits to the Wilms' tumour DWI data. IVIM provided the best fit for the normal kidney data. Mono exponential was the least appropriate fitting method for both Wilms' tumour and normal kidney data. DISCUSSION: The diffusion weighted signal in Wilms' tumours and normal kidney tissue does not exhibit a mono-exponential decay and is better described by non-Gaussian models of diffusion.

An alternative approach to contrast-enhanced imaging: diffusion-weighted imaging and T1-weighted imaging identifies and quantifies necrosis in Wilms tumour.

OBJECTIVES: Volume of necrosis in Wilms tumour is informative of chemotherapy response. Contrast-enhanced T1-weighted MRI (T1w) provides a measure of necrosis using gadolinium. This study aimed to develop a non-invasive method of identifying non-enhancing (necrotic) tissue in Wilms tumour. METHODS: In this single centre, retrospective study, post-chemotherapy MRI data from 34 Wilms tumour patients were reviewed (March 2012-March 2017). Cases with multiple b value diffusion-weighted imaging (DWI) and T1w imaging pre- and post-gadolinium were included. Fractional T1 enhancement maps were generated from the gadolinium T1w data. Multiple linear regression determined whether fitted parameters from a mono-exponential model (ADC) and bi-exponential model (IVIM - intravoxel incoherent motion) (D, D*, f) could predict fractional T1 enhancement in Wilms tumours, using normalised pre-gadolinium T1w (T1wnorm) signal as an additional predictor. Measured and predicted fractional enhancement values were compared using the Bland-Altman plot. An optimum threshold for separating necrotic and viable tissue using fractional T1 enhancement was established using ROC. RESULTS: ADC and D (diffusion coefficient) provided the strongest predictors of fractional T1 enhancement in tumour tissue (p < 0.001). Using the ADC-T1wnorm model (adjusted R2 = 0.4), little bias (mean difference = - 0.093, 95% confidence interval = [- 0.52, 0.34]) was shown between predicted and measured values of fractional enhancement and analysed via the Bland-Altman plot. The optimal threshold for differentiating viable and necrotic tissue was 33% fractional T1 enhancement (based on measured values, AUC = 0.93; sensitivity = 85%; specificity = 90%). CONCLUSIONS: Combining ADC and T1w imaging predicts enhancement in Wilms tumours and reliably identifies and measures necrotic tissue without gadolinium. KEY POINTS: • Alternative method to identify necrotic tissue in Wilms tumour without using contrast agents but rather using diffusion and T 1 weighted MRI. • A method is presented to visualise and quantify necrotic tissue in Wilms tumour without contrast. • The proposed method has the potential to reduce costs and burden to Wilms tumour patients who undergo longitudinal follow-up imaging as contrast agents are not used.

Stroke onset time estimation from multispectral quantitative magnetic resonance imaging in a rat model of focal permanent cerebral ischemia.

BACKGROUND: Quantitative T2 relaxation magnetic resonance imaging allows estimation of stroke onset time. AIMS: We aimed to examine the accuracy of quantitative T1 and quantitative T2 relaxation times alone and in combination to provide estimates of stroke onset time in a rat model of permanent focal cerebral ischemia and map the spatial distribution of elevated quantitative T1 and quantitative T2 to assess tissue status. METHODS: Permanent middle cerebral artery occlusion was induced in Wistar rats. Animals were scanned at 9.4T for quantitative T1, quantitative T2, and Trace of Diffusion Tensor (Dav) up to 4 h post-middle cerebral artery occlusion. Time courses of differentials of quantitative T1 and quantitative T2 in ischemic and non-ischemic contralateral brain tissue (ΔT1, ΔT2) and volumes of tissue with elevated T1 and T2 relaxation times (f1, f2) were determined. TTC staining was used to highlight permanent ischemic damage. RESULTS: ΔT1, ΔT2, f1, f2, and the volume of tissue with both elevated quantitative T1 and quantitative T2 (V(Overlap)) increased with time post-middle cerebral artery occlusion allowing stroke onset time to be estimated. V(Overlap) provided the most accurate estimate with an uncertainty of ±25 min. At all times-points regions with elevated relaxation times were smaller than areas with Dav defined ischemia. CONCLUSIONS: Stroke onset time can be determined by quantitative T1 and quantitative T2 relaxation times and tissue volumes. Combining quantitative T1 and quantitative T2 provides the most accurate estimate and potentially identifies irreversibly damaged brain tissue.

A spatiotemporal theory for MRI T2 relaxation time and apparent diffusion coefficient in the brain during acute ischaemia: Application and validation in a rat acute stroke model.

The objective of this study is to present a mathematical model which can describe the spatiotemporal progression of cerebral ischaemia and predict magnetic resonance observables including the apparent diffusion coefficient (ADC) of water and transverse relaxation time T2 This is motivated by the sensitivity of the ADC to the location of cerebral ischaemia and T2 to its time-course, and that it has thus far proven challenging to relate observations of changes in these MR parameters to stroke timing, which is of considerable importance in making treatment choices in clinics. Our mathematical model, called the cytotoxic oedema/dissociation (CED) model, is based on the transit of water from the extra- to the intra-cellular environment (cytotoxic oedema) and concomitant degradation of supramacromolecular and macromolecular structures (such as microtubules and the cytoskeleton). It explains experimental observations of ADC and T2, as well as identifying the rate of spread of effects of ischaemia through a tissue as a dominant system parameter. The model brings the direct extraction of the timing of ischaemic stroke from quantitative MRI closer to reality, as well as providing insight on ischaemia pathology by imaging in general. We anticipate that this may improve patient access to thrombolytic treatment as a future application.

Timing the ischaemic stroke by 1H-MRI: improved accuracy using absolute relaxation times over signal intensities.

One in four ischaemic stroke patients are ineligible for thrombolytic treatment due to unknown onset time. Quantification of absolute MR relaxation times and signal intensities are potential methods for estimating stroke duration. We compared the accuracy of these approaches and determined whether changes in relaxation times and signal intensities identify the same ischaemic tissue as diffusion MRI. Seven Wistar rats underwent permanent middle cerebral artery occlusion to induce focal ischaemia and were scanned at six time points. The trace of the diffusion tensor (DAV), T1ρ and T2 were acquired at 4.7 T. Results show relaxation times, and signal intensities of the MR relaxation parameters increase linearly with ischaemia duration (P<0.001). Using T1ρ and T2 relaxation times, an estimate of 4.5 h after occlusion has an uncertainty of ± 12 and ± 35 min, respectively, compared with over 50 min for signal intensities. In addition, we present a pixel-by-pixel method that simultaneously estimates stroke onset time and identifies potentially irreversible ischaemic tissue using absolute relaxation times. This method demonstrates signal intensity changes during ischaemia display an ambiguous pattern and highlights the possibility that diffusion MRI overestimates the true extent of irreversible ischaemia. In conclusion, quantification of absolute relaxation times at a single time point enables a more accurate estimation of stroke duration than signal intensities and provides more information about tissue status in ischaemia.