ssVERDICT: Self-supervised VERDICT-MRI for enhanced prostate tumor characterization.

PURPOSE: Demonstrating and assessing self-supervised machine-learning fitting of the VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumors) model for prostate cancer. METHODS: We derive a self-supervised neural network for fitting VERDICT (ssVERDICT) that estimates parameter maps without training data. We compare the performance of ssVERDICT to two established baseline methods for fitting diffusion MRI models: conventional nonlinear least squares and supervised deep learning. We do this quantitatively on simulated data by comparing the Pearson's correlation coefficient, mean-squared error, bias, and variance with respect to the simulated ground truth. We also calculate in vivo parameter maps on a cohort of 20 prostate cancer patients and compare the methods' performance in discriminating benign from cancerous tissue via Wilcoxon's signed-rank test. RESULTS: In simulations, ssVERDICT outperforms the baseline methods (nonlinear least squares and supervised deep learning) in estimating all the parameters from the VERDICT prostate model in terms of Pearson's correlation coefficient, bias, and mean-squared error. In vivo, ssVERDICT shows stronger lesion conspicuity across all parameter maps, and improves discrimination between benign and cancerous tissue over the baseline methods. CONCLUSION: ssVERDICT significantly outperforms state-of-the-art methods for VERDICT model fitting and shows, for the first time, fitting of a detailed multicompartment biophysical diffusion MRI model with machine learning without the requirement of explicit training labels.

Avoiding Unnecessary Biopsy after Multiparametric Prostate MRI with VERDICT Analysis: The INNOVATE Study.

Background In men suspected of having prostate cancer (PCa), up to 50% of men with positive multiparametric MRI (mpMRI) findings (Prostate Imaging Reporting and Data System [PI-RADS] or Likert score of 3 or higher) have no clinically significant (Gleason score ≤3+3, benign) biopsy findings. Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumor (VERDICT) MRI analysis could improve the stratification of positive mpMRI findings. Purpose To evaluate VERDICT MRI, mpMRI-derived apparent diffusion coefficient (ADC), and prostate-specific antigen density (PSAD) as determinants of clinically significant PCa (csPCa). Materials and Methods Between April 2016 and December 2019, men suspected of having PCa were prospectively recruited from two centers and underwent VERDICT MRI and mpMRI at one center before undergoing targeted biopsy. Biopsied lesion ADC, lesion-derived fractional intracellular volume (FIC), and PSAD were compared between men with csPCa and those without csPCa, using nonparametric tests subdivided by Likert scores. Area under the receiver operating characteristic curve (AUC) was calculated to test diagnostic performance. Results Among 303 biopsy-naive men, 165 study participants (mean age, 65 years ± 7 [SD]) underwent targeted biopsy; of these, 73 had csPCa. Median lesion FIC was higher in men with csPCa (FIC, 0.53) than in those without csPCa (FIC, 0.18) for Likert 3 (P = .002) and Likert 4 (0.60 vs 0.28, P < .001) lesions. Median lesion ADC was lower for Likert 4 lesions with csPCa (0.86 × 10-3 mm2/sec) compared with lesions without csPCa (1.12 × 10-3 mm2/sec, P = .03), but there was no evidence of a difference for Likert 3 lesions (0.97 × 10-3 mm2/sec vs 1.20 × 10-3 mm2/sec, P = .09). PSAD also showed no difference for Likert 3 (0.17 ng/mL2 vs 0.12 ng/mL2, P = .07) or Likert 4 (0.14 ng/mL2 vs 0.12 ng/mL2, P = .47) lesions. The diagnostic performance of FIC (AUC, 0.96; 95% CI: 0.93, 1.00) was higher (P = .02) than that of ADC (AUC, 0.85; 95% CI: 0.79, 0.91) and PSAD (AUC, 0.74; 95% CI: 0.66, 0.82) for the presence of csPCa in biopsied lesions. Conclusion Lesion fractional intracellular volume enabled better classification of clinically significant prostate cancer than did apparent diffusion coefficient and prostate-specific antigen density. Clinical trial registration no. NCT02689271 © RSNA, 2022 Online supplemental material is available for this article.

VERDICT MRI for Prostate Cancer: Intracellular Volume Fraction versus Apparent Diffusion Coefficient.

Background Biologic specificity of diffusion MRI in relation to prostate cancer aggressiveness may improve by examining separate components of the diffusion MRI signal. The Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors (VERDICT) model estimates three distinct signal components and associates them to (a) intracellular water, (b) water in the extracellular extravascular space, and (c) water in the microvasculature. Purpose To evaluate the repeatability, image quality, and diagnostic utility of intracellular volume fraction (FIC) maps obtained with VERDICT prostate MRI and to compare those maps with apparent diffusion coefficient (ADC) maps for Gleason grade differentiation. Materials and Methods Seventy men (median age, 62.2 years; range, 49.5-82.0 years) suspected of having prostate cancer or undergoing active surveillance were recruited to a prospective study between April 2016 and October 2017. All men underwent multiparametric prostate and VERDICT MRI. Forty-two of the 70 men (median age, 67.7 years; range, 50.0-82.0 years) underwent two VERDICT MRI acquisitions to assess repeatability of FIC measurements obtained with VERDICT MRI. Repeatability was measured with use of intraclass correlation coefficients (ICCs). The image quality of FIC and ADC maps was independently evaluated by two board-certified radiologists. Forty-two men (median age, 64.8 years; range, 49.5-79.6 years) underwent targeted biopsy, which enabled comparison of FIC and ADC metrics in the differentiation between Gleason grades. Results VERDICT MRI FIC demonstrated ICCs of 0.87-0.95. There was no significant difference between image quality of ADC and FIC maps (score, 3.1 vs 3.3, respectively; P = .90). FIC was higher in lesions with a Gleason grade of at least 3+4 compared with benign and/or Gleason grade 3+3 lesions (mean, 0.49 ± 0.17 vs 0.31 ± 0.12, respectively; P = .002). The difference in ADC between these groups did not reach statistical significance (mean, 1.42 vs 1.16 × 10-3 mm2/sec; P = .26). Conclusion Fractional intracellular volume demonstrates high repeatability and image quality and enables better differentiation of a Gleason 4 component cancer from benign and/or Gleason 3+3 histology than apparent diffusion coefficient. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Sigmund and Rosenkrantz in this issue.

Combined diffusion-relaxometry MRI to identify dysfunction in the human placenta.

PURPOSE: A combined diffusion-relaxometry MR acquisition and analysis pipeline for in vivo human placenta, which allows for exploration of coupling between T2* and apparent diffusion coefficient (ADC) measurements in a sub 10-minute scan time. METHODS: We present a novel acquisition combining a diffusion prepared spin echo with subsequent gradient echoes. The placentas of 17 pregnant women were scanned in vivo, including both healthy controls and participants with various pregnancy complications. We estimate the joint T2* -ADC spectra using an inverse Laplace transform. RESULTS: T2* -ADC spectra demonstrate clear quantitative separation between normal and dysfunctional placentas. CONCLUSIONS: Combined T2* -diffusivity MRI is promising for assessing fetal and maternal health during pregnancy. The T2* -ADC spectrum potentially provides additional information on tissue microstructure, compared to measuring these two contrasts separately. The presented method is immediately applicable to the study of other organs.

VERDICT-AMICO: Ultrafast fitting algorithm for non-invasive prostate microstructure characterization.

VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumours) estimates and maps microstructural features of cancerous tissue non-invasively using diffusion MRI. The main purpose of this study is to address the high computational time of microstructural model fitting for prostate diagnosis, while retaining utility in terms of tumour conspicuity and repeatability. In this work, we adapt the accelerated microstructure imaging via convex optimization (AMICO) framework to linearize the estimation of VERDICT parameters for the prostate gland. We compare the original non-linear fitting of VERDICT with the linear fitting, quantifying accuracy with synthetic data, and computational time and reliability (performance and precision) in eight patients. We also assess the repeatability (scan-rescan) of the parameters. Comparison of the original VERDICT fitting versus VERDICT-AMICO showed that the linearized fitting (1) is more accurate in simulation for a signal-to-noise ratio of 20 dB; (2) reduces the processing time by three orders of magnitude, from 6.55 seconds/voxel to 1.78 milliseconds/voxel; (3) estimates parameters more precisely; (4) produces similar parametric maps and (5) produces similar estimated parameters with a high Pearson correlation between implementations, r2  > 0.7. The VERDICT-AMICO estimates also show high levels of repeatability. Finally, we demonstrate that VERDICT-AMICO can estimate an extra diffusivity parameter without losing tumour conspicuity and retains the fitting advantages. VERDICT-AMICO provides microstructural maps for prostate cancer characterization in seconds.

VERDICT MRI validation in fresh and fixed prostate specimens using patient-specific moulds for histological and MR alignment.

The VERDICT framework for modelling diffusion MRI data aims to relate parameters from a biophysical model to histological features used for tumour grading in prostate cancer. Validation of the VERDICT model is necessary for clinical use. This study compared VERDICT parameters obtained ex vivo with histology in five specimens from radical prostatectomy. A patient-specific 3D-printed mould was used to investigate the effects of fixation on VERDICT parameters and to aid registration to histology. A rich diffusion data set was acquired in each ex vivo prostate before and after fixation. At both time points, data were best described by a two-compartment model: the model assumes that an anisotropic tensor compartment represents the extracellular space and a restricted sphere compartment models the intracellular space. The effect of fixation on model parameters associated with tissue microstructure was small. The patient-specific mould minimized tissue deformations and co-localized slices, so that rigid registration of MRI to histology images allowed region-based comparison with histology. The VERDICT estimate of the intracellular volume fraction corresponded to histological indicators of cellular fraction, including high values in tumour regions. The average sphere radius from VERDICT, representing the average cell size, was relatively uniform across samples. The primary diffusion direction from the extracellular compartment of the VERDICT model aligned with collagen fibre patterns in the stroma obtained by structure tensor analysis. This confirmed the biophysical relationship between ex vivo VERDICT parameters and tissue microstructure from histology.

Simplified Luminal Water Imaging for the Detection of Prostate Cancer From Multiecho T2 MR Images.

BACKGROUND: Luminal water imaging (LWI) suffers less from imaging artifacts than the diffusion-weighted imaging used in multiparametric MRI of the prostate. LWI obtains multicompartment tissue information from a multiecho T2 dataset. PURPOSE: To compare a simplified LWI technique with apparent diffusion coefficient (ADC) in classifying lesions based on groupings of PI-RADS v2 scores. Secondary aims were to investigate whether LWI differentiates between histologically confirmed tumor and normal tissue as effectively as ADC, and whether LWI is correlated with the multicompartment parameters of the vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) diffusion model. STUDY TYPE: A subset of a larger prospective study. POPULATION: In all, 65 male patients aged 49-79 were scanned. FIELD STRENGTH/SEQUENCE: A 32-echo T2 and a six b-value diffusion sequence (0, 90, 500, 1500, 2000, 3000 s/mm2 ) at 3T. ASSESSMENT: Regions of interest were placed by a board-certified radiologist in areas of lesion and benign tissue and given PI-RADS v2 scores. STATISTICAL TESTS: Receiver operating characteristic and logistic regression analyses were performed. RESULTS: LWI classifies tissue as PI-RADS 1,2 or PI-RADS 3,4,5 with an area under curve (AUC) value of 0.779, compared with 0.764 for ADC. LWI differentiated histologically confirmed malignant from nonmalignant tissue with AUC, sensitivity, and specificity values of 0.81, 75%, and 87%, compared with 0.75, 83%, and 67% for ADC. The microstructural basis of the LWI technique is further suggested by the correspondence with the VERDICT diffusion-based microstructural imaging technique, with α, A1 , A2 , and LWF showing significant correlations. DATA CONCLUSION: LWI alone can predict PI-RADS v2 score groupings and detect histologically confirmed tumors with an ability similar to ADC alone without the limitations of diffusion-weighted MRI. This is important, given that ADC has an advantage in these tests as it already informs PI-RADS v2 scoring. LWI also provides multicompartment information that has an explicit biophysical interpretation, unlike ADC. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:910-917.

Placenta microstructure and microcirculation imaging with diffusion MRI.

PURPOSE: To assess which microstructural models best explain the diffusion-weighted MRI signal in the human placenta. METHODS: The placentas of nine healthy pregnant subjects were scanned with a multishell, multidirectional diffusion protocol at 3T. A range of multicompartment biophysical models were fit to the data, and ranked using the Bayesian information criterion. RESULTS: Anisotropic extensions to the intravoxel incoherent motion model, which consider the effect of coherent orientation in both microvascular structure and tissue microstructure, consistently had the lowest Bayesian information criterion values. Model parameter maps and model selection results were consistent with the physiology of the placenta and surrounding tissue. CONCLUSION: Anisotropic intravoxel incoherent motion models explain the placental diffusion signal better than apparent diffusion coefficient, intravoxel incoherent motion, and diffusion tensor models, in information theoretic terms, when using this protocol. Future work will aim to determine if model-derived parameters are sensitive to placental pathologies associated with disorders, such as fetal growth restriction and early-onset pre-eclampsia. Magn Reson Med 80:756-766, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Measurement and modeling of diffusion time dependence of apparent diffusion coefficient and fractional anisotropy in prostate tissue ex vivo.

The purpose of this study was to measure and model the diffusion time dependence of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) derived from conventional prostate diffusion-weighted imaging methods as used in recommended multiparametric MRI protocols. Diffusion tensor imaging (DTI) was performed at 9.4 T with three radical prostatectomy specimens, with diffusion times in the range 10-120 ms and b-values 0-3000 s/mm2 . ADC and FA were calculated from DTI measurements at b-values of 800 and 1600 s/mm2 . Independently, a two-component model (restricted isotropic plus Gaussian anisotropic) was used to synthesize DTI data, from which ADC and FA were predicted and compared with the measured values. Measured ADC and FA exhibited a diffusion time dependence, which was closely predicted by the two-component model. ADC decreased by about 0.10-0.15 µm2 /ms as diffusion time increased from 10 to 120 ms. FA increased with diffusion time at b-values of 800 and 1600 s/mm2 but was predicted to be independent of diffusion time at b = 3000 s/mm2 . Both ADC and FA exhibited diffusion time dependence that could be modeled as two unmixed water pools - one having isotropic restricted dynamics, and the other unrestricted anisotropic dynamics. These results highlight the importance of considering and reporting diffusion times in conventional ADC and FA calculations and protocol recommendations, and inform the development of improved diffusion methods for prostate cancer imaging.

Microstructural models for diffusion MRI in breast cancer and surrounding stroma: an ex vivo study.

The diffusion signal in breast tissue has primarily been modelled using apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion tensor (DT) models, which may be too simplistic to describe the underlying tissue microstructure. Formalin-fixed breast cancer samples were scanned using a wide range of gradient strengths, durations, separations and orientations. A variety of one- and two-compartment models were tested to determine which best described the data. Models with restricted diffusion components and anisotropy were selected in most cancerous regions and there were no regions in which conventional ADC or DT models were selected. Maps of ADC generally related to cellularity on histology, but maps of parameters from more complex models suggest that both overall cell volume fraction and individual cell size can contribute to the diffusion signal, affecting the specificity of ADC to the tissue microstructure. The areas of coherence in diffusion anisotropy images were small, approximately 1 mm, but the orientation corresponded to stromal orientation patterns on histology.

Information-based ranking of 10 compartment models of diffusion-weighted signal attenuation in fixed prostate tissue.

This study compares the theoretical information content of single- and multi-compartment models of diffusion-weighted signal attenuation in prostate tissue. Diffusion-weighted imaging (DWI) was performed at 9.4 T with multiple diffusion times and an extended range of b values in four whole formalin-fixed prostates. Ten models, including different combinations of isotropic, anisotropic and restricted components, were tested. Models were ranked using the Akaike information criterion. In all four prostates, two-component models, comprising an anisotropic Gaussian component and an isotropic restricted component, ranked highest in the majority of voxels. Single-component models, whether isotropic (apparent diffusion coefficient, ADC) or anisotropic (diffusion tensor imaging, DTI), consistently ranked lower than multi-component models. Model ranking trends were independent of voxel size and maximum b value in the range tested (1.6-16 mm(3) and 3000-10,000 s/mm(2)). This study characterizes the two major water components previously identified by biexponential models and shows that models incorporating both anisotropic and restricted components provide more information-rich descriptions of DWI signals in prostate tissue than single- or multi-component anisotropic models and models that do not account for restricted diffusion.

INNOVATE: A prospective cohort study combining serum and urinary biomarkers with novel diffusion-weighted magnetic resonance imaging for the prediction and characterization of prostate cancer.

BACKGROUND: Whilst multi-parametric magnetic resonance imaging (mp-MRI) has been a significant advance in the diagnosis of prostate cancer, scanning all patients with elevated prostate specific antigen (PSA) levels is considered too costly for widespread National Health Service (NHS) use, as the predictive value of PSA levels for significant disease is poor. Despite the fact that novel blood and urine tests are available which may predict aggressive disease better than PSA, they are not routinely employed due to a lack of clinical validity studies. Furthermore approximately 40 % of mp-MRI studies are reported as indeterminate, which can lead to repeat examinations or unnecessary biopsy with associated patient anxiety, discomfort, risk and additional costs. METHODS/DESIGN: We aim to clinically validate a panel of minimally invasive promising blood and urine biomarkers, to better select patients that will benefit from a multiparametric prostate MRI. We will then test whether the performance of the mp-MRI can be improved by the addition of an advanced diffusion-weighted MRI technique, which uses a biophysical model to characterise tissue microstructure called VERDICT; Vascular and Extracellular Restricted Diffusion for Cytometry in Tumours. INNOVATE is a prospective single centre cohort study in 365 patients. mp-MRI will act as the reference standard for the biomarker panel. A clinical outcome based reference standard based on biopsy, mp-MRI and follow-up will be used for VERDICT MRI. DISCUSSION: We expect the combined effect of biomarkers and VERDICT MRI will improve care by better detecting aggressive prostate cancer early and make mp-MRI before biopsy economically viable for universal NHS adoption. TRIAL REGISTRATION: INNOVATE is registered on ClinicalTrials.gov, with reference NCT02689271 .

Information theoretic ranking of four models of diffusion attenuation in fresh and fixed prostate tissue ex vivo.

PURPOSE: To compare the theoretical information content of four popular models of diffusion-weighted signal attenuation. METHOD: Four whole prostates were imaged fresh unfixed and fixed at 9.4T. Biexponential, kurtosis, stretched exponential, and monoexponential models were ranked using Akaike's Information Criterion (AIC) with validation by a leave-one-out test of model prediction error. RESULTS: For unfixed tissue measurements (b-value range: 17-2104 s/mm(2)) the biexponential and kurtosis models had similar information content to each other and this was distinctly higher than for the stretched and monoexponential models. In fixed-tissue measurements (b-value range: 17-8252 s/mm(2)), the biexponential model had much higher information content than the three other models. CONCLUSION: AIC-based model ranking is consistent with an independent prediction accuracy test. Biexponential and kurtosis models consistently perform better than stretched and monoexponential models. The biexponential model has increasing superiority over all three other models as maximum b-value increases above ∼2000 s/mm(2).

Viable and fixed white matter: diffusion magnetic resonance comparisons and contrasts at physiological temperature.

PURPOSE: Fixed samples have been used extensively in diffusion MRI (dMRI) studies. However, fixation causes significant structural changes in tissue. The purpose of this study was to evaluate fixed white matter as a surrogate for viable white matter during development and validation of dMRI methods. METHODS: dMRI data was acquired from fixed and viable rat optic nerves maintained in identical conditions in a viable isolated tissue (VIT) chamber. The chamber preserves tissue integrity for 10 h at 37°C. Diffusion tensors (DT) and multi-compartment white matter signal models were fitted to the data. RESULTS: When comparing VIT and fixed tissue, DT parameters demonstrated that fixation causes significant reductions in axial diffusivity and increases in radial diffusivity. However, both tissues exhibited similar responses to changes in diffusion times and gradient strengths. Multicompartment models demonstrated differences in parameter estimates (e.g., directional diffusivities) that were analogous to differences in DT parameters. Similarities in multi-compartment model rankings suggested that tissue water populations were broadly maintained postfixation. CONCLUSIONS: The data demonstrate that fixed tissue, while maintaining the broad water environment of viable tissue, differs significantly in diffusion parameters. Results from dMRI experiments on fixed tissue may correlate with-but will not directly translate into-results from viable tissue.

A ranking of diffusion MRI compartment models with in vivo human brain data.

PURPOSE: Diffusion magnetic resonance imaging (MRI) microstructure imaging provides a unique noninvasive probe into tissue microstructure. The technique relies on biophysically motivated mathematical models, relating microscopic tissue features to the magnetic resonance (MR) signal. This work aims to determine which compartment models of diffusion MRI are best at describing measurements from in vivo human brain white matter. METHODS: Recent work shows that three compartment models, designed to capture intra-axonal, extracellular, and isotropically restricted diffusion, best explain multi-b-value data sets from fixed rat corpus callosum. We extend this investigation to in vivo by using a live human subject on a clinical scanner. The analysis compares models of one, two, and three compartments and ranks their ability to explain the measured data. We enhance the original methodology to further evaluate the stability of the ranking. RESULTS: As with fixed tissue, three compartment models explain the data best. However, a clearer hierarchical structure and simpler models emerge. We also find that splitting the scanning into shorter sessions has little effect on the ranking of models, and that the results are broadly reproducible across sessions. CONCLUSION: Three compartments are required to explain diffusion MR measurements from in vivo corpus callosum, which informs the choice of model for microstructure imaging applications in the brain.

Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI.

This work presents a biophysical model of diffusion and relaxation MRI for prostate called relaxation vascular, extracellular and restricted diffusion for cytometry in tumours (rVERDICT). The model includes compartment-specific relaxation effects providing T1/T2 estimates and microstructural parameters unbiased by relaxation properties of the tissue. 44 men with suspected prostate cancer (PCa) underwent multiparametric MRI (mp-MRI) and VERDICT-MRI followed by targeted biopsy. We estimate joint diffusion and relaxation prostate tissue parameters with rVERDICT using deep neural networks for fast fitting. We tested the feasibility of rVERDICT estimates for Gleason grade discrimination and compared with classic VERDICT and the apparent diffusion coefficient (ADC) from mp-MRI. The rVERDICT intracellular volume fraction fic discriminated between Gleason 3 + 3 and 3 + 4 (p = 0.003) and Gleason 3 + 4 and ≥ 4 + 3 (p = 0.040), outperforming classic VERDICT and the ADC from mp-MRI. To evaluate the relaxation estimates we compare against independent multi-TE acquisitions, showing that the rVERDICT T2 values are not significantly different from those estimated with the independent multi-TE acquisition (p > 0.05). Also, rVERDICT parameters exhibited high repeatability when rescanning five patients (R2 = 0.79-0.98; CV = 1-7%; ICC = 92-98%). The rVERDICT model allows for accurate, fast and repeatable estimation of diffusion and relaxation properties of PCa sensitive enough to discriminate Gleason grades 3 + 3, 3 + 4 and ≥ 4 + 3.

Prostate MR image quality of apparent diffusion coefficient maps versus fractional intracellular volume maps from VERDICT MRI using the PI-QUAL score and a dedicated Likert scale for artefacts.

PURPOSE: This study aimed to assess the image quality of apparent diffusion coefficient (ADC) maps derived from conventional diffusion-weighted MRI and fractional intracellular volume maps (FIC) from VERDICT MRI (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) in patients from the INNOVATE trial. The inter-reader agreement was also assessed. METHODS: Two readers analysed both ADC and FIC maps from 57 patients enrolled in the INNOVATE prospective trial. Image quality was assessed using the Prostate Imaging Quality (PI-QUAL) score and a subjective image quality Likert score (Likert-IQ). The image quality of FIC and ADC were compared using a Wilcoxon Signed Ranks test. The inter-reader agreement was assessed with Cohen's kappa. RESULTS: There was no statistically significant difference between the PI-QUAL score for FIC datasets compared to ADC datasets for either reader (p = 0.240 and p = 0.614). Using the Likert-IQ score, FIC image quality was higher compared to ADC (p = 0.021) as assessed by reader-1 but not for reader-2 (p = 0.663). The inter-reader agreement was 'fair' for PI-QUAL scoring of datasets with FIC maps at 0.27 (95% confidence interval; 0.08-0.46) and ADC datasets at 0.39 (95% confidence interval 0.22-0.57). For Likert scoring, the inter-reader agreement was also 'fair' for FIC maps at 0.38 (95% confidence interval; 0.10-0.65) and substantial for ADC maps at 0.62 (95% confidence interval; 0.39-0.86). CONCLUSION: Image quality was comparable for FIC and ADC. The inter-reader agreement was similar when using PIQUAL for both FIC and ADC datasets but higher for ADC maps compared to FIC maps using the image quality Likert score.

Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology.

The aim of this work was to extend the VERDICT-MRI framework for modelling brain tumours, enabling comprehensive characterisation of both intra- and peritumoural areas with a particular focus on cellular and vascular features. Diffusion MRI data were acquired with multiple b-values (ranging from 50 to 3500 s/mm2), diffusion times, and echo times in 21 patients with brain tumours of different types and with a wide range of cellular and vascular features. We fitted a selection of diffusion models that resulted from the combination of different types of intracellular, extracellular, and vascular compartments to the signal. We compared the models using criteria for parsimony while aiming at good characterisation of all of the key histological brain tumour components. Finally, we evaluated the parameters of the best-performing model in the differentiation of tumour histotypes, using ADC (Apparent Diffusion Coefficient) as a clinical standard reference, and compared them to histopathology and relevant perfusion MRI metrics. The best-performing model for VERDICT in brain tumours was a three-compartment model accounting for anisotropically hindered and isotropically restricted diffusion and isotropic pseudo-diffusion. VERDICT metrics were compatible with the histological appearance of low-grade gliomas and metastases and reflected differences found by histopathology between multiple biopsy samples within tumours. The comparison between histotypes showed that both the intracellular and vascular fractions tended to be higher in tumours with high cellularity (glioblastoma and metastasis), and quantitative analysis showed a trend toward higher values of the intracellular fraction (fic) within the tumour core with increasing glioma grade. We also observed a trend towards a higher free water fraction in vasogenic oedemas around metastases compared to infiltrative oedemas around glioblastomas and WHO 3 gliomas as well as the periphery of low-grade gliomas. In conclusion, we developed and evaluated a multi-compartment diffusion MRI model for brain tumours based on the VERDICT framework, which showed agreement between non-invasive microstructural estimates and histology and encouraging trends for the differentiation of tumour types and sub-regions.

Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison.

This paper aims to identify the minimum requirements for an accurate model of the diffusion MR signal in white matter of the brain. We construct a taxonomy of multi-compartment models of white matter from combinations of simple models for the intra- and the extra-axonal spaces. We devise a new diffusion MRI protocol that provides measurements with a wide range of imaging parameters for diffusion sensitization both parallel and perpendicular to white matter fibres. We use the protocol to acquire data from two fixed rat brains, which allows us to fit, study and compare the different models. The study examines a total of 47 analytic models, including several well-used models from the literature, which we place within the taxonomy. The results show that models that incorporate intra-axonal restriction, such as ball and stick or CHARMED, generally explain the data better than those that do not, such as the DT or the biexponential models. However, three-compartment models which account for restriction parallel to the axons and incorporate pore size explain the measurements most accurately. The best fit comes from combining a full diffusion tensor (DT) model of the extra-axonal space with a cylindrical intra-axonal component of single radius and a third spherical compartment of non-zero radius. We also measure the stability of the non-zero radius intra-axonal models and find that single radius intra-axonal models are more stable than gamma distributed radii models with similar fitting performance.

Differentiating False Positive Lesions from Clinically Significant Cancer and Normal Prostate Tissue Using VERDICT MRI and Other Diffusion Models.

False positives on multiparametric MRIs (mp-MRIs) result in many unnecessary invasive biopsies in men with clinically insignificant diseases. This study investigated whether quantitative diffusion MRI could differentiate between false positives, true positives and normal tissue non-invasively. Thirty-eight patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI, followed by transperineal biopsy. The patients were categorized into two groups following biopsy: (1) significant cancer­true positive, 19 patients; (2) atrophy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN)­false positive, 19 patients. The clinical apparent diffusion coefficient (ADC) values were obtained, and the intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted via deep learning. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) (p < 0.0001) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular−extravascular volume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were found for the VERDICT fIC (p = 0.004) and IVIM D. These results demonstrate that model-based diffusion MRI could reduce unnecessary biopsies occurring due to false positive prostate lesions and shows promising sensitivity to benign diseases.