Alexander, DC

Atkinson, D

Devine, W

Giganti, F

Johnston, E

Panagiotaki, E

Punwani, S

UCL Discovery

Two compartment fitting for Luminal Water Imaging: multi-echo T2 in Prostate CancerWilliam Devine , Francesco Giganti , Edward Johnston , Eleftheria Panagiotaki , Shonit Punwani , Daniel C. Alexander ,and David AtkinsonCentre for Medical Imaging, University College London, London, United Kingdom, Department of Radiology, University College London HospitalNHS Foundation Trust, London, United Kingdom, Centre for Medical Image Computing, University College London, London, United KingdomSynopsisThis work aims both to show that using a constrained fitting method for Luminal WaterImaging with fewer echoes produces accurate parameter estimates and that this fittingmethod is feasible for detecting and grading Prostate Cancer. Simulated signals wereproduced and the mean LWF across multiple iterations was calculated for two models. Then19 patients were imaged and the images were contoured in both cancerous and benignregions. The simulation showed that the proposed constrained method is accurate and thein-vivo imaging reinforced the idea that multi-echo T2 modelling shows promise in detectingand grading PCa.IntroductionSimilar to methods developed for imaging myelin in the brain , Luminal Water Imaging (LWI), a fitting technique for multi-echo T2 data recentlyproposed by Sabouri et al. , calculates the percentage volume of lumen within a voxel. LWI has been shown to be closely related to the luminal spacein prostatic tissue , with a recent study demonstrating the feasibility of luminal water imaging in the detection and grading of PCa . However, thecurrent method requires a 64-echo acquisition which is time consuming and may not be available without dedicated scanner research software. Thiswork seeks to show that the combination of an appropriately refined fitting method and fewer acquired echoes provide values of the Luminal WaterFraction (LWF) that are in close agreement with those of the original LWI. Correlations to Gleason score were then performed along with the AUC,sensitivity and specificity of the detection of PCa. MethodsModel The original fitting proposed by Sabouri et al used a smoothed non-negative least squares (NNLS) fitting over many variables to produce adistribution  of T2 values . The method proposed here constrains this distribution to consist of two Gaussians. This constraint was imposedbecause the vast majority of voxels in the Sabouri studies  showed two main signal components, which may provide a more robust fitting. Themodel relating the Signal  to the distribution  of T2 values is:where  is the overall magnitude of the signal and  is the echo time. For each voxel six parameters were estimated: overall scaling ( ), relativefraction between the two compartments (α), mean and standard deviation of the short T2 peak (μ  and σ ) and mean and standard deviation of thelong T2 peak (μ  and σ ). The LWF was calculated as the area under the long T2 component divided by the area under both components.Simulation Simulated signal decays were produced for two separate underlying T2 distributions (two delta functions at T2  and T2  and twoGaussian distributions with means T2  and T2 , fixed at T2  = 75ms and T2  = 600ms) with varying values of LWF, Signal-to-Noise Ratio(SNR) and Number of Echoes (NE). For each combination of parameters 50 iterations of the signal were created. For each iteration the original andnew fitting methods were tested using 64-echo and 32-echo simulation signals respectively. The mean LWF across the 50 iterations was thencalculated for both models in order to show how the two compare over a range of LWF and SNR values.Data In order to investigate the relationship between this new fitting method and Gleason Score, 19 subjects were imaged for this study. The scanparameters are included in Figure 1. A board-certified radiologist contoured 15 lesions in 19 patients, which were confirmed to be cancers followingtargeted biopsy. Regions of interest (ROIs) were also placed in 16 areas of normal tissue in each of the subjects, all of which were histologicallyconfirmed as benign.Statistics A Spearman’s rank correlation test was used to find the correlation between Gleason Score and LWF and the Area Under the Curve (AUC),sensitivity and specificity values were calculated using a Receiver Operating Characteristic (ROC) analysis with 5-fold cross validation to reduceproblems arising from multiple ROIs coming from the same image.ResultsFigure 1 shows the scan parameters of the 32-echo acquisition. Figure 2 shows that for nearly all values of LWF and SNR, using either the deltafunction or Gaussian assumption, the LWF value produced by the new method is at least as accurate as the original. Using a Spearman’s rankcorrelation test a correlation of -0.667 was found between Gleason Score and LWF in 31 ROIs. Figure 3 shows the AUC, sensitivity and specificityachieved by the new fitting method in 31 ROIs. Figure 4 is an example LWF map of the prostate. Note the higher LWF in the peripheral zone (PZ),consistent with histological findings of large regular acini and loosely woven stroma in the PZ . 1 2 1 3 1 311 23122 31 p 42,3S pS = p(T2)exp(− )dT2M0 ∫∞0TET2M0 TE M01 12 2short longshort long short long5DiscussionThe LWF values calculated in simulation using both fitting methods show that the new constrained method is just as accurate as the original. Thecorrelation between Gleason Score and LWF then reinforces the idea that multi-echo T2 modelling shows promise as a method for detecting andgrading PCa. The AUC, sensitivity and specificity of the detection of PCa are all lower in this study when compared to previous results6 but this maybe down to a much lower number of ROIs. ConclusionThis new method shows promise in being able to significantly reduce the number of measurements needed to carry out LWI, making it a moreclinically viable method. AcknowledgementsCancer Research UK through the Comprehensive Cancer Imaging Centre, Prostate Cancer UK and the NIHR funded biomedical research centre atUCLH.References1. Whittall K., et al. "In vivo measurement of T2 distributions and water contents in normal human brain." Magnetic Resonance in Medicine 37,no. 1 (1997): 34-43.2. Sabouri S., et al. “MR Measurement of Luminal Water in Prostate Gland: Quantitative Correlation Between MRI and Histology.” Journal ofMagnetic Resonance Imaging 46, no. 3 (2017): 861-8693. Sabouri S., et al. “Luminal Water Imaging: A New MR Imaging T2 Mapping Technique for Prostate Cancer Diagnosis” Radiology 284, no. 2(2017): 451-4594. Bjarnason T., et al. “AnalyzeNNLS: Magnetic resonance multiexponential decay image analysis” Journal of Magnetic Resonance 206, (2010):200–2045. McNeal J. “Normal Histology of the Prostate” The American Journal of Surgical Pathology 12, no. 8 (1988):619-6336. Sabouri S., et al. “Luminal water imaging: a novel MRI method for prostate cancer diagnosis” Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)2489 FiguresScan parameters of the 32-echo acquisition.This table shows the predicted value of the LWF for both models for diﬀerent actual LWF values and noise levels and using both delta function andGaussian signal simulations. It shows that, for the majority of values of both LWF and SNR, the constrained model is at least as close to the actualvalue as the NNLS model.AUC, sensitivity and specificity achieved by the new fitting method against the original. The LWF calculated using NNLS is taken from a previouswork for comparison .6LWF map of the prostate. Note the higher LWF in the peripheral zone (PZ), consistent with histological findings of large regular acini and looselywoven stroma in the PZ .5

Two compartment fitting for Luminal Water Imaging: multi-echo T2 in Prostate Cancer

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Two compartment fitting for Luminal Water Imaging: multi-echo T2 in Prostate Cancer

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