ADC is a potential post treatment imaging biomarker in colorectal liver metastasis however measurements are affected by respiratory motion. This is compounded by increased statistical uncertainty in ADC measurement with decreasing tumour volume. In this prospective study we applied a retrospective motion correction method to improve the image quality of 15 tumour data sets from 11 patients. We compared repeatability of ADC measurements corrected for motion artefact against non-motion corrected acquisition of the same data set. We then applied an error model that estimated the uncertainty in ADC repeatability measurements therefore taking into consideration tumour volume. Test-retest differences in ADC for each tumour, was scaled to their estimated measurement uncertainty, and 95% confidence limits were calculated, with a null hypothesis that there is no difference between the model distribution and the data. An early post treatment scan (within 7 days of starting treatment) was acquired for 12 tumours from 8 patients. When accounting for both motion artefact and statistical uncertainty due to tumour volumes, the threshold for detecting significant post treatment changes for an individual tumour in this data set, reduced from 30.3% to 1.7% (95% limits of agreement). Applying these constraints, a significant change in ADC (5 th and 20 th percentiles of the ADC histogram) was observed in 5 patients post treatment. For smaller studies, motion correcting data for small tumour volumes increased statistical efficiency to detect post treatment changes in ADC. Lower percentiles may be more sensitive than mean ADC for colorectal metastases.

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A novel method called global and local transformation consistency constraints, which combines the scale, orientation and spatial layout information of 'scale invariant feature transform' (SIFT) features, is proposed for discarding mismatches from given putative point correspondences. Experiments show that the proposed method can efficiently extract high-precision matches from low-precision putative SIFT matches for wide baseline image pairs, and outperforms or performs close to state-of-the-art approaches.Introduction: Local features are powerful tools for finding correspondences between wide baseline views of the same scenes. Some feature-based algorithms first establish putative correspondences, and then estimate the best global geometry relationship (such as homography) interpreting them. However, many well-known robust estimators (such as RANSAC [1]) perform poorly when the ratio of inliers is lower than 50% [2], while discarding mismatches before estimating this relationship yields important improvements, especially in the case where incorrect matches strongly outnumber the correct ones. Previous works (see e.g. [3,4]) for discarding mismatches mainly employ the geometrical and topological relationship among putative matches, but ignore the scale and orientation information of the potential feature pairs, which can express a similarity transformation.This Letter focuses on rejecting mismatches via evaluating the quality of each potential correspondence, which is measured by both global and local transformation consistency. To address the mismatches discarding problem, we divide the algorithm into two steps. First, using global constraint, we will be retaining a part of the matches of which the scale log-ratio and orientation difference are approximate to global scaling and rotation factor, respectively. Then, using local constraint, we will reject more incorrect matches from the first step, with a stricter constraint by requiring that neighbouring feature pairs have the similar transformation. Experiments show that the approach presented in this Letter improves the currently achieved wide baseline matching precision, with 10% fewer errors on most of the six well-known wide baseline image pairs, which were offered by Tuytellaars and Van Gool [5].

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