Improving Accuracy of Information Extraction from Quantitative Magnetic Resonance Imaging

Quantitative MRI offers the possibility to produce objective measurements of tissue physiology at different scales. Such measurements are highly valuable in applications such as drug development, treatment monitoring or early diagnosis of cancer. From microstructural information in diffusion weighted imaging (DWI) or local perfusion and permeability in dynamic contrast (DCE-) MRI to more macroscopic observations of the local intestinal contraction, a number of aspects of quantitative MRI are considered in this thesis. The main objective of the presented work is to provide pre-processing techniques and model modification in order to improve the reliability of image analysis in quantitative MRI. Firstly, the challenge of clinical DWI signal modelling is investigated to overcome the biasing effect due to noise in the data. Several methods with increasing level of complexity are applied to simulations and a series of clinical datasets. Secondly, a novel Robust Data Decomposition Registration technique is introduced to tackle the problem of image registration in DCE-MRI. The technique allows the separation of tissue enhancement from motion effects so that the latter can be corrected independently. It is successfully applied to DCE-MRI datasets of different organs. This application is extended to the correction of respiratory motion in small bowel motility quantification in dynamic MRI data acquired during free breathing. Finally, a new local model for the arterial input function (AIF) is proposed. The estimation of the arterial blood contrast agent concentration in DCE-MRI is augmented using prior knowledge on local tissue structure from DWI. This work explores several types of imaging using MRI. It contributes to clinical quantitative MRI analysis providing practical solutions aimed at improving the accuracy and consistency of the parameters derived from image data

Central pancreatectomy: comparison of results according to the type of anastomosis

INTRODUCTION: The mild pancreatic tumors are more and more treated by central pancreatectomy (CP) in alternative with the widened pancreatectomies. Indeed, their morbidity is lesser but they are however burdened by a rate of important postoperative fistulas. The purpose of our study is to compare pancreatico-jejunal anastomosis and pancreatico-gastric anastomosis. METHODS: This work was realized in a bicentric retrospective way. Twenty-five CP were included and classified according to two groups according to the pancreatic anastomosis (group 1 for pancreatico-jejunal anastomosis and group 2 for the pancreatico-gastric anastomosis). CP was realized according to a protocol standardized in both centers and the complications were classified according to the classification of Clavien and Dindo and the fistulas according to the classification of Bassi. RESULTS: Both groups were comparable. The duration operating and the blood losses were equivalent in both groups. There was a significant difference (P=0,014) as regards the rate of fistula. The pancreatico-gastric anastomosis complicated more often of a low-grade fistula. However, in both groups, the treatment was mainly medical. Our results were comparable with those found in the literature and confirmed the advantages of the CP with regard to the cephalic duodeno-pancreatectomy (DPC) or to the distal pancreatectomy (DP). However, in the literature, a meta-analysis did not report difference between both types of anastomosis but this one concerned only the DPC. CONCLUSIONS: This work showed a less important incidence of low-grade fistula after pancreatico-jejunal anastomosis in the fall of a PM. This result should be confirmed by a later study on a more important sample of PM

Pancreatico-jejunostomy decreases post-operative pancreatic fistula incidence and severity after central pancreatectomy

BACKGROUNDS: Central pancreatectomy (CP) is an alternative to pancreaticoduodenectomy and distal pancreatectomy in benign tumours of pancreatic isthmus management. It is known for a high post-operative pancreatic fistula (POPF) rate. The purpose of this study was to compare POPF incidence between pancreatico-jejunostomy (PJ) and pancreatico-gastrostomy (PG). METHODS: Fifty-eight patients (mean age 53.9 ± 1.9 years) who underwent a CP in four French University Hospitals from 1988 to 2011 were analysed. The distal pancreatic remnant was either anastomosed to the stomach (44.8%, n = 25) or to a Roux-en-Y jejunal loop (55.2%, n = 35) with routine external drainage allowing a systematic search for POPF. POPF severity was classified according to the International Study Group on Pancreatic Fistula (ISGPF) and Clavien-Dindo classifications. RESULTS: The groups were similar on sex ratio, mean age, ASA score, pancreas texture, operative time and operative blood loss. Mean follow-up was 36.2 ± 3.9 months. POPF were significantly more frequent after PG (76.9 versus 37.5%, P = 0.003). PG was associated with significantly higher grade of POPF both when graded with ISGPF classification (P = 0.012) and Clavien-Dindo classification (P = 0.044). There was no significant difference in post-operative bleeding (0.918) and delayed gastric emptying (0.877) between the two groups. Hospital length of stay was increased after PG (23.6 ± 3.5 days versus 16.5 ± 1.9 days, P = 0.071). There was no significant difference in incidence of long-term exocrine (3.8 versus 19.2%, P = 0.134) and endocrine (7.7 versus 9.4%, P = 0.575) pancreatic insufficiencies. CONCLUSION: PG was associated with a significantly higher POPF incidence and severity in CP. We recommend performing PJ especially in older patients to improve CP outcomes

Hypercalcémie sévère après substitution d’une carence prolongée en vitamine D chez une malade ayant une hyperparathyroïdie primaire

International audienc

Direct parametric reconstruction from undersampled (k, t)-space data in dynamic contrast enhanced MRI

The Magnetic Resonance Imaging (MRI) signal can be made sensitive to functional parameters that provide information about tissues. In dynamic contrast enhanced (DCE) MRI these functional parameters are related to the microvasculature environment and the concentration changes that occur rapidly after the injection of a contrast agent. Typically DCE images are reconstructed individually and kinetic parameters are estimated by fitting a pharmacokinetic model to the time-enhancement response; these methods can be denoted as "indirect". If undersampling is present to accelerate the acquisition, techniques such as kt-FOCUSS can be employed in the reconstruction step to avoid image degradation. This paper suggests a Bayesian inference framework to estimate functional parameters directly from the measurements at high temporal resolution. The current implementation estimates pharmacokinetic parameters (related to the extended Tofts model) from undersampled (k, t)-space DCE MRI. The proposed scheme is evaluated on a simulated abdominal DCE phantom and prostate DCE data, for fully sampled, 4 and 8-fold undersampled (k, t)-space data. Direct kinetic parameters demonstrate better correspondence (up to 70% higher mutual information) to the ground truth kinetic parameters (of the simulated abdominal DCE phantom) than the ones derived from the indirect methods. For the prostate DCE data, direct kinetic parameters depict the morphology of the tumour better. To examine the impact on cancer diagnosis, a peripheral zone prostate cancer diagnostic model was employed to calculate a probability map for each method

Respiratory motion correction in dynamic MRI using robust data decomposition registration - Application to DCE-MRI.

Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC60) at early enhancement

Dual registration of abdominal motion for motility assessment in free-breathing data sets acquired using dynamic MRI.

At present, registration-based quantification of bowel motility from dynamic MRI is limited to breath-hold studies. Here we validate a dual-registration technique robust to respiratory motion for the assessment of small bowel and colonic motility. Small bowel datasets were acquired in breath-hold and free-breathing in 20 healthy individuals. A pre-processing step using an iterative registration of the low rank component of the data was applied to remove respiratory motion from the free breathing data. Motility was then quantified with an existing optic-flow (OF) based registration technique to form a dual-stage approach, termed Dual Registration of Abdominal Motion (DRAM). The benefit of respiratory motion correction was assessed by (1) assessing the fidelity of automatically propagated segmental regions of interest (ROIs) in the small bowel and colon and (2) comparing parametric motility maps to a breath-hold ground truth. DRAM demonstrated an improved ability to propagate ROIs through free-breathing small bowel and colonic motility data, with median error decreased by 90% and 55%, respectively. Comparison between global parametric maps showed high concordance between breath-hold data and free-breathing DRAM. Quantification of segmental and global motility in dynamic MR data is more accurate and robust to respiration when using the DRAM approach

Noise estimation from averaged diffusion weighted images: Can unbiased quantitative decay parameters assist cancer evaluation?

Purpose: Multiexponential decay parameters are estimated from diffusion-weighted-imaging that generally have inherently low signal-to-noise ratio and non-normal noise distributions, especially at high b-values. Conventional nonlinear regression algorithms assume normally distributed noise, introducing bias into the calculated decay parameters and potentially affecting their ability to classify tumors. This study aims to accurately estimate noise of averaged diffusion-weighted-imaging, to correct the noise induced bias, and to assess the effect upon cancer classification. Methods: A new adaptation of the median-absolute-deviation technique in the wavelet-domain, using a closed form approximation of convolved probability-distribution-functions, is proposed to estimate noise. Nonlinear regression algorithms that account for the underlying noise (maximum probability) fit the biexponential/stretched exponential decay models to the diffusion-weighted signal. A logistic-regression model was built from the decay parameters to discriminate benign from metastatic neck lymph nodes in 40 patients. Results: The adapted median-absolute-deviation method accurately predicted the noise of simulated (R=0.96) and neck diffusion-weighted-imaging (averaged once or four times). Maximum probability recovers the true apparent-diffusion-coefficient of the simulated data better than nonlinear regression (up to 40%), whereas no apparent differences were found for the other decay parameters. Conclusions: Perfusion-related parameters were best at cancer classification. Noise-corrected decay parameters did not significantly improve classification for the clinical data set though simulations show benefit for lower signal-to-noise ratio acquisitions. © 2013 Wiley Periodicals, Inc

Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation

Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics – particularly in routine clinical practice. A framework is developed to automate DCE-MRI tumour segmentation, by introducing: perfusion-supervoxels to over-segment and classify DCE-MRI volumes using the dynamic contrast enhancement characteristics; and the pieces-of-parts graphical model, which adds global (anatomic) constraints that further refine the supervoxel components that comprise the tumour. The framework was evaluated on 23 DCE-MRI scans of patients with rectal adenocarcinomas, and achieved a voxelwise area-under the receiver operating characteristic curve (AUC) of 0.97 compared to expert delineations. Creating a binary tumour segmentation, 21 of the 23 cases were segmented correctly with a median Dice similarity coefficient (DSC) of 0.63, which is close to the inter-rater variability of this challenging task. A second study is also included to demonstrate the method’s generalisability and achieved a DSC of 0.71. The framework achieves promising results for the underexplored area of rectal tumour segmentation in DCE-MRI, and the methods have potential to be applied to other DCE-MRI and supervoxel segmentation problems