The prediction of radiationâ induced liver dysfunction using a local dose and regional venous perfusion model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134805/1/mp1081.pd

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

GAN and dual‐input two‐compartment model‐based training of a neural network for robust quantification of contrast uptake rate in gadoxetic acid‐enhanced MRI

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154967/1/mp14055_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154967/2/mp14055.pd

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

© 2017 The Author(s). Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis

Correction of arterial input function in dynamic contrast‐enhanced MRI of the liver

Purpose: To develop a postprocessing method to correct saturation of arterial input function (AIF) in T1‐weighted dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) for quantification of hepatic perfusion. Materials and Methods: The saturated AIF is corrected by parameterizing the first pass of the AIF as a smooth function with a single peak and minimizing a least‐squares error in fitting the liver DCE‐MRI data to a dual‐input single‐compartment model. Sensitivities of the method to the degree of saturation in the AIF first‐pass peak and the image contrast‐to‐noise ratio were assessed. The method was also evaluated by correlating portal venous perfusion with an independent overall liver function measurement. Results: The proposed method corrects the distorted AIF with a saturation ratio up to 0.45. The corrected AIF improved hepatic arterial perfusion by −23.4% and portal venous perfusion by 26.9% in a study of 12 patients with liver cancers. The correlation between the mean voxelwise portal venous perfusion and overall liver function measurement was improved by using the corrected AIFs (R 2 = 0.67) compared with the saturated AIFs (R 2 = 0.39). Conclusion: The method is robust for correcting AIF distortion and has the potential to improve quantification of hepatic perfusion for assessment of liver tissue response to treatment in patients with hepatic cancers. J. Magn. Reson. Imaging 2012;36:411–421. © 2012 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92374/1/23636_ftp.pd

Post Hepatectomy Liver Failure: Risk Factors and Prediction of Post-Operative Function using Novel Dynamic MRI

Liver surgery is an advancing specialty with improved outcomes in recent years. Liver resection is used with curative intent for both primary and metastatic cancer. Despite the rapid improvements and increasing range of surgical options, there remains a significant risk of developing Post-Hepatectomy Liver Failure (PHLF) – caused by inadequate remnant liver function after surgery. This is a condition with high mortality and morbidity and currently there are no specific treatments for it once it has developed. Its pathogenesis is complex and multifactorial, and some risk factors, particularly ageing are uncertain as to their contributing significance. This thesis aimed to investigate risk factors for PHLF development and a imaging based measurement of liver function after major liver resection. This study identified patients over-75 years have a significantly increased risk of PHLF. Development of a method to predict post-operative function is needed to aid patient selection and reduce complications for those who undergo resection. Currently, volumetry is performed but this has proven inadequate, with some patients still developing PHLF despite adequate remnant volume. Other options such as Indocyanine Green and Technetium-99m labelled Mebrofenin are not readily available. One potential solution is Dynamic Gadoxetate Enhanced (DGE) MRI of the Liver, which has been developed to investigate liver function, with promising results for demonstrating liver heterogenicity in patients with parenchymal liver diseases. Oncological staging of the liver involves MRI to plan surgical resection, and DGE-MRI can be integrated into the diagnostic protocol easily with no additional burden to the patient. This thesis aimed to demonstrate if DGE-MRI functional estimates can predict post-operative liver function after resection of colorectal liver metastases. This study demonstrated that there was good correlation of DGE-MRI-function tests with post-operative hyperbilirubinaemia, a measure of hepatic dysfunction. This could be utilised in surgical planning to improve patient selection and outcomes

Dynamic Contrast Enhanced Computed Tomography Measurement of Perfusion in Hepatic Cancer

ABSTRACT In recent years, the incidence and mortality rate for hepatocellular carcinoma (HCC) have increased due to the emergence of hepatitis B, C and other diseases that cause cirrhosis. The progression from cirrhosis to HCC is characterized by abnormal vascularization and by a shift from a venous to an arterial blood supply. A knowledge of HCC vascularity which is manifested as alterations in liver blood flow may distinguish among different stages of liver disease and can be used to monitor response to treatment. Unfortunately, conventional diagnostic imaging techniques lack the ability to accurately quantify HCC vascularity. The purpose of this thesis was to validate and assess the diagnostic capabilities of dynamic contrast enhanced computed tomography (DCE-CT) and perfusion software designed to measure hepatic perfusion. Chapter 2 described a study designed to evaluate the accuracy and precision of hepatic perfusion measurement. The results showed a strong correlation between hepatic artery blood flow measurement with DCE-CT and radioactive microspheres under steady state in a rabbit model for HCC (VX2 carcinoma). Using repeated measurements and Monte Carlo simulations, DCE-CT perfusion measurements were found to be precise; with the highest precision in the tumor rim. In Chapter 3, we used fluorine-18 fluoro-2-deoxy-D-glucose (FDG) positron emission tomography and DCE-CT perfusion to determined an inverse correlation between glucose utilization and tumor blood flow; with an R of 0.727 (P \u3c 0.05). This suggests a limited supply of oxygen (possibly hypoxia) and that the tumor cells were surviving via anaerobic glycolysis. in In Chapter 4, hepatic perfusion data showed that thalidomide caused a reduction of tumor perfusion in the responder group during the first 8 days after therapy, P \u3c 0.05; while perfusion in the partial responder and control group remained unchanged, P \u3e 0.05. These changes were attributed to vascular remodeling and maturation resulting in a more functional network of endothelial tubes lined with pericytes. The results of this thesis demonstrate the accuracy and precision of DCE-CT hepatic perfusion measurements. It also showed that DCE-CT perfusion has the potential to enhance the functional imaging ability of hybrid PET/CT scanners and evaluate the efficacy of anti-angiogenesis therapy

GPU-accelerated voxelwise hepatic perfusion quantification

Voxelwise quantification of hepatic perfusion parameters from dynamic contrast enhanced (DCE) imaging greatly contributes to assessment of liver function in response to radiation therapy. However, the efficiency of the estimation of hepatic perfusion parameters voxel-by-voxel in the whole liver using a dual-input single-compartment model requires substantial improvement for routine clinical applications. In this paper, we utilize the parallel computation power of a graphics processing unit (GPU) to accelerate the computation, while maintaining the same accuracy as the conventional method. Using compute unified device architecture-GPU, the hepatic perfusion computations over multiple voxels are run across the GPU blocks concurrently but independently. At each voxel, nonlinear least-squares fitting the time series of the liver DCE data to the compartmental model is distributed to multiple threads in a block, and the computations of different time points are performed simultaneously and synchronically. An efficient fast Fourier transform in a block is also developed for the convolution computation in the model. The GPU computations of the voxel-by-voxel hepatic perfusion images are compared with ones by the CPU using the simulated DCE data and the experimental DCE MR images from patients. The computation speed is improved by 30 times using a NVIDIA Tesla C2050 GPU compared to a 2.67 GHz Intel Xeon CPU processor. To obtain liver perfusion maps with 626 400 voxels in a patient's liver, it takes 0.9 min with the GPU-accelerated voxelwise computation, compared to 110 min with the CPU, while both methods result in perfusion parameters differences less than 10 −6 . The method will be useful for generating liver perfusion images in clinical settings.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98596/1/0031-9155_57_17_5601.pd