MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI

Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in DCE MRI/CT, ADC calculations and IVIM modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow. We present a framework for medical image fitting tasks that is included in MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-to-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre- and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth. Providing a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform pharmacokinetic analysis on DCE MRI data, with both interactive and automatized batch processing workflows.Comment: 31 pages, 11 figures URL: http://mitk.org/wiki/MITK-ModelFi

A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136452/1/nbm3695.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136452/2/nbm3695_am.pd

An extended vascular model for less biased estimation of permeability parameters in DCEâ T1 images

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137317/1/nbm3698_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137317/2/nbm3698.pd

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

Perfusion MRI quantification for Multi-Echo EPIK sequence in brain tumour patients

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2018The main goal of this thesis was to obtain fully quantifying perfusion parameters from both DSC (Dy-namic Susceptibility Contrast) and DCE (Dynamic Contrast Enhancement) techniques through usage of only one perfusion sequence–GESEEPIK (Gradient-echo, Spin-echo Echo-Planar Imaging with Key- hole). For this, twenty-two patients with a possible brain tumor were recruited for this study, and each patient was scanned data hybrid PET-MR 3T scanner. Firstly, T1-mapping data was acquired through a sequence of Inversion-Recovery EPIK. The contrast agent was then injected into the patient and perfusion images were acquired using the GESE EPIK sequence. Simultaneously, 18F-FET images were acquired which allowed the exclusion of patients who did not have any brain tumor. After the images were acquired, they were analyzed and the parameters were calculated. For starters, the information regarding the changes in T2 and T2*, already inherent to the data acquired, was analyzed. The curve of the MR signal was converted to the concentration curve. This curve was calculated using two different equations. With the calculated concentration curve, the DSC parameters were calculated. As expected, in areas affected by a tumor, there was an increase in vascularization due to angiogenesis. It was also observed, by comparison of the two methods used to calculate the concentration curves, that the non-removal of the leakage effects induced an unreal increase in the calculated parameters. In order to obtain images related to variations of T1 for DCE quantification, the images acquired were extrapolated to a echo time equal to zero. To these extrapolated images, the values obtained from the T1-mapping prior to the contrast’s injection were subtracted in order to obtain the concentration curve. A method that used the extrapolated images to obtain an initial T1-mapping was also tested, in order to avoid the need to implement the extra sequence for that purpose. The tofts kinetic model was applied in both methods, allowing the calculation of DCE parameters. In both applied methods, the results obtained were very similar, indicating the possibility of non-acquisition of the extra sequence if there are time constraints. However, not all parameters behaved as expected and a more detailed investigation of the literature was carried out. It is concluded that some parameters do not have a clear description and cannot characterize human physiology and be used in the study of pathologies. In conclusion, the initial goal of this thesis to obtain quantitative parameters DSC and DCE perfusion techniques, using only a single contrast sequence, was achieved with success. The need to remove leak age effects, which increased the calculated values and the tumor area, was also verified. However, some inconsistencies in the parameters’ interpretation were registered in the available literature. These inconsistencies had repercussions in some values obtained, whose interpretation was impossible, despite being in agreement with the literature. In addition, a method has been tested which further eliminates the need to acquireT1-mapping data prior to the contrast’s injection through an addition al sequence. Although not an objective of this thesis, It was not possible to relate the perfusion parameters to the degree of tumor severity

On The Development of a Dynamic Contrast-Enhanced Near-Infrared Technique to Measure Cerebral Blood Flow in the Neurocritical Care Unit

A dynamic contrast-enhanced (DCE) near-infrared (NIR) method to measure cerebral blood flow (CBF) in the neurocritical care unit (NCU) is described. A primary concern in managing patients with acquired brain injury (ABI) is onset of delayed ischemic injury (DII) caused by complications during the days to weeks following the initial insult, resulting in reduced CBF and impaired oxygen delivery. The development of a safe, portable, and quantitative DCE-NIR method for measuring CBF in NCU patients is addressed by focusing on four main areas: designing a clinically compatible instrument, developing an appropriate analytical framework, creating a relevant ABI animal model, and validating the method against CT perfusion. In Chapter 2, depth-resolved continuous-wave NIR recovered values of CBF in a juvenile pig show strong correlation with CT perfusion CBF during mild ischemia and hyperemia (r=0.84, p\u3c0.001). In particular, subject-specific light propagation modeling reduces the variability caused by extracerebral layer contamination. In Chapter 3, time-resolved (TR) NIR improves the signal sensitivity to brain tissue, and a relative CBF index is be both sensitive and specific to flow changes in the brain. In particular, when compared with the change in CBF measured with CT perfusion during hypocapnia, the deconvolution-based index has an error of 0.8%, compared to 21.8% with the time-to-peak method. To enable measurement of absolute CBF, a method for characterizing the AIF is described in Chapter 4, and the theoretical basis for an advanced analytical framework—the kinetic deconvolution optical reconstruction (KDOR)—is provided in Chapter 5. Finally, a multichannel TR-NIR system is combined with KDOR to quantify CBF in an adult pig model of ischemia (Chapter 6). In this final study, measurements of CBF obtained with the DCE-NIR technique show strong agreement with CT perfusion measurements of CBF in mild and moderate ischemia (r=0.86, p\u3c0.001). The principle conclusion of this thesis is that the DCE-NIR method, combining multidistance TR instrumentation with the KDOR analytical framework, can recover CBF values that are in strong agreement with CT perfusion values of CBF. Ultimately, bedside CBF measurements could improve clinical management of ABI by detecting delayed ischemia before permanent brain damage occurs

Repeatability of arterial input functions and kinetic parameters in muscle obtained by dynamic contrast enhanced MR imaging of the head and neck

BACKGROUND: Quantification of pharmacokinetic parameters in dynamic contrast enhanced (DCE) MRI is heavily dependent on the arterial input function (AIF). In the present patient study on advanced stage head and neck squamous cell carcinoma (HNSCC) we have acquired DCE-MR images before and during chemo radiotherapy. We determined the repeatability of image-derived AIFs and of the obtained kinetic parameters in muscle and compared the repeatability of muscle kinetic parameters obtained with image-derived AIF's versus a population-based AIF. MATERIALS AND METHODS: We compared image-derived AIFs obtained from the internal carotid, external carotid and vertebral arteries. Pharmacokinetic parameters (ve, Ktrans, kep) in muscle-located outside the radiation area-were obtained using the Tofts model with the image-derived AIFs and a population averaged AIF. Parameter values and repeatability were compared. Repeatability was calculated with the pre- and post-treatment data with the assumption of no DCE-MRI measurable biological changes between the scans. RESULTS: Several parameters describing magnitude and shape of the image-derived AIFs from the different arteries in the head and neck were significantly different. Use of image-derived AIFs led to higher pharmacokinetic parameters compared to use of a population averaged AIF. Median muscle pharmacokinetic parameters values obtained with AIFs in external carotids, internal carotids, vertebral arteries and with a population averaged AIF were respectively: ve (0.65, 0.74, 0.58, 0.32), Ktrans (0.30, 0.21, 0.13, 0.06), kep (0.41, 0.32, 0.24, 0.18). Repeatability of pharmacokinetic parameters was highest when a population averaged AIF was used; however, this repeatability was not significantly different from image-derived AIFs. CONCLUSION: Image-derived AIFs in the neck region showed significant variations in the AIFs obtained from different arteries, and did not improve repeatability of the resulting pharmacokinetic parameters compared with the use of a population averaged AIF. Therefore, use of a population averaged AIF seems to be preferable for pharmacokinetic analysis using DCE-MRI in the head and neck area

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

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

DIFFUSE OPTICAL MEASUREMENTS OF HEAD AND NECK TUMOR HEMODYNAMICS FOR EARLY PREDICTION OF CHEMO-RADIATION THERAPY OUTCOMES

Chemo-radiation therapy is a principal modality for the treatment of head and neck cancers, and its efficacy depends on the interaction of tumor oxygen with free radicals. In this study, we adopted a novel hybrid diffuse optical instrument combining a commercial frequency-domain tissue oximeter (Imagent) and a custom-made diffuse correlation spectroscopy (DCS) flowmeter, which allowed for simultaneous measurements of tumor blood flow and blood oxygenation. Using this hybrid instrument we continually measured tumor hemodynamic responses to chemo-radiation therapy over the treatment period of 7 weeks. We also explored monitoring dynamic tumor hemodynamic changes during radiation delivery. Blood flow data analysis was improved by simultaneously extracting multiple parameters from one single autocorrelation function curve measured by DCS. Patients were classified into two groups based on clinical outcomes: a complete response (CR) group and an incomplete response (IR) group with remote metastasis and/or local recurrence within one year. Interestingly, we found human papilloma virus (HPV-16) status largely affected tumor homodynamic responses to therapy. Significant differences in tumor blood flow index (BFI) and reduced scattering coefficient (μs’) between the IR and CR groups were observed in HPV-16 negative patients at Week 3. Significant differences in oxygenated hemoglobin concentration ([HbO2]) and blood oxygen saturation (StO2) between the two groups were found in HPV-16 positive patients at Week 1 and Week 3, respectively. Receiver operating characteristic curves were constructed and results indicated high sensitivities and specificities of these hemodynamic parameters for early (within the first three weeks of the treatment) prediction of one-year treatment outcomes. Measurement of tumor hemodynamics may serve as a predictive tool allowing treatment selection based on biologic tumor characteristics. Ultimately, reduction of side effects in patients not benefiting from radiation treatment may be feasible