Development Of Multi Parametric High Field Magnetic Resonance Imaging Techniques For Improved Characterization Of Prostate Cancer
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Development Of Multi Parametric High Field Magnetic Resonance Imaging Techniques For Improved Characterization Of Prostate Cancer
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Author : Albert Chen
language : en
Publisher:
Release Date : 2006
Development Of Multi Parametric High Field Magnetic Resonance Imaging Techniques For Improved Characterization Of Prostate Cancer written by Albert Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
The multi-parameric approach of acquiring different and unique MR data to characterize prostate cancer developed in this work may increase the usefulness and significance of MR prostate exam for the clinical management of prostate cancer.
Multi Parametric Magnetic Resonance Imaging Mri In Prostate Cancer
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Author : Deanna Lyn Langer
language : en
Publisher:
Release Date : 2010
Multi Parametric Magnetic Resonance Imaging Mri In Prostate Cancer written by Deanna Lyn Langer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Prostate cancer is extremely prevalent, with shifting patient demographics leading to an increasing number of men balancing treatment efficacy with associated side-effects. Non-invasive characterization of disease - useful for guiding biopsy, to monitor disease progression during active surveillance, or for treatment planning of focal therapies - could have a significant impact on patient management. Through its excellent anatomic imaging capabilities and its ability to characterize physiologic properties, magnetic resonance imaging (MRI) has the potential to fulfill clinical goals; however, further improvements are necessary to maximize accuracy and impact. Thus, this thesis presents: 1) the development of a multi-parametric model to combine parameters derived from measurement of T2 relaxation, diffusion weighted imaging, and dynamic contrast-enhanced MRI to improve the discrimination between normal and malignant peripheral zone tissue; 2) determination of the impact that the presence of normal tissue within regions of tumour has on the measurement of apparent diffusion coefficient (ADC) and T2 relaxation in the peripheral zone; and 3) relationships between MRI measurement and underlying prostate tissue composition. A common patient cohort was used for all studies, with prostate cancer patients having in vivo MRI prior to prostatectomy followed by whole-mount histologic sectioning of the surgical specimens, facilitating the use of pathology as a gold-standard for all analyses. In the first study, the optimal multi-parametric model combines ADC, T2, and volume transfer constant (Ktrans) to yield the probability of malignancy for each voxel. Performance of the model is better than each single parameter, but not significantly so compared to ADC. The second study demonstrates that there is no difference in ADC and T2 between tumours containing significant portions of normal tissue and the surrounding normal tissue itself, indicating that full characterization of prostate cancer with MRI may be limited. Finally, by determining relationships between MRI parameters and tissue characteristics, the third study suggests mechanisms driving MR image appearance in the prostate, including the visualization of cancer. Taken together, this thesis presents potential improvements to prostate cancer imaging, and provides further insight into the interplay between the underlying histology and MRI.
Atlas Of Multiparametric Prostate Mri
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Author : Joan C. Vilanova
language : en
Publisher: Springer
Release Date : 2017-09-28
Atlas Of Multiparametric Prostate Mri written by Joan C. Vilanova and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-28 with Medical categories.
This atlas provides a comprehensive, state of the art review of the use of multiparametric MRI (mpMRI) for the imaging of prostate cancer, covering aspects from diagnosis and loco-regional staging through to the role of the technique after treatment and follow-up. The book contains a wealth of high-resolution images, many of them in color, and displays the anatomical-MRI–pathological correlation whenever appropriate. Readers will find a helpful overview on the current standardized method for reading and reporting on mpMRI, the Prostate Imaging Reporting and Data System (PI-RADS), version 2. Dedicated chapters focus on differential diagnosis and imaging pitfalls, and the inclusion of helpful diagrams and algorithms will further assist in image interpretation, enabling readers to ease and improve their use of mpMRI. Edited and written by very experienced radiologists, pathologists, and urologists; the Atlas of Multiparametric Prostate MRI will serve as a unique source of clinically relevant information and an aid to disease management for radiologists, urologists, pathologists, radiotherapists, and oncologists.
Imaging And Focal Therapy Of Early Prostate Cancer
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Author : Thomas J. Polascik
language : en
Publisher: Springer
Release Date : 2017-02-22
Imaging And Focal Therapy Of Early Prostate Cancer written by Thomas J. Polascik and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-22 with Medical categories.
This text encompass an up-to-date, comprehensive review of the state-of-the-art for gland preserving therapies. Fully updated and revised, this text evaluates the scientific evidence for the evolving trend to treat intermediate risk, clinically localized prostate cancer in a focally ablative manner with novel gland-preserving, focal therapy methods. Various ablative devices such as high intensity focused ultrasound, irreversible electroporation, photodynamic therapy, cryotherapy and laser ablation, among others, is discussed in regard to their strengths and limitations as a therapeutic modality. Emphasis is placed on patient selection and outcomes utilizing both advanced imaging techniques and pathologic evaluation. Current and new approaches to image cancer foci within the prostate (multiparametric ultrasonography, multiparametric magnetic resonance image, etc) are presented along with various biopsy techniques, including robotics to map prostate cancer. Patient selection based on imaging and genomic classification, adjuvants to enhance therapy, treatment strategy, outcomes and patient centered concerns is discussed, providing an acceptable balance between cancer control and improved quality of life for patients. Written by experts in the field and lavishly illustrated with detailed line-art and photographs, Imaging and Focal Therapy of Early Prostate Cancer, Second Edition is designed as a comprehensive resource for urologists, radiation oncologists, medical oncologists, radiologists, uropathologists, molecular biologists, biomedical engineers, other clinicians –- residents, fellows, nurses and allied professionals -- and researchers with an interest in the diagnosis and novel treatment of prostate cancer. It will provide insight into the latest research and clinical applications of image-guided diagnosis and minimally invasive focal, gland-preserving treatment for prostate cancer.
Prostate Cancer Imaging Computer Aided Diagnosis Prognosis And Intervention
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Author : Anant Madabhushi
language : en
Publisher: Springer
Release Date : 2010-09-15
Prostate Cancer Imaging Computer Aided Diagnosis Prognosis And Intervention written by Anant Madabhushi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-15 with Computers categories.
Prostatic adenocarcinoma (CAP) is the second most common malignancy with an estimated 190,000 new cases in the USA in 2010 (Source: American Cancer Society), and is the most frequently diagnosed cancer among men. If CAP is caught early, men have a high, five-year survival rate. Unfortunately there is no standardized ima- based screening protocol for early detection of CAP (unlike for breast cancers). In the USA high levels of prostate-specific antigen (PSA) warrant a trans-rectal ultrasound (TRUS) biopsy to enable histologic confirmation of presence or absence of CAP. With recent rapid developments in multi-parametric radiological imaging te- niques (spectroscopy, dynamic contrast enhanced MR imaging, PET, RF ultrasound), some of these functional and metabolic imaging modalities are allowing for definition of high resolution, multi-modal signatures for prostate cancer in vivo. Distinct com- tational and technological challenges for multi-modal data registration and classifi- tion still remain in leveraging this multi-parametric data for directing therapy and optimizing biopsy. Additionally, with the recent advent of whole slide digital sc- ners, digitized histopathology has become amenable to computerized image analysis. While it is known that outcome of prostate cancer (prognosis) is highly correlated with Gleason grade, pathologists often have difficulty in distinguishing between interme- ate Gleason grades from histopathology. Development of computerized image analysis methods for automated Gleason grading and predicting outcome on histopathology have to confront the significant computational challenges associated with working these very large digitized images.
Improving The Quantitative Interpretation Of Multi Parametric Mri Mp Mri In Prostate Cancer
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Author : Xinran Zhong
language : en
Publisher:
Release Date : 2019
Improving The Quantitative Interpretation Of Multi Parametric Mri Mp Mri In Prostate Cancer written by Xinran Zhong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Purpose Prostate cancer (PCa) is the second leading cause of cancer-related death in men in the United States. The accurate diagnosis of PCa is crucial for proper treatment decision. Although biopsy is still the gold standard for diagnosis, it is limited to low sensitivity and invasiveness. On the other hand, as a non-invasive imaging tool, multi-parametric MRI (mp-MRI) has excellent potential in PCa diagnosis such as detection and stratification of aggressiveness. The mp-MRI includes both anatomical and functional information to be able to provide a comprehensive characterization of the tissue. However, diagnosis with mp-MRI is limited to inconsistent and qualitative interpretation. Clinically, the evaluation of mp-MRI is often through a standardized scoring system, PIRADS v2, which can lead to high inter- and intra-observer variability, and with a large amount of data for each case, the diagnosis process can be time-consuming. In order to get a more consistent quantitative evaluation, there are mainly two ways to utilize algorithms to help the diagnosis. The first one is creating quantitative biomarkers through mathematical models proposed based on assumption and understanding of physics and physiology, such as pharmacokinetic models for quantitative dynamic contrast-enhanced (DCE) MRI. The second one is using a machine learning technique to train a system with existing data to get diagnosis prediction on new data. The purpose of this work is to improve the quantitative interpretation of mp-MRI in PCa diagnosis regarding consistency and accuracy. Methods To evaluate existing B1+ estimation techniques to achieve a more consistent pre-contrast T1 estimation for quantitative DCE- MRI, 21 volunteers were prospectively recruited and scanned twice on two 3T MRI scanners, resulting in 84 variable flip angle (VFA) T1 exams. Two B1+ mapping techniques, including reference region variable flip angle (RR-VFA) and saturated turbo FLASH (satTFL), were used for B1+ correction, and T1 maps with and without B1+ correction were tested for the intra-scanner repeatability and inter-scanner reproducibility. Volumetric regions of interest were drawn on the transition zone, peripheral zone of the prostate and the obturator internus left and right muscles in the corresponding slices. The average T1 within each ROI for each scan was compared for both intra- and inter-scanner variability using concordance correlation coefficient and Bland-Altman plot. To simplify B1+ compensation for quantitative DCE MRI in clinical and clinical research settings, an analytical B1+ correction method is proposed using a Taylor series approximation to the steady-state spoiled gradient echo signal equation. The proposed approach only requires B1+ maps and uncorrected pharmacokinetic (PK) parameters as the input, and was evaluated using a prostate digital reference object (DRO) and 82 in-vivo prostate DCE-MRI cases. The approximated analytical correction was compared with the ground truth PK parameters in simulation, and compared with the reference numerical correction in in-vivo experiments, using percentage error as the metric. To develop a deep transfer learning (DTL) based model to distinguish indolent lesions from clinically significant PCa lesions using multiparametric MRI, 140 patients with 3T mp-MRI and whole-mount histopathology (WMHP) were included as the study cohort with IRB approval. The DTL based model was trained on 169 lesions in 110 arbitrarily selected patients and tested on the remaining 47 lesions in 30 patients. We compared the DTL based model with the same deep learning (DL) model architecture trained from scratch and the classification based on PIRADS v2 score with a threshold of 4 using accuracy, sensitivity, specificity, and area under curve (AUC). Bootstrapping with 2000 resamples was performed to estimate the 95% confidence interval (CI) for AUC. Results Both RR-VFA-corrected T1 and satTFL-corrected T1 showed higher intra- and inter-scanner correlation (0.89/0.87 and 0.87/0.84 respectively) than VFA T1 (0.84 and 0.74). Bland-Altman plots showed that VFA T1 had a wider 95% limits of agreement and a larger range of T1 for each tissue compared to T1 with B1+ correction. The prostate DRO results show that the proposed approach provides residual error less than 0.4% for both Ktrans and ve, compared to the ground truth. This noise-free residual error was smaller than the noise-induced error using the reference numerical correction, which had a minimum error of 2.1 4.3% with baseline SNR of 234.5. For the 82 in-vivo cases, percentage error compared to the reference numerical correction method had a mean of 0.1% (95% central range of [0.0%, 0.2%]) across the prostate volume. After training on 169 lesions in 110 patients, the AUC of discriminating indolent from clinically significant PCa lesions of the DTL based model, DL model without transfer learning and PIRADS v2 score > 4 were 0.726 (CI [0.575, 0.876]), 0.687 (CI [0.532, 0.843]) and 0.711 (CI [0.575, 0.847]), respectively in the testing set. Conclusion The application of B1+ correction (both RR-VFA and satTFL) to VFA T1 results in more repeatable and reproducible T1 estimation than VFA T1. This can potentially provide improved quantification of the prostate DCE-MRI parameters. The approximated analytical B1+ correction method provides comparable results with less than 0.3% error within 95% central range, compared to reference numerical B1+ correction. The proposed method is a practical solution for B1+ correction in prostate DCE-MRI due to its simple implementation. The DTL based model achieved higher AUC compared to the DL model without transfer learning and PIRADS v2 score > 4 in discriminating clinically significant lesions in the testing set. The DeLong test indicated that the DTL based model achieved comparable AUC compared to the classification based on PIRADS v2 score (p = 0.89).
Quantitative Integration Of Imaging And Non Imaging Data
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Author : Pallavi Tiwari
language : en
Publisher:
Release Date : 2012
Quantitative Integration Of Imaging And Non Imaging Data written by Pallavi Tiwari and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Data integration (Computer science) categories.
The problem of data integration involving imaging and non-imaging modalities is largely unexplored in the biomedical eld, mainly due to the challenges in quantitatively combining such heterogeneous modalities existing in diff erent dimensions and scales. Although several methods have been proposed in the literature involving quantitative integration of multi-protocol imaging, there has been a paucity of similar biomedical tools for quantitative integration of imaging and non-imaging data. In this work, we present novel data integration schemes to overcome the aforementioned challenges limiting the integration of imaging and non-imaging modalities, and hence improve disease characterization. Our novel data integration methods are applied to integration of multi-parametric Magnetic Resonance (MR) imaging (MP-MRI)-structural MR imaging with metabolic spectroscopic information (non-imaging) for improved prostate cancer (CaP) diagnosis, grading, and treatment evaluation post-radiation therapy (RT). To this end, we have developed novel data integration schemes such as, Multimodal Wavelet Embedding Representation for data Combination (MaWERiC), and Semi-Supervised Multi-Kernel (SeSMiK) Graph Embedding, which fi rst uniformly represent individual data modalities into a common framework using dimensionality reduction and kernel embedding techniques, followed by a seamless integration of imaging and non-imaging data in the common framework. The integrated quantitative signatures thus obtained are shown to be signifi cantly more diagnostically informative as compared to any single modality. Similar improvement in results was observed using integrated MP-MRI signatures for evaluating radiation therapy related changes in CaP patients, with an aim to identify (a) pre-RT disease extent along with extra capsule spread (if any) and (b) residual disease on post-RT MP-MRI.
Pet Ct And Mri In Prostate Cancer
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Author : Fabio Grizzi
language : en
Publisher: Frontiers Media SA
Release Date : 2024-06-03
Pet Ct And Mri In Prostate Cancer written by Fabio Grizzi and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-03 with Medical categories.
Prostate cancer remains one of the most common cancers and is among the most lethal in men worldwide. It is significant that prostate cancer is identified in the early stages as the disease can be highly metastatic leading to a low survival rate. Therefore, it is essential for patients to have a better prognosis and able to be treated early. The diagnostic tools to identify prostate cancer have developed throughout the years which includes but is not limited to transrectal ultrasound-guided prostate biopsy and histopathology prior to radical prostatectomy. However, biopsies have been found to be invasive in addition to studies demonstrating an underdiagnosis of patients who have advanced prostate cancer.
Optimizing And Advancing Multiparametric Magnetic Resonance Imaging For Biologically Guided Radiotherapy
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Author : Robert Bergen
language : en
Publisher:
Release Date : 2019
Optimizing And Advancing Multiparametric Magnetic Resonance Imaging For Biologically Guided Radiotherapy written by Robert Bergen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Multi-parametric MRI studies of the prostate often include anatomical images, as well as functional images such as perfusion-weighted images (PWI) and diffusion-weighted images (DWI). These functional images give insights into tissue micro-environment which helps physicians further differentiate between healthy and cancerous tissue. MRI can also potentially measure tissue hypoxia, the lack of oxygen within the tissue, which can introduce resistance to radiotherapy treatment and negatively affect treatment outcomes. Ideally, these data sets would be used to characterize a dominant prostate lesion, to which a radiation dose could be escalated during radiotherapy. However, incorporating DWI, PWI and oxygenation measurements into treatment planning is not routine, because the imaging requirements for MRI-guided radiotherapy are stricter than diagnostic imaging requirements. For DWI, image distortion may be a significant source of error, and therefore must be minimized. PWI imaging relies heavily on T1 mapping, but conventional T1 mapping methods can be very inaccurate and affect the localization or characterization of the dominant lesion. Finally, oxygenation measurements in the prostate using advanced imaging techniques like quantitative susceptibility mapping (QSM) requires validation. Tissue oxygen level dependent (TOLD) imaging, another method that is sensitive to oxygenation, also requires additional corrections due to its sensitivity to temperature. To improve upon conventional multi-parametric MRI, correction methods were implemented to reduce image distortion in DWI and to reduce uncertainties in T1 mapping for PWI. The correction methods were implemented both in phantom and in vivo and compared to conventional techniques. The feasibility of oxygenation measurements, using both QSM and TOLD, was also tested in phantom and in vivo, and temperature measurements were acquired so that a correction could be applied to the TOLD data. Finally, the optimized and conventional imaging methods were compared in terms of prostate lesion localization and characterization. Simulations were then performed to investigate the effects on a prostate treatment plan. Significant differences were found between the optimized and conventional DWI and PWI imaging techniques, and the feasibility of MR oxygenation measurements was demonstrated. It was shown that the improved multi-parametric MRI acquisitions had significant impacts on lesion localization and characterization, which could potentially have significant effects on treatment planning.
Monte Carlo Framework For Prostate Cancer Correction And Reconstruction In Endorectal Multi Parametric Mri
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Author : Dorothy Lui
language : en
Publisher:
Release Date : 2014
Monte Carlo Framework For Prostate Cancer Correction And Reconstruction In Endorectal Multi Parametric Mri written by Dorothy Lui and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Prostate cancer is one of the leading causes of cancer death in the male population. The detection of prostate cancer using imaging has been challenging until recently. Multi-parametric MRI has been shown to allow accurate localization of the cancers and can help direct biopsies to cancer foci which is required to plan treatment. The interpretation of MRI, however, requires a high level of expertise and review of large multi-parametric data sets. An endorectal receiver coil is often used to improve signal-to-noise ratio (SNR) and aid in detection of smaller cancer foci. Despite increased SNR, intensity bias fields can exist where nearest the endorectal coil the signal is greater than those regions farther from the coil. Weak delineation of the prostate as well as poor prostate gland visualization can greatly impact the ease and accuracy of diagnosis. For this reason, there is a need for an automated system which can correct endorectal multi-parametric MRI for enhanced visualization. A framework using Monte Carlo sampling techniques has been developed for prostate cancer correction and reconstruction in endorectal multi-parametric MRI. Its performance against state-of-the-art approaches demonstrate improved results for visualization and prostate delineation. The first step in the proposed framework involves reconstructing an intensity bias-free image. Using importance-weighted Monte Carlo sampling, the intensity bias field is estimated to approximate the bias-free result. However, the reconstruction is still pervaded by noise which becomes amplified and non-stationary as a result of intensity bias correction. The second step in the framework applies a spatially-adaptive Rician distributed Monte Carlo sampling approach while accounting for the endorectal coil's underlying SNR characteristics. To evaluate the framework, the individual steps are compared against state-of-the-art approaches using phantoms and real patient data to quantify visualization improvement. The intensity bias correction technique is critiqued based on detail preservation and delineation of the prostate from the background as well as improvement in tumor identification. The noise compensation approach is considered based on the noise suppression, contrast of tissue as well as preservation of details and texture. Utilizing quantitative and qualitative metrics in addition to visual analysis, the experimental results demonstrated that the proposed framework allows for improved visualization, with increased delineation of the prostate and preservation of tissue textures and details. This allows radiologists to more easily identify characteristics of cancerous and healthy tissue leading to more accurate and confident diagnoses.