Interactive deformation registration of endorectal prostate MRI using ITK thin plate splines

Open-source magnetic resonance imaging: Improving access, science, and education through global collaboration

Open-source practices and resources in magnetic resonance imaging (MRI) have increased substantially in recent years. This trend started with software and data being published open-source and, more recently, open-source hardware designs have become increasingly available. These developments towards a culture of sharing and establishing nonexclusive global collaborations have already improved the reproducibility and reusability of code and designs, while providing a more inclusive approach, especially for low-income settings. Community-driven standardization and documentation efforts are further strengthening and expanding these milestones. The future of open-source MRI is bright and we have just started to discover its full collaborative potential. In this review we will give an overview of open-source software and open-source hardware projects in human MRI research.

A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol

Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

Determination of the Association Between T2-weighted MRI and Gleason Sub-pattern: A Proof of Principle Study

RATIONALE AND OBJECTIVES

The study aimed to determine the relationship between T2-weighted magnetic resonance imaging (MRI) signal and histologic sub-patterns in prostate cancer areas with different Gleason grades.

MATERIALS AND METHODS

MR images of prostates (n = 25) were obtained prior to radical prostatectomy. These were processed as whole-mount specimens with tumors and the peripheral zone was annotated digitally by two pathologists. Gleason grade 3 was the most prevalent grade and was subdivided into packed, intermediate, and sparse based on gland-to-stroma ratio. Large cribriform, intraductal carcinoma, and small cribriform glands (grade 4 group) were separately annotated but grouped together for statistical analysis. The log MRI signal intensity for each contoured region (n = 809) was measured, and pairwise comparisons were performed using the open-source software R version 3.0.1.

RESULTS

Packed grade 3 sub-pattern has a significantly lower MRI intensity than the grade 4 group (P < 0.00001). Sparse grade 3 has a significantly higher MRI intensity than the packed grade 3 sub-pattern (P < 0.0001). No significant difference in MRI intensity was observed between the Gleason grade 4 group and the sparse sub-pattern grade 3 group (P = 0.54). In multivariable analysis adjusting for peripheral zone, the P values maintained significance (packed grade 3 group vs grade 4 group, P < 0.001; and sparse grade 3 sub-pattern vs packed grade 3 sub-pattern, P < 0.001).

CONCLUSIONS

This study demonstrated that T2-weighted MRI signal is dependent on histologic sub-patterns within Gleason grades 3 and 4 cancers, which may have implications for directed biopsy sampling and patient management.

Multiparametric magnetic resonance imaging of the prostate: technical aspects and role in clinical management

The heterogeneity and largely indolent nature of prostate cancer require better tools to avoid overdetection of low-risk disease and improve diagnostic accuracy in high-risk patients. During the last 3 decades, magnetic resonance imaging (MRI) has evolved to become the most accurate imaging technique for prostate cancer detection and staging, with a promising role in risk stratification. Because each MRI technique has advantages and limitations, state of the art of the so-called multiparametric MRI of the prostate is achieved combining anatomical T2-weighted imaging integrated with other techniques in which image contrast is related to the pathophysiology of the disease, such as diffusion-weighted imaging, dynamic contrast-enhanced imaging, and MR spectroscopy. After reviewing this article, readers will understand the clinical challenges in the management of patients with confirmed or suspected prostate cancer, when and how multiparametric MRI of the prostate can provide meaningful information, and how to perform and interpret it.

The Insight ToolKit image registration framework

Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK⁴) seeks to establish new standards in publicly available image registration methodology. ITK⁴ makes several advances in comparison to previous versions of ITK. ITK⁴ supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK⁴ reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK⁴ contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling.¹

Using manual prostate contours to enhance deformable registration of endorectal MRI

BACKGROUND

Endorectal MRI provides detailed images of the prostate anatomy and is useful for radiation treatment planning. Here we describe a Demons field-initialized B-spline deformable registration of prostate MRI.

MATERIAL AND METHODS

T2-weighted endorectal MRIs of five patients were used. The prostate and the tumor of each patient were manually contoured. The planning MRIs and their segmentations were simulated by warping the corresponding endorectal MRIs using thin plate spline (TPS). Deformable registration was initialized using the deformation field generated using Demons algorithm to map the deformed prostate MRI to the non-deformed one. The solution was refined with B-Spline registration. Volume overlap similarity was used to assess the accuracy of registration and to suggest a minimum margin to account for the registration errors.

RESULTS

Initialization using Demons algorithm took about 15 min on a computer with 2.8 GHz Intel, 1.3 GB RAM. Refinement B-spline registration (200 iterations) took less than 5 min. Using the synthetic images as the ground truth, at zero margin, the average (S.D.) 98 (±0.4)% for prostate coverage was 97 (±1)% for tumor. The average (±S.D.) treatment margin required to cover the entire prostate was 1.5 (±0.2)mm. The average (± S.D.) treatment margin required to cover the tumor was 0.7 (±0.1)mm. We also demonstrated the challenges in registering an in vivo deformed MRI to an in vivo non-deformed MRI.

DISCUSSION

We here present a deformable registration scheme that can overcome large deformation. This platform is expected to be useful for prostate cancer radiation treatment planning.

Learning statistical correlation for fast prostate registration in image-guided radiotherapy

PURPOSE

In adaptive radiation therapy of prostate cancer, fast and accurate registration between the planning image and treatment images of the patient is of essential importance. With the authors' recently developed deformable surface model, prostate boundaries in each treatment image can be rapidly segmented and their correspondences (or relative deformations) to the prostate boundaries in the planning image are also established automatically. However, the dense correspondences on the nonboundary regions, which are important especially for transforming the treatment plan designed in the planning image space to each treatment image space, are remained unresolved. This paper presents a novel approach to learn the statistical correlation between deformations of prostate boundary and nonboundary regions, for rapidly estimating deformations of the nonboundary regions when given the deformations of the prostate boundary at a new treatment image.

METHODS

The main contributions of the proposed method lie in the following aspects. First, the statistical deformation correlation will be learned from both current patient and other training patients, and further updated adaptively during the radiotherapy. Specifically, in the initial treatment stage when the number of treatment images collected from the current patient is small, the statistical deformation correlation is mainly learned from other training patients. As more treatment images are collected from the current patient, the patient-specific information will play a more important role in learning patient-specific statistical deformation correlation to effectively reflect prostate deformation of the current patient during the treatment. Eventually, only the patient-specific statistical deformation correlation is used to estimate dense correspondences when a sufficient number of treatment images have been acquired from the current patient. Second, the statistical deformation correlation will be learned by using a multiple linear regression (MLR) model, i.e., ridge regression (RR) model, which has the best prediction accuracy than other MLR models such as canonical correlation analysis (CCA) and principal component regression (PCR).

RESULTS

To demonstrate the performance of the proposed method, we first evaluate its registration accuracy by comparing the deformation field predicted by our method with the deformation field estimated by the thin plate spline (TPS) based correspondence interpolation method on 306 serial prostate CT images of 24 patients. The average predictive error on the voxels around 5 mm of prostate boundary is 0.38 mm for our method of RR-based correlation model. Also, the corresponding maximum error is 2.89 mm. We then compare the speed for deformation interpolation by different methods. When considering the larger region of interest (ROI) with the size of 512 × 512 × 61, our method takes 24.41 seconds to interpolate the dense deformation field while TPS method needs 6.7 minutes; when considering a small ROI (surrounding prostate) with size of 112 × 110 × 93, our method takes 1.80 seconds, while TPS method needs 25 seconds.

CONCLUSIONS

Experimental results show that the proposed method can achieve much faster registration speed yet with comparable registration accuracy, compared to the TPS-based correspondence (or deformation) interpolation approach.

Amide proton transfer MR imaging of prostate cancer: a preliminary study

PURPOSE

To evaluate the capability of amide proton transfer (APT) MR imaging for detection of prostate cancer that typically shows a higher tumor cell proliferation rate and cellular density leading to an MRI-detectable overall elevated mobile protein level in higher grade tumors.

MATERIALS AND METHODS

Twelve patients with biopsy-proven prostate cancer were imaged on a 3 Tesla MR imaging system before prostatectomy. APT-MR images were acquired by means of a single-slice single-shot turbo spin echo sequence with a saturation prepulse preparation using 33 different frequency offsets (-8 to 8 ppm, interval 0.5 ppm). For quantification we used the APT ratio (APTR) based on the asymmetry of the magnetization transfer ratio at 3.5 ppm in respect to the water signal. Tumor and peripheral zone benign regions of interest (ROIs) were delineated based on whole mount pathology slides after prostatectomy.

RESULTS

APTR in prostate cancer ROIs was 5.8% ± 3.2%, significantly higher than that in the peripheral zone benign regions (0.3% ± 3.2%, P = 0.002).

CONCLUSION

APT-MR imaging is feasible in prostate cancer detection and has the potential to discriminate between cancer and noncancer tissues.

Interactive, multi-modality image registrations for combined MRI/MRSI-planned HDR prostate brachytherapy

Purpose

This study presents the steps and criteria involved in the series of image registrations used clinically during the planning and dose delivery of focal high dose-rate (HDR) brachytherapy of the prostate.

Material and methods

Three imaging modalities – Magnetic Resonance Imaging (MRI), Magnetic Resonance Spectroscopic Imaging (MRSI), and Computed Tomography (CT) – were used at different steps during the process. MRSI is used for identification of dominant intraprosatic lesions (DIL). A series of rigid and nonrigid transformations were applied to the data to correct for endorectal-coil-induced deformations and for alignment with the planning CT. Mutual information was calculated as a morphing metric. An inverse planning optimization algorithm was applied to boost dose to the DIL while providing protection to the urethra, penile bulb, rectum, and bladder. Six prostate cancer patients were treated using this protocol.

Results

The morphing algorithm successfully modeled the probe-induced prostatic distortion. Mutual information calculated between the morphed images and images acquired without the endorectal probe showed a significant (p = 0.0071) increase to that calculated between the unmorphed images and images acquired without the endorectal probe. Both mutual information and visual inspection serve as effective diagnostics of image morphing. The entire procedure adds less than thirty minutes to the treatment planning.

Conclusion

This work demonstrates the utility of image transformations and registrations to HDR brachytherapy of prostate cancer.

The brain MR Image segmentation techniques and use of diagnostic packages

RATIONALE AND OBJECTIVES

This article provides a survey of segmentation methods for medical images. Usually, classification of segmentation methods is done based on the approaches adopted and the domain of application.

MATERIALS AND METHODS

This survey is conducted on the recent segmentation methods used in biomedical image processing and explores the methods useful for better segmentation. A critical appraisal of the current status of semiautomated and automated methods is made for the segmentation of anatomical medical images emphasizing the advantages and disadvantages. Computer-aided diagnosis (CAD) used by radiologists as a second opinion has become one of the major research areas in medical imaging and diagnostic radiology. A picture archiving communication system (PACS) is an integrated workflow system for managing images and related data that is designed to streamline operations throughout the whole patient care delivery process.

RESULTS

By using PACS, the medical image interpretation may be changed from conventional hard-copy images to soft-copy studies viewed on the systems workstations.

CONCLUSION

The automatic segmentations assist the doctors in making quick diagnosis. The CAD need not be comparable to that of physicians, but is surely complementary.