Comparison of rigid and elastic matching of dynamic magnetic resonance mammographic images by mutual information

Synthetic head and neck and phantom images for determining deformable image registration accuracy in magnetic resonance imaging

PURPOSE

Magnetic resonance imaging (MRI) provides noninvasive evaluation of patient's anatomy without using ionizing radiation. Due to this and the high soft-tissue contrast, MRI use has increased and has potential for use in longitudinal studies where changes in patients' anatomy or tumors at different time points are compared. Deformable image registration can be useful for these studies. Here, we describe two datasets that can be used to evaluate the registration accuracy of systems for MR images, as it cannot be assumed to be the same as that measured on CT images.

ACQUISITION AND VALIDATION METHODS

Two sets of images were created to test registration accuracy. (a) A porcine phantom was created by placing ten 0.35-mm gold markers into porcine meat. The porcine phantom was immobilized in a plastic container with movable dividers. T1-weighted, T2-weighted, and CT images were acquired with the porcine phantom compressed in four different ways. The markers were not visible on the MR images, due to the selected voxel size, so they did not interfere with the measured registration accuracy, while the markers were visible on the CT images and were used to identify the known deformation between positions. (b) Synthetic images were created using 28 head and neck squamous cell carcinoma patients who had MR scans pre-, mid-, and postradiotherapy treatment. An inter- and intrapatient variation model was created using these patient scans. Four synthetic pretreatment images were created using the interpatient model, and four synthetic post-treatment images were created for each of the pretreatment images using the intrapatient model.

DATA FORMAT AND USAGE NOTES

The T1-weighted, T2-weighted, and CT scans of the porcine phantom in the four positions are provided. Four T1-weighted synthetic pretreatment images each with four synthetic post-treatment images, and four T2-weighted synthetic pretreatment images each with four synthetic post-treatment images are provided. Additionally, the applied deformation vector fields to generate the synthetic post-treatment images are provided. The data are available on TCIA under the collection MRI-DIR.

POTENTIAL APPLICATIONS

The proposed database provides two sets of images (one acquired and one computer generated) for use in evaluating deformable image registration accuracy. T1- and T2-weighted images are available for each technique as the different image contrast in the two types of images may impact the registration accuracy.

Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms

PURPOSE

Accurate deformable image registration is necessary for longitudinal studies. The error associated with commercial systems has been evaluated using computed tomography (CT). Several in-house algorithms have been evaluated for use with magnetic resonance imaging (MRI), but there is still relatively little information about MRI deformable image registration. This work presents an evaluation of two deformable image registration systems, one commercial (Velocity) and one in-house (demons-based algorithm), with MRI using two different metrics to quantify the registration error.

METHODS

The registration error was analyzed with synthetic MR images. These images were generated from interpatient and intrapatient variation models trained on 28 patients. Four synthetic post-treatment images were generated for each of four synthetic pretreatment images, resulting in 16 image registrations for both the T1- and T2-weighted images. The synthetic post-treatment images were registered to their corresponding synthetic pretreatment image. The registration error was calculated between the known deformation vector field and the generated deformation vector field from the image registration system. The registration error was also analyzed using a porcine phantom with ten implanted 0.35-mm diameter gold markers. The markers were visible on CT but not MRI. CT, T1-weighted MR, and T2-weighted MR images were taken in four different positions. The markers were contoured on the CT images and rigidly registered to their corresponding MR images. The MR images were deformably registered and the distance between the projected marker location and true marker location was measured as the registration error.

RESULTS

The synthetic images were evaluated only on Velocity. Root mean square errors (RMSEs) of 0.76 mm in the left-right (LR) direction, 0.76 mm in the anteroposterior (AP) direction, and 0.69 mm in the superior-inferior (SI) direction were observed for the T1-weighted MR images. RMSEs of 1.1 mm in the LR direction, 0.75 mm in the AP direction, and 0.81 mm in the SI direction were observed for the T2-weighted MR images. The porcine phantom MR images, when evaluated with Velocity, had RMSEs of 1.8, 1.5, and 2.7 mm in the LR, AP, and SI directions for the T1-weighted images and 1.3, 1.2, and 1.6 mm in the LR, AP, and SI directions for the T2-weighted images. When the porcine phantom images were evaluated with the in-house demons-based algorithm, RMSEs were 1.2, 1.5, and 2.1 mm in the LR, AP, and SI directions for the T1-weighted images and 0.81, 1.1, and 1.1 mm in the LR, AP, and SI directions for the T2-weighted images.

CONCLUSIONS

The MRI registration error was low for both Velocity and the in-house demons-based algorithm according to both image evaluation methods, with all RMSEs below 3 mm. This implies that both image registration systems can be used for longitudinal studies using MRI.

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

A review of biomechanically informed breast image registration

Breast radiology encompasses the full range of imaging modalities from routine imaging via x-ray mammography, magnetic resonance imaging and ultrasound (both two- and three-dimensional), to more recent technologies such as digital breast tomosynthesis, and dedicated breast imaging systems for positron emission mammography and ultrasound tomography. In addition new and experimental modalities, such as Photoacoustics, Near Infrared Spectroscopy and Electrical Impedance Tomography etc, are emerging. The breast is a highly deformable structure however, and this greatly complicates visual comparison of imaging modalities for the purposes of breast screening, cancer diagnosis (including image guided biopsy), tumour staging, treatment monitoring, surgical planning and simulation of the effects of surgery and wound healing etc. Due primarily to the challenges posed by these gross, non-rigid deformations, development of automated methods which enable registration, and hence fusion, of information within and across breast imaging modalities, and between the images and the physical space of the breast during interventions, remains an active research field which has yet to translate suitable methods into clinical practice. This review describes current research in the field of breast biomechanical modelling and identifies relevant publications where the resulting models have been incorporated into breast image registration and simulation algorithms. Despite these developments there remain a number of issues that limit clinical application of biomechanical modelling. These include the accuracy of constitutive modelling, implementation of representative boundary conditions, failure to meet clinically acceptable levels of computational cost, challenges associated with automating patient-specific model generation (i.e. robust image segmentation and mesh generation) and the complexity of applying biomechanical modelling methods in routine clinical practice.

Hierarchical alignment of breast DCE-MR images by groupwise registration and robust feature matching

PURPOSE

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) shows high sensitivity in detecting breast cancer. However, its performance could be affected by patient motion during the imaging. To overcome this problem, it is necessary to correct patient motion by deformable registration, before using the DCE-MRI to detect breast cancer. However, deformable registration of DCE-MR images is challenging due to the dramatic contrast change over time (especially between the precontrast and postcontrast images). Most existing methods typically register each postcontrast image onto the precontrast image independently, without considering the dynamic contrast change after agent uptake. This could lead to the inconsistency among the aligned postcontrast images in the precontrast image space, which will eventually result in worse performance in cancer detection. In this paper, the authors present a novel hierarchical registration framework to address this problem.

METHODS

First, the authors propose a hierarchical registration framework to deploy the groupwise registration for simultaneous registration of all postcontrast images onto their group-mean image and further aligning the group-mean image of postcontrast images onto the precontrast image space for final alignment of all precontrast and postcontrast images. In this way, the postcontrast images (with similar intensity patterns) can be jointly aligned onto the precontrast image for increasing their overall consistency after registration. Second, in order to improve the registration between the precontrast image and the group-mean image of the postcontrast images, the authors propose using the contrast-invariant attribute vectors to guide the robust feature matching during the registration.

RESULTS

Our proposed hierarchical registration framework has been comprehensively evaluated and compared with affine registration and widely used deformable registration methods in both pairwise and groupwise registration formulation. The experimental results on both real and simulated images show that our method can obtain not only more accurate but also more consistent registration results than any of all other registration algorithms.

CONCLUSIONS

The authors have proposed a novel groupwise registration method to achieve accurate and consistent alignment for breast DCE-MR images. In the future, the authors will further evaluate our proposed method with more clinical datasets.

Reconstruction of 3D lung models from 2D planning data sets for Hodgkin's lymphoma patients using combined deformable image registration and navigator channels

PURPOSE

Late complications (cardiac toxicities, secondary lung, and breast cancer) remain a significant concern in the radiation treatment of Hodgkin's lymphoma (HL). To address this issue, predictive dose-risk models could potentially be used to estimate radiotherapy-related late toxicities. This study investigates the use of deformable image registration (DIR) and navigator channels (NCs) to reconstruct 3D lung models from 2D radiographic planning images, in order to retrospectively calculate the treatment dose exposure to HL patients treated with 2D planning, which are now experiencing late effects.

METHODS

Three-dimensional planning CT images of 52 current HL patients were acquired. 12 image sets were used to construct a male and a female population lung model. 23 "Reference" images were used to generate lung deformation adaptation templates, constructed by deforming the population model into each patient-specific lung geometry using a biomechanical-based DIR algorithm, MORFEUS. 17 "Test" patients were used to test the accuracy of the reconstruction technique by adapting existing templates using 2D digitally reconstructed radiographs. The adaptation process included three steps. First, a Reference patient was matched to a Test patient by thorax measurements. Second, four NCs (small regions of interest) were placed on the lung boundary to calculate 1D differences in lung edges. Third, the Reference lung model was adapted to the Test patient's lung using the 1D edge differences. The Reference-adapted Test model was then compared to the 3D lung contours of the actual Test patient by computing their percentage volume overlap (POL) and Dice coefficient.

RESULTS

The average percentage overlapping volumes and Dice coefficient expressed as a percentage between the adapted and actual Test models were found to be 89.2 +/- 3.9% (Right lung = 88.8%; Left lung = 89.6%) and 89.3 +/- 2.7% (Right = 88.5%; Left = 90.2%), respectively. Paired T-tests demonstrated that the volumetric reconstruction method made a statistically significant improvement to the population lung model shape (p < 0.05). The error in the results were also comparable to the volume overlap difference observed between inhale and exhale lung volumes during free-breathing respiratory motion (POL: p = 0.43; Dice: p = 0.20), which implies that the accuracies of the reconstruction method are within breathing constraints and would not be the confining factor in estimating normal tissue dose exposure.

CONCLUSIONS

The result findings show that the DIR-NC technique can achieve a high degree of reconstruction accuracy, and could be useful in approximating 3D dosimetric representations of historical 2D treatment. In turn, this could provide a better understanding of the biophysical relationship between dose-volume exposure and late term radiotherapy effects.

Adapting liver motion models using a navigator channel technique

Deformable registration can improve the accuracy of tumor targeting; however for online applications, efficiency as well as accuracy is important. A navigator channel technique has been developed to combine a biomechanical model-based deformable registration algorithm with a population motion model and patient specific motion information to perform fast deformable registration for application in image-guided radiation therapy. A respiratory population-based liver motion model was generated from breath-hold CT data sets of ten patients using a finite element model as a framework. The population model provides a biomechanical reference template of the average liver motions, which were found to be (absolute mean +/-SD) 0.12 +/- 0.10, 0.84 +/- 0.13, and 1.24 +/- 0.18 cm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. The population motion model was then adapted to the specific liver motion of 13 patients based on their exhale and inhale CT images. The patient motion was calculated using a navigator channel (a narrow region of interest window) on liver boundaries in the images. The absolute average accuracy of the navigator channel to predict the 1D SI and AP motions of the liver was less than 0.11, which is less than the out-of-plane image voxel size, 0.25 cm. This 1D information was then used to adapt the 4D population motion model in the SI and AP directions to predict the patient specific liver motion. The absolute average residual error of the navigator channel technique to adapt the population motion to the patients' specific motion was verified using three verification methods: (1) vessel bifurcation, (2) tumor center of mass, and (3) MORFEUS deformable algorithm. All three verification methods showed statistically similar results where the technique's accuracy was approximately on the order of the voxel image sizes. This method has potential applications in online assessment of motion at the time of treatment to improve image-guided radiotherapy and monitoring of intrafraction motion.

Information entropy measure for evaluation of image quality

This paper presents a simple and straightforward method for synthetically evaluating digital radiographic images by a single parameter in terms of transmitted information (TI). The features of our proposed method are (1) simplicity of computation, (2) simplicity of experimentation, and (3) combined assessment of image noise and resolution (blur). Two acrylic step wedges with 0-1-2-3-4-5 and 0-2-4-6-8-10 mm in thickness were used as phantoms for experiments. In the present study, three experiments were conducted. First, to investigate the relation between the value of TI and image noise, various radiation doses by changing exposure time were employed. Second, we examined the relation between the value of TI and image blurring by shifting the phantoms away from the center of the X-ray beam area toward the cathode end when imaging was performed. Third, we analyzed the combined effect of deteriorated blur and noise on the images by employing three smoothing filters. Experimental results show that the amount of TI is closely related to both image noise and image blurring. The results demonstrate the usefulness of our method for evaluation of physical image quality in medical imaging.

Breast image registration techniques: a survey

Breast cancer is the most common type of cancer in women worldwide. Image registration plays an important role in breast cancer detection. This paper gives an overview of the current state-of-the-art in the breast image registration techniques. For the intramodality registration techniques, X-ray, MRI, and ultrasound are the primary focuses of interest. Intermodality techniques will cover the combination of different modalities. Validation of breast registration methods is also discussed.

Quantitative evaluation of free-form deformation registration for dynamic contrast-enhanced MR mammography

In this paper, we present an evaluation study of a set of registration strategies for the alignment of sequences of 3D dynamic contrast-enhanced magnetic resonance breast images. The accuracy of the optimal registration strategies was determined on unseen data. The evaluation is based on the simulation of physically plausible breast deformations using finite element methods and on contrast-enhanced image pairs without visually detectable motion artifacts. The configuration of the finite element model was chosen according to its ability to predict in vivo breast deformations for two volunteers. We computed transformations for ten patients with 12 simulated deformations each. These deformations were applied to the postcontrast image to model patient motion occurring between pre- and postcontrast image acquisition. The original precontrast images were registered to the corresponding deformed postcontrast images. The performance of several registration configurations (rigid, affine, B-spline based nonrigid, single-resolution, multi-resolution, and volume-preserving) was optimized for five of the ten patients. The images were most accurately aligned with volume-preserving single-resolution nonrigid registration employing 40 or 20 mm control point spacing. When tested on the remaining five patients the optimal configurations reduced the average mean registration error from 1.40 to 0.45 mm for the whole breast tissue and from 1.20 to 0.32 mm for the enhancing lesion. These results were obtained on average within 26 (81) min for 40 (20) mm control point spacing. The visual appearance of the difference images from 30 patients was significantly improved after 20 mm volume-preserving single-resolution nonrigid registration in comparison to no registration or rigid registration. No substantial volume changes within the region of the enhancing lesions were introduced by this nonrigid registration.

Anisotropic multi-scale fluid registration: evaluation in magnetic resonance breast imaging

Registration using models of compressible viscous fluids has not found the general application of some other techniques (e.g., free-form-deformation (FFD)) despite its ability to model large diffeomorphic deformations. We report on a multi-resolution fluid registration algorithm which improves on previous work by (a) directly solving the Navier-Stokes equation at the resolution of the images, (b) accommodating image sampling anisotropy using semi-coarsening and implicit smoothing in a full multi-grid (FMG) solver and (c) exploiting the inherent multi-resolution nature of FMG to implement a multi-scale approach. Evaluation is on five magnetic resonance (MR) breast images subject to six biomechanical deformation fields over 11 multi-resolution schemes. Quantitative assessment is by tissue overlaps and target registration errors and by registering using the known correspondences rather than image features to validate the fluid model. Context is given by comparison with a validated FFD algorithm and by application to images of volunteers subjected to large applied deformation. The results show that fluid registration of 3D breast MR images to sub-voxel accuracy is possible in minutes on a 1.6 GHz Linux-based Athlon processor with coarse solutions obtainable in a few tens of seconds. Accuracy and computation time are comparable to FFD techniques validated for this application.

[Validation of an entropy-based algorithm for registration of serial 3D MR mammography data]

The aim of this study was to develop and implement an algorithm for the co-registration of 3D breast MRI sets acquired at two slightly different patient positions (repetitive examination). Combined translation and rotation with locally varying parameters were applied for the purpose of coordinate transformation. A phantom allowing selective changes of the volume of the glandular tissue model was developed, in order to prove the robustness of the proposed matcher against local changes. Serial 3D data sets of phantoms and volunteers were acquired to validate the routines. Co-registration was performed using mutual information (MI) as a similarity measure of the matching of the acquired images. In the phantom study, the phantom was deliberately shifted and rotated around horizontal and vertical axes. Starting the registration with global translations using a rigid matcher, the horizontal (phi) and vertical (theta) rotation angles were optimized in an iteration loop for each slice. This method was then applied to the breast data sets. Application of the algorithm on serial 3D MR data sets improved the co-registration especially in consideration of varying local tissue volumes. The algorithm represents a compromise between a pure rigid and an elastic 3D matcher.

Performance evaluation of image processing algorithms in CADe

One of the key limitations of existing image processing algorithms for computer-aided detection (CADe) is that they are often designed and evaluated in an ad hoc manner. This paper characterizes some of the issues and shortcomings in existing performance evaluation paradigms for image processing algorithms in breast cancer screening, particularly in the context of computer aided detection. We present the framework for establishing a performance evaluation process using standardized criteria. We conclude with some specific recommendations to improve the infrastructure for evaluation the performance of image processing algorithms.

Non-rigid image registration: theory and practice

Image registration is an important enabling technology in medical image analysis. The current emphasis is on development and validation of application-specific non-rigid techniques, but there is already a plethora of techniques and terminology in use. In this paper we discuss the current state of the art of non-rigid registration to put on-going research in context and to highlight current and future clinical applications that might benefit from this technology. The philosophy and motivation underlying non-rigid registration is discussed and a guide to common terminology is presented. The core components of registration systems are described and outstanding issues of validity and validation are confronted.

Mutual-information-based registration of medical images: a survey

An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.

Automated generation of a four-dimensional model of the liver using warping and mutual information

The use of mutual information (MI) based alignment to map changes in liver shape and position from exhale to inhale was investigated. Inhale and exhale CT scans were obtained with intravenous contrast for six patients. MI based alignment using thin-plate spine (TPS) warping was performed between each inhale and exhale image set. An expert radiation oncologist identified corresponding vessel bifurcations on the exhale and inhale CT image and the transformation for identified points was determined. This transformation was then used to determine the accuracy of the MI based alignment. The reproducibility of the vessel bifurcation identification was measured through repeat blinded vessel bifurcation identification. Reproducibility [standard deviation (SD)] in the L/R, A/P, and I/S directions was 0.11, 0.09, and 0.14 cm, respectively. The average absolute difference between the transformation obtained using MI based alignment and the vessel bifurcation in the L/R, A/P, and I/S directions was 0.13 cm (SD=0.10 cm), 0.15 cm (SD=0.12 cm), and 0.15 cm (SD-0.14 cm), respectively. These values are comparable to the reproducibility of bifurcation identification, indicating that MI based alignment using TPS warping is accurate to within measurement error and is a reliable tool to aid in describing deformation that the liver undergoes from the exhale to inhale state.

Reproducibility of mammary gland structure during repeat setups in a supine position

PURPOSE

In breast conserving therapy, complete excision of the tumor with an acceptable cosmetic outcome depends on accurate localization in terms of both the position of the lesion and its extent. We hypothesize that preoperative contrast-enhanced magnetic resonance (MR) imaging of the patient in a supine position may be used for accurate tumor localization and marking of its extent immediately prior to surgery. Our aims in this study are to assess the reproducibility of mammary gland structure during repeat setups in a supine position, to evaluate the effect of a breast immobilization device, and to derive reproducibility margins that take internal tissue shifts into account occurring between repeat setups.

MATERIALS & METHODS

The reproducibility of mammary gland structure during repeat setups in a supine position is estimated by quantification of tissue shifts in the breasts of healthy volunteers between repeat MR setups. For each volunteer fiducials are identified and registered with their counter locations in corresponding MR volumes. The difference in position denotes the shift of breast tissue. The dependence on breast volume and the part of the breast, as well as the effect of a breast immobilization cast are studied.

RESULTS

The tissue shifts are small with a mean standard deviation on the order of 1.5 mm, being slightly larger in large breasts (V> 1000 cm3), and in the posterior part (toward the pectoral muscle) of both small and large breasts. The application of a breast immobilization cast reduces the tissue shifts in large breasts. A reproducibility margin on the order of 5 mm will take the internal tissue shifts into account that occur between repeat setups.

CONCLUSION

The results demonstrate a high reproducibility of mammary gland structure during repeat setups in a supine position.

Current awareness in NMR in biomedicine

In order to keep subscribers up-to-date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of NMR in biomedicine. Each bibliography is divided into 9 sections: 1 Books, Reviews ' Symposia; 2 General; 3 Technology; 4 Brain and Nerves; 5 Neuropathology; 6 Cancer; 7 Cardiac, Vascular and Respiratory Systems; 8 Liver, Kidney and Other Organs; 9 Muscle and Orthopaedic. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted.