Semiautomatic nonrigid registration for the prostate and pelvic MR volumes

RATIONALE AND OBJECTIVES: Three-dimensional (3D) nonrigid image registration for potential applications in prostate cancer treatment and interventional magnetic resonance (iMRI) imaging-guided therapies were investigated. MATERIALS AND METHODS: An almost fully automated 3D nonrigid registration algorithm using mutual information and a thin plate spline (TPS) transformation for MR images of the prostate and pelvis were created and evaluated. In the first step, an automatic rigid body registration with special features was used to capture the global transformation. In the second step, local feature points (FPs) were registered using mutual information. An operator entered only five FPs located at the prostate center, left and right hip joints, and left and right distal femurs. The program automatically determined and optimized other FPs at the external pelvic skin surface and along the femurs. More than 600 control points were used to establish a TPS transformation for deformation of the pelvic region and prostate. Ten volume pairs were acquired from three volunteers in the diagnostic (supine) and treatment positions (supine with legs raised). RESULTS: Various visualization techniques showed that warping rectified the significant pelvic misalignment by the rigid-body method. Gray-value measures of registration quality, including mutual information, correlation coefficient, and intensity difference, all improved with warping. The distance between prostate 3D centroids was 0.7 +/- 0.2 mm after warping compared with 4.9 +/- 3.4 mm with rigid-body registration. CONCLUSION: Semiautomatic nonrigid registration works better than rigid-body registration when patient position is changed greatly between acquisitions. It could be a useful tool for many applications in the management of prostate.     

Automated image registration of gated cardiac single-photon emission computed tomography and magnetic resonance imaging

PURPOSE: To develop an automatic registration method for electrocardiogram-gated myocardial perfusion single-photon emission computed tomography (SPECT) and cardiac cine-magnetic resonance imaging (MRI). MATERIALS AND METHODS: Paired myocardial perfusion SPECT (MPS) and MRI from 20 patients were considered. MR images were presegmented by heart localization based on detection of cardiac motion and optimal thresholding. A registration algorithm based on mutual information was subsequently applied to all time frames or a selected subset from both modalities. RESULTS: A preprocessing step significantly improved the accuracy of the registration when compared to automatic registration performed without preprocessing. Errors in translation parameters (T(x), T(y), T(z)) averaged (1.0 +/- 1.5, 1.1 +/- 1.3, 0.9 +/- 0.9) pixels with MRI segmentation and (4.6 +/- 3.2, 3.4 +/- 2.6, 3.0 +/- 3.4) pixels without MRI segmentation. Errors in rotation parameters (R(x), R(y), R(z)) averaged (5.4 +/- 2.9, 3.4 +/- 2.7, 4.5 +/- 3.6) degrees with MRI segmentation and (9.3 +/- 6.1, 4.8 +/- 4.3, 14.6 +/- 12.6) degrees without MRI segmentation. Error was calculated as the absolute difference between the expert manual and the automatic registration transformation. CONCLUSION: Automatic registration of gated MPS and cine MRI is possible with the use of a mutual information-based technique when MR images are presegmented. Cardiac motion can be used to isolate the left ventricle (LV) on the MR images automatically, and subsequently the segmented MR images can be coregistered with gated MPS.     

基于体素灰度三维多模医学图像配准中相似性测度的选取

目的：在基于体素灰度医学图像配准领域，找出最适合于临床应用的多模医学图像配准相似性测度。方法：在极端的刚体配准条件下，检验出互相关系数，互信息和相关比相似性测度为适合的相似性测度。同时进一步解释了基于互信息相似性测度的医学图像配准易于陷入局部最优，而基于相关比相似性测度的方法易于保证配准得到全局最优，最后，利用加速的多分辨率配准方案和Powell‘s优化算法，对临床医学图像进行了基于相关比相似性测度的多模图像配准试验。结果：通过临床医学专家的判断，利用相关比相似性测度进行多模医学图像配准，安全能满足临床的要求，进行MR／CT，MR／PET三维多模医学图像配准时效果非常理想，结论：相比于其他相似性测度，互相关比相似性测度在基于体素灰度，三维多模医学图像配准领域，是一个更为适宜和准确的相似性测度。     

Registering histologic and MR images of prostate for image-based cancer detection

RATIONALE AND OBJECTIVES: Needle biopsy is currently the only way to confirm prostate cancer. To increase prostate cancer diagnostic rate, needles are expected to be deployed at suspicious cancer locations. High-contrast magnetic resonance (MR) imaging provides a powerful tool for detecting suspicious cancerous tissues. To do this, MR appearances of cancerous tissue should be characterized and learned from a sufficient number of prostate MR images with known cancer information. However, ground-truth cancer information is only available in histologic images. Therefore it is necessary to warp ground-truth cancerous regions in histological images to MR images by a registration procedure. The objective of this article is to develop a registration technique for aligning histological and MR images of the same prostate. MATERIAL AND METHODS: Five pairs of histological and T2-weighted MR images of radical prostatectomy specimens are collected. For each pair, registration is guided by two sets of correspondences that can be reliably established on prostate boundaries and internal salient bloblike structures of histologic and MR images. RESULTS: Our developed registration method can accurately register histologic and MR images. It yields results comparable to manual registration, in terms of landmark distance and volume overlap. It also outperforms both affine registration and boundary-guided registration methods. CONCLUSIONS: We have developed a novel method for deformable registration of histologic and MR images of the same prostate. Besides the collection of ground-truth cancer information in MR images, the method has other potential applications. An automatic, accurate registration of histologic and MR images actually builds a bridge between in vivo anatomical information and ex vivo pathologic information, which is valuable for various clinical studies.     

System for prostate brachytherapy and biopsy in a standard 1.5 T MRI scanner.

A technique for transperineal high-dose-rate (HDR) prostate brachytherapy and needle biopsy in a standard 1.5 T MRI scanner is demonstrated. In each of eight procedures (in four patients with intermediate to high risk localized prostate cancer), four MRI-guided transperineal prostate biopsies were obtained followed by placement of 14-15 hollow transperineal catheters for HDR brachytherapy. Mean needle-placement accuracy was 2.1 mm, 95% of needle-placement errors were less than 4.0 mm, and the maximum needle-placement error was 4.4 mm. In addition to guiding the placement of biopsy needles and brachytherapy catheters, MR images were also used for brachytherapy treatment planning and optimization. Because 1.5 T MR images are directly acquired during the interventional procedure, dependence on deformable registration is reduced and online image quality is maximized.     

Quantitative and clinical analysis of SPECT image registration for epilepsy studies.

This study reports quantitative measurements of the accuracy of two popular voxel-based registration algorithms--Woods' automated image registration algorithm and mutual information correlation--and compares these with conventional surface matching (SM) registration. METHODS: The registration algorithms were compared (15 different matches each) for (a) three-dimensional brain phantom images, (b) an ictal SPECT image from a patient with partial epilepsy matched to itself after modification to simulate changes in the cerebral blood flow pattern and (c) ictal/interictal SPECT images from 15 patients with partial epilepsy. Blinded visual ranking and localization of the subtraction images derived from the patient images were also performed. RESULTS: Both voxel-based registration methods were more accurate than SM registration (P < 0.0005). Automated image registration algorithm was more accurate than mutual information correlation for the computer-simulated ictal/interictal images and the patient ictal/interictal studies (P < 0.05). The subtraction SPECTs from SM were poorer in visual ranking more often than the voxel-based methods (P < 0.05). CONCLUSION: Voxel intensity-based registration algorithms provide significant improvement in ictal/interictal SPECT registration accuracy and result in a clinically detectable improvement in the subtraction SPECT images.     

Using vascular structure for CT-SPECT registration in the pelvis.

The authors outline a method for three-dimensional registration of pelvic CT and 111In-labeled monoclonal antibody capromab pendetide (111In MoAb 7E11.C5) images using 99mTc-labeled red blood cell SPECT data. METHODS: This method of CT-SPECT registration relies on the identification of major blood vessels in the CT and 99mTc SPECT images. The vessels are segmented from the image datasets by outlining them on transverse planar slices using a mouse-based drawing tool. Stacking the transverse outlines provides a three-dimensional representation of the vascular structures. Registration is performed by matching the surfaces of the segmented volumes. Dual isotope acquisition of 111In and 99mTc activities provides precise SPECT-SPECT registration so that registration in three dimensions of the 111In MoAb and CT images is achieved by applying the same transformation obtained from the 99mTc SPECT-CT registration. RESULTS: This method provided accurate registration of pelvic structures and significantly improved interpretation of 111In MoAb 7E11.C5 exams. Furthermore, sites of involvement by prostate cancer suggested by the 111In MoAb examination could be interpreted with the bony and soft tissue (nodal) anatomy seen on CT. CONCLUSION: This method is a general clinical tool for the registration of pelvic CT and SPECT imaging data. There are immediate applications in conformal radiation therapy treatment planning for certain prostate cancer patients.     

Evaluation of Four Volume-Based Image Registration Algorithms

We evaluated 4 volume-based automatic image registration algorithms from 2 commercially available treatment planning systems (Philips Syntegra and BrainScan). The algorithms based on cross correlation (CC), local correlation (LC), normalized mutual information (NMI), and BrainScan mutual information (BSMI) were evaluated with: (1) the synthetic computed tomography (CT) images, (2) the CT and magnetic resonance (MR) phantom images, and (3) the CT and MR head image pairs from 12 patients with brain tumors. For the synthetic images, the registration results were compared with known transformation parameters, and all algorithms achieved accuracy of submillimeter in translation and subdegree in rotation. For the phantom images, the registration results were compared with those provided by frame and marker-based manual registration. For the patient images, the results were compared with anatomical landmark–based manual registration to qualitatively determine how the results were close to a clinically acceptable registration. NMI and LC outperformed CC and BSMI, with the sense of being closer to a clinically acceptable result. As for the robustness, NMI and BSMI outperformed CC and LC. A guideline of image registration in our institution was given, and final visual assessment is necessary to guarantee reasonable results.     

Evaluation of Four Volume-Based Image Registration Algorithms

We evaluated 4 volume-based automatic image registration algorithms from 2 commercially available treatment planning systems (Philips Syntegra and BrainScan). The algorithms based on cross correlation (CC), local correlation (LC), normalized mutual information (NMI), and BrainScan mutual information (BSMI) were evaluated with: (1) the synthetic computed tomography (CT) images, (2) the CT and magnetic resonance (MR) phantom images, and (3) the CT and MR head image pairs from 12 patients with brain tumors. For the synthetic images, the registration results were compared with known transformation parameters, and all algorithms achieved accuracy of submillimeter in translation and subdegree in rotation. For the phantom images, the registration results were compared with those provided by frame and marker-based manual registration. For the patient images, the results were compared with anatomical landmark–based manual registration to qualitatively determine how the results were close to a clinically acceptable registration. NMI and LC outperformed CC and BSMI, with the sense of being closer to a clinically acceptable result. As for the robustness, NMI and BSMI outperformed CC and LC. A guideline of image registration in our institution was given, and final visual assessment is necessary to guarantee reasonable results.     

Multimodal and three-dimensional imaging of prostate cancer.

Accurate characterization of prostate cancer is crucial for treatment planning and patient management. Non-invasive SPECT imaging using a radiolabeled monoclonal antibody, 111In-labeled capromab pendetide, offers advantage over existing means for prostate cancer diagnosis and staging. However, there are difficulties associated with the interpretation of these SPECT images. In this study, we developed a 3D surface-volume hybrid rendering method that utilizes multi-modality image data to facilitate diagnosis of prostate cancer. SPECT and CT or MRI (or both) images were aligned either manually or automatically. 3D hybrid rendering was implemented to blend prostate tumor distribution from SPECT in pelvis with anatomic structures from CT/MRI. Feature extraction technique was also implemented within the hybrid rendering for tumor uptake enhancement. Autoradiographic imaging and histological evaluation were performed to correlate with the in-vivo SPECT images. Warping registration of histological sections was carried out to compensate the deformation of histology slices during fixation to help the alignment between histology and in-vivo images. Overall, the rendered volumetric evaluation of prostate cancer has the potential to greatly increase the confidence in the reading of radiolabeled monoclonal antibody scans, especially in patients where there is a high suspicion of prostate tumor metastasis.     

Impact of fusion of indium-111 capromab pendetide volume data sets with those from MRI or CT in patients with recurrent prostate cancer

OBJECTIVE: Our goal was to evaluate the impact of image fusion on the interpretation of indium-111 Prosta-Scint SPECT scans. MATERIALS AND METHODS: Sixty-seven consecutive patients referred for rising prostate-specific antigen (PSA) levels after initial therapy for primary prostate cancer underwent SPECT 96 hr after infusion of (111)In Prosta-Scint, with simultaneous technetium-99m blood pool imaging. Volume data sets from the SPECT scans were then fused with those from CT and MR images of the pelvis using a 3D landmark-based warping program. The SPECT scans were initially interpreted without benefit of MRI or CT fusion. The fused Prosta-Scint MRI-CT volumes were reevaluated by a nuclear radiologist and an MRI radiologist. Independent reviews before and after fusion were available in these patients. Validation of results after fusion was performed through correlation with PSA changes after radiation therapy. RESULTS: Six patients with sites that could not be evaluated and three without their original Prosta-Scint scanning reports were excluded; thus, 58 patients were studied clinically. Seventy-four of 161 prefusion-positive sites were found to be negative after fusion. These 74 sites subsequently were identified primarily as showing bowel, vessel, or marrow uptake after fusion. In two patients, nodal disease was identified although the review before perfusion indicated none. Twenty-five patients previously thought to have nodal disease appeared to have only local disease after fusion. After local radiation therapy, PSA levels decreased in 12 of 25 patients, increased in five, and were unavailable in eight. CONCLUSION: Although Prosta-Scint SPECT alone can help in the proper management of recurrent prostate cancer, fusion with MRI-CT of the pelvis can improve the specificity of the examination.     

A comparative study of warping and rigid body registration for the prostate and pelvic MR volumes.

A three-dimensional warping registration algorithm was created and compared to rigid body registration of magnetic resonance (MR) pelvic volumes including the prostate. The rigid body registration method combines the advantages of mutual information (MI) and correlation coefficient at different resolutions. Warping registration is based upon independent optimization of many interactively placed control points (CP's) using MI and a thin plate spline transformation. More than 100 registration experiments with 17 MR volume pairs determined the quality of registration under conditions simulating potential interventional MRI-guided treatments of prostate cancer. For image pairs that stress rigid body registration (e.g. supine, the diagnostic position, and legs raised, the treatment position), both visual and numerical evaluation methods showed that warping consistently worked better than rigid body. Experiments showed that approximately 180 strategically placed CP's were sufficiently expressive to capture important features of the deformation.     

Motion tracking in MR-guided liver therapy by using navigator echoes and projection profile matching

RATIONALE AND OBJECTIVES: Image registration in magnetic resonance (MR) image-guided liver therapy enhances surgical guidance by fusing preoperative multimodality images with intraoperative images, or by fusing intramodality images to correlate serial intraoperative images to monitor the effect of therapy. The objective of this paper is to describe the application of navigator echo and projection profile matching to fast two-dimensional image registration for MR-guided liver therapy. MATERIALS AND METHODS: We obtain navigator echoes along the read-out and phase-encoding directions by using modified gradient echo imaging. This registration is made possible by masking out the liver profile from the image and performing profile matching with cross-correlation or mutual information as similarity measures. The set of experiments include a phantom study with a 2.0-T experimental MR scanner, and a volunteer and a clinical study with a 0.5-T open-configuration MR scanner, and these evaluate the accuracy and effectiveness of this method for liver therapy. RESULTS: Both the phantom and volunteer study indicate that this method can perform registration in 34 ms with root-mean-square error of 1.6 mm when the given misalignment of a liver is 30 mm. The clinical studies demonstrate that the method can track liver motion of up to approximately 40 mm. Matching profiles with cross-correlation information perform better than with mutual information in terms of robustness and speed. CONCLUSION: The proposed image registration method has potential clinical impact on and advantages for MR-guided liver therapy.     

First-pass myocardial perfusion image registration by maximization of normalized mutual information

PURPOSE: To evaluate a left ventricular image registration algorithm for first-pass MR myocardial perfusion. MATERIALS AND METHODS: A normalized mutual information based motion correction algorithm was proposed and tested on 27 adenosine stressed myocardial perfusion cases consisting of pretreatment and posttreatment of 15 patients undergone autologous bone marrow mononuclear cell transplant therapy. An image mask approximately covering the left and right ventricles was manually defined to include a region of interest for registration. A two-dimensional multiresolution registration approach was used to register consecutively acquired multislice images with in-plane translations. The method was validated by manual registration and singular value deconvolution based perfusion analysis. RESULTS: The proposed image registration algorithm was found to be robust in minimizing the in-plane motion of the left ventricle in first-pass myocardial perfusion. The image mask including the left and right ventricle was found to be more robust than including the left ventricle alone. A smooth estimate of normalized mutual information coefficients were achieved for images with large contrast changes. CONCLUSION: The proposed semiautomatic multiresolution registration algorithm was able to register first-pass MR myocardial perfusion images and may be useful in quantitative perfusion analysis.     

用影像存档与通讯系统进行影像融合

目的 应用影像存档与通讯系统（PACS）和影像融合软件进行不同显像形式图像融合的方法学探索。方法 图像的原始采集和处理分别使用Siemens螺旋CT、MR和E．CAM^ 双探头带符合线路SPECT仪。经CT、MR和核医学等科室间的PACS图像查询、传输和存取，用安装在SPECT仪计算机上的Medical image merge (MIM)软件进行影像融合处理。原始影像容积数据用重新分层方法创建图像，通过平移和旋转相关的观察端面、调节影像对比度和透明度等实现融合影像。结果 通过PACS成功地进行了CT、MR和核医学影像的传输、存取和载入，进行MR与核医学脑显像、CT和核医学胸部显像等的图像融合，获得了理想的图像。结论 应用PACS和MIM软件进行影像融合能获取良好的图像。     

Image registration for targeted MRI-guided transperineal prostate biopsy

Purpose:

To develop and evaluate image registration methodology for automated re‐identification of tumor‐suspicious foci from preprocedural MR exams during MR‐guided transperineal prostate core biopsy.

Materials and Methods:

A hierarchical approach for automated registration between planning and intra‐procedural T2‐weighted prostate MRI was developed and evaluated on the images acquired during 10 consecutive MR‐guided biopsies. Registration accuracy was quantified at image‐based landmarks and by evaluating spatial overlap for the manually segmented prostate and sub‐structures. Registration reliability was evaluated by simulating initial mis‐registration and analyzing the convergence behavior. Registration precision was characterized at the planned biopsy targets.

Results:

The total computation time was compatible with a clinical setting, being at most 2 min. Deformable registration led to a significant improvement in spatial overlap of the prostate and peripheral zone contours compared with both rigid and affine registration. Average in‐slice landmark registration error was 1.3 ± 0.5 mm. Experiments simulating initial mis‐registration resulted in an estimated average capture range of 6 mm and an average in‐slice registration precision of ±0.3 mm.

Conclusion:

Our registration approach requires minimum user interaction and is compatible with the time constraints of our interventional clinical workflow. The initial evaluation shows acceptable accuracy, reliability and consistency of the method. J. Magn. Reson. Imaging 2012;36:987–992. © 2012 Wiley Periodicals, Inc.     

Automatic method to assess local CT-MR imaging registration accuracy on images of the head

BACKGROUND AND PURPOSE: Precise registration of CT and MR images is crucial in many clinical cases for proper diagnosis, decision making or navigation in surgical interventions. Various algorithms can be used to register CT and MR datasets, but prior to clinical use the result must be validated. To evaluate the registration result by visual inspection is tiring and time-consuming. We propose a new automatic registration assessment method, which provides the user a color-coded fused representation of the CT and MR images, and indicates the location and extent of poor registration accuracy. METHODS: The method for local assessment of CT-MR registration is based on segmentation of bone structures in the CT and MR images, followed by a voxel correspondence analysis. The result is represented as a color-coded overlay. The algorithm was tested on simulated and real datasets with different levels of noise and intensity non-uniformity. RESULTS: Based on tests on simulated MR imaging data, it was found that the algorithm was robust for noise levels up to 7% and intensity non-uniformities up to 20% of the full intensity scale. Due to the inability to distinguish clearly between bone and cerebro-spinal fluids in the MR image (T1-weighted), the algorithm was found to be optimistic in the sense that a number of voxels are classified as well-registered although they should not. However, nearly all voxels classified as misregistered are correctly classified. CONCLUSION: The proposed algorithm offers a new way to automatically assess the CT-MR image registration accuracy locally in all the areas of the volume that contain bone and to represent the result with a user-friendly, intuitive color-coded overlay on the fused dataset.     

Evaluation of left ventricular endocardial volumes and ejection fractions computed from gated perfusion SPECT with magnetic resonance imaging: Comparison of two methods

BACKGROUND: Two methods of computing left ventricular volumes and ejection fraction (EF) from 8-frame gated perfusion single photon emission computed tomography (SPECT) were compared with each other and with magnetic resonance (MR) imaging. METHODS AND RESULTS: Thirty-five subjects underwent 8-frame gated dual-isotope SPECT imaging and 12- to 16-frame gated MR imaging. Endocardial boundaries on short-axis MR images were hand traced by experts blinded to any SPECT results. Volumes and EF were computed with the use of Simpson's rule. SPECT images were analyzed for the same functional variables with the use of 2 automatic programs, Quantitative Gated SPECT (QGS) and the Emory Cardiac Toolbox (ECTb). The mean difference between MR and SPECT EF was 0.008 for ECTb and 0.08 for QGS. QGS showed a slight trend toward higher correlation for EF (r = 0.72, SE of the estimate = 0.08) than ECTb (r = 0.70, SE of the estimate = 0.09). For both SPECT methods, left ventricular volumes were similarly correlated with MR, although SPECT volumes were higher than MR values by approximately 30%. CONCLUSIONS: QGS and ECTb values of cardiac function computed from 8-frame gated perfusion SPECT correlate very well with each other and correlate well with MR. Averaged over all subjects, ECTb measurements of EF are not significantly different from MR values but QGS significantly underestimates the MR values.     

Image fusion of CT and MRI data enables improved target volume definition in 3D-brachytherapy treatment planning

PURPOSE: To integrate MRI into CT-based 3D-brachytherapy treatment planning using a software system for image registration and fusion. METHODS AND MATERIALS: Sixteen patients with recurrent head-and-neck cancer, vulvar cancer, liposarcoma, and cervical cancer were treated with interstitial (n=12) and endocavitary (n=4) brachytherapy. CT and MRI scans were performed after implantation and prior to treatment planning. Image registration to integrate the CT and MR information into a single geometric framework was performed using a software algorithm based on mutual information. Conventional 3D-brachytherapy planning based on CT-information alone was compared to brachytherapy planning based on fused CT and MRI data. The accuracy of the image fusion was measured using predefined corresponding landmarks in the CT and MRI data. RESULTS: The presented automated algorithm proved to be robust and reliable (mean registration error 1.8 mm, range 0.8-4.1 mm, SD 0.9 mm). Tumor visualization was difficult using CT alone in all cases. Brachytherapy treatment planning based on fused CT and MRI data enabled better definition of target volume and risk structures as compared to treatment planning based on CT alone. CONCLUSIONS: Image registration and fusion is feasible for afterloading brachytherapy treatment planning. Treatment planning based on fused CT and MRI data resulted in improved target volume and risk structure definition.     

Opacity transfer function optimization for volume-rendered computed tomography images of the prostate

RATIONALE AND OBJECTIVES: The selection of an opacity transfer function is essential for volume visualization. Computed tomography (CT) scans of the pelvis were used to determine an optimal opacity transfer function for use in radiotherapy. MATERIALS AND METHODS: On sample datasets (a mathematical phantom and a patient pelvis CT scan), standard viewing orientations were selected to render the prostate. Opacity functions were selected via (1) trapezoidal manual selection, (2) trapezoidal semiautomatic selection, and (3) histogram volume-based selection. Using an established metric, the errors using each of these methods were computed. RESULTS: Trapezoidal manual opacity function optimization resulted in visually acceptable images, but the errors were considerable (6.3-9.1 voxel units). These errors could be reduced with the use of trapezoidal semiautomatic selection (4.9-6.2 voxel units) or with histogram volume-based selection (4.8-7.9 voxel units). As each visualization algorithm focused on enhancing the boundary of the prostate using a different approach, the scene information was considerably different using the three techniques. CONCLUSION: Improved volume visualization of soft tissue interfaces was achieved using automated optimal opacity function determination, compared with manual selection.