Registration of three-dimensional MR and PET images of the human brain without markers

Axial and sagittal magnetic resonance (MR) sections and contiguous sections of axial positron emission tomographic (PET) images obtained with fludeoxyglucose F-18 were used to evaluate a new method of registering three-dimensional images of the brain. The users specified the interhemispheric fissure plane in three dimensions for both the MR and PET data sets by specifying its endpoints within several axial sections. A transformation matrix aligning the interhemispheric fissure plane in MR and PET space was calculated and used to create one resectioned PET image on the resectioned PET image, and the user specified the remaining translations and rotation by moving the overlaid outline of the MR image. MR and PET data sets in four subjects were registered. The three-dimensional error on average was less than 3.8 mm and never exceeded 7.5 mm. Less than 1 hour per patient was required for registration. The method is accurate unless the interhemispheric fissure deviates significantly from a planar configuration. It does not need thin or contiguous MR sections and provides an estimate of the total registration error for every case.     

Prospective registration of human head magnetic resonance images for reproducible slice positioning using localizer images

PURPOSE: To facilitate assessing brain tumor growth and progression of stroke lesions by reproducible slice positioning in human head magnetic resonance (MR) images, a method for prospective registration is proposed that adjusts the image slice position without moving the patient and with no additional scans. MATERIALS AND METHODS: The gradient reference frame of follow-up examinations was adjusted to achieve the same image slice positioning relative to the patient as in the previous examination. The three-dimensional geometrical transformation parameters for the gradients were determined using two-dimensional image registration of three orthogonal localizer images. The method was developed and evaluated using a phantom with arbitrarily adjustable position. Feasibility for in vivo applications was demonstrated with brain MR imaging (MRI) of healthy volunteers. RESULTS: Standard retrospective registration was used for assessing the quality of the method. The accuracy of the realignment was 0.0 mm +/- 1.2 mm and -0.2 degrees +/- 0.9 degrees (mean +/- SD) in phantom experiments. In 10 examinations of volunteers, misalignments up to 49.2 mm and 21 degrees were corrected. The accuracy of the realignment after prospective registration was 0.1 mm +/- 1.5 mm and 0.2 degrees +/- 1.5 degrees. CONCLUSION: Image-based prospective registration using localizer images of the pre- and postexaminations is a robust method for reproducible slice positioning.     

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.     

A novel registration method for interval change detection between two chest X-ray images with different rotation angles

RATIONALE AND OBJECTIVES: Registration is an important process to detect interval changes between two chest x-ray images. However, the conventional registration methods suffer from misregistration because of the difference in rotation angles of human body around an axis parallel to the x-ray films, such as anteroposterior inclination. Such difference causes permutation of the shadows between the two images, which makes registration difficult. This article proposes a novel registration method in cases where two chest x-ray images have different rotation angles. MATERIALS AND METHODS: Twelve x-ray images taken from a chest phantom and four chest photofluorograms of two patients were used to evaluate the performance. First, the proposed algorithm estimates the rotation angles of the body from the pair of two x-ray images based on the function describing the relationship between a point in the current image and that in the previous image, which is derived from a three-dimensional rotational model of the body. Then it aligns two images according to the function. RESULTS: From the results of estimating rotation angles, it was found that proposed method can estimate the angles with an error of less than 1 degrees. Then two physicians evaluated the subtraction images and confirmed that this approach makes it possible to detect the interval changes accurately even if there are permutations of shadows in the x-ray image. CONCLUSIONS: The proposed method is superior to the conventional one when two chest x-ray images have different rotation angles.     

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.     

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.     

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.     

Transmission imaging for registration of ictal and interictal single-photon emission tomography, magnetic resonance imaging and electroencephalography

A method developed for registration of ictal and interictal single-photon emission tomography (SPET), magnetic resonance imaging (MRI) and electroencephalography (EEG) is described. For SPET studies, technetium-99m ethyl cysteinate dimer (ECD) was injected intravenously while the patient was monitored on video-EEG to document the ictal or interictal state. Imaging was performed using a triple-head gamma camera equipped with a transmission imaging device using a gadolinium-153 source. The images (128x128 pixels, voxel size 3.7x3.7x3.6 mm3) were reconstructed using an iterative algorithm and postfiltered with a Wiener filter. The gold-plated silver electrodes on the patient's scalp were utilized as markers for registration of the ictal and interictal SPET images, as these metallic markers were clearly seen on the transmission images. Fitting of the marker sets was based on a non-iterative least squares method. The interictal SPET image was subtracted from the ictal image after scaling. The T1-weighted MPRAGE MR images with voxel size of 1.0x1.0x1.0 mm3 were obtained with a 1.5-T scanner. For registration of MR and subtraction SPET images, the external marker set of the ictal SPET study was fitted to the surface of the head segmented from MR images. The SPET registration was tested with a phantom experiment. Registration of ictal and interictal SPET in five patient studies resulted in a 2-mm RMS residual of the marker sets. The estimated RMS error of registration in the final result combining locations of the electrodes, subtraction SPET and MR images was 3-5 mm. In conclusion, transmission imaging can be utilized for an accurate and easily implemented registration procedure for ictal and interictal SPET, MRI and EEG.     

Magnetic resonance image registration in multiple sclerosis: comparison with repositioning error and observer-based variability

PURPOSE: To study the use of image registration in the analysis of multiple sclerosis (MS) lesion volume and compare this with repositioning error and observer-based variability. MATERIALS AND METHODS: The normalized mutual information (NMI) algorithm is evaluated in an accuracy study using a phantom, followed by a validation study on magnetic resonance (MR) data of MS patients. Further, using scan-rescan MR data, the effect of registration on MS lesion volume compared with repositioning error and observer-based variability is assessed. RESULTS: The registration accuracy was near perfect in the phantom study, while the in vivo validation study demonstrated an accuracy on the order of 0.2-0.3 mm. In the scan-rescan study, quantification accounted for 15.6% of the relative variance, repositioning for 44.4%, and registration for 40.0%. CONCLUSION: NMI resulted in robust and accurate alignment of MR brain images of MS patients. Its use in the detection of changes in MS using large serial MR imaging (MRI) data warrants future evaluation.     

Application of 3D-MR image registration to monitor diseases around the knee joint

PURPOSE: To estimate the accuracy and consistency of a method using a voxel-based MR image registration algorithm for precise monitoring of knee joint diseases. MATERIALS AND METHODS: Rigid body transformation was calculated using a normalized cross-correlation (NCC) algorithm involving simple manual segmentation of the bone region based on its anatomical features. The accuracy of registration was evaluated using four phantoms, followed by a consistency test using MR data from the 11 patients with knee joint disease. RESULTS: The registration accuracy in the phantom experiment was 0.49+/-0.19 mm (SD) for the femur and 0.56+/-0.21 mm (SD) for the tibia. The consistency value in the experiment using clinical data was 0.69+/-0.25 mm (SD) for the femur and 0.77+/-0.37 mm (SD) for the tibia. These values were all smaller than a voxel (1.25 x 1.25 x 1.5 mm). CONCLUSION: The present method based on an NCC algorithm can be used to register serial MR images of the knee joint with error on the order of a sub-voxel. This method would be useful for precisely assessing therapeutic response and monitoring knee joint diseases; normalized cross-correlation; accuracy.     

Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI.

Patient motion and image distortion induced by eddy currents cause artifacts in maps of diffusion parameters computed from diffusion-weighted (DW) images. A novel and comprehensive approach to correct for spatial misalignment of DW imaging (DWI) volumes acquired with different strengths and orientations of the diffusion sensitizing gradients is presented. This approach uses a mutual information-based registration technique and a spatial transformation model containing parameters that correct for eddy current-induced image distortion and rigid body motion in three dimensions. All parameters are optimized simultaneously for an accurate and fast solution to the registration problem. The images can also be registered to a normalized template with a single interpolation step without additional computational cost. Following registration, the signal amplitude of each DWI volume is corrected to account for size variations of the object produced by the distortion correction, and the b-matrices are properly recalculated to account for any rotation applied during registration. Both qualitative and quantitative results show that this approach produces a significant improvement of diffusion tensor imaging (DTI) data acquired in the human brain.     

Adaptive 4D MR imaging using navigator-based respiratory signal for MRI-guided therapy.

For real-time 3D visualization of respiratory organ motion for MRI-guided therapy, a new adaptive 4D MR imaging method based on navigator echo and multiple gating windows was developed. This method was designed to acquire a time series of volumetric 3D images of a cyclically moving organ, enabling therapy to be guided by synchronizing the 4D image with the actual organ motion in real time. The proposed method was implemented in an open-configuration 0.5T clinical MR scanner. To evaluate the feasibility and determine optimal imaging conditions, studies were conducted with a phantom, volunteers, and a patient. In the phantom study the root mean square (RMS) position error in the 4D image of the cyclically moving phantom was 1.9 mm and the imaging time was approximately 10 min when the 4D image had six frames. In the patient study, 4D images were successfully acquired under clinical conditions and a liver tumor was discriminated in the series of frames. The image quality was affected by the relations among the encoding direction, the slice orientation, and the direction of motion of the target organ. In conclusion, this study has shown that the proposed method is feasible and capable of providing a real-time dynamic 3D atlas for surgical navigation with sufficient accuracy and image quality.     

Prospective multiaxial motion correction for fMRI.

Corruption of the image time series due to interimage head motion limits the clinical utility of functional MRI. This paper presents a method for real-time prospective correction of rotation and translation in all six degrees of rigid body motion. By incorporating an orbital navigator (ONAV) echo for each of the sagittal, axial, and coronal planes into the fMRI pulse sequence, rotation and translation can be measured and the spatial orientation of the image acquisition sequence that follows can be corrected prospectively in as little as 160 msec. Testing of the method using a computerized motion phantom capable of performing complex multiaxial motion showed subdegree rotational and submillimeter translational accuracy over a range of +/-8 degrees and +/-8 mm of motion. In vivo images demonstrate correction of simultaneous through-plane and in-plane motion and improved detection of fMRI activation in the presence of head motion.     

Automatic registration of MR and SPECT images for treatment planning in prostate cancer

RATIONALE AND OBJECTIVES: To aid in surgical and radiation therapy planning for prostate adenocarcinoma, a general-purpose automatic registration method that is based on mutual information was used to align magnetic resonance (MR) images and single photon emission computed tomographic (SPECT) images of the pelvis and prostate. MATERIALS AND METHODS: The authors assessed the effects of various factors on alignment between pairs of MR and SPECT images, including the use of particular pulse sequences in MR imaging, image voxel intensity scaling, the use of different regions on the MR-SPECT histogram, spatial masking of nonoverlapping visual data between images, and multiresolution optimization. A mutual information algorithm was used as the cost function for automatic registration. Automatic registration was deemed acceptable when it resulted in a transformation with less than 2 voxel units (6 mm) difference in translation and less than 2 degree difference in rotation from that obtained with manual registration performed independently by nuclear medicine radiologists. RESULTS: Paired sets of MR and SPECT image volumes from four of five patients were successfully registered. For successful registration, MR images must be optimal and registration must be performed at full spatial resolution and at the full intensity range. Masking, cropping, and the normalization of mutual information, used to register partially overlapping MR-SPECT volumes, were not successful. Multiresolution optimization had little effect on the accuracy and speed of the registration. CONCLUSION: Automatic registration between MR and SPECT images of the pelvis can be achieved when data acquisition and image processing are performed properly. It should prove useful for prostate cancer diagnosis, staging, and treatment planning.     

Registration of emission and transmission whole-body scintillation-camera images.

In this work, a method for registration of whole-body (WB) scintillation-camera images is presented. The primary motive for the development is to perform activity quantification using the conjugate view method on an image basis. Accurate image registration is required for sequential anterior and posterior scans, for serial emission images for analysis of the biokinetics, and for transmission and emission images for a pixel-based attenuation correction. METHODS: Registration is performed by maximization of the mutual information. The spatial transformation has been tailored for the registration of WB images and is composed of global and local transformations, including rigid, projective, and curved transformations. A coarse registration is first performed using cross-correlation and direct pixel scaling. Optimization is then performed in a sequence, beginning with the 2 legs independently, followed by the upper body and head. Evaluation is performed for clinical images of an (131)I-labeled monoclonal antibody and for Monte Carlo-simulated images. An anthropomorphic WB computer phantom, which has been especially modified to match the patient position during WB scanning, is used for the simulations. RESULTS: For simulated images, registration errors are within 1 pixel (<3.6 mm) for a sufficient image count level. Separate evaluation of the influence of noise shows that the errors increase below a total image count of approximately 10(5) (signal-to-noise ratio, approximately 4). For clinical evaluations, the deviations between point markers are 9 +/- 5 mm. CONCLUSION: An automatic registration method for WB images has been developed, which is applicable to emission-emission and transmission-emission registration. This method has been applied in more than 50 clinical studies and has shown to be robust and reliable.     

Transmission imaging for registration of ictal and interictal single-photon emission tomography, magnetic resonance imaging and electroencephalography

A method developed for registration of ictal and interictal single-photon emission tomography (SPET), magnetic resonance imaging (MRI) and electroencephalography (EEG) is described. For SPET studies, technetium-99m ethyl cysteinate dimer (ECD) was injected intravenously while the patient was monitored on video-EEG to document the ictal or interictal state. Imaging was performed using a triple-head gamma camera equipped with a transmission imaging device using a gadolinium-153 source. The images (128×128 pixels, voxel size 3.7×3.7×3.6 mm³) were reconstructed using an iterative algorithm and postfiltered with a Wiener filter. The gold-plated silver electrodes on the patient’s scalp were utilized as markers for registration of the ictal and interictal SPET images, as these metallic markers were clearly seen on the transmission images. Fitting of the marker sets was based on a non-iterative least squares method. The interictal SPET image was subtracted from the ictal image after scaling. The T1-weighted MPRAGE MR images with voxel size of 1.0×1.0×1.0 mm³ were obtained with a 1.5-T scanner. For registration of MR and subtraction SPET images, the external marker set of the ictal SPET study was fitted to the surface of the head segmented from MR images. The SPET registration was tested with a phantom experiment. Registration of ictal and interictal SPET in five patient studies resulted in a 2-mm RMS residual of the marker sets. The estimated RMS error of registration in the final result combining locations of the electrodes, subtraction SPET and MR images was 3–5 mm. In conclusion, transmission imaging can be utilized for an accurate and easily implemented registration procedure for ictal and interictal SPET, MRI and EEG. Received 20 September and in revised form 16 October 1999     

Registration of [18F]FDG microPET and small-animal MRI

This paper describes a voxel-based method for coregistering microPET [(18)F]FDG emission images and MRI data without the need for fiducial markers. [(18)F]FDG has a well-characterized biodistribution in normal mice and thus may be useful for image registration. Female BALB/c mice were implanted with EMT-6 mouse mammary carcinoma 1 week prior to imaging. Three imaging sessions were performed in which a [(18)F]FDG microPET-R4 emission scan was taken followed by small-animal MRI with and without Gd-based contrast agent. MicroPET and MR images were registered using a voxel-based algorithm that computes rigid-body image transformations based on the alignment of intensity gradients. Registration accuracy was assessed on the basis of dual-modality external fiducial line sources incorporated into the mouse bed. The root mean square (rms) registration errors were 0.74 mm translation and 1.44 degrees rotation without contrast and 0.72 mm translation and 0.89 degrees rotation with contrast. Generally, good registration was evident upon inspection of fused microPET/MR images. Accurate automated, voxel-based microPET-MR image coregistration, utilizing image intensity gradients, is feasible. Our technique requires no manual identification of image features and makes no use of surgically implanted or external fiducial markers or stereotactic apparatus.     

MRI-SPET and SPET-SPET brain co-registration: evaluation of the performance of eight different algorithms.

The aim of this study was to assess the accuracy and computing time needed for MRI-SPET and SPET-SPET brain co-registration using eight different algorithms (Hermes software from Nuclear Diagnostics Ltd run on a SUN Ultra Sparc 2) to determine the clinically most suitable algorithm. MRI-SPET co-registration was evaluated using phantom studies. To approximate clinical dual-headed SPET studies, a Hoffman brain phantom was filled with 99Tcm. For MRI imaging (1.5 Tesla), the phantom was filled with water and doped with Gd-DTPA for contrast enhancement. For both modalities, phantom images were acquired and reconstructed using a routine clinical protocol. MRI and SPET images were matched by Downhill Simplex minimization of the sum of absolute Count Differences (CD), the sum of the Square Root of absolute count differences (SR), the Difference in Shape between the binary masks (SD), the number of Sign Changes in the subtracted image (SC), the Variance of intensities between corresponding pixels (VAR), the sum of absolute count differences between the 2D- and 3D-Gradient images (2DG-3DG) and, finally, the standard deviation of the Uniformity Index (UI), that is the intensity ratio between spatially corresponding voxels. Six degrees of freedom were allowed (three translation and three rotation parameters, three scaling parameters were constrained). The accuracy of the matching process with these different similarity measures was evaluated via the residual mismatch between external markers. We found that CD, SR, VAR nad UI give the most accurate registration compared with the other similarity measures. For the evaluation of SPET-SPET co-registration, five 99Tcm-ECD brain perfusion SPET scans were performed with a dual-headed gamma camera. These studies were then manually misaligned, and subsequently re-aligned using the methods outlined above. For this application, CD, SR and VAR were also found to give the most accurate registration. For all of these algorithms, the computing time required was clinically acceptable (i.e. less than 10 min).     

Attenuation correction of myocardial SPECT images with X-ray CT: effects of registration errors between X-ray CT and SPECT

PURPOSE: Attenuation correction with an X-ray CT image is a new method to correct attenuation on SPECT imaging, but the effect of the registration errors between CT and SPECT images is unclear. In this study, we investigated the effects of the registration errors on myocardial SPECT, analyzing data from a phantom and a human volunteer. METHODS: Registerion (fusion) of the X-ray CT and SPECT images was done with standard packaged software in three dimensional fashion, by using linked transaxial, coronal and sagittal images. In the phantom study, an X-ray CT image was shifted 1 to 3 pixels on the x, y and z axes, and rotated 6 degrees clockwise. Attenuation correction maps generated from each misaligned X-ray CT image were used to reconstruct misaligned SPECT images of the phantom filled with 201Tl. In a human volunteer, X-ray CT was acquired in different conditions (during inspiration vs. expiration). CT values were transferred to an attenuation constant by using straight lines; an attenuation constant of 0/cm in the air (CT value = -1,000 HU) and that of 0.150/cm in water (CT value = 0 HU). For comparison, attenuation correction with transmission CT (TCT) data and an external gamma-ray source (99mTc) was also applied to reconstruct SPECT images. RESULTS: Simulated breast attenuation with a breast attachment, and inferior wall attenuation were properly corrected by means of the attenuation correction map generated from X-ray CT. As pixel shift increased, deviation of the SPECT images increased in misaligned images in the phantom study. In the human study, SPECT images were affected by the scan conditions of the X-ray CT. CONCLUSION: Attenuation correction of myocardial SPECT with an X-ray CT image is a simple and potentially beneficial method for clinical use, but accurate registration of the X-ray CT to SPECT image is essential for satisfactory attenuation correction.