A comparative study on manual and automatic slice-to-volume registration of CT images

In order to assess the clinical relevance of a slice-to-volume registration algorithm, this technique was compared to manual registration. Reformatted images obtained from a diagnostic CT examination of the lower abdomen were reviewed and manually registered by 41 individuals. The results were refined by the algorithm. Furthermore, a fully automatic registration of the single slices to the whole CT examination, without manual initialization, was also performed. The manual registration error for rotation and translation was found to be 2.7±2.8 ° and 4.0±2.5 mm. The automated registration algorithm significantly reduced the registration error to 1.6±2.6 ° and 1.3±1.6 mm (p = 0.01). In 3 of 41 (7.3%) registration cases, the automated registration algorithm failed completely. On average, the time required for manual registration was 213±197 s; automatic registration took 82±15 s. Registration was also performed without any human interaction. The resulting registration error of the algorithm without manual pre-registration was found to be 2.9±2.9 ° and 1.1±0.2 mm. Here, a registration took 91±6 s, on average. Overall, the automated registration algorithm improved the accuracy of manual registration by 59% in rotation and 325% in translation. The absolute values are well within a clinically relevant range.     

Quantitative comparison of automatic and interactive methods for MRI-SPECT image registration of the brain based on 3-dimensional calculation of error.

A wide range of techniques for registration of medical images has been devised in recent years. The aim of this study is to quantify the overall spatial registration error of 3 different methods for image registration: interactive matching, surface matching, and uniformity index matching as described by Woods. METHODS: MRI and ethylcysteinate dimer-SPECT images of the brain were registered for 15 patients. The matching error was assessed by determining intra- and interobserver variability of registrations. Quantification of the registration error was based on the mean spatial distance of 5000 voxels between 2 image positions. The mean position after repeated registrations in each patient was used as the gold standard. To evaluate the coherence of the 3 different registration methods, intermethod variability was determined. RESULTS: Interactive matching showed an intraobserver/interobserver variability of 1.5+/-0.3 mm/1.6+/-0.3 mm (mean +/- SD). The time demand for this method was 11+/-5 min. Surface matching revealed a variability of 2.6+/-1.1 mm/3.8+/-1.0 mm and a time demand of 26+/-12 min. Reproducibility of Woods' algorithm was 2.2+/-0.8 mm with a time demand of 9+/-3 min. In 4 of the 15 cases, Woods' method failed. The mean deviation between all 3 methods was 2.3+/-0.8 mm. CONCLUSION: With a suitable user interface, interactive matching had the lowest registration error. The influence of subjectivity was shown to be negligible. Therefore, interactive matching is our preferred technique for image fusion of the brain.     

Decoupled automated rotational and translational registration for functional MRI time series data: The dart registration algorithm

A rapid, in-plane image registration algorithm that accurately estimates and corrects for rotational and translational motion is described. This automated, one-pass method achieves its computational efficiency by decoupling the estimation of rotation and translation, allowing the application of rapid cross-correlation and cross-spectrum techniques for the determination of displacement parameters. k-space regridding and modulation techniques are used for image correction as alternatives to linear interpolation. The performance of this method was analyzed with simulations and echo-planar image data from both phantoms and human subjects. The processing time for image registration on a Hewlett-Packard 735/125 is 7.5 s for a 128 × 128 pixel image and 1.7 s for a 64 × 64 pixel image. Imaging phantom data demonstrate the accuracy of the method (mean rotational error, ?0.09°; standard deviation = 0.17°; range, ?0.44° to + 0.31°; mean translational error = ?0.035 pixels; standard deviation = 0.054 pixels; range, ?0.16 to + 0.06 pixels). Registered human functional imaging data demonstrate a significant reduction in motion artifacts such as linear trends in pixel time series and activation artifacts due to stimulus-correlated motion. The advantages of this technique are its noniterative one-pass nature, the reduction in image degradation as compared to previous methods, and the speed of computation.     

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.     

PET-MR image fusion in soft tissue sarcoma: accuracy,reliability and practicality of interactive point-based and automated mutual information techniques

The fusion of functional positron emission tomography (PET) data with anatomical magnetic resonance (MR) or computed tomography images, using a variety of interactive and automated techniques, is becoming commonplace, with the technique of choice dependent on the specific application. The case of PET-MR image fusion in soft tissue is complicated by a lack of conspicuous anatomical features and deviation from the rigid-body model. Here we compare a point-based external marker technique with an automated mutual information algorithm and discuss the practicality, reliability and accuracy of each when applied to the study of soft tissue sarcoma. Ten subjects with suspected sarcoma in the knee, thigh, groin, flank or back underwent MR and PET scanning after the attachment of nine external fiducial markers. In the assessment of the point-based technique, three error measures were considered: fiducial localisation error (FLE), fiducial registration error (FRE) and target registration error (TRE). FLE, which represents the accuracy with which the fiducial points can be located, is related to the FRE minimised by the registration algorithm. The registration accuracy is best characterised by the TRE, which is the distance between corresponding points in each image space after registration. In the absence of salient features within the target volume, the TRE can be measured at fiducials excluded from the registration process. To assess the mutual information technique, PET data, acquired after physically removing the markers, were reconstructed in a variety of ways and registered with MR. Having applied the transform suggested by the algorithm to the PET scan acquired before the markers were removed, the residual distance between PET and MR marker-pairs could be measured. The manual point-based technique yielded the best results (RMS TRE =8.3 mm, max =22.4 mm, min =1.7 mm), performing better than the automated algorithm (RMS TRE =20.0 mm, max =30.5 mm, min =7.7 mm) when registering filtered back-projection PET images to MR. Image reconstruction with an iterative algorithm or registration of a composite emission-transmission image did not improve the overall accuracy of the registration process. We have demonstrated that, in this application, point-based PET-MR registration using external markers is practical, reliable and accurate to within approximately 5 mm towards the fiducial centroid. The automated algorithm did not perform as reliably or as accurately.     

Registration of images from sequential MR studies of the brain

For sequential studies of patients with brain tumors, the authors have designed an automated registration procedure for intra- and interexamination alignment of magnetic resonance images. This was evaluated with artificially misregistered data and data from repeat studies of six healthy volunteers and six brain tumor patients. In a subset of cases, a manual procedure based on matching of neuroanatomic landmarks was also applied for comparison. The results showed that the technique is robust and reproducible, giving an accuracy in the range of 1–2 mm, which corresponded to the spatial resolution of the images. Subject motion between imaging sequences within the same study was negligible, although adjustments (one to two section thicknesses) were required in the z direction to correlate multisection and volume images and to allow accurate image segmentation. For alignment between sequential volunteer and patient examinations, translations of up to 22 mm and rotations in the x, y, and z axes of up to 9° were required. This alignment procedure may be valuable in numerous aspects of treatment planning and patient follow-up.     

Implementation of a digital image superposition algorithm for radionuclide images: an assessment of its accuracy and reproducibility

An operator-interactive algorithm to achieve superposition of organ images has been used with a dedicated nuclear medicine computer system. Its purpose is to achieve organ registration in 128 X 128 digitized images before a direct numerical comparison of the regional distribution of a deposited radiotracer is performed. The accuracy and reproducibility of the algorithm for myocardial images has been tested by four operators, using a set of 28 image pairs in which the relative position of the heart differed by more than 10 mm for each pair. Comparing their results with the known displacements on two occasions provided an assessment of these two important parameters. The accuracy and reproducibility for superposing myocardial images by this digital technique are found to be well within the spatial resolution (FWHM) of the imaging system of the Tl-201 tracer studied.     

Automated analysis of small animal PET studies through deformable registration to an atlas

Purpose

This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model.

Methods

A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed.

Results

The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6?mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10?% in most of the organs considered.

Conclusion

The proposed automated quantification technique is reliable, robust and suitable for fast quantification of preclinical PET data in large serial studies.     

Segmentation of the quadratus lumborum muscle using statistical shape modeling

Purpose:

To compare automated segmentation of the quadratus lumborum (QL) based on statistical shape modeling (SSM) with conventional manual processing of magnetic resonance (MR) images for segmentation of this paraspinal muscle.

Materials and Methods:

The automated SSM scheme for QL segmentation was developed using an MR database of 7 mm axial images of the lumbar region from 20 subjects (cricket fast bowlers and athletic controls). Specifically, a hierarchical 3D‐SSM scheme for segmentation of the QL, and surrounding psoas major (PS) and erector spinae+multifidus (ES+MT) musculature, was implemented after image preprocessing (bias field correction, partial volume interpolation) followed by image registration procedures to develop average and probabilistic MR atlases for initializing and constraining the SSM segmentation of the QL. The automated and manual QL segmentations were compared using spatial overlap and average surface distance metrics.

Results:

The spatial overlap between the automated SSM and manual segmentations had a median Dice similarity metric of 0.87 (mean = 0.86, SD = 0.08) and mean average surface distance of 1.26 mm (SD = 0.61) and 1.32 mm (SD = 0.60) for the right and left QL muscles, respectively.

Conclusion:

The current SSM scheme represents a promising approach for future automated morphometric analyses of the QL and other paraspinal muscles from MR images. J. Magn. Reson. Imaging 2011;33:1422–1429. © 2011 Wiley‐Liss, Inc.     

MRI dosimetry: a fast quantitative MRI method to determine 3D absorbed dose distributions

RATIONALE AND OBJECTIVES: Magnetic resonance imaging (MRI) techniques seem to be very promising for 3D dosimetry studies, but long imaging acquisition time limits their use. A new fast T1 mapping protocol, easy to implement on a conventional MR imager, has been used to determine dose distributions on Fricke gels. METHODS: The method has been tested on manganese chloride (MnCl2) doped ferrous gelatin gels. The T1 measuring times range from 1 minute 40 seconds to 3 minutes 30 seconds for a 256x256 matrix image. RESULTS: The two- and three-dimensional profiles agree with those obtained with conventional dosimetry techniques (ion chambers). The precision and the spatial resolution principally depend on the signal-to-noise ratio of the used imaging RF coil. For example, for a surface coil, the accuracy is about 2.5% with a 1.56 mm spatial resolution. CONCLUSION: These preliminary results support the feasibility of the proposed technique for accurate MRI dosimetry studies and also have potential for various clinical quantitative MRI applications.     

Comparison of manual vs. automated multimodality (CT-MRI) image registration for brain tumors.

Computed tomgoraphy-magnetic resonance imaging (CT-MRI) registrations are routinely used for target-volume delineation of brain tumors. We clinically use 2 software packages based on manual operation and 1 automated package with 2 different algorithms: chamfer matching using bony structures, and mutual information using intensity patterns. In all registration algorithms, a minimum of 3 pairs of identical anatomical and preferably noncoplanar landmarks is used on each of the 2 image sets. In manual registration, the program registers these points and links the image sets using a 3-dimensional (3D) transformation. In automated registration, the 3 landmarks are used as an initial starting point and further processing is done to complete the registration. Using our registration packages, registration of CT and MRI was performed on 10 patients. We scored the results of each registration set based on the amount of time spent, the accuracy reported by the software, and a final evaluation. We evaluated each software program by measuring the residual error between "matched" points on the right and left globes and the posterior fossa for fused image slices. In general, manual registration showed higher misalignment between corresponding points compared to automated registration using intensity matching. This error had no directional dependence and was, most of the time, larger for a larger structure in both registration techniques. Automated algorithm based on intensity matching also gave the best results in terms of registration accuracy, irrespective of whether or not the initial landmarks were chosen carefully, when compared to that done using bone matching algorithm. Intensity-matching algorithm required the least amount of user-time and provided better accuracy.     

MRI‐based large deformation high dimensional mapping of the hippocampus in rats: Development and validation of the technique

Purpose

To report the detection of structural and functional biological changes in living animals using small animal in vivo MRI that complements traditional ex vivo histological techniques. We report the development and validation of the application of large deformation high dimensional mapping (HDM‐LD) segmentation for the hippocampus in the rat.

Materials and Methods

High resolution volumetric T2 weighted MRI images were acquired at 4.7 Tesla from six male in‐breed nonepileptic Wistar rats. Two HDM‐LD segmentations of the hippocampus (automated 1 and automated 2) were compared with the manual segmentations of two investigators who independently segmented the hippocampi (manual 1 and manual 2).

Results

The mean overlap for the hippocampi between automated 1 and automated 2 for the right hippocampi was 94.4% (SD 1.0) and for the left hippocampi was 94.3% (SD 2.5), while the mean overlap between automated 1 and manual 1 for the right hippocampi was 91.4% (SD 1.3) and for the left hippocampi was 91.9% (SD 1.4). Mean values for absolute differences for comparisons of all the segmentations were the following: automated 1 versus automated 2, 3.2% (SD 1.0); manual 1 versus manual 2 6.82% (SD 5.22); automated 1 versus manual 1 13.0% (SD 1.8).

Conclusion

HDM‐LD can be applied to obtain accurate and reproducible three‐dimensional segmentations of the hippocampus from rat MR images. HDM‐LD will be a useful tool for investigations of hippocampal structural changes in vivo in rat models of human disease. J. Magn. Reson. Imaging 2009;29:1027–1034. © 2009 Wiley‐Liss, Inc.     

Unsupervised estimation of myocardial displacement from tagged MR sequences using nonrigid registration.

We propose a fully automatic cardiac motion estimation technique that uses nonrigid registration between temporally adjacent images to compute the myocardial displacement field from tagged MR sequences using as inputs (sources) both horizontally and vertically tagged images. We present a new multisource nonrigid registration algorithm employing a semilocal deformation model that provides controlled smoothness. The method requires no segmentation. We apply a multiresolution optimization strategy for better speed and robustness. The accuracy of the algorithm is assessed on experimental data (animal model) and healthy volunteer data by calculating the root mean square (RMS) difference in position between the estimated tag trajectories and manual tracings outlined by an expert. For the approximately 20000 tag lines analyzed (45 slices over 20-40 time frames), the RMS difference between the automatic tag trajectories and the manually segmented tag trajectories was 0.51 pixels (0.25 mm) for the animal data and 0.49 pixels (0.49 mm) for the human volunteer data. The RMS difference in the separation between adjacent tag lines (RMS_TS) was also assessed, resulting in an RMS_TS of 0.40 pixels (0.19 mm) in the experimental data and 0.52 pixels (0.56 mm) in the volunteer data. These results confirm the subpixel accuracy achieved using the proposed methodology.     

Evaluation of software for registration of contrast-enhanced brain MR images in patients with glioblastoma multiforme

OBJECTIVE: We evaluated commercially available software that rapidly and automatically registers brain MR images on a clinical workstation, and we studied the accuracy of these registrations. SUBJECTS AND METHODS: Ten patients with a diagnosis of glioblastoma multiforme underwent contrast-enhanced inversion recovery prepared three-dimensional (3D) volumetric spoiled gradient-recalled acquisition in the steady state (SPGR) MR imaging (contiguous 1.5-mm slice thickness, 96-104 slices). After this imaging sequence, each patient was brought out of the head coil into a sitting position and then repositioned in the coil. The inversion recovery prepared 3D SPGR sequence was then repeated. A commercially available software program operating on a clinical workstation was used to automatically register the second inversion recovery prepared SPGR series to the first. The speed of registration was recorded. The accuracy of each registration was estimated by recording the coordinates of eight anatomic landmarks on the registered and reference series and by calculating the mean error among matching landmarks. RESULTS: In nine of 10 patients, the registration software produced a visually satisfactory registration. In one patient, a second registration was necessary to produce a satisfactory registration. The processing time for each iteration was 48.3 +/- 3.8 sec (mean +/- SD). The mean error in aligning matching anatomic landmarks ranged from 0.67 to 1.41 mm, with an overall mean of 1.18 mm. The largest error among matching landmarks was 2.3 mm. CONCLUSION: Commercially available registration software can automatically register 3D imaging volumes in less than 1 min. The mean error in registration was approximately equivalent to the dimensions of a single voxel.     

Model-based registration for dynamic cardiac perfusion MRI

PURPOSE: To assess the accuracy of a model-based approach for registration of myocardial dynamic contrast-enhanced (DCE)-MRI corrupted by respiratory motion. MATERIALS AND METHODS: Ten patients were scanned for myocardial perfusion on 3T or 1.5T scanners, and short- and long-axis slices were acquired. Interframe registration was done using an iterative model-based method in conjunction with a mean square difference metric. The method was tested by comparing the absolute motion before and after registration, as determined from manually registered images. Regional flow indices of myocardium calculated from the manually registered data were compared with those obtained with the model-based registration technique. RESULTS: The mean absolute motion of the heart for the short-axis data sets over all the time frames decreased from 5.3+/-5.2 mm (3.3+/-3.1 pixels) to 0.8+/-1.3 mm (0.5+/-0.7 pixels) in the vertical direction, and from 3.0+/-3.7 mm (1.7+/-2.1 pixels) to 0.9+/-1.2 mm (0.5+/-0.7 pixels) in the horizontal direction. A mean absolute improvement of 77% over all the data sets was observed in the estimation of the regional perfusion flow indices of the tissue as compared to those obtained from manual registration. Similar results were obtained with two-chamber-view long-axis data sets. CONCLUSION: The model-based registration method for DCE cardiac data is comparable to manual registration and offers a unique registration method that reduces errors in the quantification of myocardial perfusion parameters as compared to those obtained from manual registration.     

Registration of planar emission images with reprojected CT data.

Planar gamma-camera imaging is still widely used clinically. Alignment of planar images with images from tomographic modalities, such as CT, or with other planar images would be desirable. Here, we present and evaluate a method for such an alignment, using planar transmission images acquired with the emission images and reprojection of the 3-dimensional CT data. This method permits determination of which CT slice corresponds to a particular row of pixels in the gamma-camera image and which column of pixels in that CT slice corresponds to a particular pixel in the emission data. METHODS: A method based on maximization of the correlation coefficient, previously used for 3-dimensional datasets, was modified to permit 2-dimensional registrations. Planar transmission measurements were obtained using a collimated 99mTc flood source in conjunction with planar emission studies. The CT data were first reprojected to permit the 2-dimensional registration. The registration method was evaluated for its accuracy and reproducibility. RESULTS: For phantom data, the registration errors were -0.1 +/- 1.0 mm for x-translations, 1.0 +/- 1.3 mm for y-translations, and -0.2 +/- 0.3 degrees for rotations. For patient data, the errors were 1.6 +/- 0.8 mm for x-translations, 1.3 +/- 1.0 mm for y-translations, and 0.5 +/- 0.5 degrees for rotations. An examination of the need for rescaling of the attenuation data (to compensate for the different photon energies used in the respective attenuation measurements) showed no significant impact on registration error. When 5 different regions of interest were used for the correlation coefficient calculation, the mean errors attributable to region-of-interest choice alone were 1.0 mm for x-translations, 2.0 mm for y-translations, and 1.2 degrees for rotations. CONCLUSION: In almost all instances, translational registration errors were kept to subpixel levels (pixel size, 2.6 mm) and rotational errors to 1 degrees or less. The 1 exception was in the easily avoidable case of "pitch" rotations of the patient of 2 degrees or more. The modified registration method provides a simple yet reliable way to provide cross-modality evaluation of planar emission data.     

Clinical usefulness of diffusion-weighted imaging using low and high b-values to detect rectal cancer.

PURPOSE: The purpose of this study was to assess the potential role of diffusion-weighted imaging (DWI) using low and high b-values to detect rectal cancer. METHODS: The subjects were 15 patients diagnosed endoscopically with rectal cancer (m in 1 patient, sm in 0, mp in 3, ss in 7, se in 1, a in 3) and 20 patients diagnosed endoscopically with colon cancer and no other lesions (control group). Magnetic resonance imaging was performed using a 1.5T system. DWI was performed in the axial plane using echo planar imaging sequence (repetition time/echo time 1200/66, field of view 306x350 mm, reconstruction matrix 156x256, pixel size 2.0x1.4x8.0 mm) and acquired with 2 b-values (50 and 800 s/mm2). Low and high b-value DW images were analyzed visually. A lesion was positive by detection of a focal area of high signal in the rectum in high b-value images. The apparent diffusion coefficient (ADC) values of areas of high signal in high b-value images were calculated from the low and high b-value images. RESULTS: High b-value images enabled visualization of all 15 rectal cancers. In the control group, 13 cases were classified as negative and 7 cases as positive for rectal cancer. Sensitivity for detection of rectal cancer was 100% (15/15), and specificity was 65% (13/20). The mean ADC values in 7 patients with false-positive lesions and in 15 patients with rectal cancer were 1.374x10(-3) mm2/s (standard deviation [SD]: 0.157) and 1.194x10(-3) mm2/s (SD: 0.152), respectively (P=0.026). CONCLUSION: DWI with low and high b-values may be used to screen for rectal cancer.     

Enhancing accuracy of magnetic resonance image fusion by defining a volume of interest

We compared the registration accuracy for corresponding anatomical landmarks in two MR images after fusing the complete volume (CV) and a defined volume of interest (VOI) of both MRI data sets. We carried out contrast-enhanced T1-weighted gradient-echo and T2-weighted fast spin-echo MRI (matrix 256×256) in 39 cases. The CV and a defined VOI data set were each fused using prototype software. We measured and analysed the distance between 25 anatomical landmarks in predefined areas identified at levels L₁–L₅ corresponding to defined axial sections. Fusion technique, landmark areas and level of fusion were further processed using a feed-forward neural network to calculate the difference which can be expected based on the measurements. We identified 975 landmarks for both T1- and T2-weighted images and found a significant difference in registration accuracy (P<0.01) for all landmarks between CV (1.6±1.2 mm) and VOI (0.7±1.0 mm). From cranial (L₁) to caudal (L₅), mean deviations were: L₁ CV 1.5 mm, VOI 0.5 mm; L₂ CV 1.8 mm, VOI 0.4 mm; L₃ CV 1.7 mm, VOI 0.4 mm; L₄ CV 1.6 mm, VOI 0.6 mm; and L₅ CV 1.6 mm, VOI 1.6 mm. Neural network analysis predicted a higher accuracy for VOI (0.05–0.15 mm) than for CV fusion (0.9–1.6 mm). Deviations due to magnetic susceptibility changes between air and tissue seen on gradient-echo images can decrease fusion accuracy. Our VOI fusion technique improves image fusion accuracy to <0.5 mm by excluding areas with marked susceptibility changes.     

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.     

Automated algorithm for reconstruction of the complete spine from multistation 7T MR data

Recent technical developments in high‐field MRI have enabled high‐resolution imaging of the whole spine within clinically acceptable times. However, analysis of such data requires intensity inhomogeneity correction and volume stitching, both of which are typically performed manually. In this work, an automated method for reconstruction of the complete spine from multistation 7T MR data is presented. The method consists of a number of image processing steps, in particular intensity inhomogeneity correction and image registration for recovery of unknown interscan bed translations, which result in high‐quality spine volume reconstructions. The registration performance of the developed algorithm was validated on 18 datasets acquired in two or three stations. In all the test cases, our algorithm was able to produce correct reconstruction of the spine volume. The resulting mean registration error (0.53 mm) is found to be lower than the pixel size, demonstrating robustness and accuracy of the proposed method. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.