Search tips
Search criteria

Results 1-25 (1275910)

Clipboard (0)

Related Articles

1.  Effects of rigid and non-rigid image registration on test-retest variability of quantitative [18F]FDG PET/CT studies 
EJNMMI Research  2012;2:10.
[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET) is a valuable tool for monitoring response to therapy in oncology. In longitudinal studies, however, patients are not scanned in exactly the same position. Rigid and non-rigid image registration can be applied in order to reuse baseline volumes of interest (VOI) on consecutive studies of the same patient. The purpose of this study was to investigate the impact of various image registration strategies on standardized uptake value (SUV) and metabolic volume test-retest variability (TRT).
Test-retest whole-body [18F]FDG PET/CT scans were collected retrospectively for 11 subjects with advanced gastrointestinal malignancies (colorectal carcinoma). Rigid and non-rigid image registration techniques with various degrees of locality were applied to PET, CT, and non-attenuation corrected PET (NAC) data. VOI were drawn independently on both test and retest scans. VOI drawn on test scans were projected onto retest scans and the overlap between projected VOI and manually drawn retest VOI was quantified using the Dice similarity coefficient (DSC). In addition, absolute (unsigned) differences in TRT of SUVmax, SUVmean, metabolic volume and total lesion glycolysis (TLG) were calculated in on one hand the test VOI and on the other hand the retest VOI and projected VOI. Reference values were obtained by delineating VOIs on both scans separately.
Non-rigid PET registration showed the best performance (median DSC: 0.82, other methods: 0.71-0.81). Compared with the reference, none of the registration types showed significant absolute differences in TRT of SUVmax, SUVmean and TLG (p > 0.05). Only for absolute TRT of metabolic volume, significant lower values (p < 0.05) were observed for all registration strategies when compared to delineating VOIs separately, except for non-rigid PET registrations (p = 0.1). Non-rigid PET registration provided good volume TRT (7.7%) that was smaller than the reference (16%).
In particular, non-rigid PET image registration showed good performance similar to delineating VOI on both scans separately, and with smaller TRT in metabolic volume estimates.
PMCID: PMC3349514  PMID: 22404895
Positron emission tomography (PET); Test-retest variability; Image registration; Non-rigid; Rigid
2.  Multimodality Non-Rigid Image Registration for Planning, Targeting and Monitoring during CT-guided Percutaneous Liver Tumor Cryoablation 
Academic radiology  2010;17(11):1334-1344.
Rationale and Objectives
To develop non-rigid image registration between pre-procedure contrast enhanced MR images and intra-procedure unenhanced CT images, to enhance tumor visualization and localization during CT-guided liver tumor cryoablation procedures.
Materials and Methods
After IRB approval, a non-rigid registration (NRR) technique was evaluated with different pre-processing steps and algorithm parameters and compared to a standard rigid registration (RR) approach. The Dice Similarity Coefficient (DSC), Target Registration Error (TRE), 95% Hausdorff distance (HD) and total registration time (minutes) were compared using a two-sided Student’s t-test. The entire registration method was then applied during five CT-guided liver cryoablation cases with the intra-procedural CT data transmitted directly from the CT scanner, with both accuracy and registration time evaluated.
Selected optimal parameters for registration were section thickness of 5mm, cropping the field of view to 66% of its original size, manual segmentation of the liver, B-spline control grid of 5×5×5 and spatial sampling of 50,000 pixels. Mean 95% HD of 3.3mm (2.5x improvement compared to RR, p<0.05); mean DSC metric of 0.97 (13% increase); and mean TRE of 4.1mm (2.7x reduction) were measured. During the cryoablation procedure registration between the pre-procedure MR and the planning intra-procedure CT took a mean time of 10.6 minutes, the MR to targeting CT image took 4 minutes and MR to monitoring CT took 4.3 minutes. Mean registration accuracy was under 3.4mm.
Non-rigid registration allowed improved visualization of the tumor during interventional planning, targeting and evaluation of tumor coverage by the ice ball. Future work is focused on reducing segmentation time to make the method more clinically acceptable.
PMCID: PMC2952665  PMID: 20817574
non-rigid registration; B-Spline registration; liver tumor cryoablation; multimodal registration
3.  MRI Signal Intensity Based B-Spline Nonrigid Registration for Pre- and Intraoperative Imaging During Prostate Brachytherapy 
To apply an intensity-based nonrigid registration algorithm to MRI-guided prostate brachytherapy clinical data and to assess its accuracy.
Materials and Methods
A nonrigid registration of preoperative MRI to intraoperative MRI images was carried out in 16 cases using a Basis-Spline algorithm in a retrospective manner. The registration was assessed qualitatively by experts’ visual inspection and quantitatively by measuring the Dice similarity coefficient (DSC) for total gland (TG), central gland (CG), and peripheral zone (PZ), the mutual information (MI) metric, and the fiducial registration error (FRE) between corresponding anatomical landmarks for both the nonrigid and a rigid registration method.
All 16 cases were successfully registered in less than 5 min. After the nonrigid registration, DSC values for TG, CG, PZ were 0.91, 0.89, 0.79, respectively, the MI metric was −0.19 ± 0.07 and FRE presented a value of 2.3 ± 1.8 mm. All the metrics were significantly better than in the case of rigid registration, as determined by one-sided t-tests.
The intensity-based nonrigid registration method using clinical data was demonstrated to be feasible and showed statistically improved metrics when compare to only rigid registration. The method is a valuable tool to integrate pre- and intraoperative images for brachytherapy.
PMCID: PMC2801562  PMID: 19856437
prostate brachytherapy; signal intensity-based nonrigid registration; B-Spline
4.  Comparison of a flexible versus a rigid breast compression paddle: pain experience, projected breast area, radiation dose and technical image quality 
European Radiology  2014;25(3):821-829.
To compare pain, projected breast area, radiation dose and image quality between flexible (FP) and rigid (RP) breast compression paddles.
The study was conducted in a Dutch mammographic screening unit (288 women). To compare both paddles one additional image with RP was made, consisting of either a mediolateral-oblique (MLO) or craniocaudal-view (CC). Pain experience was scored using the Numeric Rating Scale (NRS). Projected breast area was estimated using computer software. Radiation dose was estimated using the model by Dance. Image quality was reviewed by three radiologists and three radiographers.
There was no difference in pain experience between both paddles (mean difference NRS: 0.08 ± 0.08, p = 0.32). Mean radiation dose was 4.5 % lower with FP (0.09 ± 0.01 p = 0.00). On MLO-images, the projected breast area was 0.79 % larger with FP. Paired evaluation of image quality indicated that FP removed fibroglandular tissue from the image area and reduced contrast in the clinically relevant retroglandular area at chest wall side.
Although FP performed slightly better in the projected breast area, it moved breast tissue from the image area at chest wall side. RP showed better contrast, especially in the retroglandular area. We therefore recommend the use of RP for standard MLO and CC views.
Key points
• Pain experience showed no difference between flexible and rigid breast compression paddles.
• Flexible paddles do not depict clinically relevant retroglandular areas as well.
• Flexible paddles move breast tissue from image area at the chest wall side.
• Rigid paddles depict more breast tissue and shows better contrast.
• Rigid breast compression paddles are recommended for standard mediolateral-oblique and craniocaudal views.
Electronic supplementary material
The online version of this article (doi:10.1007/s00330-014-3422-4) contains supplementary material, which is available to authorized users.
PMCID: PMC4328113  PMID: 25504427
Mammography; Compression paddle; Performance; Flexible compression paddle; Rigid compression paddle
5.  Inverse consistent non-rigid image registration based on robust point set matching 
BioMedical Engineering OnLine  2014;13(Suppl 2):S2.
Robust point matching (RPM) has been extensively used in non-rigid registration of images to robustly register two sets of image points. However, except for the location at control points, RPM cannot estimate the consistent correspondence between two images because RPM is a unidirectional image matching approach. Therefore, it is an important issue to make an improvement in image registration based on RPM.
In our work, a consistent image registration approach based on the point sets matching is proposed to incorporate the property of inverse consistency and improve registration accuracy. Instead of only estimating the forward transformation between the source point sets and the target point sets in state-of-the-art RPM algorithms, the forward and backward transformations between two point sets are estimated concurrently in our algorithm. The inverse consistency constraints are introduced to the cost function of RPM and the fuzzy correspondences between two point sets are estimated based on both the forward and backward transformations simultaneously. A modified consistent landmark thin-plate spline registration is discussed in detail to find the forward and backward transformations during the optimization of RPM. The similarity of image content is also incorporated into point matching in order to improve image matching.
Synthetic data sets, medical images are employed to demonstrate and validate the performance of our approach. The inverse consistent errors of our algorithm are smaller than RPM. Especially, the topology of transformations is preserved well for our algorithm for the large deformation between point sets. Moreover, the distance errors of our algorithm are similar to that of RPM, and they maintain a downward trend as whole, which demonstrates the convergence of our algorithm. The registration errors for image registrations are evaluated also. Again, our algorithm achieves the lower registration errors in same iteration number. The determinant of the Jacobian matrix of the deformation field is used to analyse the smoothness of the forward and backward transformations. The forward and backward transformations estimated by our algorithm are smooth for small deformation. For registration of lung slices and individual brain slices, large or small determinant of the Jacobian matrix of the deformation fields are observed.
Results indicate the improvement of the proposed algorithm in bi-directional image registration and the decrease of the inverse consistent errors of the forward and the reverse transformations between two images.
PMCID: PMC4304244  PMID: 25559889
Consistent image registration; Robust point matching; Correspondence; Forward transformation; Backward transformation
6.  A novel approach for establishing benchmark CBCT/CT deformable image registrations in prostate cancer radiotherapy 
Physics in medicine and biology  2013;58(22):8077-8097.
Deformable image registration (DIR) is an integral component for adaptive radiation therapy. However, accurate registration between daily cone-beam computed tomography (CBCT) and treatment planning CT is challenging, due to significant daily variations in rectal and bladder fillings as well as the increased noise levels in CBCT images. Another significant challenge is the lack of “ground-truth” registrations in the clinical setting, which is necessary for quantitative evaluation of various registration algorithms. The aim of this study is to establish benchmark registrations of clinical patient data.
Three pairs of CT/CBCT datasets were chosen for this IRB-approved retrospective study. On each image, in order to reduce the contouring uncertainty, ten independent sets of organs were manually delineated by five physicians. The mean contour set for each image was derived from the ten contours. A set of distinctive points (round natural calcifications and 3 implanted prostate fiducial markers) were also manually identified. The mean contours and point features were then incorporated as constraints into a B-spline based DIR algorithm. Further, a rigidity penalty was imposed on the femurs and pelvic bones to preserve their rigidity. A piecewise-rigid registration approach was adapted to account for the differences in femur pose and the sliding motion between bones. For each registration, the magnitude of the spatial Jacobian (|JAC|) was calculated to quantify the tissue compression and expansion. Deformation grids and finite-element-model-based unbalanced energy maps were also reviewed visually to evaluate the physical soundness of the resultant deformations. Organ DICE indices (indicating the degree of overlap between registered organs) and residual misalignments of the fiducial landmarks were quantified.
Manual organ delineation on CBCT images varied significantly among physicians with overall mean DICE index of only 0.7 among redundant contours. Seminal vesicle contours were found to have the lowest correlation amongst physicians (DICE=0.5). After DIR, the organ surfaces between CBCT and planning CT were in good alignment with mean DICE indices of 0.9 for prostate, rectum, and bladder, and 0.8 for seminal vesicles. The Jacobian magnitudes |JAC| in the prostate, rectum, and seminal vesicles were in the range of 0.4–1.5, indicating mild compression/expansion. The bladder volume differences were larger between CBCT and CT images with mean |JAC| values of 2.2, 0.7, and 1.0 for three respective patients. Bone deformation was negligible (|JAC|=~1.0). The difference between corresponding landmark points between CBCT and CT was less than 1.0 mm after DIR.
We have presented a novel method of establishing benchmark deformable image registration accuracy between CT and CBCT images in the pelvic region. The method incorporates manually delineated organ surfaces and landmark points as well as pixel similarity in the optimization, while ensuring bone rigidity and avoiding excessive deformation in soft tissue organs. Redundant contouring is necessary to reduce the overall registration uncertainty.
PMCID: PMC4090712  PMID: 24171908
deformable image registration; finite element method; adaptive radiation therapy
7.  Automated Registration of Sequential Breath-Hold Dynamic Contrast-Enhanced MRI Images: a Comparison of 3 Techniques 
Magnetic resonance imaging  2011;29(5):668-682.
Dynamic Contrast-Enhanced MRI (DCE-MRI) is increasingly in use as an investigational biomarker of response in cancer clinical studies. Proper registration of images acquired at different time-points is essential for deriving diagnostic information from quantitative pharmacokinetic analysis of these data. Motion artifacts in the presence of time-varying intensity due to contrast-enhancement make this registration problem challenging. DCE-MRI of chest and abdominal lesions is typically performed during sequential breath-holds, which introduces misregistration due to inconsistent diaphragm positions, and also places constraints on temporal resolution vis-à-vis free-breathing. In this work, we have employed a computer-generated DCE-MRI phantom to compare the performance of two published methods, Progressive Principal Component Registration and Pharmacokinetic Model-Driven Registration, with Sequential Elastic Registration (SER) to register adjacent time-sample images using a published general-purpose elastic registration algorithm. In all 3 methods, a 3-D rigid-body registration scheme with a mutual information similarity measure was used as a pre-processing step. The DCE-MRI phantom images were mathematically deformed to simulate misregistration which was corrected using the 3 schemes. All 3 schemes were comparably successful in registering large regions of interest (ROIs) such as muscle, liver, and spleen. SER was superior in retaining tumor volume and shape, and in registering smaller but important ROIs such as tumor core and tumor rim. The performance of SER on clinical DCE-MRI datasets is also presented.
PMCID: PMC3100446  PMID: 21531108
dynamic; gadolinium; MRI; mutual information; non-rigid registration; elastic registration; tracer kinetics
8.  Automatic 3D-to-2D registration for CT and dual-energy digital radiography for calcification detection 
Medical physics  2007;34(12):4934-4943.
We are investigating three-dimensional (3D) to two-dimensional (2D) registration methods for computed tomography (CT) and dual-energy digital radiography (DEDR). CT is an established tool for the detection of cardiac calcification. DEDR could be a cost-effective alternative screening tool. In order to utilize CT as the “gold standard” to evaluate the capability of DEDR images for the detection and localization of calcium, we developed an automatic, intensity-based 3D-to-2D registration method for 3D CT volumes and 2D DEDR images. To generate digitally reconstructed radiography (DRR) from the CT volumes, we developed several projection algorithms using the fast shear-warp method. In particular, we created a Gaussian-weighted projection for this application. We used normalized mutual information (NMI) as the similarity measurement. Simulated projection images from CT values were fused with the corresponding DEDR images to evaluate the localization of cardiac calcification. The registration method was evaluated by digital phantoms, physical phantoms, and clinical data sets. The results from the digital phantoms show that the success rate is 100% with a translation difference of less than 0.8 mm and a rotation difference of less than 0.2°. For physical phantom images, the registration accuracy is 0.43±0.24 mm. Color overlay and 3D visualization of clinical images show that the two images registered well. The NMI values between the DRR and DEDR images improved from 0.21±0.03 before registration to 0.25±0.03 after registration. Registration errors measured from anatomic markers decreased from 27.6±13.6 mm before registration to 2.5±0.5 mm after registration. Our results show that the automatic 3D-to-2D registration is accurate and robust. This technique can provide a useful tool for correlating DEDR with CT images for screening coronary artery calcification. © 2007 American Association of Physicists in Medicine. [DOI: 10.1118/1.2805994]
PMCID: PMC2743028  PMID: 18196818
3D-to-2D registration; dual-energy digital radiography (DEDR); computed tomography (CT); cardiac calcification; coronary artery diseases (CADs)
9.  Fusion of Color Doppler and Magnetic Resonance Images of the Heart 
Journal of Digital Imaging  2011;24(6):1024-1030.
This study was designed to establish and analyze color Doppler and magnetic resonance fusion images of the heart, an approach for simultaneous testing of cardiac pathological alterations, performance, and hemodynamics. Ten volunteers were tested in this study. The echocardiographic images were produced by Philips IE33 system and the magnetic resonance images were generated from Philips 3.0-T system. The fusion application was implemented on MATLAB platform utilizing image processing technology. The fusion image was generated from the following steps: (1) color Doppler blood flow segmentation, (2) image registration of color Doppler and magnetic resonance imaging, and (3) image fusion of different image types. The fusion images of color Doppler blood flow and magnetic resonance images were implemented by MATLAB programming in our laboratory. Images and videos were displayed and saved as AVI and JPG. The present study shows that the method we have developed can be used to fuse color flow Doppler and magnetic resonance images of the heart. We believe that the method has the potential to: fill in information missing from the ultrasound or MRI alone, show structures outside the field of view of the ultrasound through MR imaging, and obtain complementary information through the fusion of the two imaging methods (structure from MRI and function from ultrasound).
Electronic supplementary material
The online version of this article (doi:10.1007/s10278-011-9393-y) contains supplementary material, which is available to authorized users.
PMCID: PMC3212677  PMID: 21656081
Biomedical image analysis; Image fusion; Digital image processing; Digital imaging and communications in medicine (DICOM); Cardiac imaging; MR imaging
10.  Fusion of Color Doppler and Magnetic Resonance Images of the Heart 
Journal of Digital Imaging  2011;24(6):1024-1030.
This study was designed to establish and analyze color Doppler and magnetic resonance fusion images of the heart, an approach for simultaneous testing of cardiac pathological alterations, performance, and hemodynamics. Ten volunteers were tested in this study. The echocardiographic images were produced by Philips IE33 system and the magnetic resonance images were generated from Philips 3.0-T system. The fusion application was implemented on MATLAB platform utilizing image processing technology. The fusion image was generated from the following steps: (1) color Doppler blood flow segmentation, (2) image registration of color Doppler and magnetic resonance imaging, and (3) image fusion of different image types. The fusion images of color Doppler blood flow and magnetic resonance images were implemented by MATLAB programming in our laboratory. Images and videos were displayed and saved as AVI and JPG. The present study shows that the method we have developed can be used to fuse color flow Doppler and magnetic resonance images of the heart. We believe that the method has the potential to: fill in information missing from the ultrasound or MRI alone, show structures outside the field of view of the ultrasound through MR imaging, and obtain complementary information through the fusion of the two imaging methods (structure from MRI and function from ultrasound).
Electronic supplementary material
The online version of this article (doi:10.1007/s10278-011-9393-y) contains supplementary material, which is available to authorized users.
PMCID: PMC3212677  PMID: 21656081
Biomedical image analysis; Image fusion; Digital image processing; Digital imaging and communications in medicine (DICOM); Cardiac imaging; MR imaging
11.  Multimodality infarct identification for optimal image-guided intramyocardial cell injections 
Netherlands Heart Journal  2014;22(11):493-500.
Intramyocardial cell injections in the context of cardiac regenerative therapy can currently be performed using electromechanical mapping (EMM) provided by the NOGA®XP catheter injection system. The gold standard technique to determine infarct size and location, however, is late gadolinium enhanced magnetic resonance imaging (LGE-MRI). In this article we describe a practical and accurate technique to co-register LGE-MRI and NOGA®XP datasets during the injection procedures to ultimately perform image-guided injections to the border zone of the infarct determined by LGE-MRI.
Materials and methods
LGE-MRI and EMM were obtained in three pigs with chronic myocardial infarction. MRI and EMM datasets were registered using the in-house developed 3D CartBox image registration toolbox consisting of three steps: 1) landmark registration, 2) surface registration, and 3) manual optimization. The apex and the coronary ostia were used as landmarks.
Image registration was successful in all datasets, and resulted in a mean registration error of 3.22 ± 1.86 mm between the MRI surface mesh and EMM points. Visual assessment revealed that the locations and the transmural extent of the infarctions measured by LGE-MRI only partly overlap with the infarct areas identified by the EMM parameters.
The 3D CartBox image registration toolbox enables registration of EMM on pre-procedurally acquired MRI during the catheter injection procedure. This allows the operator to perform real-time image-guided cell injections into the border zone of the infarct as assessed by LGE-MRI. The 3D CartBox thereby enables, for the first time, standardisation of the injection location for cardiac regenerative therapy.
Electronic supplementary material
The online version of this article (doi:10.1007/s12471-014-0604-2) contains supplementary material, which is available to authorized users.
PMCID: PMC4391177  PMID: 25331760
Cardiac regenerative therapy; Intramyocardial injections; Electromechanical mapping; Myocardial fibrosis; MRI; Late Gadolinium enhancement
12.  Computer-assisted Navigation in Bone Tumor Surgery: Seamless Workflow Model and Evolution of Technique 
Computer-assisted navigation was recently introduced to aid the resection of musculoskeletal tumors. However, it has not always been possible to directly navigate the osteotomy with real-time manipulation of available surgical tools. Registration techniques vary, although most existing systems use some form of surface matching.
We developed and evaluated a workflow model of computer-assisted bone tumor surgery and evaluated (1) the applicability of currently available software to different bones; (2) the accuracy of the navigated excision; and (3) the accuracy of a new registration technique of fluoro-CT matching.
Our workflow involved detailed preoperative planning with CT-MRI image fusion, three-dimensional mapping of the tumor, and planning of the resection plane. Using the workflow model, we reviewed 15 navigation procedures in 12 patients, including four with joint-saving resections and three with custom implant reconstructions. Intraoperatively, registration was performed with either paired points and surface matching (Group 1, n = 10) or a new technique of fluoro-CT image matching (Group 2, n = 5). All osteotomies were performed under direct computer navigation. Postoperatively, each case was evaluated for histologic margin and gross measurement of the achieved surgical margin.
The margins were free from tumor in all resected specimens. In the Group 1 procedures, the correlation between preoperative planned margins and actual achieved margins was 0.631, whereas in Group 2 procedures (fluoro-CT matching), the correlation was 0.985.
Our findings suggest computer-assisted navigation is accurate and useful for bone tumor surgery. The new registration technique using fluoro-CT matching may allow more accurate resection of margins.
Electronic supplementary material
The online version of this article (doi:10.1007/s11999-010-1465-7) contains supplementary material, which is available to authorized users.
PMCID: PMC2947663  PMID: 20635175
13.  Nonrigid 3D Medical Image Registration and Fusion Based on Deformable Models 
For coregistration of medical images, rigid methods often fail to provide enough freedom, while reliable elastic methods are available clinically for special applications only. The number of degrees of freedom of elastic models must be reduced for use in the clinical setting to archive a reliable result. We propose a novel geometry-based method of nonrigid 3D medical image registration and fusion. The proposed method uses a 3D surface-based deformable model as guidance. In our twofold approach, the deformable mesh from one of the images is first applied to the boundary of the object to be registered. Thereafter, the non-rigid volume deformation vector field needed for registration and fusion inside of the region of interest (ROI) described by the active surface is inferred from the displacement of the surface mesh points. The method was validated using clinical images of a quasirigid organ (kidney) and of an elastic organ (liver). The reduction in standard deviation of the image intensity difference between reference image and model was used as a measure of performance. Landmarks placed at vessel bifurcations in the liver were used as a gold standard for evaluating registration results for the elastic liver. Our registration method was compared with affine registration using mutual information applied to the quasi-rigid kidney. The new method achieved 15.11% better quality with a high confidence level of 99% for rigid registration. However, when applied to the quasi-elastic liver, the method has an averaged landmark dislocation of 4.32 mm. In contrast, affine registration of extracted livers yields a significantly (P = 0.000001) smaller dislocation of 3.26 mm. In conclusion, our validation shows that the novel approach is applicable in cases where internal deformation is not crucial, but it has limitations in cases where internal displacement must also be taken into account.
PMCID: PMC3652073  PMID: 23690883
14.  Effects of Reusing Baseline Volumes of Interest by Applying (Non-)Rigid Image Registration on Positron Emission Tomography Response Assessments 
PLoS ONE  2014;9(1):e87167.
Reusing baseline volumes of interest (VOI) by applying non-rigid and to some extent (local) rigid image registration showed good test-retest variability similar to delineating VOI on both scans individually. The aim of the present study was to compare response assessments and classifications based on various types of image registration with those based on (semi)-automatic tumour delineation.
Baseline (n = 13), early (n = 12) and late (n = 9) response (after one and three cycles of treatment, respectively) whole body [18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) scans were acquired in subjects with advanced gastrointestinal malignancies. Lesions were identified for early and late response scans. VOI were drawn independently on all scans using an adaptive 50% threshold method (A50). In addition, various types of (non-)rigid image registration were applied to PET and/or CT images, after which baseline VOI were projected onto response scans. Response was classified using PET Response Criteria in Solid Tumors for maximum standardized uptake value (SUVmax), average SUV (SUVmean), peak SUV (SUVpeak), metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and the area under a cumulative SUV-volume histogram curve (AUC).
Non-rigid PET-based registration and non-rigid CT-based registration followed by non-rigid PET-based registration (CTPET) did not show differences in response classifications compared to A50 for SUVmax and SUVpeak,, however, differences were observed for MATV, SUVmean, TLG and AUC. For the latter, these registrations demonstrated a poorer performance for small lung lesions (<2.8 ml), whereas A50 showed a poorer performance when another area with high uptake was close to the target lesion. All methods were affected by lesions with very heterogeneous tracer uptake.
Non-rigid PET- and CTPET-based image registrations may be used to classify response based on SUVmax and SUVpeak. For other quantitative measures future studies should assess which method is valid for response evaluations by correlating with survival data.
PMCID: PMC3904976  PMID: 24489860
15.  Dual-isotope Acquisition for CT–SPECT Registration of Infection Studies 
Journal of Digital Imaging  2009;23(3):258-267.
The registration of CT and NM images can enhance patient diagnosis since it allows for the fusion of anatomical and functional information as well as attenuation correction of NM images. However, irrespective of the methods used, registration accuracy depends heavily on the characteristics of the input images and the degree of similarity between them. This poses a challenge for registering CT and NM images as they may have very different characteristics. To address the particular problem of CT and In-111 SPECT registration, we propose to perform a dual-isotope study which involves an additional injection of Tc-99m MDP to generate two inherently registered images: In-111 SPECT and Tc-99m SPECT. As skeletal structures are visible in both CT and Tc-99m SPECT, performing registration of these images may be much more effective. The very same spatial transformation derived can be immediately applied to complete the registration of CT and the corresponding In-111 SPECT. Accordingly, we hypothesize that the registration of CT and Tc-99m SPECT can be more accurately performed than the registration of CT and In-111 SPECT and seek to compare the accuracies between the aforementioned registrations. In this paper, we have collected three clinical datasets, with the ground-truth transformations known, and tested the proposed approach by using a mutual information-based algorithm to solve for the rigid/non-rigid misalignments introduced to them. Based on the results of our experiments, we conclude that registration using Tc-99m SPECT can achieve 100% success rate, and is thus much more superior to the registration using In-111 SPECT, which at best, achieves only 38% success rate. Clearly, the introduction of a dual-isotope acquisition can substantially improve the registration of SPECT and CT images.
PMCID: PMC3046655  PMID: 19137374
3D imaging (imaging; three-dimensional); image fusion; image processing; image registration; computed tomography; nuclear medicine; single photon emission computed tomography (SPECT); simulation; graphical user interface (GUI)
16.  Preliminary experience with a novel method of three-dimensional co-registration of prostate cancer digital histology and in vivo multiparametric MRI 
Clinical radiology  2013;68(12):10.1016/j.crad.2013.07.010.
To assess a novel method of three-dimensional (3D) co-registration of prostate cancer digital histology and in-vivo multiparametric magnetic resonance imaging (mpMRI) image sets for clinical usefulness.
A software platform was developed to achieve 3D co- registration. This software was prospectively applied to three patients who underwent radical prostatectomy. Data comprised in-vivo mpMRI [T2-weighted, dynamic contrast-enhanced weighted images (DCE); apparent diffusion coefficient (ADC)], ex-vivo T2-weighted imaging, 3D-rebuilt pathological specimen, and digital histology. Internal landmarks from zonal anatomy served as reference points for assessing co-registration accuracy and precision.
Applying a method of deformable transformation based on 22 internal landmarks, a 1.6 mm accuracy was reached to align T2-weighted images and the 3D-rebuilt pathological specimen, an improvement over rigid transformation of 32% (p = 0.003). The 22 zonal anatomy landmarks were more accurately mapped using deformable transformation than rigid transformation (p = 0.0008). An automatic method based on mutual information, enabled automation of the process and to include perfusion and diffusion MRI images. Evaluation of co-registration accuracy using the volume overlap index (Dice index) met clinically relevant requirements, ranging from 0.81–0.96 for sequences tested. Ex-vivo images of the specimen did not significantly improve co-registration accuracy.
This preliminary analysis suggests that deformable transformation based on zonal anatomy landmarks is accurate in the co-registration of mpMRI and histology. Including diffusion and perfusion sequences in the same 3D space as histology is essential further clinical information. The ability to localize cancer in 3D space may improve targeting for image-guided biopsy, focal therapy, and disease quantification in surveillance protocols.
PMCID: PMC3884198  PMID: 23993149
17.  A frequency-based approach to locate common structure for 2D-3D intensity-based registration of setup images in prostate radiotherapy 
Medical physics  2007;34(7):3005-3017.
In many radiotherapy clinics, geometric uncertainties in the delivery of 3D conformal radiation therapy and intensity modulated radiation therapy of the prostate are reduced by aligning the patient's bony anatomy in the planning 3D CT to corresponding bony anatomy in 2D portal images acquired before every treatment fraction. In this paper, we seek to determine if there is a frequency band within the portal images and the digitally reconstructed radiographs (DRRs) of the planning CT in which bony anatomy predominates over non-bony anatomy such that portal images and DRRs can be suitably filtered to achieve high registration accuracy in an automated 2D-3D single portal intensity-based registration framework. Two similarity measures, mutual information and the Pearson correlation coefficient were tested on carefully collected gold-standard data consisting of a kilovoltage cone-beam CT (CBCT) and megavoltage portal images in the anterior-posterior (AP) view of an anthropomorphic phantom acquired under clinical conditions at known poses, and on patient data. It was found that filtering the portal images and DRRs during the registration considerably improved registration performance. Without filtering, the registration did not always converge while with filtering it always converged to an accurate solution. For the pose-determination experiments conducted on the anthropomorphic phantom with the correlation coefficient, the mean (and standard deviation) of the absolute errors in recovering each of the six transformation parameters were θx:0.18(0.19)°, θy:0.04(0.04)°, θz:0.04(0.02)°, tx:0.14(0.15) mm, ty:0.09(0.05) mm, and tz: 0.49(0.40) mm. The mutual information-based registration with filtered images also resulted in similarly small errors. For the patient data, visual inspection of the superimposed registered images showed that they were correctly aligned in all instances. The results presented in this paper suggest that robust and accurate registration can be achieved with intensity-based methods by focusing on rigid bony structures in the images while diminishing the influence of artifacts with similar frequencies as soft tissue.
PMCID: PMC2796184  PMID: 17822009
2D-3D image registration; spectral analysis; prostate radiotherapy; cone beam CT; setup verification; portal image
18.  Image registration of pre-procedural MRI and intra-procedural CT images to aid CT-guided percutaneous cryoablation of renal tumors 
To determine whether a non-rigid registration (NRR) technique was more accurate than a rigid registration (RR) technique when fusing pre-procedural contrast-enhanced MR images to unenhanced CT images during CT-guided percutaneous cryoablation of renal tumors.
Both RR and NRR were applied retrospectively to 11 CT-guided percutaneous cryoablation procedures performed to treat renal tumors (mean diameter; 23 mm). Pre-procedural contrast-enhanced MR images of the upper abdomen were registered to unenhanced intra-procedural CT images obtained just prior to the ablation. RRs were performed manually, and NRRs were performed using an intensity-based approach with affine and Basis-Spline techniques used for modeling displacement. Registration accuracy for each technique was assessed using the 95% Hausdorff distance (HD), Fiducial Registration Error (FRE) and the Dice Similarity Coefficient (DSC). Statistical differences were analyzed using a two-sided Student’s t-test. Time for each registration technique was recorded.
Mean 95% HD (1.7 mm), FRE (1.7 mm) and DSC (0.96) using the NRR technique were significantly better than mean 95% HD (6.4 mm), FRE (5.0 mm) and DSC (0.88) using the RR technique (P < 0.05 for each analysis). Mean registration times of NRR and RR techniques were 15.2 and 5.7 min, respectively.
The non-rigid registration technique was more accurate than the rigid registration technique when fusing pre-procedural MR images to intra-procedural unenhanced CT images. The non-rigid registration technique can be used to improve visualization of renal tumors during CT-guided cryoablation procedures.
PMCID: PMC3050046  PMID: 20499194
Multi-modality image fusion; Cryoablation; Renal tumors; B-Spline; Non-rigid registration
19.  A Stationary Wavelet Transform Based Approach to Registration of Planning CT and Setup Cone beam-CT Images in Radiotherapy 
Journal of Medical Systems  2014;38(5):40.
Image registration between planning CT images and cone beam-CT (CBCT) images is one of the key technologies of image guided radiotherapy (IGRT). Current image registration methods fall roughly into two categories: geometric features-based and image grayscale-based. Mutual information (MI) based registration, which belongs to the latter category, has been widely applied to multi-modal and mono-modal image registration. However, the standard mutual information method only focuses on the image intensity information and overlooks spatial information, leading to the instability of intensity interpolation. Due to its use of positional information, wavelet transform has been applied to image registration recently. In this study, we proposed an approach to setup CT and cone beam-CT (CBCT) image registration in radiotherapy based on the combination of mutual information (MI) and stationary wavelet transform (SWT). Firstly, SWT was applied to generate gradient images and low frequency components produced in various levels of image decomposition were eliminated. Then inverse SWT was performed on the remaining frequency components. Lastly, the rigid registration of gradient images and original images was implemented using a weighting function with the normalized mutual information (NMI) being the similarity measure, which compensates for the lack of spatial information in mutual information based image registration. Our experiment results showed that the proposed method was highly accurate and robust, and indicated a significant clinical potential in improving the accuracy of target localization in image guided radiotherapy (IGRT).
PMCID: PMC4018509  PMID: 24729043
Image registration; Planning CT; Cone beam-CT; Stationary wavelet transform; Mutual information
20.  Effect of Volume-of-Interest Misregistration on Quantitative Planar Activity and Dose Estimation 
Physics in medicine and biology  2010;55(18):5483-5497.
In targeted radionuclide therapy (TRT), dose estimation is essential for treatment planning and tumor dose response studies. Dose estimates are typically based on a time series of whole body conjugate view planar or SPECT scans of the patient acquired after administration of a planning dose. Quantifying the activity in the organs from these studies is an essential part of dose estimation.
The Quantitative Planar (QPlanar) processing method involves accurate compensation for image degrading factors and correction for organ and background overlap via the combination of computational models of the image formation process and 3D volumes of interest defining the organs to be quantified. When the organ VOIs are accurately defined, the method intrinsically compensates for attenuation, scatter, and partial volume effects, as well as overlap with other organs and the background. However, alignment between the 3D organ volume of interest (VOIs) used in QPlanar processing and the true organ projections in the planar images is required. The goal of this research was to study the effects of VOI misregistration on the accuracy and precision of organ activity estimates obtained using the QPlanar method.
In this work, we modeled the degree of residual misregistration that would be expected after an automated registration procedure by randomly misaligning 3D SPECT/CT images, from which the VOI information was derived, and planar images. Mutual information based image registration was used to align the realistic simulated 3D SPECT images with the 2D planar images. The residual image misregistration was used to simulate realistic levels of misregistration and allow investigation of the effects of misregistration on the accuracy and precision of the QPlanar method. We observed that accurate registration is especially important for small organs or ones with low activity concentrations compared to neighboring organs. In addition, residual misregistration gave rise to a loss of precision in the activity estimates that was on the order of the loss of precision due to Poisson noise in the projection data. These results serve as a lower bound on the effects of misregistration on the accuracy and precision of QPlanar activity estimate and demonstrate that misregistration errors must be taken into account when assessing the overall precision of organ dose estimates.
PMCID: PMC3004535  PMID: 20798459
targeted radionuclide therapy (TRT); dose estimation; Quantitative Planar (QPlanar) processing method; mutual information
21.  Image Registration for Targeted MRI-guided Transperineal Prostate Biopsy 
To develop and evaluate image registration methodology for automated re-identification of tumor-suspicious foci from pre-procedural 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.
The total computation time was compatible with a clinical setting, being at most 2 minutes. Deformable registration led to a significant improvement in spatial overlap of the prostate and peripheral zone contours compared to 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.
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.
PMCID: PMC3434292  PMID: 22645031
Prostate cancer; image-guided interventions; prostate biopsy; image registration; non-rigid registration; mutual information; performance characterization
22.  A quantitative comparison of the performance of three deformable registration algorithms in radiotherapy 
We present an evaluation of various non-rigid registration algorithms for the purpose of compensating interfractional motion of the target volume and organs at risk areas when acquiring CBCT image data prior to irradiation. Three different deformable registration (DR) methods were used: the Demons algorithm implemented in the iPlan Software (BrainLAB AG, Feldkirchen, Germany) and two custom-developed piecewise methods using either a Normalized Correlation or a Mutual Information metric (featureletNC and featureletMI). These methods were tested on data acquired using a novel purpose-built phantom for deformable registration and clinical CT/CBCT data of prostate and lung cancer patients. The Dice similarity coefficient (DSC) between manually drawn contours and the contours generated by a derived deformation field of the structures in question was compared to the result obtained with rigid registration (RR). For the phantom, the piecewise methods were slightly superior, the featureletNC for the intramodality and the featureletMI for the intermodality registrations. For the prostate cases in less than 50% of the images studied the DSC was improved over RR. Deformable registration methods improved the outcome over a rigid registration for lung cases and in the phantom study, but not in a significant way for the prostate study. A significantly superior deformation method could not be identified.
PMCID: PMC3865361  PMID: 23969092
Deformable registration; radiotherapy; organ motion; Deformierbare Registrierung; Radiotherapie; Organbewegung
23.  A Novel Technique for Prealignment in Multimodality Medical Image Registration 
BioMed Research International  2014;2014:726852.
Image pair is often aligned initially based on a rigid or affine transformation before a deformable registration method is applied in medical image registration. Inappropriate initial registration may compromise the registration speed or impede the convergence of the optimization algorithm. In this work, a novel technique was proposed for prealignment in both monomodality and multimodality image registration based on statistical correlation of gradient information. A simple and robust algorithm was proposed to determine the rotational differences between two images based on orientation histogram matching accumulated from local orientation of each pixel without any feature extraction. Experimental results showed that it was effective to acquire the orientation angle between two unregistered images with advantages over the existed method based on edge-map in multimodalities. Applying the orientation detection into the registration of CT/MR, T1/T2 MRI, and monomadality images with respect to rigid and nonrigid deformation improved the chances of finding the global optimization of the registration and reduced the search space of optimization.
PMCID: PMC4055031  PMID: 25162024
24.  A method for dynamic subtraction MR imaging of the liver 
Subtraction of Dynamic Contrast-Enhanced 3D Magnetic Resonance (DCE-MR) volumes can result in images that depict and accurately characterize a variety of liver lesions. However, the diagnostic utility of subtraction images depends on the extent of co-registration between non-enhanced and enhanced volumes. Movement of liver structures during acquisition must be corrected prior to subtraction. Currently available methods are computer intensive. We report a new method for the dynamic subtraction of MR liver images that does not require excessive computer time.
Nineteen consecutive patients (median age 45 years; range 37–67) were evaluated by VIBE T1-weighted sequences (TR 5.2 ms, TE 2.6 ms, flip angle 20°, slice thickness 1.5 mm) acquired before and 45s after contrast injection. Acquisition parameters were optimized for best portal system enhancement. Pre and post-contrast liver volumes were realigned using our 3D registration method which combines: (a) rigid 3D translation using maximization of normalized mutual information (NMI), and (b) fast 2D non-rigid registration which employs a complex discrete wavelet transform algorithm to maximize pixel phase correlation and perform multiresolution analysis. Registration performance was assessed quantitatively by NMI.
The new registration procedure was able to realign liver structures in all 19 patients. NMI increased by about 8% after rigid registration (native vs. rigid registration 0.073 ± 0.031 vs. 0.078 ± 0.031, n.s., paired t-test) and by a further 23% (0.096 ± 0.035 vs. 0.078 ± 0.031, p < 0.001, paired t-test) after non-rigid realignment. The overall average NMI increase was 31%.
This new method for realigning dynamic contrast-enhanced 3D MR volumes of liver leads to subtraction images that enhance diagnostic possibilities for liver lesions.
PMCID: PMC1564010  PMID: 16759378
25.  An algorithm for longitudinal registration of PET/CT images acquired during neoadjuvant chemotherapy in breast cancer: preliminary results 
EJNMMI Research  2012;2:62.
By providing estimates of tumor glucose metabolism, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) can potentially characterize the response of breast tumors to treatment. To assess therapy response, serial measurements of FDG-PET parameters (derived from static and/or dynamic images) can be obtained at different time points during the course of treatment. However, most studies track the changes in average parameter values obtained from the whole tumor, thereby discarding all spatial information manifested in tumor heterogeneity. Here, we propose a method whereby serially acquired FDG-PET breast data sets can be spatially co-registered to enable the spatial comparison of parameter maps at the voxel level.
The goal is to optimally register normal tissues while simultaneously preventing tumor distortion. In order to accomplish this, we constructed a PET support device to enable PET/CT imaging of the breasts of ten patients in the prone position and applied a mutual information-based rigid body registration followed by a non-rigid registration. The non-rigid registration algorithm extended the adaptive bases algorithm (ABA) by incorporating a tumor volume-preserving constraint, which computed the Jacobian determinant over the tumor regions as outlined on the PET/CT images, into the cost function. We tested this approach on ten breast cancer patients undergoing neoadjuvant chemotherapy.
By both qualitative and quantitative evaluation, our constrained algorithm yielded significantly less tumor distortion than the unconstrained algorithm: considering the tumor volume determined from standard uptake value maps, the post-registration median tumor volume changes, and the 25th and 75th quantiles were 3.42% (0%, 13.39%) and 16.93% (9.21%, 49.93%) for the constrained and unconstrained algorithms, respectively (p = 0.002), while the bending energy (a measure of the smoothness of the deformation) was 0.0015 (0.0005, 0.012) and 0.017 (0.005, 0.044), respectively (p = 0.005).
The results indicate that the constrained ABA algorithm can accurately align prone breast FDG-PET images acquired at different time points while keeping the tumor from being substantially compressed or distorted.
Trial registration
PMCID: PMC3520720  PMID: 23157877
breast cancer; longitudinal registration; FDG-PET/CT; treatment response; metabolic monitoring

Results 1-25 (1275910)