Automatic registration and alignment on a template of cardiac stress and rest reoriented SPECT images

A Multi-Modality Attention Network for Coronary Artery Disease Evaluation From Routine Myocardial Perfusion Imaging and Clinical Data

Myocardial perfusion imaging (MPI) is an essential tool for diagnosing and evaluating coronary artery disease (CAD). However, the diagnosis using MPI remains laborious as it involves multi-step readouts and meticulous image processing. These challenges impact current attempts at automating image interpretation of MPI. In this paper, we propose a multi-modality attention network (MMAN) that leverages information from clinical and MPI data for CAD diagnosis. Specifically, we propose an image-correlated cross-attention (ICCA) module that fuses information from both stress and rest MPI to enhance feature representation at the image level. Furthermore, we design a clinical data-guided attention (CDGA) module that integrates clinical data with image features to improve overall feature understanding for CAD evaluation. In addition, we employ self-learning for network pre-training, which further enhances the diagnostic performance using MPI on CAD. Experiments on a myocardial perfusion imaging dataset demonstrate that the proposed method is effective for CAD evaluation using myocardial perfusion imaging and clinical data.

Normal Databases for the Relative Quantification of Myocardial Perfusion

PURPOSE OF REVIEW

Myocardial perfusion imaging (MPI) with SPECT is performed clinically worldwide to detect and monitor coronary artery disease (CAD). MPI allows an objective quantification of myocardial perfusion at stress and rest. This established technique relies on normal databases to compare patient scans against reference normal limits. In this review, we aim to introduce the process of MPI quantification with normal databases and describe the associated perfusion quantitative measures that are used.

RECENT FINDINGS

New equipment and new software reconstruction algorithms have been introduced which require the development of new normal limits. The appearance and regional count variations of normal MPI scan may differ between these new scanners and standard Anger cameras. Therefore, these new systems may require the determination of new normal limits to achieve optimal accuracy in relative myocardial perfusion quantification. Accurate diagnostic and prognostic results rivaling those obtained by expert readers can be obtained by this widely used technique.

SUMMARY

Throughout this review, we emphasize the importance of the different normal databases and the need for specific databases relative to distinct imaging procedures. use of appropriate normal limits allows optimal quantification of MPI by taking into account subtle image differences due to the hardware and software used, and the population studied.

Registration of coronary arteries in computed tomography angiography images using Hidden Markov Model

Computed tomography angiography (CTA) allows for not only diagnosis of coronary artery disease (CAD) with high spatial resolution but also monitoring the remodeling of vessel walls in the progression of CAD. Alignment of coronary arteries in CTA images acquired at different times (with a 3-7 years interval) is required to visualize and analyze the geometric and structural changes quantitatively. Previous work in image registration primarily focused on large anatomical structures and leads to suboptimal results when applying to registration of coronary arteries. In this paper, we develop a novel method to directly align the straightened coronary arteries in the cylindrical coordinate system guided by the extracted centerlines. By using a Hidden Markov Model (HMM), image intensity information from CTA and geometric information of extracted coronary arteries are combined to align coronary arteries. After registration, the pathological features in two straightened coronary arteries can be directly visualized side by side by synchronizing the corresponding cross-sectional slices and circumferential rotation angles. By evaluating with manually labeled landmarks, the average distance error is 1.6 mm.

Is it Feasible to Use the Commercially Available Autoquantitation Software for the Evaluation of Myocardial Viability on Small-Animal Cardiac F-18 FDG PET Scan?

PURPOSE

To evaluate the reliability of quantitation of myocardial viability on cardiac F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) scans with three different methods of visual scoring system, autoquantitation using commercially available autoquantitation software, and infarct-size measurement using histogram-based maximum pixel threshold identification on polar-map in rat hearts.

METHODS

A myocardial infarct (MI) model was made by left anterior descending artery (LAD) ligation in rat hearts. Eighteen MI rats underwent cardiac FDG-PET-computed tomography (CT) twice within a 4-week interval. Myocardium was partitioned into 20 segments for the comparison, and then we quantitated non-viable myocardium on cardiac FDG PET-CT with three different methods: method A-infarct-size measurement using histogram-based maximum pixel threshold identification on polar-map; method B-summed MI score (SMS) by a four-point visual scoring system; method C-metabolic non-viable values by commercially available autoquantitation software. Changes of non-viable myocardium on serial PET-CT scans with three different methods were calculated by the change of each parameter. Correlation and reproducibility were evaluated between the different methods.

RESULTS

Infarct-size measurement, visual SMS, and non-viable values by autoquantitation software presented proportional relationship to each other. All the parameters of methods A, B, and C showed relatively good correlation between each other. Among them, infarct-size measurement (method A) and autoquantitation software (method C) showed the best correlation (r = 0.87, p < 0.001). When we evaluated the changes of non-viable myocardium on the serial FDG-PET-CT- however, autoquantitation program showed less correlation with the other methods. Visual assessment (method B) and those of infarct size (method A) showed the best correlation (r = 0.54, p = 0.02) for the assessment of interval changes.

CONCLUSIONS

Commercially available quantitation software could be applied to measure the myocardial viability on small animal cardiac FDG-PET-CT scan. This kind of quantitation showed good correlation with infarct size measurement by histogram-based maximum pixel threshold identification. However, this method showed the weak correlation when applied in the measuring the changes of non-viable myocardium on the serial scans, which means that the caution will be needed to evaluate the changes on the serial monitoring.

Validation of an automated method to quantify stress-induced ischemia and infarction in rest-stress myocardial perfusion SPECT

BACKGROUND

Myocardial perfusion SPECT (MPS) is one of the frequently used methods for quantification of perfusion defects in patients with known or suspected coronary artery disease. This article describes open access software for automated quantification in MPS of stress-induced ischemia and infarction and provides phantom and in vivo validation.

METHODS AND RESULTS

A total of 492 patients with known or suspected coronary artery disease underwent both stress and rest MPS. The proposed perfusion analysis algorithm (Segment) was trained in 140 patients and validated in the remaining 352 patients using visual scoring in MPS by an expert reader as reference standard. Furthermore, validation was performed with simulated perfusion defects in an anthropomorphic computer model. Total perfusion deficit (TPD, range 0-100), including both extent and severity of the perfusion defect, was used as the global measurement of the perfusion defects. Mean bias ± SD between TPD by Segment and the simulated TPD was 3.6 ± 3.8 (R(2) = 0.92). Mean bias ± SD between TPD by Segment and the visual scoring in the patients was 1.2 ± 2.9 (R (2)= 0.64) for stress-induced ischemia and -0.3 ± 3.1 (R(2) = 0.86) for infarction.

CONCLUSION

The proposed algorithm can detect and quantify perfusion defects in MPS with good agreement to expert readers and to simulated values in a computer phantom.

Evaluating Similarity Measures for Brain Image Registration

Evaluation of similarity measures for image registration is a challenging problem due to its complex interaction with the underlying optimization, regularization, image type and modality. We propose a single performance metric, named robustness, as part of a new evaluation method which quantifies the effectiveness of similarity measures for brain image registration while eliminating the effects of the other parts of the registration process. We show empirically that similarity measures with higher robustness are more effective in registering degraded images and are also more successful in performing intermodal image registration. Further, we introduce a new similarity measure, called normalized spatial mutual information, for 3D brain image registration whose robustness is shown to be much higher than the existing ones. Consequently, it tolerates greater image degradation and provides more consistent outcomes for intermodal brain image registration.

Deformable medical image registration: a survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

Analysis of carotid artery deformation in different head and neck positions for maxillofacial catheter navigation in advanced oral cancer treatment

Background

To improve the accuracy of catheter navigation, it is important to develop a method to predict shifts of carotid artery (CA) bifurcations caused by intraoperative deformation. An important factor affecting the accuracy of electromagnetic maxillofacial catheter navigation systems is CA deformations. We aimed to assess CA deformation in different head and neck positions.

Methods

Using two sets of computed tomography angiography (CTA) images of six patients, displacements of the skull (maxillofacial segments), C1–C4 cervical vertebrae, mandible (mandibular segment), and CA along with its branches were analyzed. Segmented rigid bones around CA were considered the main causes of CA deformation. After superimposition of maxillofacial segments, C1–C4 and mandible segments were superimposed separately for displacement measurements. Five bifurcation points (vA–vE) were assessed after extracting the CA centerline. A new standardized coordinate system, regardless of patient-specific scanning positions, was employed. It was created using the principal axes of inertia of the maxillofacial bone segments of patients. Position and orientation parameters were transferred to this coordinate system. CA deformation in different head and neck positions was assessed.

Results

Absolute shifts in the center of gravity in the bone models for different segments were C1, 1.02 ± 0.9; C2, 2.18 ± 1.81; C3, 4.25 ± 3.85; C4, 5.90 ± 5.14; and mandible, 1.75 ± 2.76 mm. Shifts of CA bifurcations were vA, 5.52 ± 4.12; vB, 4.02 ± 3.27; vC, 4.39 ± 2.42; vD, 4.48 ± 1.88; and vE, 2.47 ± 1.32. Displacements, position changes, and orientation changes of C1–C4 segments as well as the displacements of all CA bifurcation points were similar in individual patients.

Conclusions

CA deformation was objectively proven as an important factor contributing to errors in maxillofacial navigation. Our study results suggest that small movements of the bones around CA can result in small CA deformations. Although patients’ faces were not fixed properly during CT scanning, C1–C4 and vA–vE displacements were similar in individual patients. We proposed a novel method for accumulation of the displacement data, and this study indicated the importance of surrounding bone displacements in predicting CA bifurcation.

Automatic PET-CT image registration method based on mutual information and genetic algorithms

Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets. In this paper, we present a registration algorithm that is able to automatically align PET/CT cardiac images. The algorithm bases on mutual information (MI) as registration metric and on genetic algorithm as optimization method. A multiresolution approach was used to optimize the processing time. The algorithm was tested on computerized models of volumetric PET/CT cardiac data and on real PET/CT datasets. The proposed automatic registration algorithm smoothes the pattern of the MI and allows it to reach the global maximum of the similarity function. The implemented method also allows the definition of the correct spatial transformation that matches both synthetic and real PET and CT volumetric datasets.

Sparse registration for three-dimensional stress echocardiography

Three-dimensional (3-D) stress echocardiography is a novel technique for diagnosing cardiac dysfunction. It involves evaluating wall motion of the left ventricle, by visually analyzing ultrasound images obtained in rest and in different stages of stress. Since the acquisitions are performed minutes apart, variabilities may exist in the visualized cross-sections. To improve anatomical correspondence between rest and stress, aligning the images is essential. We developed a new intensity-based, sparse registration method to retrieve standard anatomical views from 3-D stress images that were equivalent to the manually selected views in the rest images. Using sparse image planes, the influence of common image artifacts could be reduced. We investigated different similarity measures and different levels of sparsity. The registration was tested using data of 20 patients and quantitatively evaluated based on manually defined anatomical landmarks. Alignment was best using sparse registration with two long-axis and two short-axis views; registration errors were reduced significantly, to the range of interobserver variabilities. In 91% of the cases, the registration result was qualitatively assessed as better than or equal to the manual alignment. In conclusion, sparse registration improves the alignment of rest and stress images, with a performance similar to manual alignment. This is an important step towards objective quantification in 3-D stress echocardiography.

A review of geometric transformations for nonrigid body registration

This paper provides a comprehensive and quantitative review of spatial transformations models for nonrigid image registration. It explains the theoretical foundation of the models and classifies them according to this basis. This results in two categories, physically based models described by partial differential equations of continuum mechanics (e.g., linear elasticity and fluid flow) and basis function expansions derived from interpolation and approximation theory (e.g., radial basis functions, B-splines and wavelets). Recent work on constraining the transformation so that it preserves the topology or is diffeomorphic is also described. The final section reviews some recent evaluation studies. The paper concludes by explaining under what conditions a particular transformation model is appropriate.

Spillover Compensation in the Presence of Respiratory Motion Embedded in SPECT Perfusion Data

Spillover from adjacent significant accumulations of extra-cardiac activity decreases diagnostic accuracy of SPECT perfusion imaging in especially the inferior/septal cardiac region. One method of compensating for the spillover at some location outside of a structure is to estimate it as the counts blurred into this location when a template (3D model) of the structure undergoes simulated imaging followed by reconstruction. The objective of this study was to determine what impact uncorrected respiratory motion has on such spillover compensation of extra-cardiac activity in the right coronary artery (RCA) territory, and if it is possible to use manual segmentation to define the extra-cardiac activity template(s) used in spillover correction. Two separate MCAT phantoms (128(3) matrices) were simulated to represent the source and attenuation distributions of patients with and without respiratory motion. For each phantom the heart was modeled: 1) with a normal perfusion pattern and 2) with an RCA defect equal to 50% of the normal myocardium count level. After Monte Carlo simulation of 64 × 64 × 120 projections with appropriate noise, data were reconstructed using the rescaled block iterative (RBI) algorithm with 30 subsets and 5 iterations with compensation for attenuation, scatter and resolution. A 3D Gaussian post-filter with a sigma of 0.476 cm was used to suppress noise. Manual segmentation of the liver in filtered emission slices was used to create 3D binary templates. The true liver distribution (with and without respiratory motion included) was also used as binary templates. These templates were projected using a ray-driven projector simulating the imaging system with the exclusion of Compton scatter and reconstructed using the same protocol as for the emission data, excluding scatter compensation. Reconstructed templates were scaled using reconstructed emission count levels from the liver, and spillover subtracted outside the template. It was evident from the polar maps that the manually segmented template reconstructions were unable to remove all the spillover originating in the liver from the inferior wall. This was especially noticeable when a perfusion defect is present. Templates based on the true liver distribution appreciably improved spillover correction. Thus the emerging combined SPECT/CT technology may play a vital role in identifying and segmenting extra-cardiac structures more reliably thereby facilitating spillover correction. This study also indicates that compensation for respiratory motion might play an important role in spillover compensation.

Assessment of automated scintigram alignment in the stackgram domain for cardiac stress–rest SPECT studies

Single photon emission computed tomography (SPECT) provides a method to obtain three-dimensional images of blood perfusion. In cardiac stress-rest SPECT studies, coronary artery disease can be examined by comparing intensity differences of the acquired images of the heart at stress and rest. The stress-rest image pair needs to be aligned into the same spatial position and orientation in order to compare the images in an automated and reproducible manner. In this work, we study a novel stackgram-based alignment algorithm for cardiac stress-rest SPECT data. The stackgram approach allows the alignment of the acquired raw data prior to image reconstruction, unlike conventional alignment techniques.

Quantitative evaluation of myocardial function by a volume-normalized map generated from relative blood flow

Our study aimed to quantitatively evaluate blood flow in the left ventricle (LV) of apical hypertrophic cardiomyopathy (APH) by combining wall thickness obtained from cardiac magnetic resonance imaging (MRI) and myocardial perfusion from single-photon emission computed tomography (SPECT). In this study, we considered paired MRI and myocardial perfusion SPECT from ten patients with APH and ten normals. Myocardial walls were detected using a level set method, and blood flow per unit myocardial volume was calculated using 3D surface-based registration between the MRI and SPECT images. We defined relative blood flow based on the maximum in the whole myocardial region. Accuracies of wall detection and registration were around 2.50 mm and 2.95 mm, respectively. We finally created a bull's-eye map to evaluate wall thickness, blood flow (cardiac perfusion) and blood flow per unit myocardial volume. In patients with APH, their wall thicknesses were over 10 mm. Decreased blood flow per unit myocardial volume was detected in the cardiac apex by calculation using wall thickness from MRI and blood flow from SPECT. The relative unit blood flow of the APH group was 1/7 times that of the normals in the apex. This normalization by myocardial volume distinguishes cases of APH whose SPECT images resemble the distributions of normal cases.

Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation

We propose a new spatio-temporal elastic registration algorithm for motion reconstruction from a series of images. The specific application is to estimate displacement fields from two-dimensional ultrasound sequences of the heart. The basic idea is to find a spatio-temporal deformation field that effectively compensates for the motion by minimizing a difference with respect to a reference frame. The key feature of our method is the use of a semi-local spatio-temporal parametric model for the deformation using splines, and the reformulation of the registration task as a global optimization problem. The scale of the spline model controls the smoothness of the displacement field. Our algorithm uses a multiresolution optimization strategy to obtain a higher speed and robustness. We evaluated the accuracy of our algorithm using a synthetic sequence generated with an ultrasound simulation package, together with a realistic cardiac motion model. We compared our new global multiframe approach with a previous method based on pairwise registration of consecutive frames to demonstrate the benefits of introducing temporal consistency. Finally, we applied the algorithm to the regional analysis of the left ventricle. Displacement and strain parameters were evaluated showing significant differences between the normal and pathological segments, thereby illustrating the clinical applicability of our method.

Registration of real-time 3-D ultrasound images of the heart for novel 3-D stress echocardiography

Stress echocardiography is a routinely used clinical procedure to diagnose cardiac dysfunction by comparing wall motion information in prestress and poststress ultrasound images. Incomplete data, complicated imaging protocols and misaligned prestress and poststress views, however, are known limitations of conventional stress echocardiography. We discuss how the first two limitations are overcome via the use of real-time three-dimensional (3-D) ultrasound imaging, an emerging modality, and have called the new procedure "3-D stress echocardiography." We also show that the problem of misaligned views can be solved by registration of prestress and poststress 3-D image sequences. Such images are misaligned because of variations in placing the ultrasound transducer and stress-induced anatomical changes. We have developed a technique to temporally align 3-D images of the two sequences first and then to spatially register them to rectify probe placement error while preserving the stress-induced changes. The 3-D spatial registration is mutual information-based. Image registration used in conjunction with 3-D stress echocardiography can potentially improve the diagnostic accuracy of stress testing.

Cine MPR: interactive multiplanar reformatting of four-dimensional cardiac data using hardware-accelerated texture mapping

Four-dimensional (4-D) imaging to capture the three-dimensional (3-D) structure and motion of the heart in real time is an emerging trend. We present here our method of interactive multiplanar reformatting (MPR), i.e., the ability to visualize any chosen anatomical cross section of 4-D cardiac images and to change its orientation smoothly while maintaining the original heart motion. Continuous animation to show the time-varying 3-D geometry of the heart and smooth dynamic manipulation of the reformatted planes, as well as large image size (100-300 MB), make MPR challenging. Our solution exploits the hardware acceleration of 3-D texture mapping capability of high-end commercial PC graphics boards. Customization of volume subdivision and caching concepts to periodic cardiac data allows us to use this hardware effectively and efficiently. We are able to visualize and smoothly interact with real-time 3-D ultrasound cardiac images at the desired frame rate (25 Hz). The developed methods are applicable to MPR of one or more 3-D and 4-D medical images, including 4-D cardiac images collected in a gated fashion.

Traitement des images et radiothérapie

Medical images are of great importance in radiotherapy, which became a privileged application field for image processing techniques. Moreover, because of the progression of the computers' performances, these techniques are also in full expansion. Today, the recent developments of the radiotherapy (3DCR, IMRT) offer a huge place to them. Effectively, they can potentially answer to the precision requirements of the modern radiotherapy, and may then contribute to improve the delivered treatments. The purpose of this article is to present the different image processing techniques that are currently used in radiotherapy (including image matching and segmentation) as they are described in the literature.

Registration of 3D CT angiography and cardiac MR images in coronary artery disease patients

A method for the registration of 3D cardiac CT angiography (CTA) and magnetic resonance (MR) data sets based on their myocardial epicardial surfaces is introduced. The approach relies on temporally registered data sets obtained based on the electrocardiogram recorded during the CTA acquisition and the timing characteristics of the MR acquisition. The myocardial epicardial surfaces were identified in the reformatted CTA and MR data sets using a 3D semi-automated segmentation algorithm. This algorithm was implemented, evaluated on clinical data, and compared to a set of manual outlines during the course of this study. The registration of the CTA and MR data sets was based on the iterative closest point algorithm, which minimizes the distance between the surfaces defined by the epicardial outlines in each data set. The proposed technique was applied to data obtained from 11 patients with coronary artery disease. The CTA data was reformatted based on the registration results and the location of the MR imaging planes. The resulting CTA-MR image pairs were evaluated qualitatively by two experts, who graded the majority of the cases as either excellent or acceptable (11 of 11 cases for one reader, and 9 of 11 for the other). The results were evaluated quantitatively based on the distance between the registered epicardial surfaces. The quantitative measures indicated that the registered surfaces were within two pixels of one another (on average). The registration results were used to generate combined 3D renderings of information extracted from both data sets for visualization purposes.

Co-registration of function and anatomy in frameless stereotaxy by contour fitting

We investigated a co-registration algorithm using a contour-fitting procedure to integrate functional data from magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) for frameless stereotaxy. In fMRI the shape of the head was reconstructed from anatomical images, in MEG it was scanned using an electromagnetic sensor position indicator. Functional information was transferred to the 3D-MR image set used for frameless stereotaxy by fitting the digitized (MEG) and reconstructed head shape (fMRI) to the 3D-MR images. The mean residual error of the contour fit was 2.3 mm for the MEG and 1.3 mm for the fMRI registration. According to computer simulations, the achievable transformation error is 0.75 and 0.5 mm, respectively. This method enables independent recording of functional and anatomical measurements with a co-registration accuracy better than 2 mm.

A 3-D model-based registration approach for the PET, MR and MCG cardiac data fusion

In this paper, a new approach is presented for the assessment of a 3-D anatomical and functional model of the heart including structural information from magnetic resonance imaging (MRI) and functional information from positron emission tomography (PET) and magnetocardiography (MCG). The method uses model-based co-registration of MR and PET images and marker-based registration for MRI and MCG. Model-based segmentation of MR anatomical images results in an individualized 3-D biventricular model of the heart including functional parameters from PET and MCG in an easily interpretable 3-D form.

Three-dimensional motion tracking of coronary arteries in biplane cineangiograms

A three-dimensional (3-D) method for tracking the coronary arteries through a temporal sequence of biplane X-ray angiography images is presented. A 3-D centerline model of the coronary vasculature is reconstructed from a biplane image pair at one time frame, and its motion is tracked using a coarse-to-fine hierarchy of motion models. Three-dimensional constraints on the length of the arteries and on the spatial regularity of the motion field are used to overcome limitations of classical two-dimensional vessel tracking methods, such as tracking vessels through projective occlusions. This algorithm was clinically validated in five patients by tracking the motion of the left coronary tree over one cardiac cycle. The root mean square reprojection errors were found to be submillimeter in 93% (54/58) of the image pairs. The performance of the tracking algorithm was quantified in three dimensions using a deforming vascular phantom. RMS 3-D distance errors were computed between centerline models tracked in the X-ray images and gold-standard centerline models of the phantom generated from a gated 3-D magnetic resonance image acquisition. The mean error was 0.69 (+/- 0.06) mm over eight temporal phases and four different biplane orientations.

Method for three-dimensional data registration from disparate imaging modalities in the NOGA Myocardial Viability Trial

Region-by-region comparison of data concerning left ventricular (LV) status is difficult to perform quantitatively if the data was acquired from disparate imaging modalities. We validated a method for comparing measurements obtained by electromechanical mapping (EMM) catheter with dobutamine stress echocardiography (DSE) via biplane contrast ventriculography, with the assistance of three-dimensional (3-D) echocardiographic data. The ventriculograms were traced and the borders were used to reconstruct the LV in 3-D with the aid of a database of 3-D echocardiographic studies. The 3-D LV was oriented to the EMM data based on the body coordinates and then manually scaled and translated to fit. The EMM data were mapped to the 3-D surface. The 3-D surface was divided into the 16 regions defined for echocardiographic assessment. The mean EMM value for local linear shortening, a parameter of function, was computed in each segment. The EMM and semiquantitative echocardiographic assessments of regional myocardial function were compared by segment, and the volume of the 3-D LV was compared with the volume computed from the ventriculogram. The volume of the 3-D surface correlated closely with that of the ventriculogram (r = 0.97, SEE = 27.4 ml) but with a significant overestimation of 63 +/- 35 ml. There was a highly significant (p < 0.0001) agreement in regional function between EMM and echo. Local linear shortening correlated significantly (p < 0.0001) with echocardiographic severity of wall motion, averaging 9.5 +/- 6.5, 8.1 +/- 5.4, 5.9 +/- 4.8, and 6.2 +/- 3.3 in segments read as normal, hypokinetic, akinetic, and dyskinetic, respectively. The method presented is valid for comparing cardiac parameters derived from disparate image data on a region-by-region basis by employing anatomic landmarks on 3-D reconstructions of the LV endocardial surface.

3-D freehand echocardiography for automatic left ventricle reconstruction and analysis based on multiple acoustic windows

A new method is proposed to reconstruct and analyze the left ventricle (LV) from multiple acoustic window three-dimensional (3-D) ultrasound acquired using a transthoracic 3-D rotational probe. Prior research in this area has been based on one acoustic window acquisition. However, the data suffers from several limitations that degrade the reconstruction and reduce the clinical value of interpretation, such as the presence of shadow due to bone (ribs) and air (in the lungs) and motion of the probe during the acquisition. In this paper, we show how to overcome these limitations by automatically fusing information from multiple acoustic window sparse-view acquisitions and using a position sensor to track the probe in real time. Geometric constraints of the object shape, and spatiotemporal information relating to the image acquisition process, are used in new algorithms for 1) grouping endocardial edge cues from an initial image segmentation and 2) defining a novel reconstruction method that utilizes information from multiple acoustic windows. The new method has been validated on a phantom and three real heart data sets. In the phantom study, one finger of a latex glove was scanned from two acoustic windows and reconstructed using the new method. The volume error was measured to be less than 4%. In the clinical case study, 3-D ultrasound and magnetic resonance imaging (MRI) scanning were performed on the same healthy volunteers. Quantitative ejection fractions (EFs) and volume-time curves over a cardiac cycle were estimated using the new method and compared to cardiac MRI measurements. This showed that the new method agrees better with MRI measurements than the previous approach we have developed based on a single acoustic window. The EF errors of the new method with respect to MRI measurements were less than 6%. A more extensive clinical validation is required to establish whether these promising first results translate to a method suitable for routine clinical use.

Automated 3-D echocardiography analysis compared with manual delineations and SPECT MUGA

A major barrier for using 3-D echocardiography for quantitative analysis of heart function in routine clinical practice is the absence of accurate and robust segmentation and tracking methods necessary to make the analysis automatic. In this paper, we present an automated three-dimensional (3-D) echocardiographic acquisition and image-processing methodology for assessment of left ventricular (LV) function. We combine global image information provided by a novel multiscale fuzzy-clustering segmentation algorithm, with local boundaries obtained with phase-based acoustic feature detection. We then use the segmentation results to fit and track the LV endocardial surface using a 3-D continuous transformation. To our knowledge, this is the first report of a completely automated method. The protocol is evaluated in a small clinical case study (nine patients). We compare ejection fractions (EFs) computed with the new approach to those obtained using the standard clinical technique, single-photon emission computed tomography multigated acquisition. Errors on six datasets were found to be within six percentage points. A further two, with poor image quality, improved upon EFs from manually delineated contours, and the last failed due to artifacts in the data. Volume-time curves were derived and the results compared to those from manual segmentation. Improvement over an earlier published version of the method is noted.

A review of cardiac image registration methods

In this paper, the current status of cardiac image registration methods is reviewed. The combination of information from multiple cardiac image modalities, such as magnetic resonance imaging, computed tomography, positron emission tomography, single-photon emission computed tomography, and ultrasound, is of increasing interest in the medical community for physiologic understanding and diagnostic purposes. Registration of cardiac images is a more complex problem than brain image registration because the heart is a nonrigid moving organ inside a moving body. Moreover, as compared to the registration of brain images, the heart exhibits much fewer accurate anatomical landmarks. In a clinical context, physicians often mentally integrate image information from different modalities. Automatic registration, based on computer programs, might, however, offer better accuracy and repeatability and save time.

Bayesian learning for cardiac SPECT image interpretation

In this paper, we describe a system for automating the diagnosis of myocardial perfusion from single-photon emission computerized tomography (SPECT) images of male and female hearts. Initially we had several thousand of SPECT images, other clinical data and physician-interpreter's descriptions of the images. The images were divided into segments based on the Yale system. Each segment was described by the physician as showing one of the following conditions: normal perfusion, reversible perfusion defect, partially reversible perfusion defect, fixed perfusion defect, defect showing reverse redistribution, equivocal defect or artifact. The physician's diagnosis of overall left ventricular (LV) perfusion, based on the above descriptions, categorizes a study as showing one or more of eight possible conditions: normal, ischemia, infarct and ischemia, infarct, reverse redistribution, equivocal, artifact or LV dysfunction. Because of the complexity of the task, we decided to use the knowledge discovery approach, consisting of these steps: problem understanding, data understanding, data preparation, data mining, evaluating the discovered knowledge and its implementation. After going through the data preparation step, in which we constructed normal gender-specific models of the LV and image registration, we ended up with 728 patients for whom we had both SPECT images and corresponding diagnoses. Another major contribution of the paper is the data mining step, in which we used several new Bayesian learning classification methods. The approach we have taken, namely the six-step knowledge discovery process has proven to be very successful in this complex data mining task and as such the process can be extended to other medical data mining projects.

Automatic detection of coronary artery disease in myocardial perfusion SPECT using image registration and voxel to voxel statistical comparisons

The purpose of this study was to compare the performance of automatic detection of coronary artery disease (CAD) with that of expert observers. A male and female normal image template was constructed from normal stress technetium-99m single photon emission computed tomography (SPECT) studies. Mean and standard deviation images for each sex were created by registering normal studies to a standard shape and position. The test group consisted of 104 patients who had been routinely referred for SPECT and angiography. The gold standard for CAD was defined by angiography. The test group studies were registered to the respective templates and the Z-score was calculated for each voxel. Voxels with a Z-score greater than 5 indicated the presence of CAD. The performance of this method and that of three observers were compared by continuous receiver operating characteristic (CROC) analysis. The overall sensitivity and specificity for automatic detection were 73% and 92%, respectively. The area (Az) under the CROC curve (+/-1 SE) for automatic detection of CAD was 0.88+/-0.06. There was no statistically significant difference between the performances of the three observers in terms of Az and that of automatic detection (P> or =0.25, univariate Z-score test). The use of this automated statistical mapping approach shows a performance comparable with experienced observers, but avoids inter-observer and intra-observer variability.

Nonrigid registration of 3-D free-hand ultrasound images of the breast

Three-dimensional (3-D) ultrasound imaging of the breast enables better assessment of diseases than conventional two-dimensional (2-D) imaging. Free-hand techniques are often used for generating 3-D data from a sequence of 2-D slice images. However, the breast deforms substantially during scanning because it is composed primarily of soft tissue. This often causes tissue mis-registration in spatial compounding of multiple scan sweeps. To overcome this problem, in this paper, instead of introducing additional constraints on scanning conditions, we use image processing techniques. We present a fully automatic algorithm for 3-D nonlinear registration of free-hand ultrasound data. It uses a block matching scheme and local statistics to estimate local tissue deformation. A Bayesian regularization method is applied to the sample displacement field. The final deformation field is obtained by fitting a B-spline approximating mesh to the sample displacement field. Registration accuracy is evaluated using phantom data and similar registration errors are achieved with (0.19 mm) and without (0.16 mm) gaps in the data. Experimental results show that registration is crucial in spatial compounding of different sweeps. The execution time of the method on moderate hardware is sufficiently fast for fairly large research studies.

Interhemispheric asymmetry of regional cerebral blood flow in prepubescent boys with attention deficit hyperactivity disorder

The prefrontal cortex is asymmetric in both structure and function. In normal subjects, the right prefrontal cortex is activated more than the left during response inhibition. Patients with attention deficit hyperactivity disorder (ADHD) have impaired response inhibition and altered structural interhemispheric asymmetry. This study was conducted to examine the functional interhemispheric asymmetry during response inhibition in children with ADHD. Subjects were divided into three groups according to the level of motor hyperactivity. Blood flow tracer (99m)Tc-ethyl cysteinate dimer was injected while subjects were performing a response inhibition task (RIT), followed by single photon emission computerized tomography (SPECT). After three-dimensional reconstruction, filtering and smoothing, individual scans were morphed to a template. Three average group images were created from individual scans. Each average group image was subtracted voxel-by-voxel from its mirror image to compare the regional cerebral blood flow (rCBF) in the right and left cerebral hemispheres, yielding images of significant interhemispheric rCBF asymmetry. The severe hyperactivity group exhibited most prefrontal left>right rCBF asymmetry and left>right occipitoparietal asymmetry. Reversal of functional prefrontal asymmetry in boys with severe motor hyperactivity supports the hypothesis of right prefrontal cortex dysfunction in ADHD.

Medical image registration

Radiological images are increasingly being used in healthcare and medical research. There is, consequently, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. This article reviews registration techniques used to solve this problem, and describes the wide variety of applications to which these techniques are applied. Applications of image registration include combining images of the same subject from different modalities, aligning temporal sequences of images to compensate for motion of the subject between scans, image guidance during interventions and aligning images from multiple subjects in cohort studies. Current registration algorithms can, in many cases, automatically register images that are related by a rigid body transformation (i.e. where tissue deformation can be ignored). There has also been substantial progress in non-rigid registration algorithms that can compensate for tissue deformation, or align images from different subjects. Nevertheless many registration problems remain unsolved, and this is likely to continue to be an active field of research in the future.

Three-dimensional modeling for functional analysis of cardiac images: a review

Three-dimensional (3-D) imaging of the heart is a rapidly developing area of research in medical imaging. Advances in hardware and methods for fast spatio-temporal cardiac imaging are extending the frontiers of clinical diagnosis and research on cardiovascular diseases. In the last few years, many approaches have been proposed to analyze images and extract parameters of cardiac shape and function from a variety of cardiac imaging modalities. In particular, techniques based on spatio-temporal geometric models have received considerable attention. This paper surveys the literature of two decades of research on cardiac modeling. The contribution of the paper is three-fold: 1) to serve as a tutorial of the field for both clinicians and technologists, 2) to provide an extensive account of modeling techniques in a comprehensive and systematic manner, and 3) to critically review these approaches in terms of their performance and degree of clinical evaluation with respect to the final goal of cardiac functional analysis. From this review it is concluded that whereas 3-D model-based approaches have the capability to improve the diagnostic value of cardiac images, issues as robustness, 3-D interaction, computational complexity and clinical validation still require significant attention.

Left ventricular motion reconstruction from planar tagged MR images: a comparison

Through recent development of MR techniques, it is now possible to assess regional myocardial wall function in a non-invasive way. Using MR tagging, space is marked with planes which deform with the tissue, providing markers for tracking the local motion of the myocardium. Numerous methods to reconstruct the three-dimensional displacement field have been developed. The aim of this article is to provide a framework to quantitatively compare the performance of four methods the authors have developed. Five sets of experiments are described, and their results are reported. Instructions are also provided to perform similar tests on any method using the same data. The experiments show that some characteristic properties of the methods, such as sensitivity to noise or spatial resolution, can be quantitatively classified. Cross-comparison of performances show what range values for these properties can be considered acceptable.