Medical images consist of image structures of varying scales, with different scales representing different components.
For example, in cardiac images, left ventricle, myocardium and blood pool are the large scale structures, whereas infarct and noise are represented by relatively small scale structures. Thus, extracting different scales in an image i.e. multiscale image representation, is a valuable tool in medical image processing. There are various multiscale representation techniques based on different image decomposition algorithms and denoising methods. Gaussian blurring with varying standard deviation can be considered as a multiscale representation, but it diffuses the image isotropically, thereby diffusing main edges. On the other hand, inverse scale representations based on variational formulations preserve edges; but they tend to be time consuming and thus unsuitable for real-time applications.
In the present work, we propose a fast multiscale representation technique, motivated by successive decomposition of smooth parts based on total variation (TV ) minimization. Thus, we smooth a given image at an increasing scale, producing a multiscale TV representation. As noise is a small scale component of an image, we can effectively use the proposed method for denoising . We also prove that the denoising speed, up to the time-step, is determined by the user, making the algorithm well-suited for real-time applications. The proposed method inherits edge preserving property from total variation flow. Using this property, we propose a novel multiscale image registration algorithm, where we register corresponding scales in images, thereby registering images efficiently and accurately.
In this work, we propose an automated infarct heterogeneity analysis method for cardiac delayed enhancement magnetic
resonance images (DE-MRI). Advantages of this method include that it eliminates manual contouring of the left ventricle
and automatically distinguishes infarct, "gray zone" (heterogeneous mixture of healthy and infarct tissue), and healthy
tissue pixels despite variability in intensity and noise across images. Quantitative evaluation was performed on 12
patients. The automatically determined infarct core size and gray zone size showed high correlation with that derived
from manual delineation (R2 = 0.91 for infarct core size and R2 = 0.87 for gray zone size). The automatic method
shortens the evaluation to 5.6 ±2.2 s per image, compared with 3 min for the manual method. These results indicate a
promising method for automatic analysis of infarct heterogeneity with DE-MRI that should be beneficial for reducing
variability in quantitative analysis and improving workflow.
Cardiac interventional procedures such as myocardial stem cell delivery and radiofrequency ablation require a high degree of accuracy and efficiency. Real-time, 2-D MR technology is being developed to guide such procedures; the associated challenges include the relatively low resolution and image quality in real-time images. Real-time MR guidance can be enhanced by acquiring a 4-D (3-D + phase) volume prior to the procedure and aligning it to the 2-D real-time images, so that corresponding features in the prior volume can be integrated into the real-time image visualization. This technique provides spatial context with high resolution and SNR. A left ventricular (LV) myocardial wall contour tracking system was developed to maintain spatial alignment of prior volume images to real-time MR images. Over 9 test images sequences, each comprising 100 frames of simulated respiratory motion, the tracker maintained alignment with a mean displacement error of 1.61mm in a region of interest around the LV, as compared to a mean displacement error of 5.2mm without tracking.
The constrained, localized warping (CLW) algorithm was developed to minimize the registration errors caused by hypoperfusion lesions. SPECT brain perfusion images from 21 Alzheimer patients and 35 controls were analyzed. CLW automatically determines homologous landmarks on patient and template images. CLW was constrained by anatomy and where lesions were probable. CLW was compared with 3rd-degree, polynomial warping (AIR 3.0). Accuracy was assessed by correlation, overlap, and variance. 16 lesion types were simulated, repeated with 5 images. The errors in defect volume and intensity after registration were estimated by comparing the images resulting from warping transforms calculated when the defects were or were not present. Registration accuracy of normal studies was very similar between CLW and polynomial warping methods, and showed marked improvement over linear registration. The lesions had minimal effect on the CLW algorithm accuracy, with small errors in volume (> -4%) and intensity (< +2%). The accuracy improvement compared with not warping was nearly constant regardless of defect: +1.5% overlap and +0.001 correlation. Polynomial warping caused larger errors in defect volume (< -10%) and intensity (> +2.5%) for most defects. CLW is recommended because it caused small errors in defect estimation and improved the registration accuracy in all cases.
We present an operator-independent software technique for segmentation, realignment and analysis of brain perfusion images, with both voxel-wise and regional quantitation methods. Inter-subject registration with normalized mutual information was tested with simulated defects. Brain perfusion images (HMPAO-SPECT) from 56 subjects (21 AD; 35 controls) were retrospectively analyzed. Templates were created from the 3-D registration of the controls. Automatic segmentation was developed to remove extraneous activity that disrupts registration. Two new registration methods, robust least squares (RLS) and normalized mutual information (NMI) were implemented and compared with sum of absolute differences (CD). The automatic segmentation method caused a registration displacement of 0.4 +/- 0.3 pixels compared with manual segmentation. NMI registration proved to be less adversely effected by simulated defects than RLS or CD. The error in quantitating the patient-template parietal ratio due to mis- registration was 2.0% and 0.5% for 70% and 85% hypoperfusion defects, respectively. The registration processing time was 1.6 min (233 MHz Pentium). The most accurate discriminant utilized a logistic equation parameterized by mean counts of the parietal and temporal regions of the map, (91 +/- 8% Se, 97 +/- 5% Sp). BRASS is a fast, objective software package for single-step analysis of brain SPECT, suitable to aid diagnosis of AD.
We developed a novel clinical tool (PERFIT) for automated 3-D voxel-based quantification of myocardial perfusion, validated it with a wide spectrum of angiographically correlated cases, compared it to previous approaches, and tested its agreement with visual expert reading. A multistage, 3-D iterative inter- subject registration of patient images to normal stress and rest cardiac templates was applied, including automated masking of external activity before final fit. The reference templates were adjusted to the individual left ventricles by template erosion, for further shape correction. 125 angiographically correlated cases including multi-vessel disease, infarction, and dilated ventricles were tested. In addition, standard polar maps were generated automatically from the registered data. Results of consensus visual reading (V) and PERFIT (P) were compared. The iterative fitting was successful in 245/250 (99%) stress and rest images. PERFIT found defects on stress in 2/29 normal patients and 95/96 abnormal patients. Overall correlation between V and P findings was r equals 0.864. In all abnormal groups (n equals 96), PERFIT average defect sizes expressed as the percentage the myocardial volume were 9.6% for rest and 22.3% for stress, versus 11.4% (rest) and 23% (stress) for visual reading. Automatic quantification by PERFIT is consistent with visual analysis; it can be applied to the analysis whole spectrum of clinical images, and can aid physicians in interpretation of myocardial perfusion.