KEYWORDS: Ultrasonography, Image registration, 3D image reconstruction, 3D acquisition, Calibration, 3D image processing, Prostate, Medical imaging, Biopsy, Solids
Reconstructed 3D ultrasound of prostate gland finds application in several medical areas such as image guided biopsy,
therapy planning and dose delivery. In our application, we use an end-fire probe rotated about its axis to acquire a
sequence of rotational slices to reconstruct 3D TRUS (Transrectal Ultrasound) image. The image acquisition system
consists of an ultrasound transducer situated on a cradle directly attached to a rotational sensor. However, due to system
tolerances, axis of probe does not align exactly with the designed axis of rotation resulting in artifacts in the 3D
reconstructed ultrasound volume.
We present a rigid registration based automatic probe calibration approach. The method uses a sequence of phantom
images, each pair acquired at angular separation of 180 degrees and registers corresponding image pairs to compute the
deviation from designed axis. A modified shadow removal algorithm is applied for preprocessing. An attribute vector is
constructed from image intensity and a speckle-insensitive information-theoretic feature. We compare registration
between the presented method and expert-corrected images in 16 prostate phantom scans. Images were acquired at
multiple resolutions, and different misalignment settings from two ultrasound machines. Screenshots from 3D
reconstruction are shown before and after misalignment correction. Registration parameters from automatic and manual
correction were found to be in good agreement. Average absolute differences of translation and rotation between
automatic and manual methods were 0.27 mm and 0.65 degree, respectively. The registration parameters also showed
lower variability for automatic registration (pooled standard deviation σtranslation = 0.50 mm, σrotation = 0.52 degree)
compared to the manual approach (pooled standard deviation σtranslation = 0.62 mm, σrotation = 0.78 degree).
Standard clinical radiological techniques for determining lesion volume changes in interval exams are, as far as we
know, quantitatively non-descriptive or approximate at best. We investigate two new registration based methods
that help sketch an improved quantitative picture of lesion volume changes in hepatic interval CT exams. The
first method, Jacobian Integration, employs a constrained Thin Plate Spline warp to compute the deformation
of the lesion of interest over the intervals. The resulting jacobian map of the deformation is integrated to yield
the net lesion volume change. The technique is fast, accurate and requires no segmentation, but is sensitive
to misregistration. The second scheme uses a Weighted Gray Value Difference image of two registered interval
exams to estimate the change in lesion volume. A linear weighting and trimming curve is used to accurately
account for the contribution of partial voxels. This technique is insensitive to slight misregistration and useful
in analyzing simple lesions with uniform contrast or lesions with insufficient mutual information to allow the
computation of an accurate warp. The methods are tested on both synthetic and in vivo liver lesions and results
are evaluated against estimates obtained through careful manual segmentation of the lesions. Our findings so far
have given us reason to believe that the estimators are reliable. Further experiments on numerous in vivo lesions
will probably establish the improved efficacy of these methods in supporting earlier detection of new disease or
conversion from stable to progressive disease in comparison to existing clinical estimation techniques.
KEYWORDS: Biopsy, Prostate, Cancer, Image segmentation, 3D image processing, Ultrasonography, Image registration, 3D modeling, 3D acquisition, Prostate cancer
Prostate cancer is a multifocal disease and lesions are not distributed uniformly within the gland. Several biopsy
protocols concerning spatially specific targeting have been reported urology literature. Recently a statistical
cancer atlas of the prostate was constructed providing voxelwise probabilities of cancers in the prostate. Additionally
an optimized set of biopsy sites was computed with 94 - 96% detection accuracy was reported using only 6-7 needles. Here we discuss the warping of this atlas to prostate segmented side-fire ultrasound images of the patient. A shape model was used to speed up registration. The model was trained from over 38 expert segmented subjects off-line. This training yielded as few as 15-20 degrees of freedom that were optimized to warp the atlas surface to the patient's ultrasound image followed by elastic interpolation of the 3-D atlas. As a result the atlas is completely mapped to the patient's prostate anatomy along with optimal predetermined needle locations for biopsy. These do not preclude the use of additional biopsies if desired. A color overlay of the atlas is also displayed on the ultrasound image showing high cancer zones within the prostate. Finally current biopsy locations are saved in the atlas space and may be used to update the atlas based on the pathology report. In addition to the optimal atlas plan, previous biopsy locations and alternate plans can also be stored in the atlas space and warped to the patient with no additional time overhead.
KEYWORDS: Prostate, Image segmentation, 3D image processing, Ultrasonography, Biopsy, 3D acquisition, Error analysis, Image processing algorithms and systems, 3D image reconstruction, Prostate cancer
Prostate volume is an indirect indicator for several prostate diseases. Volume estimation is a desired requirement during
prostate biopsy, therapy and clinical follow up. Image segmentation is thus necessary. Previously, discrete dynamic contour (DDC) was implemented in orthogonal unidirectional on the slice-by-slice basis
for prostate boundary estimation. This suffered from the glitch that it needed stopping criteria during the propagation of
segmentation procedure from slice-to-slice. To overcome this glitch, axial DDC was implemented and this suffered from the fact that central axis never remains fixed and wobbles during propagation of segmentation from slice-to-slice. The effect of this was a multi-fold reconstructed surface. This paper presents a bidirectional DDC approach, thereby removing the two glitches. Our bidirectional DDC protocol was tested on a clinical dataset on 28 3-D ultrasound image volumes acquired using side fire Philips transrectal ultrasound. We demonstrate the orthogonal bidirectional DDC strategy achieved the most accurate volume estimation compared with previously published orthogonal unidirectional DDC and axial DDC methods. Compared to the ground truth, we show that the mean volume estimation errors were: 18.48%, 9.21% and 7.82% for unidirectional, axial and bidirectional DDC methods, respectively. The segmentation architecture is implemented in Visual C++ in Windows environment.
Prostate cancer is the most commonly diagnosed cancer in males in the United States and the second leading
cause of cancer death. While the exact cause is still under investigation, researchers agree on certain risk factors
like age, family history, dietary habits, lifestyle and race. It is also widely accepted that cancer distribution
within the prostate is inhomogeneous, i.e. certain regions have a higher likelihood of developing cancer. In
this regard extensive work has been done to study the distribution of cancer in order to perform biopsy more
effectively. Recently a statistical cancer atlas of the prostate was demonstrated along with an optimal biopsy
scheme achieving a high detection rate.
In this paper we discuss the complete construction and application of such an atlas that can be used in a
clinical setting to effectively target high cancer zones during biopsy. The method consists of integrating intensity
statistics in the form of cancer probabilities at every voxel in the image with shape statistics of the prostate in
order to quickly warp the atlas onto a subject ultrasound image. While the atlas surface can be registered to a
pre-segmented subject prostate surface or instead used to perform segmentation of the capsule via optimization
of shape parameters to segment the subject image, the strength of our approach lies in the fast mapping of cancer
statistics onto the subject using shape statistics. The shape model was trained from over 38 expert segmented
prostate surfaces and the atlas registration accuracy was found to be high suggesting the use of this method to
perform biopsy in near real time situations with some optimization.
Real-time knowledge of capsule volume of an organ provides a valuable clinical tool for 3D biopsy applications. It is
challenging to estimate this capsule volume in real-time due to the presence of speckles, shadow artifacts, partial volume
effect and patient motion during image scans, which are all inherent in medical ultrasound imaging.
The volumetric ultrasound prostate images are sliced in a rotational manner every three degrees. The automated
segmentation method employs a shape model, which is obtained from training data, to delineate the middle slices of
volumetric prostate images. Then a "DDC" algorithm is applied to the rest of the images with the initial contour
obtained. The volume of prostate is estimated with the segmentation results.
Our database consists of 36 prostate volumes which are acquired using a Philips ultrasound machine using a Side-fire
transrectal ultrasound (TRUS) probe. We compare our automated method with the semi-automated approach. The mean
volumes using the semi-automated and complete automated techniques were 35.16 cc and 34.86 cc, with the error of
7.3% and 7.6% compared to the volume obtained by the human estimated boundary (ideal boundary), respectively. The
overall system, which was developed using Microsoft Visual C++, is real-time and accurate.
Prostate repeat biopsy has become one of the key requirements in today's prostate cancer detection. Urologists are
interested in knowing previous 3-D biopsy locations during the current visit of the patient. Eigen has developed a system
for performing 3-D Ultrasound image guided prostate biopsy. The repeat biopsy tool consists of three stages: (1)
segmentation of the prostate capsules from previous and current ultrasound volumes; (2) registration of segmented
surfaces using adaptive focus deformable model; (3) mapping of old biopsy sites onto new volume via thin-plate splines
(TPS). The system critically depends on accurate 3-D segmentation of capsule volumes. In this paper, we study the
effect of automated segmentation technique on the accuracy of 3-D ultrasound guided repeat biopsy. Our database
consists of 38 prostate volumes of different patients which are acquired using Philips sidefire transrectal ultrasound
(TRUS) probe. The prostate volumes were segmented in three ways: expert segmentation, semi-automated segmentation,
and fully automated segmentation. New biopsy sites were identified in the new volumes from different segmentation
methods, and we compared the mean squared distance between biopsy sites. It is demonstrated that the performance of
our fully automated segmentation tool is comparable to that of semi-automated segmentation method.
Estimation of volume change of structures in response to treatment or growth during breast screening exams is a challenge primarily because of ill-defined boundary. Some treatment procedures alter the lesion completely out of its original shape. In this paper, we present an overview of our recent work on identifying a technique based on Image Volume Based Registration (IVBaR) for estimation of volume. We propose that as long as a region of interest around the lesion can be identified, the exact boundary information would not be necessary. Here, we assume that the surrounding tissue remains nearly unaffected by the treatment procedure, an assumption that is valid in many cases. It is the motion of this tissue in response to changes in the central tumor that would be tracked and used to estimate the change in tumor volume.
Image registration is now a well understood problem and several techniques using a combination of cost functions,
transformation models and optimizers have been reported in medical imaging literature. Parametric methods
often rely on the efficient placement of control points in the images, that is, depending on the location and scale
at which images are mismatched. Poor choice of parameterization results in deformations not being modeled
accurately or over parameterization, where control points may lie in homogeneous regions with low sensitivity to
cost. This lowers computational efficiency due to the high complexity of the search space and might also provide
transformations that are not physically meaningful, and possibly folded.
Adaptive methods that parameterize based on mismatch in images have been proposed. In such methods, the
cost measure must be normalized, heuristics such as how many points to pick, resolution of the grids, choosing
gradient thresholds and when to refine scale would have to be ascertained in addition to the limitation of working
only at a few discrete scales.
In this paper we identify mismatch by searching the entire image and a wide range of smooth spatial scales.
The mismatch vector, containing location and scale of mismatch is computed from peaks in the local joint
entropy. Results show that this method can be used to quickly and effectively locate mismatched regions in
images where control points can be placed in preference to other regions speeding up registration.
Registration of medical images (intra- or multi-modality) is the first step before any analysis is performed.
The analysis includes treatment monitoring, diagnosis, volumetric measurements or classification to mention a
few. While pairwise registration, i.e., aligning a floating image to a fixed reference, is straightforward, it is not
immediately clear what cost measures could be exploited for the groupwise alignment of several images (possibly
multimodal) simultaneously. Recently however there has been increasing interest in this problem applied to atlas
construction, statistical shape modeling, or simply joint alignment of images to get a consistent correspondence
of voxels across all images based on a single cost measure.
The aim of this paper is twofold, a) propose a cost function - alpha mutual information computed using
entropic graphs that is a natural extension to Shannon mutual information for pairwise registration and b)
compare its performance with the pairwise registration of the image set. We show that this measure can be
reliably used to jointly align several images to a common reference. We also test its robustness by comparing
registration errors for the registration process repeated at varying noise levels.
In our experiments we used simulated data, applying different B-spline based geometric transformations to the
same image and adding independent filtered Gaussian noise to each image. Non-rigid registration was employed
with Thin Plate Splines(TPS) as the geometric interpolant.
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