Lung Parenchymal Signal Intensity in MRI: A Technical Review with Educational Aspirations Regarding Reversible Versus Irreversible Transverse Relaxation Effects in Common Pulse Sequences

Lung parenchyma is challenging to image with proton MRI. The large air space results in ~l/5th as many signal-generating protons compared to other organs. Air/tissue magnetic susceptibility differences lead to strong magnetic field gradients throughout the lungs and to broad frequency distributions, much broader than within other organs. Such distributions have been the subject of experimental and theoretical analyses which may reveal aspects of lung microarchitecture useful for diagnosis. Their most immediate relevance to current imaging practice is to cause rapid signal decays, commonly discussed in terms of short T2* values of 1 ms or lower at typical imaging field strengths. Herein we provide a brief review of previous studies describing and interpreting proton lung spectra. We then link these broad frequency distributions to rapid signal decays, though not necessarily the exponential decays generally used to define T2* values. We examine how these decays influence observed signal intensities and spatial mapping features associated with the most prominent torso imaging sequences, including spoiled gradient and spin echo sequences. Effects of imperfect refocusing pulses on the multiple echo signal decays in single shot fast spin echo (SSFSE) sequences and effects of broad frequency distributions on balanced steady state free precession (bSSFP) sequence signal intensities are also provided. The theoretical analyses are based on the concept of explicitly separating the effects of reversible and irreversible transverse relaxation processes, thus providing a somewhat novel and more general framework from which to estimate lung signal intensity behavior in modern imaging practice.

MRI signal intensity based B-spline nonrigid registration for pre- and intraoperative imaging during prostate brachytherapy

PURPOSE

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.

RESULTS

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.

CONCLUSION

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.

Prostate contouring in MRI guided biopsy

With MRI possibly becoming a modality of choice for detection and staging of prostate cancer, fast and accurate outlining of the prostate is required in the volume of clinical interest. We present a semi-automatic algorithm that uses a priori knowledge of prostate shape to arrive at the final prostate contour. The contour of one slice is then used as initial estimate in the neighboring slices. Thus we propagate the contour in 3D through steps of refinement in each slice. The algorithm makes only minimum assumptions about the prostate shape. A statistical shape model of prostate contour in polar transform space is employed to narrow search space. Further, shape guidance is implicitly imposed by allowing only plausible edge orientations using template matching. The algorithm does not require region-homogeneity, discriminative edge force, or any particular edge profile. Likewise, it makes no assumption on the imaging coils and pulse sequences used and it is robust to the patient's pose (supine, prone, etc.). The contour method was validated using expert segmentation on clinical MRI data. We recorded a mean absolute distance of 2.0 ± 0.6 mm and dice similarity coefficient of 0.93 ± 0.3 in midsection. The algorithm takes about 1 second per slice.

MR-guided prostate interventions

In this article the current issues of diagnosis and detection of prostate cancer are reviewed. The limitations for current techniques are highlighted and some possible solutions with MR imaging and MR-guided biopsy approaches are reviewed. There are several different biopsy approaches under investigation. These include transperineal open magnet approaches to closed-bore 1.5T transrectal biopsies. The imaging, image processing, and tracking methods are also discussed. In the arena of therapy, MR guidance has been used in conjunction with radiation methods, either brachytherapy or external delivery. The principles of the radiation treatment, the toxicities, and use of images are outlined. The future role of imaging and image-guided interventions lie with providing a noninvasive surrogate for cancer surveillance or monitoring treatment response. The shift to minimally invasive focal therapies has already begun and will be very exciting when MR-guided focused ultrasound surgery reaches its full potential.

Geodesic-loxodromes for diffusion tensor interpolation and difference measurement

In algorithms for processing diffusion tensor images, two common ingredients are interpolating tensors, and measuring the distance between them. We propose a new class of interpolation paths for tensors, termed geodesic-loxodromes, which explicitly preserve clinically important tensor attributes, such as mean diffusivity or fractional anisotropy, while using basic differential geometry to interpolate tensor orientation. This contrasts with previous Riemannian and Log-Euclidean methods that preserve the determinant. Path integrals of tangents of geodesic-loxodromes generate novel measures of over-all difference between two tensors, and of difference in shape and in orientation.

Transperineal prostate biopsy under magnetic resonance image guidance: a needle placement accuracy study

PURPOSE

To quantify needle placement accuracy of magnetic resonance image (MRI)-guided core needle biopsy of the prostate.

MATERIALS AND METHODS

A total of 10 biopsies were performed with 18-gauge (G) core biopsy needle via a percutaneous transperineal approach. Needle placement error was assessed by comparing the coordinates of preplanned targets with the needle tip measured from the intraprocedural coherent gradient echo images. The source of these errors was subsequently investigated by measuring displacement caused by needle deflection and needle susceptibility artifact shift in controlled phantom studies. Needle placement error due to misalignment of the needle template guide was also evaluated.

RESULTS

The mean and standard deviation (SD) of errors in targeted biopsies was 6.5 +/- 3.5 mm. Phantom experiments showed significant placement error due to needle deflection with a needle with an asymmetrically beveled tip (3.2-8.7 mm depending on tissue type) but significantly smaller error with a symmetrical bevel (0.6-1.1 mm). Needle susceptibility artifacts observed a shift of 1.6 +/- 0.4 mm from the true needle axis. Misalignment of the needle template guide contributed an error of 1.5 +/- 0.3 mm.

CONCLUSION

Needle placement error was clinically significant in MRI-guided biopsy for diagnosis of prostate cancer. Needle placement error due to needle deflection was the most significant cause of error, especially for needles with an asymmetrical bevel.

Role of endorectal coil magnetic resonance imaging in treatment of patients with prostate cancer and in determining radical prostatectomy surgical margin status: report of a single surgeon's practice

OBJECTIVES

To evaluate the role of the combination of endorectal coil and external multicoil array magnetic resonance imaging (MRI) in the management of prostate cancer and predicting the surgical margin status in a single-surgeon practice.

METHODS

We reviewed all patients referred by a single surgeon from January 1993 to May 2002 for staging prostate MRI before selecting treatment. All MRI examinations were performed using 1.5T (Signa, GE Medical Systems) with a combination of endorectal and pelvic multicoil array. The tumor size, stage, and total gland volume on MRI, prostate-specific antigen (PSA) level, and Gleason score were all compared with the pathologic stage and diagnosis of positive surgical margins (PSMs).

RESULTS

A total of 232 patients were evaluated, of whom 110 underwent radical prostatectomy, all performed by one surgeon (group 1), and 122 did not (group 2). The results showed that MRI stage, PSA level, and age were all significantly different (P <0.001). In group 1, the results showed a high specificity (99%) and accuracy (91%) for MRI staging of T3 cancer. The postoperative follow-up (median 4.5 years) revealed that 90% of men had PSA levels of less than 0.1 ng/mL. The PSM rate was 16%. No significant difference was found on MRI between the PSM group and non-PSM group. A single tumor length greater than 1.8 cm was the cutpoint above which PSMs were found (P = 0.002).

CONCLUSIONS

The results of our study have shown that the combined use of clinical data and endorectal MRI can help optimize patient treatment and selection for surgery and, in a single surgeon's practice, lead to successful outcomes.

An image morphing technique based on optimal mass preserving mapping

Image morphing, or image interpolation in the time domain, deals with the metamorphosis of one image into another. In this paper, a new class of image morphing algorithms is proposed based on the theory of optimal mass transport. The L(2) mass moving energy functional is modified by adding an intensity penalizing term, in order to reduce the undesired double exposure effect. It is an intensity-based approach and, thus, is parameter free. The optimal warping function is computed using an iterative gradient descent approach. This proposed morphing method is also extended to doubly connected domains using a harmonic parameterization technique, along with finite-element methods.

Shape-driven 3D segmentation using spherical wavelets

This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details.

Multiscale 3-D shape representation and segmentation using spherical wavelets

This paper presents a novel multiscale shape representation and segmentation algorithm based on the spherical wavelet transform. This work is motivated by the need to compactly and accurately encode variations at multiple scales in the shape representation in order to drive the segmentation and shape analysis of deep brain structures, such as the caudate nucleus or the hippocampus. Our proposed shape representation can be optimized to compactly encode shape variations in a population at the needed scale and spatial locations, enabling the construction of more descriptive, nonglobal, nonuniform shape probability priors to be included in the segmentation and shape analysis framework. In particular, this representation addresses the shortcomings of techniques that learn a global shape prior at a single scale of analysis and cannot represent fine, local variations in a population of shapes in the presence of a limited dataset. Specifically, our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We further refine the shape representation by separating into groups wavelet coefficients that describe independent global and/or local biological variations in the population, using spectral graph partitioning. We then learn a prior probability distribution induced over each group to explicitly encode these variations at different scales and spatial locations. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to two different brain structures, the caudate nucleus and the hippocampus, of interest in the study of schizophrenia. We show: 1) a reconstruction task of a test set to validate the expressiveness of our multiscale prior and 2) a segmentation task. In the reconstruction task, our results show that for a given training set size, our algorithm significantly improves the approximation of shapes in a testing set over the Point Distribution Model, which tends to oversmooth data. In the segmentation task, our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm, by capturing finer shape details.

Harmonic skeleton guided evaluation of stenoses in human coronary arteries

This paper presents a novel approach that three-dimensionally visualizes and evaluates stenoses in human coronary arteries by using harmonic skeletons. A harmonic skeleton is the center line of a multi-branched tubular surface extracted based on a harmonic function, which is the solution of the Laplace equation. This skeletonization method guarantees smoothness and connectivity and provides a fast and straightforward way to calculate local cross-sectional areas of the arteries, and thus provides the possibility to localize and evaluate coronary artery stenosis, which is a commonly seen pathology in coronary artery disease.

Deformable structure registration of bladder through surface mapping

Cumulative dose distributions in fractionated radiation therapy depict the dose to normal tissues and therefore may permit an estimation of the risk of normal tissue complications. However, calculation of these distributions is highly challenging because of interfractional changes in the geometry of patient anatomy. This work presents an algorithm for deformable structure registration of the bladder and the verification of the accuracy of the algorithm using phantom and patient data. In this algorithm, the registration process involves conformal mapping of genus zero surfaces using finite element analysis, and guided by three control landmarks. The registration produces a correspondence between fractions of the triangular meshes used to describe the bladder surface. For validation of the algorithm, two types of balloons were inflated gradually to three times their original size, and several computerized tomography (CT) scans were taken during the process. The registration algorithm yielded a local accuracy of 4 mm along the balloon surface. The algorithm was then applied to CT data of patients receiving fractionated high-dose-rate brachytherapy to the vaginal cuff, with the vaginal cylinder in situ. The patients' bladder filling status was intentionally different for each fraction. The three required control landmark points were identified for the bladder based on anatomy. Out of an Institutional Review Board (IRB) approved study of 20 patients, 3 had radiographically identifiable points near the bladder surface that were used for verification of the accuracy of the registration. The verification point as seen in each fraction was compared with its predicted location based on affine as well as deformable registration. Despite the variation in bladder shape and volume, the deformable registration was accurate to 5 mm, consistently outperforming the affine registration. We conclude that the structure registration algorithm presented works with reasonable accuracy and provides a means of calculating cumulative dose distributions.

Magnetic resonance and the human brain: anatomy, function and metabolism

The introduction and development, over the last three decades, of magnetic resonance (MR) imaging and MR spectroscopy technology for in vivo studies of the human brain represents a truly remarkable achievement, with enormous scientific and clinical ramifications. These effectively non-invasive techniques allow for studies of the anatomy, the function and the metabolism of the living human brain. They have allowed for new understandings of how the healthy brain works and have provided insights into the mechanisms underlying multiple disease processes which affect the brain. Different MR techniques have been developed for studying anatomy, function and metabolism. The primary focus of this review is to describe these different methodologies and to briefly review how they are being employed to more fully appreciate the intricacies associated with the organ, which most distinctly differentiates the human species from the other animal forms on earth.

Flattening maps for the visualization of multibranched vessels

In this paper, we present two novel algorithms which produce flattened visualizations of branched physiological surfaces, such as vessels. The first approach is a conformal mapping algorithm based on the minimization of two Dirichlet functionals. From a triangulated representation of vessel surfaces, we show how the algorithm can be implemented using a finite element technique. The second method is an algorithm which adjusts the conformal mapping to produce a flattened representation of the original surface while preserving areas. This approach employs the theory of optimal mass transport. Furthermore, a new way of extracting center lines for vessel fly-throughs is provided.

Automated atlas-based clustering of white matter fiber tracts from DTMRI

A new framework is presented for clustering fiber tracts into anatomically known bundles. This work is motivated by medical applications in which variation analysis of known bundles of fiber tracts in the human brain is desired. To include the anatomical knowledge in the clustering, we invoke an atlas of fiber tracts, labeled by the number of bundles of interest. In this work, we construct such an atlas and use it to cluster all fiber tracts in the white matter. To build the atlas, we start with a set of labeled ROIs specified by an expert and extract the fiber tracts initiating from each ROI. Affine registration is used to project the extracted fiber tracts of each subject to the atlas, whereas their B-spline representation is used to efficiently compare them to the fiber tracts in the atlas and assign cluster labels. Expert visual inspection of the result confirms that the proposed method is very promising and efficient in clustering of the known bundles of fiber tracts.

Mass preserving registration for heart MR images

This paper presents a new algorithm for non-rigid registration between two doubly-connected regions. Our algorithm is based on harmonic analysis and the theory of optimal mass transport. It assumes an underlining continuum model, in which the total amount of mass is exactly preserved during the transformation of tissues. We use a finite element approach to numerically implement the algorithm.

Multiscale 3D shape analysis using spherical wavelets

Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data.

Combining classifiers using their receiver operating characteristics and maximum likelihood estimation

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.

The tale of Phineas Gage, digitally remastered

The injury of Phineas Gage has fueled research on and fascination with the localization of cerebral functions in the past century and a half. Most physicians and anatomists believed that Gage sustained a largely bilateral injury to the frontal lobes. However, previous studies seem to have overlooked a few less obvious, but essential details. This has led us to reanalyze the injury using three-dimensional reconstruction and quantitative computer-aided techniques and to propose a new biomechanical model, in order to determine the location and extent of the injury and explain Gage's improbable survival. Unlike previous studies on this subject, our findings are based on computer-generated three-dimensional reconstructions of a thin-slice computed tomography scan (CAT) of Phineas Gage's skull. The results of our image analysis were corroborated with the clinical findings, thoroughly recorded by Dr. Harlow in 1848, as well as with a systematic examination of the original skull specimen. Our results show that the cerebral injury was limited to the left frontal lobe, did not extend to the contralateral side, did not affect the ventricular system, and did not involve vital intracranial vascular structures. Although modern neuroscience has perhaps outgrown the speculations prompted by this famous case, it is still a living part of the medical folklore and education. Setting the record straight based on clinical reasoning, observation of the physical evidence, and sound quantitative computational methods is more than mere minutia and of interest for the broad medical community.

Workflow modeling and analysis of computer guided prostate brachytherapy under MR imaging control

We demonstrate that classical Business Process Reengineering (BPR) methods can be successfully applied to Computer Aided Surgery while increasing safety and efficiency of the overall procedure through an integrated Workflow Management System. Computer guided Prostate Brachytherapy, as a sophisticated treatment by an interdisciplinary team, is perfectly suited to apply our method. Detailed suggestions for improvement of the whole procedure could be derived by our modified BPR method.

Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier

A multichannel statistical classifier for detecting prostate cancer was developed and validated by combining information from three different magnetic resonance (MR) methodologies: T2-weighted, T2-mapping, and line scan diffusion imaging (LSDI). From these MR sequences, four different sets of image intensities were obtained: T2-weighted (T2W) from T2-weighted imaging, Apparent Diffusion Coefficient (ADC) from LSDI, and proton density (PD) and T2 (T2 Map) from T2-mapping imaging. Manually segmented tumor labels from a radiologist, which were validated by biopsy results, served as tumor "ground truth." Textural features were extracted from the images using co-occurrence matrix (CM) and discrete cosine transform (DCT). Anatomical location of voxels was described by a cylindrical coordinate system. A statistical jack-knife approach was used to evaluate our classifiers. Single-channel maximum likelihood (ML) classifiers were based on 1 of the 4 basic image intensities. Our multichannel classifiers: support vector machine (SVM) and Fisher linear discriminant (FLD), utilized five different sets of derived features. Each classifier generated a summary statistical map that indicated tumor likelihood in the peripheral zone (PZ) of the prostate gland. To assess classifier accuracy, the average areas under the receiver operator characteristic (ROC) curves over all subjects were compared. Our best FLD classifier achieved an average ROC area of 0.839(+/-0.064), and our best SVM classifier achieved an average ROC area of 0.761(+/-0.043). The T2W ML classifier, our best single-channel classifier, only achieved an average ROC area of 0.599(+/-0.146). Compared to the best single-channel ML classifier, our best multichannel FLD and SVM classifiers have statistically superior ROC performance (P=0.0003 and 0.0017, respectively) from pairwise two-sided t-test. By integrating the information from multiple images and capturing the textural and anatomical features in tumor areas, summary statistical maps can potentially aid in image-guided prostate biopsy and assist in guiding and controlling delivery of localized therapy under image guidance.

Multislice CT colonography: current status and limitations

CT colonography (CTC) is a promising method for colorectal screening providing a full structural evaluation of the entire colon and gaining in popularity due to a superior safety profile, a low rate of complications, and high patient acceptance. Multislice CT (MSCT) has further improved the diagnostic potential of CTC by generating high-resolution CT images of the abdomen and pelvis in shorter acquisition times than was previously possible. Over the past year, multiple studies have been published on every aspect of CTC including techniques, image display, image reconstruction, and clinical trial results assessing the feasibility of CTC as a screening tool. Yet despite increasing clinical use, the appropriate role of CTC in colorectal cancer screening remains undefined and barriers to widespread adoption remain. In particular, though the test is generally regarded as easy to perform, accurate interpretation requires a steep learning curve. While several large studies have found high sensitivity and specificity, the accuracy of CTC in a screening population has yet to be verified and almost no health insurance plans reimburse for its use in colorectal screening. Ongoing research in computer-aided detection and new software tools, however, have the potential to increase accuracy and ease of interpretation significantly, accelerating its acceptance as a colorectal screening tool.

A unified variational approach to denoising and bias correction in MR

We propose a novel bias correction method for magnetic resonance (MR) imaging that uses complementary body coil and surface coil images. The former are spatially homogeneous but have low signal intensity; the latter provide excellent signal response but have large bias fields. We present a variational framework where we optimize an energy functional to estimate the bias field and the underlying image using both observed images. The energy functional contains smoothness-enforcing regularization for both the image and the bias field. We present extensions of our basic framework to a variety of imaging protocols. We solve the optimization problem using a computationally efficient numerical algorithm based on coordinate descent, preconditioned conjugate gradient, half-quadratic regularization, and multigrid techniques. We show qualitative and quantitative results demonstrating the effectiveness of the proposed method in producing debiased and denoised MR images.

Nondistorting flattening maps and the 3-D visualization of colon CT images

In this paper, we consider a novel three-dimensional (3-D) visualization technique based on surface flattening for virtual colonoscopy. Such visualization methods could be important in virtual colonoscopy because they have the potential for noninvasively determining the presence of polyps and other pathologies. Further, we demonstrate a method that presents a surface scan of the entire colon as a cine, and affords the viewer the opportunity to examine each point on the surface without distortion. We use certain angle-preserving mappings from differential geometry to derive an explicit method for flattening surfaces obtained from 3-D colon computed tomography (CT) imagery. Indeed, we describe a general method based on a discretization of the Laplace-Beltrami operator for flattening a surface into the plane in a conformal manner. From a triangulated surface representation of the colon, we indicate how the procedure may be implemented using a finite element technique, which takes into account special boundary conditions. We also provide simple formulas that may be used in a real-time cine to correct for distortion.

Knowledge-based segmentation of SAR data with learned priors

An approach for the segmentation of still and video synthetic aperture radar (SAR) images is described. A priori knowledge about the objects present in the image, e.g., target, shadow and background terrain, is introduced via Bayes' rule. Posterior probabilities obtained in this way are then anisotropically smoothed, and the image segmentation is obtained via MAP classifications of the smoothed data. When segmenting sequences of images, the smoothed posterior probabilities of past frames are used to learn the prior distributions in the succeeding frame. We show with examples from public data sets that this method provides an efficient and fast technique for addressing the segmentation of SAR data.

On the Laplace-Beltrami operator and brain surface flattening

In this paper, using certain conformal mappings from uniformization theory, we give an explicit method for flattening the brain surface in a way which preserves angles. From a triangulated surface representation of the cortex, we indicate how the procedure may be implemented using finite elements. Further, we show how the geometry of the brain surface may be studied using this approach.