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.

Incorporating reversible and irreversible transverse relaxation effects into Steady State Free Precession (SSFP) signal intensity expressions for fMRI considerations.

Among the multiple sequences available for functional magnetic resonance imaging (fMRI), the Steady State Free Precession (SSFP) sequence offers the highest signal-to-noise ratio (SNR) per unit time as well as distortion free images not feasible with the more commonly employed single-shot echo planar imaging (EPI) approaches. Signal changes occurring with activation in SSFP sequences reflect underlying changes in both irreversible and reversible transverse relaxation processes. The latter are characterized by changes in the central frequencies and widths of the inherent frequency distribution present within a voxel. In this work, the well-known frequency response of the SSFP signal intensity is generalized to include the widths and central frequencies of some common frequency distributions on SSFP signal intensities. The approach, using a previously unnoted series expansion, allows for a separation of reversible from irreversible transverse relaxation effects on SSFP signal intensity changes. The formalism described here should prove useful for identifying and modeling mechanisms associated with SSFP signal changes accompanying neural activation.

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.

On high b diffusion imaging in the human brain: ruminations and experimental insights.

Interest in the manner in which brain tissue signal decays with b factor in diffusion imaging schemes has grown in recent years following the observation that the decay curves depart from purely monoexponential decay behavior. Regardless of the model or fitting function proposed for characterizing sufficiently sampled decay curves (vide infra), the departure from monoexponentiality spells increased tissue characterization potential. The degree to which this potential can be harnessed to improve specificity, sensitivity and spatial localization of diseases in brain, and other tissues, largely remains to be explored. Furthermore, the degree to which currently popular diffusion tensor imaging methods, including visually impressive white matter fiber "tractography" results, have almost completely ignored the nonmonoexponential nature of the basic signal decay with b factor is worthy of communal introspection. Here we limit our attention to a review of the basic experimental features associated with brain water signal diffusion decay curves as measured over extended b-factor ranges, the simple few parameter fitting functions that have been proposed to characterize these decays and the more involved models, e.g.,"ruminations," which have been proposed to account for the nonmonoexponentiality to date.

Carr-Purcell-Meiboom-Gill imaging of prostate cancer: quantitative T2 values for cancer discrimination.

Quantitative, apparent T(2) values of suspected prostate cancer and healthy peripheral zone tissue in men with prostate cancer were measured using a Carr-Purcell-Meiboom-Gill (CPMG) imaging sequence in order to assess the cancer discrimination potential of tissue T(2) values. The CPMG imaging sequence was used to image the prostates of 18 men with biopsy-proven prostate cancer. Whole gland coverage with nominal voxel volumes of 0.54 x 1.1 x 4 mm(3) was obtained in 10.7 min, resulting in data sets suitable for generating high-quality images with variable T(2)-weighting and for evaluating quantitative T(2) values on a pixel-by-pixel basis. Region-of-interest analysis of suspected healthy peripheral zone tissue and suspected cancer, identified on the basis of both T(1)- and T(2)-weighted signal intensities and available histopathology reports, yielded significantly (P<.0001) longer apparent T(2) values in suspected healthy tissue (193+/-49 ms) vs. suspected cancer (100+/-26 ms), suggesting potential utility of this method as a tissue specific discrimination index for prostate cancer. We conclude that CPMG imaging of the prostate can be performed in reasonable scan times and can provide advantages over T(2)-weighted fast spin echo (FSE) imaging alone, including quantitative T(2) values for cancer discrimination as well as proton density maps without the point spread function degradation associated with short effective echo time FSE sequences.

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.

Multimodal Registration of White Matter Brain Data via Optimal Mass Transport.

The elastic registration of medical scans from different acquisition sequences is becoming an important topic for many research labs that would like to continue the post-processing of medical scans acquired via the new generation of high-field-strength scanners. In this note, we present a parameter-free registration algorithm that is well suited for this scenario as it requires no tuning to specific acquisition sequences. The algorithm encompasses a new numerical scheme for computing elastic registration maps based on the minimizing flow approach to optimal mass transport. The approach utilizes all of the gray-scale data in both images, and the optimal mapping from image A to image B is the inverse of the optimal mapping from B to A. Further, no landmarks need to be specified, and the minimizer of the distance functional involved is unique. We apply the algorithm to register the white matter folds of two different scans and use the results to parcellate the cortex of the target image. To the best of our knowledge, this is the first time that the optimal mass transport function has been applied to register large 3D multimodal data sets.

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.

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.

Complimentary aspects of diffusion imaging and fMRI: II. Elucidating contributions to the fMRI signal with diffusion sensitization.

Tissue water molecules reside in different biophysical compartments. For example, water molecules in the vasculature reside for variable periods of time within arteries, arterioles, capillaries, venuoles and veins, and may be within blood cells or blood plasma. Water molecules outside of the vasculature, in the extravascular space, reside, for a time, either within cells or within the interstitial space between cells. Within these different compartments, different types of microscopic motion that water molecules may experience have been identified and discussed. These range from Brownian diffusion to more coherent flow over the time scales relevant to functional magnetic resonance imaging (fMRI) experiments, on the order of several 10s of milliseconds. How these different types of motion are reflected in magnetic resonance imaging (MRI) methods developed for "diffusion" imaging studies has been an ongoing and active area of research. Here we briefly review the ideas that have developed regarding these motions within the context of modern "diffusion" imaging techniques and, in particular, how they have been accessed in attempts to further our understanding of the various contributions to the fMRI signal changes sought in studies of human brain activation.

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.

Biexponential characterization of prostate tissue water diffusion decay curves over an extended b-factor range.

Detailed measurements of water diffusion within the prostate over an extended b-factor range were performed to assess whether the standard assumption of monoexponential signal decay is appropriate in this organ. From nine men undergoing prostate MR staging examinations at 1.5 T, a single 10-mm-thick axial slice was scanned with a line scan diffusion imaging sequence in which 14 equally spaced b factors from 5 to 3,500 s/mm(2) were sampled along three orthogonal diffusion sensitization directions in 6 min. Due to the combination of long scan time and limited volume coverage associated with the multi-b-factor, multidirectional sampling, the slice was chosen online from the available T2-weighted axial images with the specific goal of enabling the sampling of presumed noncancerous regions of interest (ROIs) within the central gland (CG) and peripheral zone (PZ). Histology from prescan biopsy (n=9) and postsurgical resection (n=4) was subsequently employed to help confirm that the ROIs sampled were noncancerous. The CG ROIs were characterized from the T2-weighted images as primarily mixtures of glandular and stromal benign prostatic hyperplasia, which is prevalent in this population. The water signal decays with b factor from all ROIs were clearly non-monoexponential and better served with bi- vs. monoexponential fits, as tested using chi(2)-based F test analyses. Fits to biexponential decay functions yielded intersubject fast diffusion component fractions in the order of 0.73+/-0.08 for both CG and PZ ROIs, fast diffusion coefficients of 2.68+/-0.39 and 2.52+/-0.38 microm(2)/ms and slow diffusion coefficients of 0.44+/-0.16 and 0.23+/-0.16 um(2)/ms for CG and PZ ROIs, respectively. The difference between the slow diffusion coefficients within CG and PZ was statistically significant as assessed with a Mann-Whitney nonparametric test (P<.05). We conclude that a monoexponential model for water diffusion decay in prostate tissue is inadequate when a large range of b factors is sampled and that biexponential analyses are better suited for characterizing prostate diffusion decay curves.

Complementary aspects of diffusion imaging and fMRI; I: structure and function.

Studying the intersection of brain structure and function is an important aspect of modern neuroscience. The development of magnetic resonance imaging (MRI) over the last 25 years has provided new and powerful tools for the study of brain structure and function. Two tools in particular, diffusion imaging and functional MRI (fMRI), are playing increasingly important roles in elucidating the complementary aspects of brain structure and function. In this work, we review basic technical features of diffusion imaging and fMRI for studying the integrity of white matter structural components and for determining the location and extent of cortical activation in gray matter, respectively. We then review a growing body of literature in which the complementary aspects of diffusion imaging and fMRI, applied as separate examinations but analyzed in tandem, have been exploited to enhance our knowledge of brain structure and function.

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.

Magnetic resonance spectroscopy-guided transperineal prostate biopsy and brachytherapy for recurrent prostate cancer.

Brachytherapy targeted to the peripheral zone with magnetic resonance imaging (MRI) guidance is a prostate cancer treatment option with potentially fewer complications than other treatments. Follow-up MRI when failure is suspected is, however, difficult because of radiation-induced changes. Furthermore, MR spectroscopy (MRS) is compromised by susceptibility artifacts from radioactive seeds in the peripheral zone. We report a case in which combined MRI/MRS was useful for the detection of prostate cancer in the transitional zone in patients previously treated with MR-guided brachytherapy. We propose that MRI/MRS can help detect recurrent prostate cancer, guide prostate biopsy, and help manage salvage treatment decisions.

A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

OBJECTIVE: Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap. DESIGN: A simple log (or logit) and a more flexible Box-Cox normality transformations were applied to untransformed or transformed data from two clinical studies to predict complications following percutaneous coronary interventions (PCIs) and for image-guided neurosurgical resection results predicted by tumor volume, respectively. We compared a non-parametric with a parametric binormal estimate of the underlying ROC curve. To construct such a GOF test, we used the non-parametric and parametric areas under the curve (AUCs) as the metrics, with a resulting p value reported. RESULTS: In the interventional cardiology example, logit and Box-Cox transformations of the predictive probabilities led to satisfactory AUCs (AUC=0.888; p=0.78, and AUC=0.888; p=0.73, respectively), while in the brain tumor resection example, log and Box-Cox transformations of the tumor size also led to satisfactory AUCs (AUC=0.898; p=0.61, and AUC=0.899; p=0.42, respectively). In contrast, significant departures from GOF were observed without applying any transformation prior to assuming a binormal model (AUC=0.766; p=0.004, and AUC=0.831; p=0.03), respectively. CONCLUSIONS: In both studies the p values suggested that transformations were important to consider before applying any binormal model to estimate the AUC. Our analyses also demonstrated and confirmed the predictive values of different classifiers for determining the interventional complications following PCIs and resection outcomes in image-guided neurosurgery.