Low Tissue Contrast Research Articles

Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation

Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination processes. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6–8months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889±0.008 for white matter and 0.870±0.006 for gray matter.

Semi-automatic segmentation of subcutaneous tumours from micro-computed tomography images

This paper outlines the first attempt to segment the boundary of preclinical subcutaneous tumours, which are frequently used in cancer research, from micro-computed tomography (microCT) image data. MicroCT images provide low tissue contrast, and the tumour-to-muscle interface is hard to determine, however faint features exist which enable the boundary to be located. These are used as the basis of our semi-automatic segmentation algorithm. Local phase feature detection is used to highlight the faint boundary features, and a level set-based active contour is used to generate smooth contours that fit the sparse boundary features. The algorithm is validated against manually drawn contours and micro-positron emission tomography (microPET) images. When compared against manual expert segmentations, it was consistently able to segment at least 70% of the tumour region (n = 39) in both easy and difficult cases, and over a broad range of tumour volumes. When compared against tumour microPET data, it was able to capture over 80% of the functional microPET volume. Based on these results, we demonstrate the feasibility of subcutaneous tumour segmentation from microCT image data without the assistance of exogenous contrast agents. Our approach is a proof-of-concept that can be used as the foundation for further research, and to facilitate this, the code is open-source and available from www.setuvo.com.

A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

Abstract Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

Incremental learning with selective memory (ILSM): towards fast prostate localization for image guided radiotherapy.

Image-guided radiotherapy (IGRT) requires fast and accurate localization of prostate in treatment CTs, which is challenging due to low tissue contrast and large anatomical variations across patients. On the other hand, in IGRT workflow, a series of CT images is acquired from the same patient under treatment, which contains valuable patient-specific information yet is often neglected by previous works. In this paper, we propose a novel learning framework, namely incremental learning with selective memory (ILSM), to effectively learn the patient-specific appearance characteristics from these patient-specific images. Specifically, starting with a population-based discriminative appearance model, ILSM aims to "personalize" the model to fit patient-specific appearance characteristics. Particularly, the model is personalized with two steps, backward pruning that discards obsolete population-based knowledge, and forward learning that incorporates patient-specific characteristics. By effectively combining the patient-specific characteristics with the general population statistics, the incrementally learned appearance model can localize the prostate of the specific patient much more accurately. Validated on a large dataset (349 CT scans), our method achieved high localization accuracy (DSC approximately 0.87) in 4 seconds.

Optimizing T1-weighted imaging of cortical myelin content at 3.0T

With increases in the sensitivity and resolution of anatomical MRI for the brain, methods for mapping the organization of the cerebral cortex by imaging its myelin content have emerged. This identifies major sensory and motor regions and could be used in studies of cortical organization, particularly if patterns of myelination can be visualized over the cortical surface robustly in individual subjects. The imaging problem is difficult, however, because of the relative thinness of the cerebral cortex and the low intracortical tissue contrast. In this paper, we optimize the contrast of T(1)-weighted MRI to help better visualize patterns of myelination. We measure a small but statistically significant difference in T(1) of 171 ± 40 ms between cortical regions with low and high myelin contents in the human cortex at 3T, and then perform simulations to choose parameters for an inversion-recovery pulse sequence that utilizes this T(1) difference to increase contrast within the cortex. We show that lengthening the delay between signal acquisition and the next inversion pulse in the sequence increases intracortical contrast more effectively than does image averaging. Using the optimized sequence, we show that major myelinated regions that are relatively thick, such as the primary motor and auditory regions, can be visualized well in individuals at 3T using whole-cortex 3D images made at 1mm isotropic resolution, while thinner regions, such as the primary visual cortex, can be visualized using targeted 3D images made at 0.5mm isotropic resolution. Our findings demonstrate that patterns of myelination can be better visualized in individual subjects when the imaging is optimized to highlight intracortical contrast and can help to pave the way for the creation of matched maps of microanatomy and function in the cortex of living individual humans.

Atlas-Free Surface Reconstruction of the Cortical Grey-White Interface in Infants

BackgroundThe segmentation of the cortical interface between grey and white matter in magnetic resonance images (MRI) is highly challenging during the first post-natal year. First, the heterogeneous brain maturation creates important intensity fluctuations across regions. Second, the cortical ribbon is highly folded creating complex shapes. Finally, the low tissue contrast and partial volume effects hamper cortex edge detection in parts of the brain.Methods and FindingsWe present an atlas-free method for segmenting the grey-white matter interface of infant brains in T2-weighted (T2w) images. We used a broad characterization of tissue using features based not only on local contrast but also on geometric properties. Furthermore, inaccuracies in localization were reduced by the convergence of two evolving surfaces located on each side of the inner cortical surface. Our method has been applied to eleven brains of one- to four-month-old infants. Both quantitative validations against manual segmentations and sulcal landmarks demonstrated good performance for infants younger than two months old. Inaccuracies in surface reconstruction increased with age in specific brain regions where the tissue contrast decreased with maturation, such as in the central region.ConclusionsWe presented a new segmentation method which achieved good to very good performance at the grey-white matter interface depending on the infant age. This method should reduce manual intervention and could be applied to pathological brains since it does not require any brain atlas.

Investigations of the origin of phase differences seen with ultrashort TE imaging of short T2 meniscal tissue

Ultrashort echo time MRI requires specialized pulse sequences with nominal echo times as low as a few microseconds to detect signals from the short T(2) tissues frequently encountered in the musculoskeletal system. Usually, magnitude images are reconstructed and these often show low tissue contrast. Ultrashort echo time phase images of the meniscus show surprisingly high contrast despite their very short echo time. In this article, we investigated the source of this contrast using the Bloch equations, simulations, phantom experiments, and tissue studies. Phase evolution was shown to occur in ultrashort echo time sequences during the finite radiofrequency pulse and readout periods, and previously unrecognized susceptibility differences between fiber groups were observed in the meniscus.

Rui Liao's work on patient-specific 3-D model guidance for interventional and hybrid-operating-room applications.

Compared to surgery, interventional and hybrid-operating-room (OR) approaches diagnose or treat pathology with the most minimally invasive techniques possible. By minimizing the physical trauma to the patient, peripheral or hybrid approaches can reduce infection rates and recovery time as well as shorten hospital stays. Minimally invasive approaches therefore are the trend and often the preferred choice, and may even be the only option for the patients associated with high surgery risks. Common interventional imaging modalities include 2-D X-ray fluoroscopy and ultrasound. However, fluoroscopic images do not display the anatomic structures without a contrast agent, which on the other hand, needs to be minimized for patients' safety. Ultrasound images suffer from relatively low image quality and tissue contrast problems. To augment the doctor's view of the patient's anatomy and help doctors navigate the devices to the targeted area with more confidence and a higher accuracy, high-resolution pre-operative volumetric data such as computed tomography and/or magnetic resonance can be fused with intra-operative 2-D images during interventions. A seamless workflow and accurate 2-D/3-D registration as well as cardiac and/or respiratory motion compensation are the key components for a successful image guidance system using a patient-specific 3-D model. Dr. Liao's research has been focused on developing methods and systems of 3-D model guidance for various interventions and hybrid-OR applications. Dr. Liao's work has led to several Siemens products with high clinical and/or market impact and a good number of scientific publications in leading journals/conferences on medical imaging.

Correction of motion artifacts in cone‐beam CT using a patient‐specific respiratory motion model

Respiratory motion adversely affects CBCT image quality and limits its localization accuracy for image-guided radiation treatment. Motion correction methods in CBCT have focused on the thorax because of its higher soft tissue contrast, whereas low-contrast tissue in abdomen remains a challenge. The authors report on a method to correct respiration-induced motion artifacts in 1 min CBCT scans that is applicable in both thorax and abdomen, using a motion model adapted to the patient from a respiration-correlated image set. Model adaptation consists of nonrigid image registration that maps each image to a reference image in the respiration-correlated set, followed by a principal component analysis to reduce errors in the nonrigid registration. The model parametrizes the deformation field in terms of observed surrogate (diaphragm or implanted marker) position and motion (inhalation or exhalation) between the images. In the thorax, the model is obtained from the same CBCT images that are to be motion-corrected, whereas in the abdomen, the model uses respiration-correlated CT (RCCT) images acquired prior to the treatment session. The CBCT acquisition is a single 360 degrees rotation lasting 1 min, while simultaneously recording patient breathing. The approximately 600 projection images are sorted into six (in thorax) or ten (in abdomen) subsets and reconstructed to obtain a set of low-quality respiration-correlated RC-CBCT images. Application of the motion model deforms each of the RC-CBCT images to a chosen reference image in the set; combining all images yields a single high-quality CBCT image with reduced blurring and motion artifacts. Repeated application of the model with different reference images produces a series of motion-corrected CBCT images over the respiration cycle, for determining the motion extent of the tumor and nearby organs at risk. The authors also investigate a simpler correction method, which does not use PCA and correlates motion state with respiration phase, thus assuming repeatable breathing patterns. Comparison of contrast-to-noise ratios of pixel intensities within anatomical structures relative to surrounding background tissue provides a quantitative assessment of relative organ visibility. Evaluation in lung phantom, two patient cases in thorax and two in upper abdomen, shows that blurring and streaking artifacts are visibly reduced with motion correction. The boundaries of tumors in the thorax, liver, and kidneys are sharper and more discernible. Repeat application of the method in one thorax case, with reference images chosen at end expiration and end inspiration, indicates its feasibility for observing tumor motion extent. Phase-based motion correction without PCA reduces blurring less effectively; in addition, implanted markers appear broken up, indicating inconsistencies in the phase-based correction. In structures showing 1 cm or more motion excursion, PCA-based motion correction shows the highest contrast-to-noise ratios in the cases examined. Motion correction of CBCT is feasible and yields observable improvement in the thorax and abdomen. The PCA-based model is an important component: First, by reducing deformation errors caused by the nonrigid registration and second, by relating deformation to surrogate position rather than phase, thus accommodating breathing pattern changes between imaging sessions. The accuracy of the method requires confirmation in further patient studies.

Efficient Tumor Targeting with High-Affinity Designed Ankyrin Repeat Proteins: Effects of Affinity and Molecular Size

Slow-clearing, tumor-targeting proteins such as monoclonal antibodies typically exhibit high tumor accumulation but low tissue contrast, whereas intermediate-sized proteins such as scFvs show faster clearance but only moderate tumor accumulation. For both, tumor targeting does not seem to improve further above an optimal affinity. We show here that with very small high-affinity proteins such as designed ankyrin repeat proteins (DARPins), these limits can be overcome. We have systematically investigated the influence of molecular mass and affinity on tumor accumulation with DARPins with specificity for HER2 in SK-OV-3.ip nude mouse xenografts. DARPins with a mass of 14.5 kDa and affinities between 270 nmol/L and 90 pmol/L showed a strong correlation of tumor accumulation with affinity to HER2, with the highest affinity DARPin reaching 8% ID/g after 24 hours and 6.5% ID/g after 48 hours (tumor-to-blood ratio >60). Tumor autoradiographs showed good penetration throughout the tumor mass. Genetic fusion of two DARPins (30 kDa) resulted in significantly lower tumor accumulation, similar to values observed for scFvs, whereas valency had no influence on accumulation. PEGylation of the DARPins increased the circulation half-life, leading to higher tumor accumulation (13.4% ID/g after 24 hours) but lower tumor-to-blood ratios. Affinity was less important for tumor uptake of the PEGylated constructs. We conclude that two regimes exist for delivering high levels of drug to a tumor: small proteins with very high affinity, such as unmodified DARPins, and large proteins with extended half-life, such as PEGylated DARPins, in which the importance of affinity is less pronounced.

Robust brain registration using adaptive probabilistic atlas.

Elastic image registration is widely used to adapt brain images to a common template space, and, in complementary fashion, to adapt an anatomical template to a subject's anatomy. Although HAMMER is a very accurate image-registration algorithm, it requires a 3-class segmentation step prior to registration, and its performance is affected by segmentation quality. We here propose a new framework to improve this algorithm's robustness to poor initial segmentation. Our new framework is based on Adaptive Generalized Expectation Maximization (AGEM) for unified segmentation and registration, in which we use an adaptive strategy to incorporate spatial information from a probabilistic atlas to improve segmentation and registration simultaneously. Our experiments using real MR brain images indicate that our integrated approach improves registration accuracy; we have also found that our iterative approach renders HAMMER robust to low tissue contrast, which hinders 3-class segmentation.

Benchmarking a novel ultrasound-CT fusion system for respiratory motion management in radiotherapy: Assessment of spatio-temporal characteristics and comparison to 4DCT

Management of respiratory motion during radiation therapy requires treatment planning and simulation using imaging modalities that possess sufficient spatio-temporal accuracy and precision. An investigation into the use of a novel ultrasound (US) imaging system for assessment of respiratory motion is presented, exploiting its good soft tissue contrast and temporal precision. The system dynamically superimposes the appropriate image plane sampled from a reference CT data set with the corresponding US B-mode image. An articulating arm is used for spatial registration. While the focus of the study was to quantify the system's ability to track respiratory motion, certain unique spatial calibration procedures were devised that render the software potentially valuable to the general research community. These include direct access to all transformation matrix elements and image scaling factors, a manual latency correction function, and a three-point spatial registration procedure that allows the system to be used in any room possessing a traditional radiotherapy laser localization system. Counter-intuitively, it was discovered that a manual procedure for calibrating certain transformation matrix elements produced superior accuracy to that of an algorithmic Levenberg-Marquardt optimization method. The absolute spatial accuracy was verified by comparing the physical locations of phantom test objects measured using the spatially registered US system, and using data from a 3DCT scan of the phantom as a reference. The spatial accuracy of the display superposition was also tested in a similar manner. The system's dynamic properties were then assessed using three methods. First, the overall system response time was studied using a programmable motion phantom. This included US video update, articulating arm update, CT data set resampling, and image display. The next investigation verified the system's ability to measure the range of motion of a moving anatomical test phantom that possessed both high and low contrast test objects. Finally, the system's performance was compared to that of a four-dimensional CT (4DCT) data set. The absolute spatial and display superposition accuracy was found to be better than 2 mm and typically 1 mm. Overall dynamic system response was adequate to produce a mean relative positional error of less than 1 mm if an empiric latency correction of 3 video frames was incorporated. The dynamic CT/US display mode was able to assess phantom motion for both high and low contrast test objects to within 1 mm, and compared favorably to the 4DCT data. The 4DCT movie loop accurately assessed the target motion for both of the high and low contrast objects tested, but the minimum intensity and average intensity reconstructions did not. This investigation demonstrated that this US system possesses sufficient spatio-temporal accuracy to properly assess respiratory motion. Future work will seek to demonstrate efficacy in its clinical application to respiratory motion assessment, particularly for sites in the upper abdomen, where low tissue contrast is evident.

Adaptive Spectral Sensitive Filter for Tissue Harmonic Imaging

Developments of tissue and contrast harmonics imaging in medical ultrasound technologies have enabled the display of low-contrast tissue structures that have not been previously recognized using conventional B-mode imaging. Images are constructed using harmonics generated while transmitted ultrasonic waves propagate through the tissue. Two techniques can be used to achieve this: the first uses a band-pass digital filter for the harmonic frequency band, and the second applies two consecutive transmit pulses that are 180° out of phase. In the present study, an alternative technique for tissue harmonic imaging is proposed. A pair of adaptive filters is employed for each receiving RF signal acquired by the ultrasonic transducer array. The technique uses a single transmit pulse but cancels out the fundamental frequency of the received signal by appropriately changing the delay terms of the adaptive filters. The resulting signal is improved at the second harmonic frequency. In order to meet the optimum design requirements, 9-tap delays are applied to each adaptive filter.

Image contrast in sections of epoxy resin-embedded biological material: maintenance of a proper anhydride-epoxy ratio during tissue impregnation.

Epoxy resins are mixtures of several components formulated in exact proportions to assure proper polymerization. It is necessary that this composition be maintained throughout the tissue being infiltrated to avoid embedding artifacts. Unfortunately, the separate resin components will not penetrate the tissue at the same rate unless they all have the same viscosity and molecular size. Failure to meet these criteria will result in component separation during tissue infiltration and may result in severe embedding defects. This and several ancillary problems such as low tissue contrast, damage to diamond knives, and poor postsection staining are explored in this brief discussion.

A low contrast-spatial resolution phantom for MR imaging using surface coils

A phantom for magnetic resonance imaging (MRI) using surface coils was developed. This phantom, which is made from a combination of a single signal-producing material and a non-signal-producing material, can be used to simulate small low-contrast regions in the surrounding tissue. MR images of this phantom showed changes in contrast resolution and spatial resolution corresponding to the contrast characteristics detected under varying scan conditions. These changes in contrast resolution and spatial resolution were visually confirmed on MR images of the phantom. The results indicate that this phantom can simulate low-contrast tissue, mimicking regions in the surrounding simulated normal tissue.

An image processing/stereological analysis system for transmission electron microscopy.

This study examines the feasibility of combining computer image digitization, image enhancement, and point counting stereological techniques to quantify video images from transmission electron microscopes (TEM). The essential hardware consists of an IBM PC/AT, a Matrox imaging board, a digitizing tablet, a high resolution black and white monitor, and a portable mass storage device. In addition a video camera must be mounted to the TEM. The software is written in three modules which have numerous routines for image acquisition, enhancement, and quantification. Quantification is achieved by selecting an electronic lattice and superimposing it on the cell image. A cursor is moved on the lattice (via the digitizing tablet) and the points are entered into a spreadsheet. One of the major limitations of the system was the reduced resolution inherent in the current hardware. However, sampling experiments showed that one could compensate for the reduced resolution by increasing the magnification of the digitized images, and the stereological values from digitized images compared favorably to those from electron micrographs. Furthermore, the system proved advantageous by eliminating the usual darkroom work, and in enhancing low contrast tissue. In spite of several hardware limitations, the concept of quantifying computer digitized TEM images appears promising.

Daily Growth Rings in Otoliths of Young-of-the-Year Largemouth Bass

Daily rings were found in the otoliths of laboratory-raised, young-of-the-year largemouth bass Micropterus salmoides for at least 151 days after swim-up. Ring formation began at hatching but the total complement of prolarval rings was visible for only 10–15 days after swim-up. Subdaily rings were visible in the posterior field but were easily distinguished from daily rings. Both high- and low-contrast tissues were present in otoliths from fish over 45 days old. Sagittal and frontal sections yielded ages of similar accuracy but sagittal sections were much easier and faster to prepare and nearly twice as many were readable. Otolith growth was directly proportional to body growth and the regression intercept was not substantially different from 0.