This PDF file contains the editorial “JMI 2014 List of Reviewers” for JMI Vol. 2 Issue 02

This PDF file contains the editorial “Introduction to the LUNGx Challenge” for JMI Vol. 2 Issue 02

This PDF file contains the editorial “Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned” for JMI Vol. 2 Issue 02

Semiconductor photon-counting detectors based on high atomic number, high density materials [cadmium zinc telluride (CZT)/cadmium telluride (CdTe)] for x-ray computed tomography (CT) provide advantages over conventional energy-integrating detectors, including reduced electronic and Swank noise, wider dynamic range, capability of spectral CT, and improved signal-to-noise ratio. Certain CT applications require high spatial resolution. In breast CT, for example, visualization of microcalcifications and assessment of tumor microvasculature after contrast enhancement require resolution on the order of 100  μm. A straightforward approach to increasing spatial resolution of pixellated CZT-based radiation detectors by merely decreasing the pixel size leads to two problems: (1) fabricating circuitry with small pixels becomes costly and (2) inter-pixel charge spreading can obviate any improvement in spatial resolution. We have used computer simulations to investigate position estimation algorithms that utilize charge sharing to achieve subpixel position resolution. To study these algorithms, we model a simple detector geometry with a 5×5 array of 200  μm pixels, and use a conditional probability function to model charge transport in CZT. We used COMSOL finite element method software to map the distribution of charge pulses and the Monte Carlo package PENELOPE for simulating fluorescent radiation. Performance of two x-ray interaction position estimation algorithms was evaluated: the method of maximum-likelihood estimation and a fast, practical algorithm that can be implemented in a readout application-specific integrated circuit and allows for identification of a quadrant of the pixel in which the interaction occurred. Both methods demonstrate good subpixel resolution; however, their actual efficiency is limited by the presence of fluorescent K-escape photons. Current experimental breast CT systems typically use detectors with a pixel size of 194  μm, with 2×2 binning during the acquisition giving an effective pixel size of 388  μm. Thus, it would be expected that the position estimate accuracy reported in this study would improve detection and visualization of microcalcifications as compared to that with conventional detectors.

Clinical studies including thousands of magnetic resonance imaging (MRI) scans offer potential for pathogenesis research in osteoarthritis. However, comprehensive quantification of all bone, cartilage, and meniscus compartments is challenging. We propose a segmentation framework for fully automatic segmentation of knee MRI. The framework combines multiatlas rigid registration with voxel classification and was trained on manual segmentations with varying configurations of bones, cartilages, and menisci. The validation included high- and low-field knee MRI cohorts from the Center for Clinical and Basic Research, the osteoarthritis initiative (QAI), and the segmentation of knee images10 (SKI10) challenge. In total, 1907 knee MRIs were segmented during the evaluation. No segmentations were excluded. Our resulting OAI cartilage volume scores are available upon request. The precision and accuracy performances matched manual reader re-segmentation well. The cartilage volume scan-rescan precision was 4.9% (RMS CV). The Dice volume overlaps in the medial/lateral tibial/femoral cartilage compartments were 0.80 to 0.87. The correlations with volumes from independent methods were between 0.90 and 0.96 on the OAI scans. Thus, the framework demonstrated precision and accuracy comparable to manual segmentations. Finally, our method placed second for cartilage segmentation in the SKI10 challenge. The comprehensive validation suggested that automatic segmentation is appropriate for cohorts with thousands of scans.

An image-processing technique for separating bones from soft tissue in static chest radiographs has been developed. The present study was performed to evaluate the usefulness of dynamic bone images in quantitative analysis of rib movement. Dynamic chest radiographs of 16 patients were obtained using a dynamic flat-panel detector and processed to create bone images by using commercial software (Clear Read BS, Riverain Technologies). Velocity vectors were measured in local areas on the dynamic images, which formed a map. The velocity maps obtained with bone and original images for scoliosis and normal cases were compared to assess the advantages of bone images. With dynamic bone images, we were able to quantify and distinguish movements of ribs from those of other lung structures accurately. Limited rib movements of scoliosis patients appeared as a reduced rib velocity field, resulting in an asymmetrical distribution of rib movement. Vector maps in all normal cases exhibited left/right symmetric distributions of the velocity field, whereas those in abnormal cases showed asymmetric distributions because of locally limited rib movements. Dynamic bone images were useful for accurate quantitative analysis of rib movements. The present method has a potential for an additional functional examination in chest radiography.

We propose a framework that efficiently employs intensity, gradient, and textural features for three-dimensional (3-D) segmentation of medical (MRI/CT) volumes. Our methodology commences by determining the magnitude of intensity variations across the input volume using a 3-D gradient detection scheme. The resultant gradient volume is utilized in a dynamic volume growing/formation process that is initiated in voxel locations with small gradient magnitudes and is concluded at sites with large gradient magnitudes, yielding a map comprising an initial set of partitions (or subvolumes). This partition map is combined with an entropy-based texture descriptor along with intensity and gradient attributes in a multivariate analysis-based volume merging procedure that fuses subvolumes with similar characteristics to yield a final/refined segmentation output. Additionally, a semiautomated version of the aforestated algorithm that allows a user to interactively segment a desired subvolume of interest as opposed to the entire volume is also discussed. Our approach was tested on several MRI and CT datasets and the results show favorable performance in comparison to the state-of-the-art ITK-SNAP technique.

Current four-dimensional computed tomography (4-D CT) lung image reconstruction methods rely on respiratory gating, such as surrogate, to sort the large number of axial images captured during multiple breathing cycles into serial three-dimensional CT images of different respiratory phases. Such sorting methods may be subject to external surrogate signal noises due to poor reproducibility of breathing cycles. New image-matching-based reconstruction algorithms refine the 4-D CT reconstruction by matching neighboring image slices, and they generally work better for the cine mode of 4-D CT acquisition than the helical mode due to different table positions of axial images in the helical mode. We propose a Bayesian model (BM) based automated 4-D CT lung image reconstruction for helical mode scans. BM allows for applying new spatial and temporal anatomical constraints in the optimization procedure. Using an iterative optimization procedure, each axial image is assigned to a respiratory phase to make sure the anatomical structures are spatially and temporally smooth based on the BM framework. In experiments, we visually and quantitatively compared the results of the proposed BM-based 4-D CT reconstruction with the respiratory surrogate and the normalized cross-correlation based image matching method using both simulated and actual 4-D patient scans. The results indicated that the proposed algorithm yielded more accurate reconstruction and fewer artifacts in the 4-D CT image series.

Current density imaging (CDI) is a magnetic resonance (MR) imaging technique that could be used to study current pathways inside the tissue. The current distribution is measured indirectly as phase changes. The inherent noise in the MR imaging technique degrades the accuracy of phase measurements leading to imprecise current variations. The outcome can be affected significantly, especially at a low signal-to-noise ratio (SNR). We have shown the residual noise distribution of the phase to be Gaussian-like and the noise in CDI images approximated as a Gaussian. This finding matches experimental results. We further investigated this finding by performing comparative analysis with denoising techniques, using two CDI datasets with two different currents (20 and 45 mA). We found that the block-matching and three-dimensional (BM3D) technique outperforms other techniques when applied on current density (J). The minimum gain in noise power by BM3D applied to J compared with the next best technique in the analysis was found to be around 2 dB per pixel. We characterize the noise profile in CDI images and provide insights on the performance of different denoising techniques when applied at two different stages of current density reconstruction.

Liver segmentation continues to remain a major challenge, largely due to its intense complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography (CT) data. We present an approach to reconstructing the liver surface in low contrast CT. The main contributions are: (1) a stochastic resonance-based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulting from the cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and medical image computing and computer-assisted interventions grand challenge workshop. Various parameters in the algorithm, such as w, Δt, z, α, μ, α₁, and α₂, play imperative roles, thus their values are precisely selected. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method.

X-ray video fluoroscopy along with two-dimensional–three-dimensional (2D-3D) registration techniques is widely used to study joints in vivo kinematic behaviors. These techniques, however, are generally very sensitive to the initial alignment of the 3-D model. We present an automatic initialization method for 2D-3D registration of medical images. The contour of the knee bone or implant was first automatically extracted from a 2-D x-ray image. Shape descriptors were calculated by normalized elliptical Fourier descriptors to represent the contour shape. The optimal pose was then determined by a hybrid classifier combining k-nearest neighbors and support vector machine. The feasibility of the method was first validated on computer synthesized images, with 100% successful estimation for the femur and tibia implants, 92% for the femur and 95% for the tibia. The method was further validated on fluoroscopic x-ray images with all the poses of the testing cases successfully estimated. Finally, the method was evaluated as an initialization of a feature-based 2D-3D registration. The initialized and uninitialized registrations had success rates of 100% and 50%, respectively. The proposed method can be easily utilized for 2D-3D image registration on various medical objects and imaging modalities.

We have previously proposed an intersection profile method for reconstructing four-dimensional (4-D) magnetic resonance imaging (MRI) consisting of one breathing cycle of the thoracoabdominal region. This method captures a set of temporal sequence images in a proper sagittal plane and sets of temporal sequence images in continuous coronal slices. The former set is used as a navigator slice and the latter sets are used as data slices. A 4-D MRI is reconstructed by synchronizing the respiratory pattern found in the navigator slice and the data slices. We propose a prospective method to reduce the acquisition time for data slices. During data slice acquisition, the synchronization process between the respiratory pattern found in the navigator slice and one data slice is monitored in real time. Data acquisition will be terminated and moved to the next data slice based on a threshold value. We used 14 data sets (seven patients with certain pulmonary disease and seven healthy volunteers) previously obtained for the original intersection profile method for a simulation using the proposed method to evaluate the time reduction and impact on image quality. Each of the data set was tested using three different threshold values and the acquisition time can be reduced up to 75%. Although the quantitative evaluation of image quality was slightly worse than that by the conventional method, the difference based on the visual inspection was subtle to human eyes.

A challenge when using current magnetic resonance (MR)-based attenuation correction in positron emission tomography/MR imaging (PET/MRI) is that the MRIs can have a signal void around the dental fillings that is segmented as artificial air-regions in the attenuation map. For artifacts connected to the background, we propose an extension to an existing active contour algorithm to delineate the outer contour using the nonattenuation corrected PET image and the original attenuation map. We propose a combination of two different methods for differentiating the artifacts within the body from the anatomical air-regions by first using a template of artifact regions, and second, representing the artifact regions with a combination of active shape models and k-nearest-neighbors. The accuracy of the combined method has been evaluated using 25 ¹⁸F-fluorodeoxyglucose PET/MR patients. Results showed that the approach was able to correct an average of 97±3% of the artifact areas.

An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges–Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., <63  pixels²) and with a larger offset length (i.e., <7  pixels), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

Water resonance lineshapes observed in breast lesions imaged with high spectral and spatial resolution (HiSS) magnetic resonance imaging have been shown to contain diagnostically useful non-Lorentzian components. The purpose of this work is to update a previous method of breast lesion diagnosis by including phase-corrected absorption and dispersion spectra. This update includes information about the shape of the complex water resonance, which could improve the performance of a computer-aided diagnosis breast lesion classification scheme. The non-Lorentzian characteristics observed in complex breast lesion water resonance spectra are characterized by comparing a plot of the real versus imaginary components of the spectrum to that of a perfect complex Lorentzian spectrum, a “dispersion versus absorption” (DISPA) analysis technique. Distortion in the shape of the observed spectra indicates underlying physiologic changes, which have been shown to be correlated with malignancy. These spectral shape distortions in each lesion voxel are quantified by summing the deviations in DISPA radius from an ideal complex Lorentzian spectrum over all Fourier components, yielding a “total radial difference” (TRD). We limited our analysis to those voxels in each lesion with the largest TRD. The number of voxels considered was dependent on the lesion size. The TRD was used to classify voxels from 15 malignant and 8 benign lesions (∼2400 voxels after voxel elimination). Lesion discrimination performance was evaluated for both the average and variance of the TRD within each lesion. Area under the receiver operating characteristic curve (ROC AUC) was used to assess both the voxel- and lesion-based discrimination methods in the task of distinguishing between malignant and benign. In the task of distinguishing voxels from malignant and benign lesions, TRD yielded an AUC of 0.89 (95% confidence interval [0.84, 0.91]). In the task of distinguishing malignant from benign lesions, the average radial difference yielded an AUC of 0.90 (95% confidence interval [0.71, 1.00]) and the variance in the radial difference yielded an AUC of 0.84 (95% confidence interval [0.61, 0.99]). We have applied the DISPA spectroscopic analysis method to HiSS data in order to identify and quantify voxels in breast lesions displaying non-Lorentzian characteristics. We have shown that a breast lesion classification scheme based on the absorption and dispersion spectral data obtained from HiSS acquisitions may outperform a similar classifier based on single off-peak component analysis, as it uses shape details of the entire spectrum instead of the magnitude at a single spectral location.

Magnetic resonance imaging is a technique for the diagnosis and classification of brain tumors. Discrete compactness is a morphological feature of two-dimensional and three-dimensional objects. This measure determines the compactness of a discretized object depending on the sum of the areas of the connected voxels and has been used for understanding the morphology of nonbrain tumors. We hypothesized that regarding brain tumors, we may improve the malignancy grade classification. We analyzed the values in 20 patients with different subtypes of primary brain tumors: astrocytoma, oligodendroglioma, and glioblastoma multiforme subdivided into the contrast-enhanced and the necrotic tumor regions. The preliminary results show an inverse relationship between the compactness value and the malignancy grade of gliomas. Astrocytomas exhibit a mean of 973±14, whereas oligodendrogliomas exhibit a mean of 942±21. In contrast, the contrast-enhanced region of the glioblastoma presented a mean of 919±43, and the necrotic region presented a mean of 869±66. However, the volume and area of the enclosing surface did not show a relationship with the malignancy grade of the gliomas. Discrete compactness appears to be a stable characteristic between primary brain tumors of different malignancy grades, because similar values were obtained from different patients with the same type of tumor.

The two-dimensional S-transform (ST-2D) is a time-frequency representation that is widely used in medical image processing but prohibitive in both storage and computation time. The high computation time required for generating local spectrum discourages the use of ST-2D for analyzing textures in medical images. A two-dimensional fast time-frequency transform (FTFT-2D) for computing the local spectrum instantaneously and accurately is proposed. It can also be used to compute the complete redundant discrete ST-2D output, if needed. It reduces the storage requirement by generating a compressed form of the ST-2D. In addition, the memory efficient and adaptive nature of the FTFT-2D make it suitable for user-specific requirements.

The aim of this study was to investigate the diagnostic performance of radiological technologists (RTs) in the detection of malignant microcalcifications and to evaluate how much computer-aided detection (CADe) improved their performances compared with those by expert breast radiologists (BRs). Six board-certified breast RTs and four board-certified BRs participated in a free-response receiver operating characteristic observer study. The dataset consisted of 75 cases (25 malignant, 25 benign, and 25 normal cases) of digital mammograms, selected from the digital database for screening mammography provided by the University of South Florida. Average figure of merit (FOM) of the RTs’ performances was statistically analyzed using jack-knife free-response receiver operating characteristic and compared with that of expert BRs. The detection performance of RTs was significantly improved by using CADe; average sensitivity was increased from 46.7% to 56.7%, with a decrease in the average number of false positives per case from 0.19 to 0.13. Detection accuracy of an average FOM was improved from 0.680 to 0.816 (p=0.001) and the difference in FOMs between RTs and radiologists failed to reach statistical significance. RTs’ performances for the identification of malignant microcalcifications on digital mammography were sufficiently high and comparable to those of radiologists by using CADe.

Recent studies show that the response rate to cardiac resynchronization therapy (CRT) could be improved if the left ventricle (LV) is paced at the site of the latest mechanical activation, but away from the myocardial scar. A prototype system for CRT lead placement guidance that combines LV functional information from ultrasound with live x-ray fluoroscopy was developed. Two mean anatomical models, each containing LV epi-, LV endo- and right ventricle endocardial surfaces, were computed from a database of 33 heart failure patients as a substitute for a patient-specific model. The sphericity index was used to divide the observed population into two groups. The distance between the mean and the patient-specific models was determined using a signed distance field metric (reported in mm). The average error values for LV epicardium were −0.4±4.6 and for LV endocardium were −0.3±4.4. The validity of using average LV models for a CRT procedure was tested by simulating coronary vein selection in a group of 15 CRT candidates. The probability of selecting the same coronary branch, when basing the selection on the average model compared to a patient-specific model, was estimated to be 95.3±2.9%. This was found to be clinically acceptable.

Registration of three-dimensional (3-D) magnetic resonance (MR) to 3-D transrectal ultrasound (TRUS) prostate images is an important step in the planning and guidance of 3-D TRUS guided prostate biopsy. In order to accurately and efficiently perform the registration, a nonrigid landmark-based registration method is required to account for the different deformations of the prostate when using these two modalities. We describe a nonrigid landmark-based method for registration of 3-D TRUS to MR prostate images. The landmark-based registration method first makes use of an initial rigid registration of 3-D MR to 3-D TRUS images using six manually placed approximately corresponding landmarks in each image. Following manual initialization, the two prostate surfaces are segmented from 3-D MR and TRUS images and then nonrigidly registered using the following steps: (1) rotationally reslicing corresponding segmented prostate surfaces from both 3-D MR and TRUS images around a specified axis, (2) an approach to find point correspondences on the surfaces of the segmented surfaces, and (3) deformation of the surface of the prostate in the MR image to match the surface of the prostate in the 3-D TRUS image and the interior using a thin-plate spline algorithm. The registration accuracy was evaluated using 17 patient prostate MR and 3-D TRUS images by measuring the target registration error (TRE). Experimental results showed that the proposed method yielded an overall mean TRE of 3.50±1.34  mm for the rigid registration and 2.24±0.71  mm for the nonrigid registration, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm. A landmark-based nonrigid 3-D MR-TRUS registration approach is proposed, which takes into account the correspondences on the prostate surface, inside the prostate, as well as the centroid of the prostate. Experimental results indicate that the proposed method yields clinically sufficient accuracy.

We aim to develop a better understanding of perception of similarity in focal computed tomography (CT) liver images to determine the feasibility of techniques for developing reference sets for training and validating content-based image retrieval systems. In an observer study, four radiologists and six nonradiologists assessed overall similarity and similarity in 5 image features in 136 pairs of focal CT liver lesions. We computed intra- and inter-reader agreements in these similarity ratings and viewed the distributions of the ratings. The readers’ ratings of overall similarity and similarity in each feature primarily appeared to be bimodally distributed. Median Kappa scores for intra-reader agreement ranged from 0.57 to 0.86 in the five features and from 0.72 to 0.82 for overall similarity. Median Kappa scores for inter-reader agreement ranged from 0.24 to 0.58 in the five features and were 0.39 for overall similarity. There was no significant difference in agreement for radiologists and nonradiologists. Our results show that developing perceptual similarity reference standards is a complex task. Moderate to high inter-reader variability precludes ease of dividing up the workload of rating perceptual similarity among many readers, while low intra-reader variability may make it possible to acquire large volumes of data by asking readers to view image pairs over many sessions.

Retinal fundus evaluation is learned through experience and training. This study aimed to determine the image presentation characteristics and the accompanying evaluation techniques, which led to the most accurate and efficient retinal pathology detection method. Phase I included 25 novice clinicians asked to evaluate 14 different pathologies using spatial versus temporal image presentations. Phase II included 25 different novice clinicians asked to evaluate five different simulated pathologies at three different pixel sizes presented in both spatial and temporal image presentations. Accuracy and speed of recognition were evaluated between the spatial and temporal presentations of the same simulated pathology. In phase l, subjects were significantly faster at simulated pathology detection using a temporal presentation with a 95% accuracy rate versus a spatial presentation with a 79% accuracy rate. In phase II, subjects demonstrated significant differences in speed of detection using the temporal technique at all 3 pixel number sizes with the greatest difference in detection times shown at the smallest retinal defects. Accuracy and speed of recognition in simulated pathology assessment were improved in a temporal presentation and the greatest improvements were demonstrated at the smallest pixel numbers.

Determining whether glioblastoma multiforme (GBM) is progressing despite treatment is challenging due to the pseudoprogression phenomenon seen on conventional MRIs, but relative cerebral blood volume (CBV) has been shown to be helpful. As CBV’s calculation from perfusion-weighted images is not standardized, we investigated whether there were differences between three FDA-cleared software packages in their CBV output values and subsequent performance regarding predicting survival/progression. Forty-five postradiation therapy GBM cases were retrospectively identified as having indeterminate MRI findings of progression versus pseudoprogression. The dynamic susceptibility contrast MR images were processed with different software and three different relative CBV metrics based on the abnormally enhancing regions were computed. The intersoftware intraclass correlation coefficients were 0.8 and below, depending on the metric used. No statistically significant difference in progression determination performance was found between the software packages, but performance was better for the cohort imaged at 3.0 T versus those imaged at 1.5 T for many relative CBV metric and classification criteria combinations. The results revealed clinically significant variation in relative CBV measures based on the software used, but minimal interoperator variation. We recommend against using specific relative CBV measurement thresholds for GBM progression determination unless the same software or processing algorithm is used.

Twelve healthy subjects underwent hydrogen-1 magnetic resonance spectroscopy (¹H-MRS) acquisition (15×15×15  mm³), diffusion tensor imaging (DTI) with a b-value of 600  s mm⁻², and fat-water magnetic resonance imaging (MRI) using the Dixon method. Subject-specific muscle fiber orientation, derived from DTI, was used to estimate the lipid proton spectral chemical shift. Pennation angles were measured as 23.78 deg in vastus lateralis (VL), 17.06 deg in soleus (SO), and 8.49 deg in tibialis anterior (TA) resulting in a chemical shift between extramyocellular lipids (EMCL) and intramyocellular lipids (IMCL) of 0.15, 0.17, and 0.19 ppm, respectively. IMCL concentrations were 8.66±1.24  mmol kg⁻¹, 6.12±0.77  mmol kg⁻¹, and 2.33±0.19  mmol kg⁻¹ in SO, VL, and TA, respectively. Significant differences were observed in IMCL and EMCL pairwise comparisons in SO, VL, and TA (p<0.05). Strong correlations were observed between total fat fractions from ¹H-MRS and Dixon MRI for VL (r=0.794), SO (r=0.655), and TA (r=0.897). Bland-Altman analysis between fat fractions (FF_MRS and FF_MRI) showed good agreement with small limits of agreement (LoA): bias=−0.21% (LoA: −1.12% to 0.69%) in VL, bias=0.025% (LoA: −1.28% to 1.33%) in SO, and bias=−0.13% (LoA: −0.74% to 0.47%) in TA. The results of this study demonstrate the variation in muscle fiber orientation and lipid concentrations in these three skeletal muscle types.

Optical coherence tomography (OCT) provides structural information of laryngeal tissue which is comparable to histopathological analysis of biopsies taken under general anesthesia. In awake patients, movements impede clinically useful OCT acquisition. Therefore, an automatic compensation of movements was implemented into a swept source OCT-laryngoscope. Video and OCT beam path were combined in one tube of 10-mm diameter. Segmented OCT images served as distance sensor and a feedback control adjusted the working distance between 33 and 70 mm by synchronously translating the reference mirror and focusing lens. With this motion compensation, the tissue was properly visible in up to 88% of the acquisition time. During quiet respiration, OCT contrasted epithelium and lamina propria. Mean epithelial thickness was measured to be 109 and 135  μm in female and male, respectively. Furthermore, OCT of mucosal wave movements during phonation enabled estimation of the oscillation frequency and amplitude. Regarding clinical issues, the OCT-laryngoscope with automated working distance adjustment may support the estimation of the depth extent of epithelial lesions and contribute to establish an indication for a biopsy. Moreover, OCT of the vibrating vocal folds provides functional information, possibly giving further insight into mucosal behavior during the vibratory cycle.

The digital imaging and communications in medicine (DICOM) information model combines pixel data and its metadata in a single object. There are user scenarios that only need metadata manipulation, such as deidentification and study migration. Most picture archiving and communication system use a database to store and update the metadata rather than updating the raw DICOM files themselves. The multiseries DICOM (MSD) format separates metadata from pixel data and eliminates duplicate attributes. This work promotes storing DICOM studies in MSD format to reduce the metadata processing time. A set of experiments are performed that update the metadata of a set of DICOM studies for deidentification and migration. The studies are stored in both the traditional single frame DICOM (SFD) format and the MSD format. The results show that it is faster to update studies’ metadata in MSD format than in SFD format because the bulk data is separated in MSD and is not retrieved from the storage system. In addition, it is space efficient to store the deidentified studies in MSD format as it shares the same bulk data object with the original study. In summary, separation of metadata from pixel data using the MSD format provides fast metadata access and speeds up applications that process only the metadata.

A dual-element needle transducer for intravascular ultrasound imaging has been developed. A low-frequency element and a high-frequency element were integrated into one device to obtain images which conveyed both low- and high-frequency information from a single scan. The low-frequency element with a center frequency of 48 MHz was fabricated from the single crystal form of lead magnesium niobate-lead titanate solid solution with two matching layers (MLs) and the high frequency element with a center frequency of 152 MHz was fabricated from lithium niobate with one ML. The measured axial and lateral resolutions were 27 and 122  μm, respectively, for the low-frequency element, and 14 and 40  μm, respectively, for the high-frequency element. The performance of the dual-element needle transducer was validated by imaging a tissue-mimicking phantom with lesion-mimicking area, and ex vivo rabbit aortas in water and rabbit whole blood. The results suggest that a low-frequency element effectively provides depth resolved images of the whole vessel and its adjacent tissue, and a high-frequency element visualizes detailed structure near the surface of the lumen wall in the presence of blood within the lumen. The advantages of a dual-element approach for intravascular imaging are also discussed.