This PDF file contains the editorial “The Spectrum of Medical Imaging in the 2015 International Year of Light” for JMI Vol. 2 Issue 03

We present a framework for optical tomography based on a path integral. Instead of directly solving the radiative transport equations, which have been widely used in optical tomography, we use a path integral that has been developed for rendering participating media based on the volume rendering equation in computer graphics. For a discretized two-dimensional layered grid, we develop an algorithm to estimate the extinction coefficients of each voxel with an interior point method. Numerical simulation results are shown to demonstrate that the proposed method works well.

Variations among detector channels in computed tomography can lead to ring artifacts in the reconstructed images and biased estimates in projection-based material decomposition. Typically, the ring artifacts are corrected by compensation methods based on flat fielding, where transmission measurements are required for a number of material-thickness combinations. Phantoms used in these methods can be rather complex and require an extensive number of transmission measurements. Moreover, material decomposition needs knowledge of the individual response of each detector channel to account for the detector inhomogeneities. For this purpose, we have developed a spectral response model that binwise predicts the response of a multibin photon-counting detector individually for each detector channel. The spectral response model is performed in two steps. The first step employs a forward model to predict the expected numbers of photon counts, taking into account parameters such as the incident x-ray spectrum, absorption efficiency, and energy response of the detector. The second step utilizes a limited number of transmission measurements with a set of flat slabs of two absorber materials to fine-tune the model predictions, resulting in a good correspondence with the physical measurements. To verify the response model, we apply the model in two cases. First, the model is used in combination with a compensation method which requires an extensive number of transmission measurements to determine the necessary parameters. Our spectral response model successfully replaces these measurements by simulations, saving a significant amount of measurement time. Second, the spectral response model is used as the basis of the maximum likelihood approach for projection-based material decomposition. The reconstructed basis images show a good separation between the calcium-like material and the contrast agents, iodine and gadolinium. The contrast agent concentrations are reconstructed with more than 94% accuracy.

Metal artifacts have been a problem associated with computed tomography (CT) since its introduction. Recent techniques to mitigate this problem have included utilization of high-energy (keV) virtual monochromatic spectral (VMS) images, produced via dual-energy CT (DECT). A problem with these high-keV images is that contrast enhancement provided by all commercially available contrast media is severely reduced. Contrast agents based on higher atomic number elements can maintain contrast at the higher energy levels where artifacts are reduced. This study evaluated three such candidate elements: bismuth, tantalum, and tungsten, as well as two conventional contrast elements: iodine and barium. A water-based phantom with vials containing these five elements in solution, as well as different artifact-producing metal structures, was scanned with a DECT scanner capable of rapid operating voltage switching. In the VMS datasets, substantial reductions in the contrast were observed for iodine and barium, which suffered from contrast reductions of 97% and 91%, respectively, at 140 versus 40 keV. In comparison under the same conditions, the candidate agents demonstrated contrast enhancement reductions of only 20%, 29%, and 32% for tungsten, tantalum, and bismuth, respectively. At 140 versus 40 keV, metal artifact severity was reduced by 57% to 85% depending on the phantom configuration.

The objective was to characterize the changes seen from incident Monte Carlo-based scatter distributions in dedicated three-dimensional (3-D) breast single-photon emission computed tomography, with emphasis on the impact of scatter correction using the dual-energy window (DEW) method. Changes in scatter distributions with 3-D detector position were investigated for prone breast imaging with an ideal detector. Energy spectra within a high-energy scatter window measured from simulations were linearly fit, and the slope was used to characterize scatter distributions. The impact of detector position on the measured scatter fraction within various photopeak windows and the k value (ratio of scatter within the photopeak and scatter energy windows) useful for scatter correction was determined. Results indicate that application of a single k value with the DEW method in the presence of anisotropic object scatter distribution is not appropriate for trajectories including the heart and liver. The scatter spectra’s slope demonstrates a strong correlation to measured k values. Reconstructions of fixed-tilt 3-D acquisition trajectories with a single k value show quantification errors up to 5% compared to primary-only reconstructions. However, a variable-tilt trajectory provides improved sampling and minimizes quantification errors, and thus allows for a single k value to be used with the DEW method leading to more accurate quantification.

Various applications require information on breast parameters, such as breast length and volume. An optical system was designed and tested for measuring these parameters with subjects in a prone position. The study results were used for optimizing patient positioning and handling for a future breast computed tomography (BCT) system. Measurements were conducted using an optical measurement system. To test the functionality and accuracy of the system, measurements were performed using reference phantoms. Additionally, 20 women and 5 men were examined to calculate breast parameters in alternative positions and breathing states. The results of the optical measurements were compared with magnetic resonance imaging (MRI) measurements. Volume and length of the reference phantoms were determined with errors below 2%. The patient study demonstrated a mean breast volume of 530.7 ml for women during normal breathing. During an exhalation state, breast volume increased significantly by 17.7 ml in comparison with normal breathing. Differences with MRI measurements were found to be 3% for breast length and 9% for breast volume on average. The proposed optical measurement system was found to be suitable for measuring the dimensional parameters of the breast in a prone position and provides a tool for evaluating breast coverage for BCT.

Volumes reconstructed from tracked planar ultrasound images often contain regions where no information was recorded. Existing interpolation methods introduce image artifacts and tend to be slow in filling large missing regions. Our goal was to develop a computationally efficient method that fills missing regions while adequately preserving image features. We use directional sticks to interpolate between pairs of known opposing voxels in nearby images. We tested our method on 30 volumetric ultrasound scans acquired from human subjects, and compared its performance to that of other published hole-filling methods. Reconstruction accuracy, fidelity, and time were improved compared with other methods.

This study aimed to investigate imaging statistical approaches for classifying three-dimensional (3-D) osteoarthritic morphological variations among 169 temporomandibular joint (TMJ) condyles. Cone-beam computed tomography scans were acquired from 69 subjects with long-term TMJ osteoarthritis (OA), 15 subjects at initial diagnosis of OA, and 7 healthy controls. Three-dimensional surface models of the condyles were constructed and SPHARM-PDM established correspondent points on each model. Multivariate analysis of covariance and direction-projection-permutation (DiProPerm) were used for testing statistical significance of the differences between the groups determined by clinical and radiographic diagnoses. Unsupervised classification using hierarchical agglomerative clustering was then conducted. Compared with healthy controls, OA average condyle was significantly smaller in all dimensions except its anterior surface. Significant flattening of the lateral pole was noticed at initial diagnosis. We observed areas of 3.88-mm bone resorption at the superior surface and 3.10-mm bone apposition at the anterior aspect of the long-term OA average model. DiProPerm supported a significant difference between the healthy control and OA group (p-value=0.001). Clinically meaningful unsupervised classification of TMJ condylar morphology determined a preliminary diagnostic index of 3-D osteoarthritic changes, which may be the first step towards a more targeted diagnosis of this condition.

This paper presents a fully automated method for detection and segmentation of liver metastases in serial computed tomography (CT) examinations. Our method uses a given two-dimensional baseline segmentation mask for identifying the lesion location in the follow-up CT and locating surrounding tissues, using nonrigid image registration and template matching, in order to reduce the search area for segmentation. Adaptive region growing and mean-shift clustering are used to obtain the lesion segmentation. Our database contains 127 cases from the CT abdomen unit at Sheba Medical Center. Development of the methodology was conducted using 22 of the cases, and testing was conducted on the remaining 105 cases. Results show that 94 of the 105 lesions were detected, for an overall matching rate of 90% making the correct RECIST 1.1 assessment in 88% of the cases. The average Dice index was 0.83±0.08, the average sensitivity was 0.82±0.13, and the positive predictive value was 0.87±0.11. In 92% of the rated cases, the results were classified by the radiologists as acceptable or better. The segmentation performance, matching rate, and RECIST assessment results hence appear promising.

Inflammatory rheumatic diseases are the leading causes of disability and constitute a frequent medical disorder, leading to inability to work, high comorbidity, and increased mortality. The standard for diagnosing and differentiating arthritis is based on clinical examination, laboratory exams, and imaging findings, such as synovitis, bone edema, or joint erosions. Contrast-enhanced ultrasound (CEUS) examination of the small joints is emerging as a sensitive tool for assessing vascularization and disease activity. Quantitative assessment is mostly performed at the region of interest level, where the mean intensity curve is fitted with an exponential function. We showed that using a more physiologically motivated perfusion curve, and by estimating the kinetic parameters separately pixel by pixel, the quantitative information gathered is able to more effectively characterize the different perfusion patterns. In particular, we demonstrated that a random forest classifier based on pixelwise quantification of the kinetic contrast agent perfusion features can discriminate rheumatoid arthritis from different arthritis forms (psoriatic arthritis, spondyloarthritis, and arthritis in connective tissue disease) with an average accuracy of 97%. On the contrary, clinical evaluation (DAS28), semiquantitative CEUS assessment, serological markers, or region-based parameters do not allow such a high diagnostic accuracy.

Cancer progression has been linked to mechanics. Therefore, there has been recent interest in developing noninvasive imaging tools for cancer assessment that are sensitive to changes in tissue mechanical properties. We have developed one such method, modality independent elastography (MIE), that estimates the relative elastic properties of tissue by fitting anatomical image volumes acquired before and after the application of compression to biomechanical models. The aim of this study was to assess the accuracy and reproducibility of the method using phantoms and a murine breast cancer model. Magnetic resonance imaging data were acquired, and the MIE method was used to estimate relative volumetric stiffness. Accuracy was assessed using phantom data by comparing to gold-standard mechanical testing of elasticity ratios. Validation error was <12%. Reproducibility analysis was performed on animal data, and within-subject coefficients of variation ranged from 2 to 13% at the bulk level and 32% at the voxel level. To our knowledge, this is the first study to assess the reproducibility of an elasticity imaging metric in a preclinical cancer model. Our results suggest that the MIE method can reproducibly generate accurate estimates of the relative mechanical stiffness and provide guidance on the degree of change needed in order to declare biological changes rather than experimental error in future therapeutic studies.

The aim of this study was to establish an advanced analytical platform for complex in vivo pathologies. We have developed a software program, QuantitativeT2, for voxel-based real-time quantitative T2 magnetic resonance imaging. We analyzed murine brain tumors to confirm feasibility of our method for neurological conditions. Anesthetized mice (with invasive gliomas, and controls) were imaged on a 9.4 Tesla scanner using a Carr–Purcell–Meiboom–Gill sequence. The multiecho T2 decays from axial brain slices were analyzed using QuantitativeT2. T2 distribution histograms demonstrated substantial characteristic differences between normal and pathological brain tissues. Voxel-based quantitative maps of tissue water fraction (WF) and geometric mean T2 (gmT2) revealed the heterogeneous alterations to water compartmentalization caused by pathology. The numeric distribution of WF and gmT2 indicated the extent of tumor infiltration. Relative evaluations between in vivo scans and ex vivo histology indicated that the T2s between 30 and 150 ms were related to cellular density and the integrity of the extracellular matrix. Overall, QuantitativeT2 has demonstrated significant advancements in qT2 analysis with real-time operation. It is interactive with an intuitive workflow; can analyze data from many MR manufacturers; and is released as open-source code to encourage examination, improvement, and expansion of this method.

Hybrid optical modality imaging is a special type of multimodality imaging significantly used in the recent past in order to harness the strengths of different imaging methods as well as to furnish complementary information beyond that provided by any individual method. We present a hybrid-modality imaging system based on a commercial clinical ultrasound imaging (USI) system using a linear array ultrasound transducer (UST) and a tunable nanosecond pulsed laser as the source. The integrated system uses photoacoustic imaging (PAI) and USI for ocular imaging to provide the complementary absorption and structural information of the eye. In this system, B-mode images from PAI and USI are acquired at 10 Hz and about 40 Hz, respectively. A linear array UST makes the system much faster compared to other ocular imaging systems using a single-element UST to form B-mode images. The results show that the proposed instrumentation is able to incorporate PAI and USI in a single setup. The feasibility and efficiency of this developed probe system was illustrated by using enucleated pig eyes as test samples. It was demonstrated that PAI could successfully capture photoacoustic signals from the iris, anterior lens surface, and posterior pole, while USI could accomplish the mapping of the eye to reveal the structures like the cornea, anterior chamber, lens, iris, and posterior pole. This system and the proposed methodology are expected to enable ocular disease diagnostic applications and can be used as a preclinical imaging system.

The objective of this study is to demonstrate the feasibility of needle-based optical coherence tomography (OCT) and functional analysis of OCT data along the full pullback trajectory of the OCT measurement in the prostate, correlated with pathology. OCT images were recorded using a commercially available C7-XR™ OCT Intravascular Imaging System interfaced to a C7 Dragonfly™ intravascular 0.9-mm-diameter imaging probe. A computer program was constructed for automated image attenuation analysis. First, calibration of the OCT system for both the point spread function and the system roll-off was achieved by measurement of the OCT signal attenuation from an extremely weakly scattering medium (Intralipid® 0.0005 volume%). Second, the data were arranged in 31 radial wedges (pie slices) per circular segments consisting of 16 A-scans per wedge and 5 axial B-scans, resulting in an average A-scan per wedge. Third, the decay of the OCT signal is analyzed over 50 pixels (500  μm) in depth, starting from the first found maximum data point. Fourth, for visualization, the data were grouped with a corresponding color representing a specific μ_oct range according to their attenuation coefficient. Finally, the analyses were compared to histopathology. To ensure that each single use sterile imaging probe is comparable to the measurements of the other imaging probes, the probe-to-probe variations were analyzed by measuring attenuation coefficients of 0.03, 6.5, 11.4, 17, and 22.7 volume% Intralipid®. Experiments were repeated five times per probe for four probes. Inter- and intraprobe variation in the measured attenuation of Intralipid samples with scattering properties similar to that of the prostate was <8% of the mean values. Mean attenuation coefficients in the prostate were 3.8  mm⁻¹ for parts of the tissue that were classified as benign (SD: 0.8  mm⁻¹, minimum: 2.2  mm⁻¹, maximum: 8.9  mm⁻¹) and 4.1  mm⁻¹ for parts of tissue that were classified as malignant (SD: 1.2  mm⁻¹, minimum: 2.5  mm⁻¹, maximum: 9.0  mm⁻¹). In benign areas, the tissue looked homogeneous, whereas in malignant areas, small glandular structures were seen. However, not all areas in which a high attenuation coefficient became apparent corresponded to areas of prostate cancer. This paper describes the first in-tissue needle-based OCT imaging and three-dimensional optical attenuation analysis of prostate tissue that indicates a correlation with pathology. Fully automated attenuation coefficient analysis was performed at 1300 nm over the full pullback. Correlation with pathology was achieved by coregistration of three-dimensional (3-D) OCT attenuation maps with 3-D pathology of the prostate. This may contribute to the current challenge of prostate imaging and the rising interest in focal therapy for reduction of side effects occurring with current therapies.

A methodology to study the relationship between clinical variables [e.g., prostate specific antigen (PSA) or Gleason score] and cancer spatial distribution is described. Three-dimensional (3-D) models of 216 glands are reconstructed from digital images of whole mount histopathological slices. The models are deformed into one prostate model selected as an atlas using a combination of rigid, affine, and B-spline deformable registration techniques. Spatial cancer distribution is assessed by counting the number of tumor occurrences among all glands in a given position of the 3-D registered atlas. Finally, a difference between proportions is used to compare different spatial distributions. As a proof of concept, we compare spatial distributions from patients with PSA greater and less than 5  ng/ml and from patients older and younger than 60 years. Results suggest that prostate cancer has a significant difference in the right zone of the prostate between populations with PSA greater and less than 5  ng/ml. Age does not have any impact in the spatial distribution of the disease. The proposed methodology can help to comprehend prostate cancer by understanding its spatial distribution and how it changes according to clinical parameters. Finally, this methodology can be easily adapted to other organs and pathologies.