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Background: The prevalence of liver diseases, especially steatosis, requires a more convenient and noninvasive tool for liver diagnosis, which can be a surrogate for the gold standard biopsy. Magnetic resonance (MR) measurement offers potential, however ultrasound (US) has better accessibility than MR. Purpose: This study aims to suggest a multiparametric US approach which demonstrates better quantification and imaging performance than MR imaging‐based proton density fat fraction (MRI‐PDFF) for hepatic steatosis assessment. Methods: We investigated early‐stage steatosis to evaluate our approach. An in vivo (within the living) animal study was performed. Fat inclusions were accumulated in the animal livers by feeding a methionine and choline deficient (MCD) diet for 2 weeks. The animals (n = 19) underwent US and MR imaging, and then their livers were excised for histological staining. From the US, MR, and histology images, fat accumulation levels were measured and compared: multiple US parameters; MRI‐PDFF; histology fat percentages. Seven individual US parameters were extracted using B‐mode measurement, Burr distribution estimation, attenuation estimation, H‐scan analysis, and shear wave elastography. Feature selection was performed, and the selected US features were combined, providing quantification of fat accumulation. The combined parameter was used for visualizing the localized probability of fat accumulation level in the liver; This procedure is known as disease‐specific imaging (DSI). Results: The combined US parameter can sensitively assess fat accumulation levels, which is highly correlated with histology fat percentage (R = 0.93, p‐value < 0.05) and outperforms the correlation between MRI‐PDFF and histology (R = 0.89, p‐value < 0.05). Although the seven individual US parameters showed lower correlation with histology compared to MRI‐PDFF, the multiparametric analysis enabled US to outperform MR. Furthermore, this approach allowed DSI to detect and display gradual increases in fat accumulation. From the imaging output, we measured the color‐highlighted area representing fatty tissues, and the fat fraction obtained from DSI and histology showed strong agreement (R = 0.93, p‐value < 0.05). Conclusions: We demonstrated that fat quantification utilizing a combination of multiple US parameters achieved higher performance than MRI‐PDFF; therefore, our multiparametric analysis successfully combined selected features for hepatic steatosis characterization. We anticipate clinical use of our proposed multiparametric US analysis, which could be beneficial in assessing steatosis in humans. [ABSTRACT FROM AUTHOR]

Background: Interventional photoacoustic imaging (iPAI) could improve ultrasound‐guided minimally invasive procedures by enabling high precision needle steering, target detection, and molecular and physiologic tissue assessment. However, iPAI capabilities including visualization field, imaging depth, and spatial resolution are not well understood in biological tissues commonly encountered in clinical practice. Therefore, the potential clinical utility of iPAI remains unclear. We aim to experimentally determine iPAI capabilities in a variety of biological tissues, to assess its potential for clinical translation. Methods: We constructed an iPAI system capable of simultaneous real‐time ultrasound (US) and photoacoustic imaging. This system delivers light directly into tissues using optical fiber integrated into a 16‐gauge needle and detects photoacoustic signals with an external linear array ultrasound probe. iPAI's geometric visualization field, maximum imaging depth, and spatial resolution were experimentally determined in fat, muscle, kidney, and liver tissues by processing photoacoustic signal intensities of reference targets placed circumferentially around the fiber tip. The maximum detection depths of blood and indocyanine green (ICG), important common endogenous and exogenous contrast agents, respectively, were estimated in each tissue type by comparing their signal intensities with the reference target signal. Results: iPAI could be performed in real‐time concurrently with US and achieved a nearly spherical visualization field centering around the optical fiber tip in all tissues. Maximum imaging depths from the fiber tip were 54.1 ± 1.3, 50.0 ± 1.5, 32.7 ± 1.1, and 16.9 ± 1.3 mm in fat, muscle, kidney, and liver tissues, respectively. Calculated maximum detection depths for blood were 41.5 ± 3.0, 39.5 ± 2.1, 24.4 ± 4.0, and 8.6 ± 2.0 mm and detection depths for ICG at 0.05 mg/mL concentration were 46.6 ± 2.5, 42.6 ± 1.4, 28.2 ± 3.9, and 12.1 ± 1.5 mm in fat, muscle, kidney, and liver, respectively. Sub‐100μ m axial resolution and submillimeter lateral resolution were achieved in all tissues, and resolution did not significantly vary with distance from the fiber tip. Conclusions: Interventional photoacoustic imaging (iPAI) allows real‐time visualization of a circumferential volume of tissue around an optical fiber tip, with submillimeter spatial resolution and tissue‐dependent imaging depth. Our data strongly support further development of clinical iPAI systems as they could improve needle steering, target detection, and molecular and physiologic tissue assessment during minimally invasive procedures. [ABSTRACT FROM AUTHOR]

Purpose: The stoichiometric calibration method for dual‐energy CT (DECT) proposed by Bourque et al. (Phys Med Biol. 59:2059; 2014), which provides estimators of the electron density and the effective atomic number, is adapted to a maximum a posteriori (MAP) framework to increase the model's robustness to noise and biases in CT data, specifically for human tissues. Robust physical parameter estimation from noisy DECT scans is required to maximize the precision of quantities used for radiotherapy treatment planning such as the proton stopping power (SPR). Methods: Estimation of electron density and effective atomic number is performed by constraining their variation to the natural range of values expected for human tissues, while maximizing attenuation data fidelity. The MAP framework is first compared against the original method using theoretical CT numbers with Gaussian noise. The quantitative accuracy of the MAP framework is then validated experimentally on the Gammex 467 phantom. Then, using two clinical datasets, the advantages of the approach are experimentally evaluated, qualitatively, and quantitatively. Results: The theoretical study shows that the root‐mean‐square error on the electron density, the effective atomic number and the SPR are, respectively, reduced from 2.3 to 1.5, 5.7 to 3.2 and 2.8 to 1.7% with the adapted framework, when analyzing soft tissues and bone together. The experimental validation study shows that the standard deviation in Gammex inserts can be reduced, on average, by factors of 1.4 (electron density), 2.7 (effective atomic number), and 1.9 (SPR), while the quantitative accuracy of the three physical parameters is preserved, on average. Evaluation on clinical datasets show apparent noise reduction in maps of all estimated physical quantities, and suggests that the MAP framework has increased robustness to beam hardening and photon starvation artifacts. Mean values for the electron density, the effective atomic number, and the SPR averaged in four uniform regions of interest (brain, muscle, adipose, and cranium), respectively, differ by 0.7, 1.8, and 0.9% between both frameworks. The standard deviation in the same regions of interest is also reduced, on average, by factors of 1.8, 6.6, and 3.2 with the MAP framework. Differences in mean value and standard deviations are statistically significant. Conclusion: Theoretical and experimental results suggest that the MAP framework produces more accurate and precise estimates of the electron density and SPR. Thus, the present approach limits the propagation of noise in DECT attenuation data to radiotherapy‐related parameters maps such as the SPR and the electron density. Using a MAP framework with DECT for radiotherapy treatment planning can help maximizing the precision of dose calculation. The method also provides more precise estimates of the effective atomic number. The MAP methodology is presented in a general way such that it can be adapted to any DECT image‐based tissue characterization method. [ABSTRACT FROM AUTHOR]

Purpose Dual-energy CT ( DECT) promises improvements in estimating stopping power ratios ( SPRs) for proton therapy treatment planning. Although several comparable mathematical formalisms have been proposed in literature, the optimal techniques to characterize human tissue SPRs with DECT in a clinical environment are not fully established. The aim of this work is to compare the most robust DECT methods against conventional single-energy CT ( SECT) in conditions reproducing a clinical environment, where CT artifacts and noise play a major role on the accuracy of these techniques. Methods Available DECT tissue characterization methods are investigated and their ability to predict SPRs is compared in three contexts: (a) a theoretical environment using the XCOM cross section database; (b) experimental data using a dual-source CT scanner on a calibration phantom; (c) simulations of a virtual humanoid phantom with the ImaSim software. The latter comparison accounts for uncertainties caused by CT artifacts and noise, but leaves aside other sources of uncertainties such as CT grid size and the I-values. To evaluate the clinical impact, a beam range calculation model is used to predict errors from the probability distribution functions determined with ImaSim simulations. Range errors caused by SPR errors in soft tissues and bones are investigated. Results Range error estimations demonstrate that DECT has the potential of reducing proton beam range uncertainties by 0.4% in soft tissues using low noise levels of 12 and 8 HU in DECT, corresponding to 7 HU in SECT. For range uncertainties caused by the transport of protons through bones, the reduction in range uncertainties for the same levels of noise is found to be up to 0.6 to 1.1 mm for bone thicknesses ranging from 1 to 5 cm, respectively. We also show that for double the amount noise, i.e., 14 HU in SECT and 24 and 16 HU for DECT, the advantages of DECT in soft tissues are lost over SECT. In bones however, the reduction in range uncertainties is found to be between 0.5 and 0.9 mm for bone thicknesses ranging from 1 to 5 cm, respectively. Conclusion DECT has a clear potential to improve proton beam range predictions over SECT in proton therapy. However, in the current state high levels of noise remain problematic for DECT characterization methods and do not allow getting the full benefits of this technology. Future work should focus on adapting DECT methods to noise and investigate methods based on raw-data to reduce CT artifacts. [ABSTRACT FROM AUTHOR]

Purpose: To characterize mass and density changes of lung parenchyma in non-small cell lung cancer (NSCLC) patients following midtreatment resolution of atelectasis and to quantify the impact this large geometric change has on normal tissue dose. Methods: Baseline and midtreatment CT images and contours were obtained for 18 NSCLC patients with atelectasis. Patients were classified based on atelectasis volume reduction between the two scans as having either full, partial, or no resolution. Relative mass and density changes from baseline to midtreatment were calculated based on voxel intensity and volume for each lung lobe. Patients also had clinical treatment plans available which were used to assess changes in normal tissue dose constraints from baseline to midtreatment. The midtreatment image was rigidly aligned with the baseline scan in two ways: (1) bony anatomy and (2) carina. Treatment parameters (beam apertures, weights, angles, monitor units, etc.) were transferred to each image. Then, dose was recalculated. Typical IMRT dose constraints were evaluated on all images, and the changes from baseline to each midtreatment image were investigated. Results: Atelectatic lobes experienced mean (stdev) mass changes of -2.8% (36.6%), -24.4% (33.0%), and -9.2% (17.5%) and density changes of -66.0% (6.4%), -25.6% (13.6%), and -17.0% (21.1%) for full, partial, and no resolution, respectively. Means (stdev) of dose changes to spinal cord Dmax, esophagus Dmean, and lungs Dmean were 0.67 (2.99), 0.99 (2.69), and 0.50 Gy (2.05 Gy), respectively, for bone alignment and 0.14 (1.80), 0.77 (2.95), and 0.06 Gy (1.71 Gy) for carina alignment. Dose increases with bone alignment up to 10.93, 7.92, and 5.69 Gy were found for maximum spinal cord, mean esophagus, and mean lung doses, respectively, with carina alignment yielding similar values. 44% and 22% of patients had at least one metric change by at least 5 Gy (dose metrics) or 5% (volume metrics) for bone and carina alignments, respectively. Investigation of GTV coverage showed mean (stdev) changes in VRx, Dmax, and Dmin of -5.5% (13.5%), 2.5% (4.2%), and 0.8% (8.9%), respectively, for bone alignment with similar results for carina alignment. Conclusions: Resolution of atelectasis caused mass and density decreases, on average, and introduced substantial changes in normal tissue dose metrics in a subset of the patient cohort. [ABSTRACT FROM AUTHOR]

Purpose: Statistical modeling of an ultrasound backscattered echo envelope is used for tissue characterization. However, in the presence of complex structures within the analyzed area, estimation of parameters is disturbed and unreliable, e.g., in the case of breast tumor classification. In order to improve the differentiation of breast lesions, the authors proposed a method based on the segmentation of homodyned K distribution parameter maps. Regions within lesions of different scattering properties were extracted and analyzed. In order to improve the classification, the best-performing features were selected from various regions and then combined. Methods: A radio-frequency data set consisting of 103 breast lesions was used in the authors' analysis. Maps of homodyned K distribution parameters were created using an algorithm based on signal-to-noise ratio, kurtosis, and skewness of fractional-order envelope moments. A Markov random field model was used to segment parametric maps. Features of different segments were extracted and evaluated based on bootstrapping and the receiver operating characteristic curve. To determine the best-performing feature subset, the authors applied the joint mutual information criterion. Results: It was found that there were individual features which performed better than the ones commonly used for lesion characterization, like the parameter obtained through averaging of values over the whole lesion. The authors selected and discussed the best-performing features. Properties of different extracted regions were important and improved the distinction between benign and malignant tumors. The best performance was obtained by combining four features with the area under the receiver operating curve of 0.84. Conclusions: The study showed that the analysis of internal changes in lesion parametric maps leads to a better classification of breast tumors. The authors recommend combining multiple features for characterization, instead of using only one parameter, especially in the case of heterogeneous lesions. [ABSTRACT FROM AUTHOR]

Purpose: Newly developed spectral computed tomography (CT) such as photon counting detector CT enables more accurate tissue-type identification through material decomposition technique. Many iterative reconstruction methods, including those developed for spectral CT, however, employ a regularization term whose penalty transition is designed using pixel value of CT image itself. Similarly, the tissue-type identification methods are then applied after reconstruction; thus, it is impossible to take into account probability distribution obtained from projection likelihood. The purpose of this work is to develop comprehensive image reconstruction and tissue-type identification algorithm which improves quality of both reconstructed image and tissue-type map. Methods: The authors propose a new framework to jointly perform image reconstruction, material decomposition, and tissue-type identification for photon counting detector CT by applying maximum a posteriori estimation with voxel-based latent variables for the tissue types. The latent variables are treated using a voxel-based coupled Markov random field to describe the continuity and discontinuity of human organs and a set of Gaussian distributions to incorporate the statistical relation between the tissue types and their attenuation characteristics. The performance of the proposed method is quantitatively compared to that of filtered backprojection and a quadratic penalized likelihood method by 100 noise realization. Results: Results showed a superior trade-off between image noise and resolution to current reconstruction methods. The standard deviation (SD) and bias of reconstructed image were improved from quadratic penalized likelihood method: bias, -0.9 vs -0.1 Hounsfield unit (HU); SD, 46.8 vs 27.4 HU. The accuracy of tissue-type identification was also improved from quadratic penalized likelihood method: 80.1% vs 86.9%. Conclusions: The proposed method makes it possible not only to identify tissue types more accurately but also to reconstruct CT images with decreased noise and enhanced sharpness owing to the information about the tissue types. [ABSTRACT FROM AUTHOR]

Purpose: Tumor grading is an important part of breast cancer diagnosis and currently requires biopsy as its standard. Here, the authors investigate quantitative ultrasound parameters in locally advanced breast cancers that can potentially separate tumors from normal breast tissue and differentiate tumor grades. Methods: Ultrasound images and radiofrequency data from 42 locally advanced breast cancer patients were acquired and analyzed. Parameters related to the linear regression of the power spectrum-midband fit, slope, and 0-MHz-intercept-were determined from breast tumors and normal breast tissues. Mean scatterer spacing was estimated from the spectral autocorrelation, and the effective scatterer diameter and effective acoustic concentration were estimated from the Gaussian form factor. Parametric maps of each quantitative ultrasound parameter were constructed from the gated radiofrequency segments in tumor and normal tissue regions of interest. In addition to the mean values of the parametric maps, higher order statistical features, computed from gray-level co-occurrence matrices were also determined and used for characterization. Finally, linear and quadratic discriminant analyses were performed using combinations of quantitative ultrasound parameters to classify breast tissues. Results: Quantitative ultrasound parameters were found to be statistically different between tumor and normal tissue (p < 0.05). The combination of effective acoustic concentration and mean scatterer spacing could separate tumor from normal tissue with 82% accuracy, while the addition of effective scatterer diameter to the combination did not provide significant improvement (83% accuracy). Furthermore, the two advanced parameters, including effective scatterer diameter and mean scatterer spacing, were found to be statistically differentiating among grade I, II, and III tumors (p = 0.014 for scatterer spacing, p = 0.035 for effective scatterer diameter). The separation of the tumor grades further improved when the textural features of the effective scatterer diameter parametric map were combined with the mean value of the map (p = 0.004). Conclusions: Overall, the binary classification results (tumor versus normal tissue) were more promising than tumor grade assessment. Combinations of advanced parameters can further improve the separation of tumors from normal tissue compared to the use of linear regression parameters. While the linear regression parameters were sufficient for characterizing breast tumors and normal breast tissues, advanced parameters and their textural features were required to better characterize tumor subtypes. [ABSTRACT FROM AUTHOR]

Purpose: Proton radiotherapy is rapidly becoming a standard treatment option for cancer. However, even though experimental data show an increase of the relative biological effectiveness (RBE) with depth, particularly at the distal end of the treatment field, a generic RBE of 1.1 is currently used in proton radiotherapy. This discrepancy might affect the effective penetration depth of the proton beam and thus the dose to the surrounding tissue and organs at risk. The purpose of this study was thus to analyze the impact of a tissue and dose dependent RBE of protons on the effective range of the proton beam in comparison to the range based on a generic RBE of 1.1. Methods: Factors influencing the biologically effective proton range were systematically analyzed by means of treatment planning studies using the Local Effect Model (LEM IV) and the treatment planning software TRiP98. Special emphasis was put on the comparison of passive and active range modulation techniques. Results: Beam energy, tissue type, and dose level significantly affected the biological extension of the treatment field at the distal edge. Up to 4 mm increased penetration depth as compared to the depth based on a constant RBE of 1.1. The extension of the biologically effective range strongly depends on the initial proton energy used for the most distal layer of the field and correlates with the width of the distal penumbra. Thus, the range extension, in general, was more pronounced for passive as compared to active range modulation systems, whereas the maximum RBE was higher for active systems. Conclusions: The analysis showed that the physical characteristics of the proton beam in terms of the width of the distal penumbra have a great impact on the RBE gradient and thus also the biologically effective penetration depth of the beam. [ABSTRACT FROM AUTHOR]

Purpose: To characterize and quantify free breathing lung tissue motion distributions. Methods: Forty seven patient data sets were acquired using a 4DCT protocol consisting of 25 ciné scans at abutting couch positions on a 16-slice scanner. The tidal volume of each scan was measured by simultaneously acquiring spirometry and an abdominal pneumatic bellows. The concept of a characteristic breath was developed to manage otherwise natural breathing pattern variations. The characteristic breath was found by first dividing the breathing traces into individual breaths, from maximum exhalation to maximum exhalation. A linear breathing drift model was assumed and the drift removed for each breath. Breaths that exceeded one standard deviation in period or amplitude were removed from further analysis. A characteristic breath was defined by normalizing each breath to a common amplitude, aligning the peak inhalation times for all of the breaths, and determining the average time at each tidal volume, keeping inhalation and exhalation separate. Breathing motion trajectories were computed using a previously published five-dimensional lung tissue trajectory model which expresses the position of internal lung tissue, X, as: X(v,f:X0)=X0+α(X0)v+β(X0)f, where X0 is the internal lung tissue position at zero tidal volume and zero airflow, the scalar values v and f are the measured tidal volume and airflow, respectively, and the vectors α and β are fitted free parameters. In order to characterize the motion patterns, the trajectory elongations were examined throughout the subject's lungs. Elongation was defined here by generating a rectangular bounding box with one side parallel to the α vector and the box oriented in the plane defined by the α and β motion vectors. Hysteresis motion was defined as the ratio of the box dimensions aligned orthogonal to and parallel to the α vector. The 15th and 85th percentile of the elongation were used to characterize tissue trajectory hysteresis. Results: The 15th and 85th percentile bounding box elongations were 0.090 ± 0.005 and 0.083 ± 0.013 in the upper left lung and 0.187 ± 0.037 and 0.203 ± 0.053, in the lower left lung. The 15th and 85th percentiles for the upper right lung were 0.092 ± 0.006 and 0.085 ± 0.013, and 0.184 ± 0.038, and 0.196 ± 0.043 in the lower right lung. Both percentiles were calculated for tidal volume displacements between 5 and 15 mm. In the left lung, the average elongations in the upper and lower lung were ζ=0.120±0.064 and ζ=0.090±0.055, respectively. The average elongations in the upper and lower right lung were ζ=0.107±0.060 and ζ=0.082±0.048, respectively. The elongation varied smoothly throughout the lungs. Conclusions: The hysteresis motion was relatively small compared to the volume-filling motion, contributing between 8% and 20% of the overall motion. Statistically significant differences were observed in the range of hysteresis contribution for upper and lower lung regions. The characteristic breath process provided an excellent method for defining an average breath. The characteristic breath had continuous tidal volume and airflow characteristics when the breath was continuously repeated,useful for generating patterns representative of realistic motion for breathing motion studies. [ABSTRACT FROM AUTHOR]

Purpose: To quantify the variation in CT number generated by the Simulix Evolution CBCT with changes in scan length and phantom thickness., Methods: Three phantoms were used in this study: CIRS Model 610 AAPM CT Phantom, Gammex 467 Tissue Characterization Phantom, and Catphan 600 phantom. The AAPM Phantom was used to assess the variation of HU with phantom thickness. Scans were acquired with two field size settings (full- and half-beam) with and without a 3.5 cm thick ring. The Catphan and Gammex phantoms were used to assess the Simulix's capability of producing a consistent CT-to-ED conversion table with different scan lengths, ranging from 1 cm (very thin) to 20 cm (clinical use). The data were also compared to data acquired with our in-house CT Sim (GE HiLite LightSpeed 16 slice)., Results: The AAPM phantom scans with and without the ring yielded an average difference in HU of 145 HU (full-beam) and 74 HU (half-beam) for each of five inserts. The HU for Cortical Bone (SB3) [largest Gammex electron density insert; 1.69] ranged from 923 to 1170 HU for the 4 cm and 1 cm scan lengths, respectively. The HU for Teflon [largest Catphan electron density insert; 1.867] ranged from 657 to 951 for the 20 cm and 1 cm scan lengths, respectively. The HU for air in Catphan ranged from -749 to -905, and the HU for LDPE [electron density 0.944] ranged from -82 to -42, for the 20 cm and 1 cm scan lengths, respectively., Conclusions: Results show a large variability in the calculated CT number with differences in phantom thickness, as evidenced by the results with the AAPM phantom. In addition, there appears to be a dependence on scan length, attributed to increased scatter contribution. Further tests will be done to evaluate the appropriateness of the use of the Simulix CBCT unit for heterogeneity corrected external beam treatment planning. The author has received no funding during the course of this research., (© 2012 American Association of Physicists in Medicine.)

Purpose: Modern treatment planning systems require lookup tables to convert Hounsfield Units (HU) to relative electron density (RED) for use in heterogeneity corrections during dose calculations. The purpose of this work is to illustrate the impact of using different model CT phantoms to determine HU to RED curves for treatment planning., Methods: A GAMMEX model 467 tissue characterization phantom and a CATPHAN model 500 multipurpose CT phantom were imaged using CT scanners in four different cancer centers and the HU to RED curves derived from each phantom were imported into an Eclipse 8.10 treatment planning system. Dose calculation were performed on a heterogeneity phantom and then compared to measurements. A comparison of isodose and DVH were performed by calculating 3D and IMRT plans onto identical CT datasets with different HU to RED curves to determine the clinical significance. Analysis based on effective atomic number of the phantom inserts was also performed., Results: The HU to RED curves from the GAMMEX and CATPHAN phantoms were found to be reasonably self-consistent across the different CT scanners. However, observable differences for higher density materials were observed between the two phantom models. The differences for the larger HU values can be attributed to the effective atomic number of the materials. In kV range of a CT scanner x-ray spectrum, photon interactions are partially due to the photoelectric effect which has a larger dependence on atomic number than Compton scatter which depends most directly on electron density., Conclusion: The HU to RED curve is more dependent on the phantom model than CT scanner. The HU to RED curve from the GAMMEX phantom produced better agreement between Eclipse AAA calculations and measured dose distributions on a heterogeneity phantom than that from the CATPHAN. However, DVH and isodose data on patient plans show small differences for common treatment sites., (© 2012 American Association of Physicists in Medicine.)