Method For Tissue Characterization Research Articles

Abstract PS3-28: Three-dimensional H-scan ultrasound imaging for acute detection of breast cancer response to neoadjuvant treatment - Initial results using a preclinical animal model

Abstract Introduction: The use of noninvasive ultrasound (US) for tumor tissue characterization is an exciting prospect for anticancer treatment response monitoring. To that end, our group has developed a new technology termed H-scan US imaging, which links differences in the raw backscattered US signals to various-sized tissue structures. The purpose of this research project was to develop a 3-dimensional (3D) H-scan US imaging system and method for tissue characterization in volume space and evaluate using a preclinical animal model of breast cancer. Methodology: Preliminary studies were conducted using female nude athymic mice (N = 20, Charles River Laboratories) implanted in the mammary fat pad with 1 million breast cancer cells (MDA-MB-231, ATCC). Once tumors reached about 1 cm in size, animals were sorted into four groups so that mean tumor size in each was comparable (N = 5 per group). Animals were then US imaged at baseline and before receiving intraperitoneal injections, namely: (1) 0.3 mg sterile saline (control), (2) 0.2 mg of agnostic TRA-8 monoclonal antibody to human death receptor 5 (DR5) + 0.1 mg sterile saline, (3) 0.1 mg paclitaxel + 0.2 mg sterile saline, and (4) 0.1 mg TRA-8 + 0.2 mg paclitaxel. Image data was acquired using a programmable US scanner (Vantage 256, Verasonics Inc) equipped with a volumetric imaging transducer (4DL7, Vermon) at baseline and again every 24 hours for 3 days. To generate the H-scan US images, a set of Gaussian-weighted Hermite filters were convolved with the radiofrequency (RF) data to measure the relative strength of the received signals. The signal envelope for each of the filtered signal then was calculated using a Hilbert transformation. Finally, the lower frequency backscattered signals were assigned to a red (R) channel and the higher frequency components to a blue (B) channel. The unfiltered original RF signal was assigned to the green (G) channel to complete the RGB colormap and 3D H-scan US image display. After US imaging on day 3, animals were humanely euthanized and tumors excised for histological processing using the TUNEL HRP-DAB assay and immunofluorescent staining for activated caspase-3, PARP1, Ki-67, and DAPI. Results: The in vivo results show that 3D H-scan US imaging is considerably more sensitive to tumor changes after neoadjuvant treatment as compared to traditional B-scan US. While there was no difference at baseline (p = 0.52), repeat H-scan US results from treated tumors exhibited progressive increases in image intensity (164.9 ± 23.4%, 180.29 ± 11.6%, and 229.2 ± 14.6% for groups 2, 3, and 4 at day 3, respectively; p < 0.05) due to cancer cell nuclear condensation and apoptotic activity. Moreover, 3D H-scan US exhibited less variance than planar measurements due to increased sample size and statistical averaging. Histological findings also confirmed increased apoptosis and decreased proliferation that matched H-scan US data trends. Conclusions: 3D H-scan US imaging is a promising technique that allows visualization of the heterogenous tissue microenvironment and improves the evaluation of treatment at an early stage of therapy as validated by histologic findings. Citation Format: Haowei Tai, Shreya Reddy, Jane Song, Mawia Khairalseed, Kenneeth Hoyt. Three-dimensional H-scan ultrasound imaging for acute detection of breast cancer response to neoadjuvant treatment - Initial results using a preclinical animal model [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-28.

Quantifying scattering from dense media using two-dimensional impedance maps.

A better understanding of ultrasound scattering in a three-dimensional (3D) medium can provide more accurate methods for ultrasound tissue characterization. The possibility of using two-dimensional impedance maps (2DZMs) based on correlation coefficients has shown promise in the case of isotropic and sparse medium [Luchies and Oelze, J. Acoust. Soc. Am. 139, 1557-1564 (2016)]. The present study investigates the use of 2DZMs in order to quantify 3D scatterer properties of dense media from two-dimensional (2D) histological slices. Two 2DZM approaches were studied: one based on the correlation coefficient and the other based on the 2D Fourier transform of 2DZMs. Both 2DZM approaches consist in estimating the backscatter coefficient (BSC) from several 2DZMs, and then the resulting BSC was fit to the theoretical polydisperse structure factor model to yield 3D scatterer properties. Simulation studies were performed to evaluate the ability of both 2DZM approaches to quantify scattering of a 3D medium containing randomly distributed polydisperse spheres or monodisperse ellipsoids. Experimental studies were also performed using the histology photomicrographs obtained from HT29 cell pellet phantoms. Results demonstrate that the 2DZM Fourier transform-based approach was more suitable than the correlation coefficient-based approach for estimating scatterer properties when using a small number of 2DZMs.

Designing Decellularized Extracellular Matrix-Based Bioinks for 3D Bioprinting.

3D bioprinting is an emerging technology to fabricate tissues and organs by precisely positioning cells into 3D structures using printable cell-laden formulations known as bioinks. Various bioinks are utilized in 3D bioprinting applications; however, developing the perfect bioink to fabricate constructs with biomimetic microenvironment and mechanical properties that are similar to native tissues is a challenging task. In recent years, decellularized extracellular matrix (dECM)-based bioinks have received an increasing attention in 3D bioprinting applications, since they are derived from native tissues and possess unique, complex tissue-specific biochemical properties. This review focuses on designing dECM-based bioinks for tissue and organ bioprinting, including commonly used decellularization and decellularized tissue characterization methods, bioink formulation and characterization, applications of dECM-based bioinks, and most recent advancements in dECM-based bioink design.

Electron density and effective atomic number estimation in a maximum a posteriori framework for dual-energy computed tomography.

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). 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. 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. 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.

A rapid volume of interest-based approach of radiomics analysis of breast MRI for tumor decoding and phenotyping of breast cancer.

BackgroundRecently, radiomics has emerged as a non-invasive, imaging-based tissue characterization method in multiple cancer types. One limitation for robust and reproducible analysis lies in the inter-reader variability of the tumor annotations, which can potentially cause differences in the extracted feature sets and results. In this study, the diagnostic potential of a rapid and clinically feasible VOI (Volume of Interest)-based approach to radiomics is investigated to assess MR-derived parameters for predicting molecular subtype, hormonal receptor status, Ki67- and HER2-Expression, metastasis of lymph nodes and lymph vessel involvement as well as grading in patients with breast cancer.MethodsA total of 98 treatment-naïve patients (mean 59.7 years, range 28.0–89.4) with BI-RADS 5 and 6 lesions who underwent a dedicated breast MRI prior to therapy were retrospectively included in this study. The imaging protocol comprised dynamic contrast-enhanced T1-weighted imaging and T2-weighted imaging. Tumor annotations were obtained by drawing VOIs around the primary tumor lesions followed by thresholding. From each segmentation, 13.118 quantitative imaging features were extracted and analyzed with machine learning methods. Validation was performed by 5-fold cross-validation with 25 repeats.ResultsPredictions for molecular subtypes obtained AUCs of 0.75 (HER2-enriched), 0.73 (triple-negative), 0.65 (luminal A) and 0.69 (luminal B). Differentiating subtypes from one another was highest for HER2-enriched vs triple-negative (AUC 0.97), followed by luminal B vs triple-negative (0.86). Receptor status predictions for Estrogen Receptor (ER), Progesterone Receptor (PR) and Hormone receptor positivity yielded AUCs of 0.67, 0.69 and 0.69, while Ki67 and HER2 Expressions achieved 0.81 and 0.62. Involvement of the lymph vessels could be predicted with an AUC of 0.8, while lymph node metastasis yielded an AUC of 0.71. Models for grading performed similar with an AUC of 0.71 for Elston-Ellis grading and 0.74 for the histological grading.ConclusionOur preliminary results of a rapid approach to VOI-based tumor-annotations for radiomics provides comparable results to current publications with the perks of clinical suitability, enabling a comprehensive non-invasive platform for breast tumor decoding and phenotyping.

A Domain Enriched Deep Learning Approach to Classify Atherosclerosis using Intravascular Ultrasound Imaging.

Intravascular ultrasound (IVUS) imaging is widely used for diagnostic imaging in interventional cardiology. The detection and quantification of atherosclerosis from acquired images is typically performed manually by medical experts or by virtual histology IVUS (VH-IVUS) software. VH-IVUS analyzes backscattered radio frequency (RF) signals to provide a color-coded tissue map, and is the method of choice for assessing atherosclerotic plaque in situ. However, a significant amount of tissue cannot be analyzed in reasonable time because the method can be applied just once per cardiac cycle. Furthermore, only hardware and software compatible with RF signal acquisition and processing may be used. We present an image-based tissue characterization method that can be applied to entire acquisition sequences post hoc for the assessment of diseased vessels. The pixel-based method utilizes domain knowledge of arterial pathology and physiology, and leverages technological advances of convolutional neural networks to segment diseased vessel walls into the same tissue classes as virtual histology using only grayscale IVUS images. The method was trained and tested on patches extracted from VH-IVUS images acquired from several patients, and achieved overall accuracy of 93.5% for all segmented tissue. Imposing physically-relevant spatial constraints driven by domain knowledge was key to achieving such strong performance. This enriched approach offers capabilities akin to VH-IVUS without the constraints of RF signals or limited once-per-cycle analysis, offering superior potential information acquisition speed, reduced hardware and software requirements, and more widespread applicability. Such an approach may well yield promise for future clinical and research applications.

Universal Sample Preparation Unlocking Multimodal Molecular Tissue Imaging.

A new tissue sample embedding and processing method is presented that provides downstream compatibility with numerous different histological, molecular biology, and analytical techniques. The methodology is based on the low temperature embedding of fresh frozen specimens into a hydrogel matrix composed of hydroxypropyl methylcellulose (HPMC) and polyvinylpyrrolidone (PVP) and sectioning using a cryomicrotome. The hydrogel was expected not to interfere with standard tissue characterization methods, histologically or analytically. We assessed the compatibility of this protocol with various mass spectrometric imaging methods including matrix-assisted laser desorption ionization (MALDI), desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS). We also demonstrated the suitability of the universal protocol for extraction based molecular biology techniques such as rt-PCR. The integration of multiple analytical modalities through this universal sample preparation protocol offers the ability to study tissues at a systems biology level and directly linking results to tissue morphology and cellular phenotype.

Molecular Effects and Tissue Penetration Depth of Physical Plasma in Human Mucosa Analyzed by Contact- and Marker-Independent Raman Microspectroscopy.

Noninvasive epithelial tissue treatment with cold atmospheric plasma (CAP) is a promising option for local treatment of chronic inflammatory and precancerous lesions as well as various mucosal cancer diseases. Atmospheric pressure plasma jets (APPJ) are well-characterized and medically approved plasma sources. There are numbers of medically approved plasma sources for the treatment of epithelial diseases; however, little is known about the biochemical effects of CAP at the plasma-tissue interface. Furthermore, the actual penetration depth of CAP into tissue is currently unclear. Noninvasive and marker-independent Raman microspectroscopy was employed to assess the molecular effects of CAP on single cells and primary human cervical tissue samples. CAP treatment showed immediate and persisting changes of specific molecular tissue components determined by multivariate analysis. Raman imaging identified CAP-dependent changes in the morphology of the tissue, as well as molecular tissue components. The expression of the different components was not significantly altered within 24 h of incubation. DNA and lipids showed the strongest changes upon CAP treatment, which were traced to the basal cell layer of cervical epithelium, corresponding to an average functional plasma penetration depth of roughly 270 μm. In this study, Raman microspectroscopy is shown to be a promising method for molecular single-cell and solid tissue characterization. Regarding CAP treatment of tissues, Raman microspectroscopy could be suitable for the screening of biological mechanisms as well as for future contact- and marker-independent monitoring of plasma tissue effects.

Ultrasound tissue characterization based on the Lempel–Ziv complexity with application to breast lesion classification

Building upon the recent successes in the application of information-theoretic concepts (e.g. Shannon entropy) in quantitative ultrasound, the authors propose a novel tissue characterization method based on the Lempel–Ziv complexity. In this procedure, standard ultrasound B-Mode images are mapped onto words over finite alphabets before the corresponding Lempel–Ziv complexity of ultrasound images is calculated. Such complexity metric may be used to differentiate between types of tissues. Here, the method is utilized as a binary classifier for the malignancy of breast lesions. The method is tested on OASBUD – an open-access breast lesions image database. Images of 48 malignant and 48 benign lesions were used – two images for each lesion. The new procedure slightly outperforms the state-of-art classifier based on pixel entropy as measured in the size of area under the receiver operating curve (ROC AUC), which suggests that it may serve as a basis for computer-assisted breast cancer ultrasound diagnosis and possibly in other standard applications of the quantitative ultrasound.

Interpretability and generalizability of a one-dimensional convolutional neural network method for hepatic steatosis characterization

Nonalcoholic fatty liver disease (NAFLD) affects 25% of the population globally. We developed a one-dimensional convolutional neural network (1-D CNN) method for the ultrasound tissue characterization using radio-frequency (RF) data and demonstrated its potential for the liver fat classification and quantification. We investigate herein the interpretability and generalizability of the method to understand why it works, and whether the performance is affected by settings, transducers, and platforms. We studied under various conditions the performances of 1-D CNN for predicting steatosis using magnetic resonance imaging-estimated proton density fat fraction (MRI-PDFF) as reference (steatosis: PDFF > 5%). Three datasets were used, each containing ultrasound RF data acquired from adults and same-day MRI-PDFF estimates: (1) 200 normal and NAFLD participants scanned using the Siemens S2000 ultrasound system with the 4C1 transducer (1–4 MHz); (2) 87 participants with known/suspected NAFLD scanned using Siemens S3000 with the 4C1 and/or 6C1HD transducers (1.5–6 MHz); and (3) 46 participants with known/suspected NAFLD scanned using GE Logiq e9 with the C1-6 transducer (1–6 MHz). The 1-D CNN method is generalizable among various instrumentation settings (e.g., focal depth, time gain compensation), betweentransducers of similar frequencies, between platforms, but not between the fundamental and tissue harmonic image modes. [Work supported by R01DK106419.] Nonalcoholic fatty liver disease (NAFLD) affects 25% of the population globally. We developed a one-dimensional convolutional neural network (1-D CNN) method for the ultrasound tissue characterization using radio-frequency (RF) data and demonstrated its potential for the liver fat classification and quantification. We investigate herein the interpretability and generalizability of the method to understand why it works, and whether the performance is affected by settings, transducers, and platforms. We studied under various conditions the performances of 1-D CNN for predicting steatosis using magnetic resonance imaging-estimated proton density fat fraction (MRI-PDFF) as reference (steatosis: PDFF > 5%). Three datasets were used, each containing ultrasound RF data acquired from adults and same-day MRI-PDFF estimates: (1) 200 normal and NAFLD participants scanned using the Siemens S2000 ultrasound system with the 4C1 transducer (1–4 MHz); (2) 87 participants with known/suspected NAFLD scanned using Siemens ...

Diagnosing and sports counselling of athletes with myocarditis

Myocarditis is defined as an inflammation of the heart muscle and its presentation, especially in athletes, is heterogeneous. Underlying causes include in most of the cases viruses, and less often bacteria, toxins, vasculitic diseases or pharmaceutical agents. Cardiac magnetic resonance (CMR) imaging is the primary imaging tool to diagnose myocarditis following laboratory test, electrocardiogram and echocardiography. In certain cases, endomyocardial biopsy is required, especially in unclear cases with reduced systolic left ventricular ejection fraction. Although, athletes and sport physicians face the dilemma of significant performance decline in competitive athletes against the risk of adverse cardiac events, currently abstinence from competitive sports is recommended for at least 3–6 months in myocarditis. Sports recommendations are currently based mainly on autopsy studies and experts’ opinions and better risk stratification tools are imperatively needed. New tissue characterization methods, namely T1 mapping and T2 mapping in CMR continue to improve sensitivity and specificity of diagnosing myocarditis and may further enhance individual risk assessment. In the future, sports physicians may be able to rely more on these novel noninvasive tissue characterisation methods in risk stratification and sports restriction recommendations of athletes with suspected myocarditis.

Robust motion correction for cardiac T1 and ECV mapping using a T1 relaxation model approach.

T1 and ECV mapping are quantitative methods for myocardial tissue characterization using cardiac MRI, and are highly relevant for the diagnosis of diffuse myocardial diseases. Since the maps are calculated pixel-by-pixel from a set of MRI images with different T1-weighting, it is critical to assure exact spatial correspondence between these images. However, in practice, different sources of motion e.g. cardiac motion, respiratory motion or patient motion, hamper accurate T1 and ECV calculation such that retrospective motion correction is required. We propose a new robust non-rigid registration framework combining a data-driven initialization with a model-based registration approach, which uses a model for T1 relaxation to avoid direct registration of images with highly varying contrast. The registration between native T1 and enhanced T1 to obtain a motion free ECV map is also calculated using information from T1 model-fitting. The method was validated on three datasets recorded with two substantially different acquisition protocols (MOLLI (dataset 1 (n=15) and dataset 2 (n=29)) and STONE (dataset 3 (n = 210))), one in breath-hold condition and one free-breathing. The average Dice coefficient increased from 72.6 ± 12.1% to 82.3 ± 7.4% (P < 0.05) and mean boundary error decreased from 2.91 ± 1.51mm to 1.62 ± 0.80mm (P < 0.05) for motion correction in a single T1-weighted image sequence (3 datasets) while average Dice coefficient increased from 63.4 ± 22.5% to 79.2 ± 8.5% (P < 0.05) and mean boundary error decreased from 3.26 ± 2.64mm to 1.77 ± 0.86mm (P < 0.05) between native and enhanced sequences (dataset 1 and 2). Overall, the native T1 SD error decreased from 67.32 ± 32.57ms to 58.11 ± 21.59ms (P < 0.05), enhanced SD error from 30.15 ± 25ms to 22.74 ± 8.94ms (P < 0.05) and ECV SD error from 10.08 ± 9.59% to 5.42 ± 3.21% (P < 0.05) (dataset 1 and 2).

Estimation of the thermo-physical property of biological tissues from temperature rise due to ultrasound exposure

This study discusses the feasibility of a method for ultrasonic tissue characterization according to the thermo-physical properties of biological tissues. Since this method measures the ratio of sound velocity variation due to ultrasonic heating, it is significant to clarify the relationship between the ratio and thermo-physical properties. The ratio of sound velocity variation of a tissue sample was measured from phase shifts of echoes before and after the temperature rise by transmitting an ultrasonic pulsed wave. In the experiments the transducer used for heating was a resonance frequency of 5.0 MHz, concave ring shape, inner diameter of 5 mm, and outer diameter of 12 mm. It was arranged coaxially on the outer perimeter of the transducer used for the measurement. This transducer was a resonance frequency of 10 MHz, a focal distance of 15 mm, and a diameter of 4 mm. The exposure time of heating ultrasound is 100 ms. As a result of heating sound pressure of 1.0 MPa, 1.5 MPa and 2.0 MPa, the estimated values of temperature rise were 0.56 ˚C, 1.1 ˚C and 2.0 ˚C, respectively. [This study was supported by MEXT-Supported Program for the Strategic Research Foundation at Private Universities, 2013-2017.]This study discusses the feasibility of a method for ultrasonic tissue characterization according to the thermo-physical properties of biological tissues. Since this method measures the ratio of sound velocity variation due to ultrasonic heating, it is significant to clarify the relationship between the ratio and thermo-physical properties. The ratio of sound velocity variation of a tissue sample was measured from phase shifts of echoes before and after the temperature rise by transmitting an ultrasonic pulsed wave. In the experiments the transducer used for heating was a resonance frequency of 5.0 MHz, concave ring shape, inner diameter of 5 mm, and outer diameter of 12 mm. It was arranged coaxially on the outer perimeter of the transducer used for the measurement. This transducer was a resonance frequency of 10 MHz, a focal distance of 15 mm, and a diameter of 4 mm. The exposure time of heating ultrasound is 100 ms. As a result of heating sound pressure of 1.0 MPa, 1.5 MPa and 2.0 MPa, the estimated val...

Verification of echo amplitude envelope analysis method in skin tissues for quantitative follow-up of healing ulcers

We aim to develop an ultrasonic tissue characterization method for the follow-up of healing ulcers by diagnosing collagen fibers properties. In this paper, we demonstrated a computer simulation with simulation phantoms reflecting irregularly distributed collagen fibers to evaluate the relationship between physical properties, such as number density and periodicity, and the estimated characteristics of the echo amplitude envelope using the homodyned-K distribution. Moreover, the consistency between echo signal characteristics and the structures of ex vivo human tissues was verified from the measured data of normal skin and nonhealed ulcers. In the simulation study, speckle or coherent signal characteristics are identified as periodically or uniformly distributed collagen fibers with high number density and high periodicity. This result shows the effectiveness of the analysis using the homodyned-K distribution for tissues with complicated structures. Normal skin analysis results are characterized as including speckle or low-coherence signal components, and a nonhealed ulcer is different from normal skin with respect to the physical properties of collagen fibers.

Dosimetric impact of dual-energy CT tissue segmentation for low-energy prostate brachytherapy: a Monte Carlo study

The purpose of this study is to evaluate the impact of a novel tissue characterization method using dual-energy over single-energy computed tomography (DECT and SECT) on Monte Carlo (MC) dose calculations for low-dose rate (LDR) prostate brachytherapy performed in a patient like geometry. A virtual patient geometry is created using contours from a real patient pelvis CT scan, where known elemental compositions and varying densities are overwritten in each voxel. A second phantom is made with additional calcifications. Both phantoms are the ground truth with which all results are compared. Simulated CT images are generated from them using attenuation coefficients taken from the XCOM database with a 100 kVp spectrum for SECT and 80 and 140Sn kVp for DECT. Tissue segmentation for Monte Carlo dose calculation is made using a stoichiometric calibration method for the simulated SECT images. For the DECT images, Bayesian eigentissue decomposition is used. A LDR prostate brachytherapy plan is defined with 125I sources and then calculated using the EGSnrc user-code Brachydose for each case. Dose distributions and dose-volume histograms (DVH) are compared to ground truth to assess the accuracy of tissue segmentation. For noiseless images, DECT-based tissue segmentation outperforms the SECT procedure with a root mean square error (RMS) on relative errors on dose distributions respectively of 2.39% versus 7.77%, and provides DVHs closest to the reference DVHs for all tissues. For a medium level of CT noise, Bayesian eigentissue decomposition still performs better on the overall dose calculation as the RMS error is found to be of 7.83% compared to 9.15% for SECT. Both methods give a similar DVH for the prostate while the DECT segmentation remains more accurate for organs at risk and in presence of calcifications, with less than 5% of RMS errors within the calcifications versus up to 154% for SECT. In a patient-like geometry, DECT-based tissue segmentation provides dose distributions with the highest accuracy and the least bias compared to SECT. When imaging noise is considered, benefits of DECT are noticeable if important calcifications are found within the prostate.

MRI characterization of brown adipose tissue under thermal challenges in normal weight, overweight, and obese young men.

To implement quantitative Dixon magnetic resonance imaging (MRI) methods for brown adipose tissue (BAT) characterization at inactive and cold-activated states in normal weight, overweight, and obese subjects. The hypotheses are that MRI characteristics of BAT would differentiate between nonobese and obese subjects, and activation of BAT in response to thermal challenges that are detected by MRI would be correlated with BAT activity measured by positron emission tomography / computed tomography (PET/CT). Fifteen male subjects (20.7 ± 1.5 years old) including six normal weight, five overweight, and four obese subjects participated in the study. A multiecho Dixon MRI sequence was performed on a 1.5T scanner. MRI was acquired under thermoneutral, nonshivering thermogenesis, and subsequent warm-up conditions. Fat fraction (FF), R2*, and the number of double bonds (ndb) were measured by solving an optimization problem that fits in- and out-of-phase MR signal intensities to the fat-water interference models. Imaging acquisition and postprocessing were performed by two MRI physicists. In each subject, Dixon MRI measurements of FF, R2*, and ndb were calculated for each voxel within all BAT regions of interest (ROIs) under each thermal condition. Mean FF, R2*, and ndb were compared between nonobese (ie, normal-weight/overweight) and obese subjects using the two-sample t-test. Receiver operating characteristic (ROC) analyses were performed to differentiate nonobese vs. obese subjects. BAT MRI measurement changes in response to thermal condition changes were compared with hypermetabolic BAT volume/activity measured by PET/CT using the Pearson's correlation. In addition, BAT MRI measurements were compared with body adiposity using the Pearson's correlation. P < 0.05 was considered statistically significant. Obese subjects showed higher FF and lower R2* than nonobese subjects under all three thermal conditions (P < 0.01). ROC analyses demonstrated that FF and R2* were excellent predictors for the differentiation of nonobese from obese subjects (100% specificity and 100% sensitivity). FF changes under thermal challenges were correlated with hypermetabolic BAT volume (r = -0.55, P = 0.04 during activation, and r = 0.72, P = 0.003 during deactivation), and with BAT activity (r = 0.69, P = 0.006 during deactivation), as measured by PET/CT. FF and R2* under all three thermal conditions were highly correlated with body adiposity (P ≤ 0.002). MRI characteristics of BAT differentiated between nonobese and obese subjects in both inactivated and activated states. BAT activation detected by Dixon MRI in response to thermal challenges were correlated with glucose uptake of metabolically active BAT. 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:936-947.

The potential of dual-energy CT to reduce proton beam range uncertainties.

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. 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. 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 8HU in DECT, corresponding to 7HU 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 1.1mm for bone thicknesses ranging from 1 to 5cm, respectively. We also show that for double the amount noise, i.e., 14HU in SECT and 24 and 16HU 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 0.9mm for bone thicknesses ranging from 1 to 5cm, respectively. 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.

A general method to derive tissue parameters for Monte Carlo dose calculation with multi-energy CT

To develop a general method for human tissue characterization with dual- and multi-energy CT and evaluate its performance in determining elemental compositions and quantities relevant to radiotherapy Monte Carlo dose calculation. Ideal materials to describe human tissue are obtained applying principal component analysis on elemental weight and density data available in literature. The theory is adapted to elemental composition for solving tissue information from CT data. A novel stoichiometric calibration method is integrated to the technique to make it suitable for a clinical environment. The performance of the method is compared with two techniques known in literature using theoretical CT data. In determining elemental weights with dual-energy CT, the method is shown to be systematically superior to the water-lipid-protein material decomposition and comparable to the parameterization technique. In determining proton stopping powers and energy absorption coefficients with dual-energy CT, the method generally shows better accuracy and unbiased results. The generality of the method is demonstrated simulating multi-energy CT data to show the potential to extract more information with multiple energies. The method proposed in this paper shows good performance to determine elemental compositions from dual-energy CT data and physical quantities relevant to radiotherapy dose calculation. The method is particularly suitable for Monte Carlo calculations and shows promise in using more than two energies to characterize human tissue with CT.

Characterizing cellular morphology by photoacoustic spectrum analysis with an ultra-broadband optical ultrasonic detector.

Photoacoustic spectrum analysis (PASA) has been demonstrated as a new method for quantitative tissue imaging and characterization. The ability of PASA in evaluating micro-size tissue features was limited by the bandwidth of detectors for photoacoustic (PA) signal acquisition. We improve upon such a limit, and report on developments of PASA facilitated by an optical ultrasonic detector based on micro-ring resonator. The detector's broad and flat frequency response significantly improves the performance of PASA and extents its characterization capability from the tissue level to cellular level. The performance of the system in characterizing cellular level (a few microns) stochastic objects was first shown via a study on size-controlled optically absorbing phantoms. As a further demonstration of PASA's potential clinical application, it was employed to characterize the morphological changes of red blood cells (RBCs) from a biconcave shape to a spherical shape as a result of aging. This work demonstrates that PASA equipped with the micro-ring ultrasonic detectors is an effective technique in characterizing cellular-level micro-features of biological samples.

TU-AB-BRC-03: Accurate Tissue Characterization for Monte Carlo Dose Calculation Using Dual-and Multi-Energy CT Data

Purpose: To develop a general method for human tissue characterization with dual-and multi-energy CT and evaluate its performance in determining elemental compositions and the associated proton stopping power relative to water (SPR) and photon mass absorption coefficients (EAC). Methods: Principal component analysis is used to extract an optimal basis of virtual materials from a reference dataset of tissues. These principal components (PC) are used to perform two-material decomposition using simulated DECT data. The elemental mass fraction and the electron density in each tissue is retrieved by measuring the fraction of each PC. A stoichiometric calibration method is adapted to the technique to make it suitable for clinical use. The present approach is compared with two others: parametrization and three-material decomposition using the water-lipid-protein (WLP) triplet. Results: Monte Carlo simulations using TOPAS for four reference tissues shows that characterizing them with only two PC is enough to get a submillimetric precision on proton range prediction. Based on the simulated DECT data of 43 references tissues, the proposed method is in agreement with theoretical values of protons SPR and low-kV EAC with a RMS error of 0.11% and 0.35%, respectively. In comparison, parametrization and WLP respectively yield RMS errors of 0.13% and 0.29% on SPR, and 2.72% and 2.19% on EAC. Furthermore, the proposed approach shows potential applications for spectral CT. Using five PC and five energy bins reduces the SPR RMS error to 0.03%. Conclusion: The proposed method shows good performance in determining elemental compositions from DECT data and physical quantities relevant to radiotherapy dose calculation and generally shows better accuracy and unbiased results compared to reference methods. The proposed method is particularly suitable for Monte Carlo calculations and shows promise in using more than two energies to characterize human tissue with CT.