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1.INTRODUCTIONWith more than 900,000 yearly new cases worldwide, liver cancer is the fifth most common cancer in men and the ninth most common cancer in women1. Percutaneous thermal ablation techniques provide minimally invasive and inexpensive focal treatment strategies for hepatic tumors2,3. They are considered a first-line treatment for patients with small hepatocellular carcinomas (HCC)4, the most common primary malignancy in the liver and the second leading cause of cancer-related mortality in the world5, and are used to bridge patients to liver transplantation6. Safe and effective ablation treatments rely on complete coverage of the target lesion with lethal temperatures7 (≥60 °C), while sparing as much surrounding tissue as possible and keeping safety margins to adjacent critical structures6. However, despite technological advancements over the past years, local recurrence rates are high (Figure 1), burdening patients and healthcare systems. Figure 1:Our analysis of post-ablation liver cancer recurrence rates from twenty-five past studies shows high recurrence rates of up to 17% for microwave ablation and even higher rates, up to 37%, for radiofrequency ablation. Disc sizes represent the number of patients included in each study. ![]() Published clinical requirements include temperature accuracy of ≤2 °C, spatial resolution of ≤2 mm, short acquisition, and image generation times for volumetric coverage (≤1 minute), metal artifact suppression, and radiation dose levels that meet safety standards8. CT-thermometry provides the most promising solution for monitoring thermal ablation treatments. This is mainly because most ablation treatments are already performed under CT guidance, the compatibility of CT with all commercially available ablation systems9, and the ability to detect immediate complications, e.g., bleeding. However, despite decades of CT-based thermometry investigations10, there is a growing demand for solutions to monitor temperatures during ablation procedures. Temperature dependence of CT Hounsfield units (HU) has been observed since the late 1970s10. Since then, several research groups partially addressed some of the clinical requirements for image-based thermometry listed above with ex vivo and in vivo experiments. However, several crucial obstacles remain for the anticipated clinical translation of CT-based thermometry. Temperature assessments from conventional CT rely on attributing shifts in HU to changes in tissue density, which in turn are affected by temperature changes according to thermal volumetric expansion ρ(T)/ρ𝑜(T0) = (1 + α∆T)–1, where ρ(T) is the tissue density at temperature T, ρ𝑜 is the tissue density at a baseline temperature T0, ΔΤ is the change from baseline temperature (in °C), and α is the thermal expansion coefficient associated with the tissue10. All previous CT-based thermometry studies employed linear or quadratic approximations of the relation above (assuming small ΔΤ) to correlate temperature and HU changes, which are assumed to be proportional to ρ. While conventional HU depend linearly on tissue density, they are also affected (non-linearly) by changes in tissue composition, which previous studies report as a limiting factor that corrupts temperature accuracy10. In addition, conventional HU are greatly affected by the scanner model, e.g., tube filtration, and acquisition parameters, e.g., kVp, as well as the patient habitus. This leads to large inconsistencies in thermal sensitivity values seen in previous studies (Figure 2). We propose to employ the exact mathematical relationship between tissue density and temperature changes10, ΔΤ ∝ 𝜌𝑜 (T0)/ρ(T), by utilizing our recently developed spectral physical density quantifications11. Figure 2:Dispersity of reported thermal sensitivity values. A large range of HU changes, from 0.5 HU to 4 HU, are associated with an increase of 2 °C. ![]() To systematically evaluate our method, we require dedicated phantoms designed to support the development of CT-based solutions for thermometry applications. Tissue-mimicking phantoms that emulate various physical properties of biological tissue are central for the development and evaluation of novel clinical technologies and applications. Compared to ex vivo and in vivo experiments, phantoms simplify safety, logistical, and cost considerations. For CT-based thermometry applications, phantoms are required to exhibit similar thermal and x-ray to those of human tissue, i.e., thermal conductivity and diffusivity, linear attenuation coefficients within the entire photon energy range relevant to clinical CT imaging. Such phantoms will enable repeatable and controlled experiments that allow rigorous comparisons of temperature sensitivity and reliability on different CT platforms at different imaging protocols. In this work we report on recent developments aimed for making non-invasive real-time thermometry a reality. We have previously demonstrated accurate physical density quantifications from clinical virtual mono-energetic images (VMI) and effective atomic number (Zeff) spectral results in phantom experiments11. Here we further validate the accuracy of these new spectral maps by utilizing them for non-invasive whole-organ mass estimations on ex vivo tissue. While our excellent results attest to the high accuracy of our spectral physical density quantifications, they also present opportunities for novel stand-alone clinical applications. In addition, we demonstrate high correlations between temperature changes and physical density quantifications on ex vivo tissue and on thermo-spectral tissue-mimicking phantoms that we developed specifically for this purpose. 2.METHODS2.1Thermo-spectral tissue-mimicking phantom developmentOur phantoms were developed by iteratively modifying the synthesis method detailed by Negussie and Mikhail et al.12,13 in order to match the x-ray attenuation curve to that of human liver tissue calculated from well-accepted elemental composition and physical density values14. These phantoms are particularly useful for thermal therapy experiments since they present comparable thermal properties to those of human tissue. Briefly, 287.5 ml of deionized water in a 1000 ml flask were degassed by purging N2 for 15 minutes. After degassing, 202.5 ml of 40% (w/v) acrylamide/bis-acrylamide solution was added to the degassed water to achieve 490 ml solution of 16.5% (w/v) acrylamide/bis-acrylamide. While stirring, 6 grams of calcium chloride dissolved in 10 ml of deionized water was added to the acrylamide solution. A single gram of ammonium persulfate in 2 ml of deionized water and a single milliliter of N,N,N’,N’-tetramethylethylenediamine were subsequently added to the solution. After stirring for additional 15 seconds, the final solution was immediately transferred to a 475 ml plastic jar. 2.2Physical density spectral map generationA complete description of the development of our spectral physical density model, its optimization, and its verification on a tissue characterization phantom (Gammex Model 467, Sun Nuclear) was provided before11. Briefly, 70 keV VMI and Zeff voxel values, which are clinically available on any spectral CT platform, are converted into physical density values through a parametrized Alvarez-Macovski model15. 2.3Non-invasive mass measurementsEx vivo bovine muscle physical density quantifications were evaluated to determine the effect of acquisition parameters on the resulting accuracy. The specimen was weighed with a precision balance (Fisher Scientific Education Precision Balance, Fisher Scientific) to provide ground-truth mass values. Next, the sample was placed on a rectangular block of polyfoam within the 20 cm bore a multi-energy CT phantom (MECT, Sun Nuclear) and scanned with a spectral detector dual-energy CT (IQon spectral scanner, Philips Healthcare) (Figure 3A). Figure 3:Experimental setup for two potential applications of physical density quantifications with spectral CT. An ex vivo muscle was (A) scanned within a multi-energy CT phantom on a spectral detector dual-energy CT to investigate non-invasive mass measurements, and (B) subjected to a range of temperatures to evaluate the corresponding effect on physical density. ![]() Scans were repeated three times at a tube voltage of 120 kVp for each set of collimations {16x0.625, 64x0.625 mm}, dose levels {15.2, 30.3, 45.5 mGy}, and acquisition mode {axial, helical} combinations. No helical scan was acquired with 16x0.625 mm at 45.5 mGy due to tube output limitations. Images were reconstructed with a clinical standard body kernel, a field of view of 350 mm, and at a slice thickness of 2.5 mm with 2.5 mm slice intervals. 70 keV VMIs and Zeff maps were reconstructed from every acquisition and corresponding physical density maps were generated using our spectral physical density model. Regions of interest (ROI) with a diameter of 13.6 mm were positioned in the center of the sample on four consecutive image slices to assess physical density quantification and their dependence on scan parameters. Finally, the sample was weighed after scanning to account for any losses in blood or minor changes in temperature. To calculate the total mass of the specimen, physical density values were summed from non-air voxels, i.e., 70 keV VMI values larger than -950 HU, and multiplied by the voxel volume (0.68 x 0.68 x 2.5 mm3). Calculated mass was compared to the average of the two weights, pre- and post-scanning, to evaluate its accuracy with different scanning parameters. 2.4Non-invasive temperature monitoringTo assess the correlation between changes in temperature and changes in physical density, relative to the physical density values at baseline temperature, optical fiber temperature probes were inserted into the same sample used in the non-invasive mass measurement experiment, as well as three of our dedicated thermo-spectral tissue-mimicking phantoms, using 13-gauge medical trocars to continuously record local internal temperatures (Figure 3B). The sample or the phantom was placed in a plastic container, and pre-heated water was poured in to completely submerge it, consequently subjecting it to a wide range of temperatures. After the sample, or phantom, reached an equilibrium temperature, ice was added to cool the water. During heating and cooling, scans were performed approximately every minute with a spectral detector dual-energy CT at a tube voltage 120 kVp, a 16x0.625 mm collimation, a revolution time of 0.75 seconds, and three different radiation dose levels {15.2, 30.3, 45.5 mGy}. For each scan, physical density maps were generated from 70 keV VMI and Zeff spectral results using the same reconstruction parameters detailed in the section above. The locations of optical fiber temperature probes were determined by thresholding 70 keV VMIs at 90 HU. 4.1 mm diameter ROIs were placed adjacent to the tip of the optical fiber to measure physical density. To elucidate thermal volumetric expansion, physical density values were normalized by dividing the last temporal physical density value with the physical density at a given timepoint. Similarly, the change in temperature was determined relative to the last temporal scan. Linear regressions were fit to the data, where the slopes were associated with the thermal volumetric expansion coefficient. R-values were determined to characterize the correlation between normalized physical density and temperature change. 3.RESULTS3.1Thermo-spectral tissue-mimicking phantom developmentAttenuation curves that were measured on multiple VMIs, at energies between 40 and 200 keV, with increments of 10 keV, are presented in Figure 4. The curves present the iterative developmental process. It enabled us to achieve a maximum error of 4 HU compared to human liver tissue across the entire energy range (Iteration #0 is the scale-down formulation from Negussie and Mikhail et al.). Since human livers differ in their elemental compositions, e.g., different fat contents, we deduce that these attenuation errors were sufficiently small for the intended purposes of these phantoms, i.e., the development and testing of spectral CT thermometry approaches. 3.2Non-invasive mass measurementsSpectral physical density quantifications from scans at different doses, collimations, and axial/helical scans revealed that with dose matched scans and different collimations, physical density decreased approximately by 0.003 g/ml with increased collimation. In addition, we observed no effect of dose and axial/helical scans on physical density quantifications. Non-invasive “whole-organ” mass estimations of the ex vivo bovine muscle from scans of varying collimation and dose levels illustrated extremely high accuracies (Figure 5). Similar to the physical density quantification, dose levels did not impact the mass value. Between the five different combinations of collimation and dose, estimated mass values were within ±1.1 grams of ground-truth mass measurements with a scale. For 16x0.625 and 64x0.625 mm collimations, the errors in mass were -0.34% and -0.04%, respectively. The accuracy of estimated mass further validates our physical density results and demonstrates a clinical application of non-invasive mass measurements for determining the presence of pathology, which is currently utilized only in post-mortem autopsies. 3.3Non-invasive temperature monitoringIn addition to non-invasive mass measurements, physical density quantifications also enabled non-invasive temperature monitoring as temperature changes are reflected in physical density changes (Figure 6). Specifically, a linear fit between normalized physical density and change in temperature demonstrated a slope of 0.00042 ± 0.00001 °C-1 and an intercept of 1.000 ± 0.0003 for temperatures between 22.0 and 45.5 °C. These fit parameters correspond to a 0.42% decrease in physical density with an increase of 10 °C. High linear correlation (R = 0.9781) between normalized physical density and change in temperature recapitulated the theoretical relation. 4.CONCLUSIONWe have demonstrated the quantitative accuracy of our physical density model on ex vivo tissue and on dedicated thermo-spectral phantoms that we have developed specifically for CT-based thermometry applications. In addition, our results demonstrate our ability to employ a well-established and direct relation, i.e., approximation-free, between changes in physical density estimations obtained from spectral CT and changes in temperature. This ability can serve as the backbone of future non-invasive real-time thermometry that is based on non- retrieved spectral information. 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