Computed Tomography Imaging of Primary Lung Cancer in Mice Using a Liposomal-Iodinated Contrast Agent

To investigate the utility of a liposomal-iodinated nanoparticle contrast agent and computed tomography (CT) imaging for characterization of primary nodules in genetically engineered mouse models of non-small cell lung cancer.Primary lung cancers with mutations in K-ras alone (Kras(LA1)) or in combination with p53 (LSL-Kras(G12D);p53(FL/FL)) were generated. A liposomal-iodine contrast agent containing 120 mg Iodine/mL was administered systemically at a dose of 16 µl/gm body weight. Longitudinal micro-CT imaging with cardio-respiratory gating was performed pre-contrast and at 0 hr, day 3, and day 7 post-contrast administration. CT-derived nodule sizes were used to assess tumor growth. Signal attenuation was measured in individual nodules to study dynamic enhancement of lung nodules.A good correlation was seen between volume and diameter-based assessment of nodules (R(2)>0.8) for both lung cancer models. The LSL-Kras(G12D);p53(FL/FL) model showed rapid growth as demonstrated by systemically higher volume changes compared to the lung nodules in Kras(LA1) mice (p<0.05). Early phase imaging using the nanoparticle contrast agent enabled visualization of nodule blood supply. Delayed-phase imaging demonstrated significant differential signal enhancement in the lung nodules of LSL-Kras(G12D);p53(FL/FL) mice compared to nodules in Kras(LA1) mice (p<0.05) indicating higher uptake and accumulation of the nanoparticle contrast agent in rapidly growing nodules.The nanoparticle iodinated contrast agent enabled visualization of blood supply to the nodules during the early-phase imaging. Delayed-phase imaging enabled characterization of slow growing and rapidly growing nodules based on signal enhancement. The use of this agent could facilitate early detection and diagnosis of pulmonary lesions as well as have implications on treatment response and monitoring

Hybrid spectral CT reconstruction.

Current photon counting x-ray detector (PCD) technology faces limitations associated with spectral fidelity and photon starvation. One strategy for addressing these limitations is to supplement PCD data with high-resolution, low-noise data acquired with an energy-integrating detector (EID). In this work, we propose an iterative, hybrid reconstruction technique which combines the spectral properties of PCD data with the resolution and signal-to-noise characteristics of EID data. Our hybrid reconstruction technique is based on an algebraic model of data fidelity which substitutes the EID data into the data fidelity term associated with the PCD reconstruction, resulting in a joint reconstruction problem. Within the split Bregman framework, these data fidelity constraints are minimized subject to additional constraints on spectral rank and on joint intensity-gradient sparsity measured between the reconstructions of the EID and PCD data. Following a derivation of the proposed technique, we apply it to the reconstruction of a digital phantom which contains realistic concentrations of iodine, barium, and calcium encountered in small-animal micro-CT. The results of this experiment suggest reliable separation and detection of iodine at concentrations ≥ 5 mg/ml and barium at concentrations ≥ 10 mg/ml in 2-mm features for EID and PCD data reconstructed with inherent spatial resolutions of 176 μm and 254 μm, respectively (point spread function, FWHM). Furthermore, hybrid reconstruction is demonstrated to enhance spatial resolution within material decomposition results and to improve low-contrast detectability by as much as 2.6 times relative to reconstruction with PCD data only. The parameters of the simulation experiment are based on an in vivo micro-CT experiment conducted in a mouse model of soft-tissue sarcoma. Material decomposition results produced from this in vivo data demonstrate the feasibility of distinguishing two K-edge contrast agents with a spectral separation on the order of the energy resolution of the PCD hardware

Three-dimensional reconstruction in free-space whole-body fluorescence tomography of mice using optically reconstructed surface and atlas anatomy

We present a 3-D image reconstruction method for free-space fluorescence tomography of mice using hybrid anatomical prior information. Specifically, we use an optically reconstructed surface of the experimental animal and a digital mouse atlas to approximate the anatomy of the animal as structural priors to assist image reconstruction. Experiments are carried out on a cadaver of a nude mouse with a fluorescent inclusion (2.4-mm-diam cylinder) implanted in the chest cavity. Tomographic fluorescence images are reconstructed using an iterative algorithm based on a finite element method. Coregistration of the fluorescence reconstruction and micro-CT (computed tomography) data acquired afterward show good localization accuracy (localization error 1.2±0.6 mm). Using the optically reconstructed surface, but without the atlas anatomy, image reconstruction fails to show the fluorescent inclusion correctly. The method demonstrates the utility of anatomical priors in support of free-space fluorescence tomography

User-specified parameters.

<p>User-specified parameters.</p

Hybrid, spectral CT reconstruction in an <i>in vivo</i> mouse model of soft tissue sarcoma.

<p>(A) 2D, sagittal slice through the algebraic initialization results shown for the least noisy data set (26 keV threshold) and the most noisy data set (45 keV threshold) by column (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180324#pone.0180324.g001" target="_blank">Fig 1</a>, step 5; X_L + X_S). A yellow oval denotes the location of the sarcoma tumor on the flank of the mouse. Red squares denote a region of interest (“ROI”) which is magnified, at right, for both thresholds. (B) Final PCD only reconstruction results (X; 6 iterations of regularized reconstruction). (C) Final hybrid reconstruction results for X_L (6 iterations of regularized reconstruction). Red arrows within the magnified region of interest denote high-contrast features which appear to be better resolved in the hybrid reconstruction. (D) Final hybrid reconstruction results for X = X_L + X_S. Magenta arrows denote attenuation artifacts around bone.</p

Comparison of hybrid and PCD only reconstruction results using identical PCD projection data.

<p>(A) Expected reconstruction results in a single 2D slice through the center of each material disk (37 keV threshold). The magenta box denotes a set of line pairs that are magnified and contrast-enhanced for comparison (single 2D slice; 2.84 lp/mm). The red box denotes the spherical lesions used for detectability analysis in (G). (B) Final PCD only reconstruction results (X). (C) Absolute difference between (A) and (B). (D) Final hybrid reconstruction results (X_L + X_S). (E) Absolute difference between (A) and (D). (F) Gaussian MTFs fitted from MT measurements taken in all 3 material disks and all 5 energy thresholds (error bars: ±1 SD). (G) The increase in the detectability index associated with hybrid reconstruction over PCD only reconstruction for each of the spherical lesions. The results are organized by material disk, diameter, and material concentration (in mg/ml) and are averaged over all 5 energy thresholds.</p

Figures 14 and 15. In vivo material decomposition.

Supplemental data for: Figures 14 and 15. In vivo material decomposition.<div><br></div><div>PCD Only, final material decomposition<br></div><div>Hybrid, final material decomposition<br></div

Preprocessing applied to the PCD projection data.

<p>(A) Example log-transformed PCD projection prior to processing (threshold: 26 keV). The yellow, dotted line denotes a single detector row. The readout of this row is shown as a function of angle in the bottom row of this figure (sinogram). (B) Corresponding PCD projection following the three forms of correction described in the text. In the single projection (row 1), an inset (corresponding yellow boxes) and red arrows highlight overly dark pixels before (A) and after (B) ring artifact prevention. In the sinogram (row 2), similar insets and arrows denote detector pixel readouts notably affected by ring artifact correction. (C) Absolute difference computed between (A) and (B). Note bright bands where the detector gaps were interpolated (red arrows). Also note the differences in windowing between columns (A) and (B) (below (A)) and column (C) (below (C)).</p