Tomography, Volume 5, Issue 3 (September 2019) – 5 articles

Preclinical imaging is critical in the development of translational strategies to detect diseases and monitor response to therapy. The National Cancer Institute Co-Clinical Imaging Resource Program was launched, in part, to develop best practices in preclinical imaging. In this context, the objective of this work was to develop a 1-hour, multiparametric magnetic resonance image-acquisition pipeline with triple-negative breast cancer patient-derived xenografts (PDXs). The 1-hour, image-acquisition pipeline includes T1- and T2-weighted scans, quantitative T1, T2, and apparent diffusion coefficient (ADC) parameter maps, and dynamic contrast-enhanced (DCE) time-course images. Quality-control measures used phantoms. The triple-negative breast cancer PDXs used for this study averaged 174 ± 73 μL in volume, with region of interest–averaged T1, T2, and ADC values of 1.9 ± 0.2 seconds, 62 ± 3 milliseconds, and 0.71 ± 0.06 μm²/ms (mean ± SD), respectively. Specific focus was on assessing the within-subject test–retest coefficient-of-variation (CVWS) for each of the magnetic resonance imaging metrics. Determination of PDX volume via manually drawn regions of interest is highly robust, with ∼1% CVWS. Determination of T2 is also robust with a ∼3% CVWS. Measurements of T1 and ADC are less robust with CVWS values in the 6%–11% range. Preliminary DCE test–retest time-course determinations, as quantified by area under the curve and Ktrans from 2-compartment exchange (extended Tofts) modeling, suggest that DCE is the least robust protocol, with ∼30%–40% CVWS. Full article

Effective transverse relaxivity of gadolinium-based contrast agents is often neglected in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Here, we assess time and tissue dependence of R2* enhancement and its impact on pharmacokinetic parameter quantification and treatment monitoring. Multiecho DCE-MRI was performed at 7 T on mice bearing subcutaneous TOV-21G human ovarian cancer xenografts (n = 8) and on the transgenic adenocarcinoma of the mouse prostate (TRAMP) model (n = 7). Subsequently, the TOV-21G tumor-bearing mice were treated with bevacizumab and rescanned 2 days later. Pharmacokinetic analysis (extended Tofts model) was performed using either the first echo signal only (standard single-echo DCE-MRI) or the estimated signal at TE = 0 derived from exponential fitting of R2* relaxation (R2*-corrected). Neglecting R2* enhancement causes underestimation of Gd-DOTA concentration (peak enhancement underestimated by 9.4%–16% in TOV-21G tumors and 13%–20% in TRAMP prostates). Median K^trans and v_e were underestimated in every mouse (TOV-21G K^trans: 11%–19%, TOV-21G v_e: 5.3%–8.9%; TRAMP K^trans: 8.6%–19%, TRAMP v_e: 12%–21%). Bevacizumab treatment reduced K^trans in all TOV-21G tumors after 48 hours. Treatment effect was significantly greater in all tumors after R2* correction (median change of −0.050 min−1 in R2*-corrected K^trans vs. −0.037 min−1 in uncorrected K^trans). R2* enhancement in DCE-MRI is both time- and tissue-dependent and may not be negligible at 7 T in tissue with high K^trans. This has consequences for the use of K^trans and other DCE-MRI parameters as biomarkers, because treatment effect size can be underestimated when R2* enhancement is neglected. Full article

We investigated a projection interpolation method for reconstructing dynamic contrast-enhanced (DCE) heart images from undersampled x-ray projections with filtered backprojecton (FBP). This method may facilitate the application of sparse-view dynamic acquisition for ultralow-dose quantitative computed tomography (CT) myocardial perfusion (MP) imaging. We conducted CT perfusion studies on 5 pigs with a standard full-view acquisition protocol (984 projections). We reconstructed DCE heart images with FBP from all and a quarter of the measured projections evenly distributed over 360°. We interpolated the sparse-view (quarter) projections to a full-view setting using a cubic-spline interpolation method before applying FBP to reconstruct the DCE heart images (synthesized full-view). To generate MP maps, we used 3 sets of DCE heart images, and compared mean MP values and biases among the 3 protocols. Compared with synthesized full-view DCE images, sparse-view DCE images were more affected by streak artifacts arising from projection undersampling. Relative to the full-view protocol, mean bias in MP measurement associated with the sparse-view protocol was 10.0 mL/min/100 g (95%CI: −8.9 to 28.9), which was >3 times higher than that associated with the synthesized full-view protocol (3.3 mL/min/100 g, 95% CI: −6.7 to 13.2). The cubic-spline-view interpolation method improved MP measurement from DCE heart images reconstructed from only a quarter of the full projection set. This method can be used with the industry-standard FBP algorithm to reconstruct DCE images of the heart, and it can reduce the radiation dose of a whole-heart quantitative CT MP study to <2 mSv (at 8-cm coverage). Full article

Precise tumor segmentation is a crucial task in radiation therapy planning. Convolutional neural networks (CNNs) are among the highest scoring automatic approaches for tumor segmentation. We investigate the difference in segmentation performance of geometrically distorted and corrected diffusion-weighted data using data of patients with head and neck tumors; 18 patients with head and neck tumors underwent multiparametric magnetic resonance imaging, including T2w, T1w, T2*, perfusion (k_trans), and apparent diffusion coefficient (ADC) measurements. Owing to strong geometrical distortions in diffusion-weighted echo planar imaging in the head and neck region, ADC data were additionally distortion corrected. To investigate the influence of geometrical correction, first 14 CNNs were trained on data with geometrically corrected ADC and another 14 CNNs were trained using data without the correction on different samples of 13 patients for training and 4 patients for validation each. The different sets were each trained from scratch using randomly initialized weights, but the training data distributions were pairwise equal for corrected and uncorrected data. Segmentation performance was evaluated on the remaining 1 test-patient for each of the 14 sets. The CNN segmentation performance scored an average Dice coefficient of 0.40 ± 0.18 for data including distortion-corrected ADC and 0.37 ± 0.21 for uncorrected data. Paired t test revealed that the performance was not significantly different (P = .313). Thus, geometrical distortion on diffusion-weighted imaging data in patients with head and neck tumor does not significantly impair CNN segmentation performance in use. Full article

We used T₂ relaxation, chemical exchange saturation transfer (CEST), and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to assess whether bacterial infection can be differentiated from inflammation in a myositis-induced mouse model. We measured the T₂ relaxation time constants, %CEST at 5 saturation frequencies, and area under the curve (AUC) from DCE-MRI after maltose injection from infected, inflamed, and normal muscle tissue models. We applied principal component analysis (PCA) to reduce dimensionality of entire CEST spectra and DCE signal evolutions, which were analyzed using standard classification methods. We extracted features from dimensional reduction as predictors for machine learning classifier algorithms. Normal, inflamed, and infected tissues were evaluated with H&E and gram-staining histological studies, and bacterial-burden studies. The T₂ relaxation time constants and AUC of DCE-MRI after injection of maltose differentiated infected, inflamed, and normal tissues. %CEST amplitudes at −1.6 and −3.5 ppm differentiated infected tissues from other tissues, but these did not differentiate inflamed tissue from normal tissue. %CEST amplitudes at 3.5, 3.0, and 2.5 ppm, AUC of DCE-MRI for shorter time periods, and relative K^trans and k_ep values from DCE-MRI could not differentiate tissues. PCA and machine learning of CEST-MRI and DCE-MRI did not improve tissue classifications relative to traditional analysis methods. Similarly, PCA and machine learning did not further improve tissue classifications relative to T₂ MRI. Therefore, future MRI studies of infection models should focus on T₂-weighted MRI and analysis of T₂ relaxation times. Full article