Hybrid spectral CT system with clinical rapid kVp-switching x-ray tube and dual-layer detector for improved iodine quantification.

Spectral computed tomography (CT) is a powerful diagnostic tool offering quantitative material decomposition results that enhance clinical imaging by providing physiologic and functional insights. Iodine, a widely used contrast agent, improves visualization in various clinical contexts. However, accurately detecting low-concentration iodine presents challenges in spectral CT systems, particularly crucial for conditions like pancreatic cancer assessment. In this study, we present preliminary results from our hybrid spectral CT instrumentation which includes clinical-grade hardware (rapid kVp-switching x-ray tube, dual-layer detector). This combination expands spectral datasets from two to four channels, wherein we hypothesize improved quantification accuracy for low-dose and low-iodine concentration cases. We modulate the system duty cycle to evaluate its impact on quantification noise and bias. We evaluate iodine quantification performance by comparing two hybrid weighting strategies alongside rapid kVp-switching. This evaluation is performed with a polyamide phantom containing seven iodine inserts ranging from 0.5 to 20 mg/mL. In comparison to alternative methodologies, the maximum separation configuration, incorporating data from both the 80 kVp, low photon energy detector layer and the 140 kVp, high photon energy detector layer produces spectral images containing low quantitative noise and bias. This study presents initial evaluations on a hybrid spectral CT system, leveraging clinical hardware to demonstrate the potential for enhanced precision and sensitivity in spectral imaging. This research holds promise for advancing spectral CT imaging performance across diverse clinical scenarios.

Evaluation of lesion detectability in positron emission tomography when using a convergent penalized likelihood image reconstruction method.

We have previously developed a convergent penalized likelihood (PL) image reconstruction algorithm using the relative difference prior (RDP) and showed that it achieves more accurate lesion quantitation compared to ordered subsets expectation maximization (OSEM). We evaluated the detectability of low-contrast liver and lung lesions using the PL-RDP algorithm compared to OSEM. We performed a two-alternative forced choice study using a channelized Hotelling observer model that was previously validated against human observers. Lesion detectability showed a stronger dependence on lesion size for PL-RDP than OSEM. Lesion detectability was improved using time-of-flight (TOF) reconstruction, with greater benefit for the liver compared to the lung and with increasing benefit for decreasing lesion size and contrast. PL detectability was statistically significantly higher than OSEM for 20 mm liver lesions when contrast was [Formula: see text] ([Formula: see text]), and TOF PL detectability was statistically significantly higher than TOF OSEM for 15 and 20 mm liver lesions with contrast [Formula: see text] and [Formula: see text], respectively. For all other cases, there was no statistically significant difference between PL and OSEM ([Formula: see text]). For the range of studied lesion properties, lesion detectability using PL-RDP was equivalent or improved compared to using OSEM.

Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET.

Ordered subset expectation maximization (OSEM) is the most widely used algorithm for clinical PET image reconstruction. OSEM is usually stopped early and post-filtered to control image noise and does not necessarily achieve optimal quantitation accuracy. As an alternative to OSEM, we have recently implemented a penalized likelihood (PL) image reconstruction algorithm for clinical PET using the relative difference penalty with the aim of improving quantitation accuracy without compromising visual image quality. Preliminary clinical studies have demonstrated visual image quality including lesion conspicuity in images reconstructed by the PL algorithm is better than or at least as good as that in OSEM images. In this paper we evaluate lesion quantitation accuracy of the PL algorithm with the relative difference penalty compared to OSEM by using various data sets including phantom data acquired with an anthropomorphic torso phantom, an extended oval phantom and the NEMA image quality phantom; clinical data; and hybrid clinical data generated by adding simulated lesion data to clinical data. We focus on mean standardized uptake values and compare them for PL and OSEM using both time-of-flight (TOF) and non-TOF data. The results demonstrate improvements of PL in lesion quantitation accuracy compared to OSEM with a particular improvement in cold background regions such as lungs.

Objective PET lesion segmentation using a spherical mean shift algorithm.

PET imagery is a valuable oncology tool for characterizing lesions and assessing lesion response to therapy. These assessments require accurate delineation of the lesion. This is a challenging task for clinicians due to small tumor sizes, blurred boundaries from the large point-spread-function and respiratory motion, inhomogeneous uptake, and nearby high uptake regions. These aspects have led to great variability in lesion assessment amongst clinicians. In this paper, we describe a segmentation algorithm for PET lesions which yields objective segmentations without operator variability. The technique is based on the mean shift algorithm, applied in a spherical coordinate frame to yield a directional assessment of foreground and background and a varying background model. We analyze the algorithm using clinically relevant hybrid digital phantoms and illustrate its effectiveness relative to other techniques.

Quantitation of small-animal (124)I activity distributions using a clinical PET/CT scanner.

UNLABELLED: Time-dependent PET imaging can be an important tool in the assessment of radiotracer performance in murine models. We have performed a quantitative analysis of PET images of (124)I, acquired on a clinical PET system using a small-animal phantom. We then compared the recovered activity concentrations with the known activity concentration in the phantom spheres. The recovery coefficients found from the phantom data were applied to in vivo (124)I anti-HER2/neu C6.5 diabody PET data and compared with necropsy biodistribution data from the same tumor-bearing immunodeficient mouse. METHODS: The small-animal phantom consisted of a 4 x 8 cm water-filled acrylic cylinder with hollow spheres filled with water ranging in volume from 0.0625 to 1.0 mL and activity concentration of 27 +/- 2 kBq/mL. The background activity concentrations varied from 0 to 0.05 to 0.10 of the spheres. Data were acquired at 0, 5, and 10 cm from the scanner longitudinal axis. Recovery coefficients were theoretically calculated for spheres of different volume, background-to-target concentrations, and distance from the scanner's longitudinal axis. The theoretic recovery coefficients were applied to the maximum sphere activity concentration measured from the PET images, thus obtaining a recovered activity concentration to be compared with the known activity concentration of the spheres. RESULTS: The mean recovered activity concentration for the phantom spheres was 25 +/- 2 kBq/mL. The (124)I diabody PET image of a mouse with a tumor xenograft was then analyzed using the techniques described. The tumor percentage injected dose per gram estimated from the murine PET image (4.8 +/- 0.4) compared well with those obtained from necropsy studies (5.1). CONCLUSION: This study indicates the feasibility of performing quantitative imaging on murine (124)I antibody fragment PET images using a large-bore clinical scanner, which enables high-throughput studies to evaluate the performance of PET tracers in a timely and cost-effective manner by imaging multiple animals simultaneously. Tracers deemed promising by this screening method can then be further evaluated using traditional necropsy studies. Our group is currently conducting time-dependent (124)I diabody PET and necropsy comparative studies with larger numbers of mice.