Efficient spectral data reduction for accurate iodine quantification in multi-energy CT.

This study proposes a spectral data reduction method for multi-channel computed tomography (CT) that optimizes material decomposition accuracy while minimizing data complexity. Spectral CT enables quantitative assessments by utilizing multiple spectral channels, yet the associated noise and computational demands can limit its clinical application. We introduce a weighting scheme that reduces acquired four spectral channels-derived from a dual-layer, rapid kVp-switching (kVp-S) CT setup-into two optimized input channels for material decomposition.

Hybrid Spectral CT: Combining rapid kVp-switching and dual-layer detectors for high sensitivity iodine imaging.

Over the past two decades, spectral computed tomography (CT) has undergone significant advancements, particularly in the realm of diagnostic accuracy, prompting a surge in clinical studies. This research examines the development of a new hybrid spectral CT system that combines a clinical-grade rapid kVp-switching X-ray tube with a dual-layer detector, aiming to boost quantitative spectral imaging performance in different clinical applications. The performance of the system was evaluated using varying tube voltages, duty cycles, and rotation times to enhance spectral outcomes.

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.

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.

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.

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.

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.