Motivation: Effective management of liver tumors through thermal ablation requires precise monitoring of the ablation zone to ensure successful treatment outcomes. Computed tomography (CT) thermometry offers a promising non-invasive solution to monitor if tumor cells have been heated to the lethal temperature threshold. However, achieving reproducible, precise, and accurate temperature measurements remains a challenge, particularly due to metal artifacts introduced by the ablation equipment.

Purpose: This study investigates the applicability of spectral CT thermometry in monitoring liver microwave ablation. It compares the reproducibility, precision and accuracy of CT thermometry on attenuation value images, with CT thermometry on physical density maps using spectral CT. Furthermore, it identifies the optimal metal artifact reduction (MAR) method — among O-MAR, deep learning-MAR, spectral CT, or a combination — to reduce needle artifacts and improve CT thermometry precision.

Materials and Methods: Four liver-mimicking gel phantoms embedded with temperature sensors underwent a 10-minute, 60W microwave ablation imaged by dual-layer spectral CT using a Philips CT7500 scanner. Each scan was processed to reconstruct standard 120 kVp images alongside physical density maps, which were derived from virtual monochromatic imaging (70 - 150 keV) and effective atomic number maps. During each procedure, 23 CT scans were acquired to monitor attenuation and physical density values in proximity of the ablation antenna over time. Attenuation-based and physical density-based thermometry models were tested for reproducibility (coefficient of variation) over three repetitions; a fourth repetition focused on accuracy (Bland-Altman analysis). MAR techniques were applied to a single repetition to evaluate temperature precision in artifact-corrupted slices.

Results: The correlation between CT value and temperature was highly linear with an R-squared value exceeding 96% for both attenuation and physical density-based thermometry. Model parameters for attenuation-based and physical density-based thermometry were -0.38 HU/ºC and 0.00039 ºC-1, with coefficients of variation of 0.023 and 0.067, respectively, indicating a high reproducibility. CT thermometry precision increased with distance from the ablation antenna, the use of attenuation maps and deep learning-MAR. Physical density maps generated at 150 keV alone and in combination with O-MAR and deep learning-MAR reduced needle artifacts by 73% on average (p=0.003) compared to attenuation images. Bland-Altman analysis reveals limits-of-agreement of -7.7°C to 5.3°C and -9.5°C to 8.1°C for attenuation and physical density-based thermometry, respectively.

Conclusion: Spectral CT has the potential to make CT thermometry more universally applicable. This study demonstrates the effectiveness of spectral CT thermometry for non-invasive temperature monitoring during liver microwave ablation. It shows that using spectral physical density maps at 150 keV, alongside deep learning-MAR and O-MAR, enhances temperature accuracy and minimizes metal artifacts. However, standardizing thermometry parameters across different patient conditions remains a challenge. Future enhancements in photon counting CT and deep learning technologies could further refine this method, ultimately reducing the risk of local tumor recurrence.

Objective. Iodine uptake in tumor lesions can be quantitatively calculated in patients scanned with dual energy computed tomography (DECT). This study investigated the possibility of using DECT obtained iodine concentration as a prognostic factor for percutaneous hepatic perfusion (PHP) treatment response in patients with uveal melanoma metastases.

Methods. Thirty-eight patients with unresectable hepatic uveal melanoma metastases who underwent DECT before and after PHP were retrospectively enrolled. Regions of interest (ROI) were drawn for the healthy liver parenchyma and target lesions (TL). The average iodine concentration of each ROI was calculated. The TL concentration was normalized using the healthy liver concentration. The progression free survival (PFS), overall survival (OS) and hepatic progression free survival (hPFS) were determined for each patient. Univariate survival analyses using Cox proportional hazards model were performed to find significant prognostic factors. The analysis was performed for the following variable at baseline: normalized iodine concentration (BL NIC), diameter, lactate dehydrogenase, alkaline phosphatase, tumor load (≤5 or >5 hepatic lesions), sex, age, and co-morbidities (yes or no). Next, the absolute NIC difference (AD NIC), relative NIC difference (RD NIC), absolute diameter difference (AD diameter), and relative diameter difference (RD diameter) between baseline and follow-up were evaluated. Multivariate survival analyses were performed individually for BL NIC, AD NIC and RD NIC with sex, age, and tumor-load. Kaplan-Meier analyses were performed to visualize the results of the survival analyses. The Pearson and Spearman correlations were examined between the change in diameter and change in NIC.

Results. BL NIC was not a significant prognostic factor. Only the baseline diameter was significant for OS (p = 0,008). RD NIC was significant for PFS (p = 0,014/0,007) and hPFS (p = 0,004/0,002) in both the univariate and multivariate analysis. AD NIC was significant for PFS (p = 0,008) in the multivariate analysis. The multivariate hazard ratio (HR) for AD NIC was 1,119 (confident interval (CI): 1,048-4,528) for PFS. RD NIC had a HR of 1,064 (CI:1,017-1,112) for PFS and a HR of 1,084 (CI: 1,031-1,140) for hPFS. The results of the Kaplan-Meier analyses were on par with the survival analyses. No correlation was found between the change in diameter and change in NIC.

Conclusion. This study showed that RD NIC between baseline and the first follow-up after PHP treatment is a prognostic factor for PFS and hPFS, and that AD NIC is a prognostic factor for PFS in patients with hepatic uveal melanoma metastases. Patients who showed an increase in normalized iodine uptake between the baseline and follow-up scans had lower PFS and hPFS, suggesting that the lesion's vitality increased regardless of the PHP treatment received. Identifying these differences at the first follow-up after treatment can provide insight into the likelihood of a patient developing progression and can aid in further decision making.