• CT volumetry
• end-stage liver disease
• graft weight
• liver transplantation
• living donor liver transplant

• Humans
• Liver Transplantation/standards
• Germany
• Gastroenterology/standards
• Practice Guidelines as Topic
• Societies, Medical

• Artificial intelligence
• Deep learning
• Diagnostic imaging
• Liver transplantation
• Living-donor
• Machine learning

• gastrointestinal system
• liver diseases

• Humans
• Liver Transplantation
• Living Donors
• Deep Learning
• Organ Size
• Tomography, X-Ray Computed/methods
• Image Processing, Computer-Assisted/methods

• Handong Global University Research Grants
• Fund of Biomedical Research Institute, Jeonbuk National University Hospital

• Graft
• Indonesia
• Length of stay
• Liver transplantation
• Living donors

CT volumetry (CTV) has been widely used for pre-operative graft weight (GW) estimation in living-donor liver transplantation (LDLT), and the use of a deep-learning algorithm (DLA) may further improve its efficiency. However, its accuracy has not been well determined. To evaluate the efficiency and accuracy of DLA-assisted CTV in GW estimation, we performed a retrospective study including 581 consecutive LDLT donors who donated a right-lobe graft. Right-lobe graft volume (GV) was measured on CT using the software implemented with the DLA for automated liver segmentation. In the development group (n = 207), a volume-to-weight conversion formula was constructed by linear regression analysis between the CTV-measured GV and the intraoperative GW. In the validation group (n = 374), the agreement between the estimated and measured GWs was assessed using the Bland–Altman 95% limit-of-agreement (LOA). The mean process time for GV measurement was 1.8 ± 0.6 min (range, 1.3–8.0 min). In the validation group, the GW was estimated using the volume-to-weight conversion formula (estimated GW [g] = 206.3 + 0.653 × CTV-measured GV [mL]), and the Bland–Altman 95% LOA between the estimated and measured GWs was −1.7% ± 17.1%. The DLA-assisted CT volumetry allows for time-efficient and accurate estimation of GW in LDLT.

• Computed tomography
• Liver tumour
• Resection planning
• Segmentation

• Abdomen
• Algorithms
• Hepatectomy
• Humans
• Imaging, Three-Dimensional
• Liver/diagnostic imaging
• Liver/surgery
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/surgery
• Tomography, X-Ray Computed

To evaluate dual-energy CT (DE) and dedicated metal artifact reduction algorithms (iMAR) during CT-guided biopsy in comparison to single-energy CT (SE).

A trocar was placed in the liver of six pigs. CT acquisitions were performed with SE and dose equivalent DE at four dose levels(1.7–13.5mGy). Iterative reconstructions were performed with and without iMAR. ROIs were placed in four positions e.g. at the trocar tip(TROCAR) and liver parenchyma adjacent to the trocar tip(LIVER-1) by two independent observers for quantitative analysis using CT numbers, noise, SNR and CNR. Qualitative image analysis was performed regarding overall image quality and artifacts generated by iMAR.

There were no significant differences in CT numbers between DE and SE at TROCAR and LIVER-1 irrespective of iMAR. iMAR significantly reduced metal artifacts at LIVER-1 for all exposure settings for DE and SE(p = 0.02-0.04), but not at TROCAR. SNR, CNR and noise were comparable for DE and SE. SNR was best for high dose levels of 6.7/13.5mGy. Mean difference in the Blant-Altman analysis was -8.43 to 0.36. Cohen’s kappa for qualitative interreader-agreement was 0.901.

iMAR independently reduced metal artifacts more effectively and efficiently than CT acquisition in DE at any dose setting and its application is feasible during CT-guided liver biopsy.

• Algorithms
• Animals
• Artifacts
• Image Processing, Computer-Assisted
• Image-Guided Biopsy
• Liver/diagnostic imaging
• Liver/pathology
• Metals/chemistry
• Radiation Dosage
• Signal-To-Noise Ratio
• Swine
• Tomography, X-Ray Computed/methods

• Medical Faculty of the University of Heidelberg

• X-ray computed tomography
• ablation techniques
• electroporation
• ethiodized oil
• hepatocellular carcinoma

• CT volumetry
• Liver transplantation
• Living donors
• Right-sided ligamentum teres

To compare image quality and metal artifact reduction between virtual monochromatic spectral imaging (VMSI), linearly blended dual-energy (DE) and single-energy (SE) images, each with and without dedicated iterative metal artifact reduction (iMAR) for CT-guided biopsy.

A biopsy trocar was positioned in the liver of six pigs. DE (Sn140/100kV_p) and SE (120kV_p/200mAs) acquisitions were performed with equivalent dose. From dual-energy datasets DE Q30-3 images and VMSI between 40–180 keV in steps of 20 keV were generated. From SE datasets I30-3 images were reconstructed. All images were reconstructed with and without iMAR. Objective image quality was analyzed applying density measurements at standardized positions (e.g. trocar tip and liver parenchyma adjacent to the trocar tip) and semi-automated threshold based segmentation. Subjective image quality was performed using semi-quantitative scores. Analyses were performed by two observers.

At the trocar tip quantitative image analysis revealed significant difference in CT numbers between reconstructions with iMAR compared to reconstructions without iMAR for VMSI at lower keV levels (80 and 100 keV; p = 0.03) and DE (p = 0.03). For liver parenchyma CT numbers were significantly higher in VMSI at high keV compared to low keV (p≤0.01). VMSI at high keV also showed higher CT numbers compared to DE and SE images, though not the level of statistical significance. The best signal-to-noise ratio for VMSI was at 80 keV and comparable to DE and SE. Noise was lowest at 80 keV and lower than in DE and SE. Subjective image quality was best with VMSI at 80 keV regardless of the application of iMAR. iMAR significantly improved image quality at levels of 140 keV and 160 keV. Interreader-agreement was good for quantitative and qualitative analysis.

iMAR improved image quality in all settings. VMSI with iMAR provided metal artifact reduction and better image quality at 80 keV and thus could improve the accurate positioning in CT-guided needle biopsy. In comparison, DE imaging did not improve image quality compared to SE.

• Algorithms
• Animals
• Artifacts
• Biopsy, Needle
• Image Processing, Computer-Assisted/methods
• Image-Guided Biopsy
• Liver/diagnostic imaging
• Observer Variation
• Radiographic Image Interpretation, Computer-Assisted/methods
• Radiography, Dual-Energy Scanned Projection
• Signal-To-Noise Ratio
• Swine
• Tomography, X-Ray Computed
• Vena Cava, Inferior/diagnostic imaging

• CT volumetry
• Liver volume measurements
• liver transplantation
• living donor
• right posterior section

• CT-guidance
• Iterative metal artifact reduction
• computed tomography
• image quality
• microwave ablation

• Abdominal CT
• Atlas-based identification
• Liver segmental anatomy
• Surgical planning

• Diagnostic imaging
• Liver
• Portal hypertension

• Carcinoma, Hepatocellular/diagnostic imaging
• Case-Control Studies
• Disease Progression
• Female
• Hepatectomy
• Humans
• Liver Cirrhosis/diagnostic imaging
• Liver Cirrhosis/mortality
• Liver Cirrhosis/surgery
• Liver Neoplasms/diagnostic imaging
• Male
• Middle Aged
• Organ Size
• Predictive Value of Tests
• Prognosis
• Retrospective Studies
• Survival Rate
• Tomography, X-Ray Computed

• I01 CX000361 CSRD VA
• R01 AA025208 NIAAA NIH HHS

• Computed tomography volumetry
• Hemihepatectomy
• Hepatic malignancy

• Adult
• Aged
• Aged, 80 and over
• Cone-Beam Computed Tomography/methods
• Female
• Hepatectomy/adverse effects
• Humans
• Liver/pathology
• Liver/surgery
• Liver Failure/etiology
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/pathology
• Liver Neoplasms/surgery
• Male
• Middle Aged
• Radiographic Image Interpretation, Computer-Assisted
• Treatment Outcome
• Tumor Burden

• Siemens

Liver volumetry is a vital component in living donor liver transplantation to determine an adequate graft volume that meets the metabolic demands of the recipient and at the same time ensures donor safety. Most institutions use preoperative contrast-enhanced CT image-based software programs to estimate graft volume. The objective of this study was to evaluate the accuracy of 2 liver volumetry programs (Rapidia vs. Dr. Liver) in preoperative right liver graft estimation compared with real graft weight.

Data from 215 consecutive right lobe living donors between October 2013 and August 2015 were retrospectively reviewed. One hundred seven patients were enrolled in Rapidia group and 108 patients were included in the Dr. Liver group. Estimated graft volumes generated by both software programs were compared with real graft weight measured during surgery, and further classified into minimal difference (≤15%) and big difference (>15%). Correlation coefficients and degree of difference were determined. Linear regressions were calculated and results depicted as scatterplots.

Minimal difference was observed in 69.4% of cases from Dr. Liver group and big difference was seen in 44.9% of cases from Rapidia group (P = 0.035). Linear regression analysis showed positive correlation in both groups (P < 0.01). However, the correlation coefficient was better for the Dr. Liver group (R² = 0.719), than for the Rapidia group (R² = 0.688).

Dr. Liver can accurately predict right liver graft size better and faster than Rapidia, and can facilitate preoperative planning in living donor liver transplantation.

• Donor selection
• Living donors
• Organ size

• acute
• chronic
• clinical research
• practice
• rejection
• subclinical
• vascularized composite and reconstructive transplantation