Three-phase Computed Tomography Research Articles

New trends in diagnosis and management of gallbladder carcinoma.

Gallbladder (GB) carcinoma, although relatively rare, is the most common biliary tree cholangiocarcinoma with aggressiveness and poor prognosis. It is closely associated with cholelithiasis and long-standing large (> 3 cm) gallstones in up to 90% of cases. The other main predisposing factors for GB carcinoma include molecular factors such as mutated genes, GB wall calcification (porcelain) or mainly mucosal microcalcifications, and GB polyps ≥ 1 cm in size. Diagnosis is made by ultrasound, computed tomography (CT), and, more precisely, magnetic resonance imaging (MRI). Preoperative staging is of great importance in decision-making regarding therapeutic management. Preoperative staging is based on MRI findings, the leading technique for liver metastasis imaging, enhanced three-phase CT angiography, or magnetic resonance angiography for major vessel assessment. It is also necessary to use positron emission tomography (PET)-CT or 18F-FDG PET-MRI to more accurately detect metastases and any other occult deposits with active metabolic uptake. Staging laparoscopy may detect dissemination not otherwise found in 20%-28.6% of cases. Multimodality treatment is needed, including surgical resection, targeted therapy by biological agents according to molecular testing gene mapping, chemotherapy, radiation therapy, and immunotherapy. It is of great importance to understand the updated guidelines and current treatment options. The extent of surgical intervention depends on the disease stage, ranging from simple cholecystectomy (T1a) to extended resections and including extended cholecystectomy (T1b), with wide lymph node resection in every case or IV-V segmentectomy (T2), hepatic trisegmentectomy or major hepatectomy accompanied by hepaticojejunostomy Roux-Y, and adjacent organ resection if necessary (T3). Laparoscopic or robotic surgery shows fewer postoperative complications and equivalent oncological outcomes when compared to open surgery, but much attention must be paid to avoiding injuries. In addition to surgery, novel targeted treatment along with immunotherapy and recent improvements in radiotherapy and chemotherapy (neoadjuvant-adjuvant capecitabine, cisplatin, gemcitabine) have yielded promising results even in inoperable cases calling for palliation (T4). Thus, individualized treatment must be applied.

CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study

BackgroundTo construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively.MethodsWe retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).ResultsThe support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature.ConclusionThe proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.

Dual-energy computed tomography in reducing the effective radiation dose of computed tomography urography in patients with urinary calculi.

To evaluate the diagnostic performance of split-bolus single-phase dual-energy computed tomography (DECT) with virtual non-contrast computed tomography (VNCT) compared to three-phase computed tomography (CT) urography in patients with urinary calculi, and to examine the performance of split-bolus single-phase DECT when reducing the effective dose. A total of 48 patients with abdominal pain or hematuria suggestive of unilateral urinary calculi were enrolled and randomly divided into the experimental and control groups, with 24 cases in each group. Patients in the experimental group underwent split-bolus single-phase DECT to obtain a mixed nephrographic excretory phase. Patients in the control group accepted a single-bolus three-phase CT urography scan (non-contrast, nephrographic phase, and excretory phase). The CT values and the contrast-to-noise ratio (CNR) of 7 segments of the urinary tract were measured and compared between the two groups by using the Mann-Whitney U test. The dose-length product (DLP) and effective dose of each patient were compared between the two groups using an independent t-test. Among all 48 patients, 35 calculi were detected in the experimental group (n=24), and 47 calculi were detected in the control group (n=24). There was no significant difference between the two groups in both CT value measurements and the CNR. The mean DLP and mean effective dose of the experimental group were significantly lower than those of the control group, and the effective dose in the experimental group was decreased by 40% compared with the control group. The application of DECT combined with split-bolus nephrographic excretory phase CT urography can reveal the urinary calculi covered by a contrast medium and also reduce the effective dose exposure to patients.

Using CT radiomic features based on machine learning models to subtype adrenal adenoma

BackgroundFunctioning and non-functioning adrenocortical adenoma are two subtypes of benign adrenal adenoma, and their differential diagnosis is crucial. Current diagnostic procedures use an invasive method, adrenal venous sampling, for endocrinologic assessment.MethodsThis study proposes establishing an accurate differential model for subtyping adrenal adenoma using computed tomography (CT) radiomic features and machine learning (ML) methods. Dataset 1 (289 patients with adrenal adenoma) was collected to develop the models, and Dataset 2 (54 patients) was utilized for external validation. Cuboids containing the lesion were cropped from the non-contrast, arterial, and venous phase CT images, and 1,967 features were extracted from each cuboid. Ten discriminative features were selected from each phase or the combined phases. Random forest, support vector machine, logistic regression (LR), Gradient Boosting Machine, and eXtreme Gradient Boosting were used to establish prediction models.ResultsThe highest accuracies were 72.7%, 72.7%, and 76.1% in the arterial, venous, and non-contrast phases, respectively, when using radiomic features alone with the ML classifier of LR. When features from the three CT phases were combined, the accuracy of LR reached 83.0%. After adding clinical information, the area under the receiver operating characteristic curve increased for all the machine learning methods except for LR. In Dataset 2, the accuracy of LR was the highest, reaching 77.8%.ConclusionThe radiomic features of the lesion in three-phase CT images can potentially suggest the functioning or non-functioning nature of adrenal adenoma. The resulting radiomic models can be a non-invasive, low-cost, and rapid method of minimizing unnecessary testing in asymptomatic patients with incidentally discovered adrenal adenoma.

Differentiating renal epithelioid angiomyolipoma from clear cell carcinoma: using a radiomics model combined with CT imaging characteristics.

This study aims to assess the computed tomography (CT) findings of renal epithelioid angiomyolipoma (EAML) and develop a radiomics-based model for differentiating EAMLs and clear cell renal cell carcinomas (RCCs). This two-center retrospective study included 28 histologically confirmed EAMLs and 56 size-matched clear cell RCCs with preoperative three-phase kidney CTs. We conducted subjective image analysis to determine the CT parameters that can distinguish EAMLs from clear cell RCCs. Training and test sets were divided by chronological order of CT scans, and radiomics model was built using ten selected features among radiomics and CT features. The diagnostic performance of the radiomics model was compared with that of the three radiologists using the area under the receiver-operating characteristic curve (AUC). The mean size of the EAMLs was 6.2 ± 5.0cm. On multivariate analysis, a snowman or ice cream cone tumor shape (OR 16.3; 95% CI 1.7-156.9, P = 0.02) and lower tumor-to-cortex (TOC) enhancement ratio in the corticomedullary phase (OR 33.4; 95% CI 5.7-197, P < 0.001) were significant independent factors for identifying EAMLs. The diagnostic performance of the radiomics model (AUC 0.89) was similar to those of genitourinary radiologists (AUC 0.78 and 0.81, P > 0.05) and superior to that of a third-year resident (AUC 0.63, P = 0.04). A snowman or ice cream cone shape and lower TOC ratio were more closely associated with EAMLs than with clear cell RCCs. A CT radiomics model was useful for differentiating EAMLs from clear cell RCCs with better diagnostic performance than an inexperienced radiologist.

Clinical Efficacy of Interventional Chemotherapy Embolization Combined with Monopolar Radiofrequency Ablation on Patients with Liver Cancer.

Due to the greater prevalence of chronic hepatitis B infection, liver tumor is especially popular in China. In China, it is the 4th most prevalent tumor and the 3rd main reason for cancer fatalities. Hepatocellular carcinomas (HCCs) account for more than 91% of every liver tumor case, and chemotherapy and immunotherapy are the better therapy choices. It is a serious threat to the lives and health of Chinese citizens. Patients diagnosed with liver tumors have a bad prognosis. Surgical resection, liver transplantation, chemotherapeutic embolization, and radiofrequency ablation (RFA) are all choices for patients who are detected early. More effective therapies can result in a better prognosis. This paper analyzes the clinical efficiency of interventional transarterial chemoembolization (TACE) integrated with monopolar radiofrequency ablation (RFA) on patients with a liver tumor. Initially, the dataset is collected and the patients are treated with combined TACE and RFA. The computed tomography (CT) images are obtained using three-phase CT imaging. The images are segmented using adaptive U-Net-based segmentation. The clinical efficiency of the patients is evaluated using Robust Residual Convolutional Neural Network (RR-CNN) which is optimized using Firefly Particle Swarm Optimization (FPSO) algorithm. The performance of the system is analyzed using the MATLAB simulation tool. In performance analysis, the proposed method of RR-CNN is high when compared to the existing method of CNN, logistic regression using genetic algorithm and KNN in overall parameters are accuracy, sensitivity, F1-score, and specificity. These integrated treatments have a suggested greater response frequency, indicating a synergistic impact by combination treatment in the initial stages.

Impact on liver position under breath-hold by computed tomography contrast agents in stereotactic body radiotherapy of liver cancer.

BackgroundWe investigated variations in liver position relative to the vertebral bone for liver cancer treated with stereotactic body radiation therapy under expiratory phase breath-hold (BH) for treatment with contrast-enhanced-computed tomography (CECT), non-CECT, and cone-beam computed tomography (CBCT).Materials and methodsSeventeen consecutive patients using a contrast enhancement (CE) agent for the CT simulation session for this retrospective study were selected. The first computed tomography (CT) scan without the use of CE agent in the expiratory phase was used for treatment planning (pCT). The remaining three CT scans without a CE agent under expiratory phase BH were acquired successively without repositioning to evaluate the intra-fraction variation in liver position. Furthermore, a three-phase CT scan (arterial, portal, and late phases) accompanied by a CE agent under expiratory phase BH was acquired for target delineation. CBCT scans without the use of a CE agent under expiratory phase BH were acquired for treatment. Inter-fractional variations (non-CECT or CECT) in liver position were measured using the difference between CBCT and pCT or each 3 phase CECT images, respectively.ResultsThe average ± standard deviations for intrafractional, non-CECT interfractional variations, and CECT interfractional variations were 1.0 ± 1.3, 2.5 ± 2.6, and 6.4 ± 6.4 mm, respectively, in the craniocaudal (CC) direction. Intra- and inter-fractional variations in liver position were relatively small for non-CECT. However, significant inter-fractional liver position variations in CECT were observed in the expiratory phase BH. The position of the liver should be carefully considered when applying CECT images for image-guided radiotherapy.

Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features.

To develop and externally validate a multiphase computed tomography (CT)-based machine learning (ML) model for staging liver fibrosis (LF) by using whole liver slices. The development dataset comprised 232 patients with pathological analysis for LF, and the test dataset comprised 100 patients from an independent outside institution. Feature extraction was performed based on the precontrast (PCP), arterial (AP), portal vein (PVP) phase, and three-phase CT images. CatBoost was utilized for ML model investigation by using the features with good reproducibility. The diagnostic performance of ML models based on each single- and three-phase CT image was compared with that of radiologists' interpretations, the aminotransferase-to-platelet ratio index, and the fibrosis index based on four factors (FIB-4) by using the receiver operating characteristic curve with the area under the curve (AUC) value. Although the ML model based on three-phase CT image (AUC = 0.65-0.80) achieved higher AUC value than that based on PCP (AUC = 0.56-0.69) and PVP (AUC = 0.51-0.74) in predicting various stage of LF, significant difference was not found. The best CT-based ML model (AUC = 0.65-0.80) outperformed the FIB-4 in differentiating advanced LF and cirrhosis and radiologists' interpretation (AUC = 0.50-0.76) in the diagnosis of significant and advanced LF. All PCP, PVP, and three-phase CT-based ML models can be an acceptable in assessing LF, and the performance of the PCP-based ML model is comparable to that of the enhanced CT image-based ML model.

Functional transition: Inconsistently parallel to the increase in future liver remnant volume after preoperative portal vein embolization.

BACKGROUNDPreoperative portal vein embolization (PVE) is a widely used strategy to enable major hepatectomy in patients with insufficient liver remnant. PVE induces hypertrophy of the future liver remnant (FLR) and a shift of the functional reserve to the FLR. However, whether the increase of the FLR volume (FLRV) corresponds to the functional transition after PVE remains unclear.AIMTo investigate the sequential relationship between the increase in FLRV and functional transition after preoperative PVE using 3-dimensional (3D) computed tomography (CT) and 99mTc-galactosyl-human serum albumin (99mTc-GSA) single-photon emission computed tomography (SPECT) fusion images. METHODSThirty-three patients who underwent major hepatectomy following PVE at the Department of Gastroenterological Surgery I, Hokkaido University Hospital between October 2013 and March 2018 were enrolled. Three-phase dynamic multidetector CT and 99mTc-GSA SPECT scintigraphy were performed at pre-PVE, and at 1 and 2 wk after PVE; 3D 99mTc-GSA SPECT CT-fused images were constructed from the Digital Imaging and Communications in Medicine data using 3D image analysis system. Functional FLRV (FFLRV) was defined as the total liver volume × (FLR volume counts/total liver volume counts) on the 3D 99mTc-GSA SPECT CT-fused images. The calculated FFLRV was compared with FLRV.RESULTSFFLRV increased by a significantly larger extent than FLRV at 1 and 2 wk after PVE (P < 0.01). The increase in FFLRV and FLRV was 55.1% ± 41.6% and 26.7% ± 17.8% (P < 0.001), respectively, at 1 wk after PVE, and 64.2% ± 33.3% and 36.8% ± 18.9% (P < 0.001), respectively, at 2 wk after PVE. In 3 of the 33 patients, FFLRV levels decreased below FLRV at 2 wk. One of the three patients showed rapidly progressive fatty changes in FLR. The biopsy at 4 wk after PVE showed macro- and micro-vesicular steatosis of more than 40%, which improved to 10%. Radical resection was performed at 13 wk after PVE. The patient recovered uneventfully without any symptoms of pos-toperative liver failure.CONCLUSIONThe functional transition lagged behind the increase in FLRV after PVE in some cases. Evaluating both volume and function is needed to determine the optimal timing of hepatectomy after PVE.

Differentiation of pulmonary sclerosing pneumocytoma from solid malignant pulmonary nodules by radiomic analysis on multiphasic CT.

PurposeTo investigate the diagnostic value and feasibility of radiomics‐based texture analysis in differentiating pulmonary sclerosing pneumocytoma (PSP) from solid malignant pulmonary nodules (SMPN) on single‐ and three‐phase computed tomography (CT) images.Materials and MethodsA total of 25 PSP patients and 35 SMPN patients with pathologically confirmed results were retrospectively included in this study. For each patient, the tumor regions were manually labeled in images acquired at the noncontrast phase (NCP), arterial phase (AP), and venous phase (VP). The least absolute shrinkage and selection operator (LASSO) method was used to select the most useful predictive features extracted from the CT images. The predictive models that discriminate PSP from SMPN based on single‐phase CT images (NCP, AP, and VP) or three‐phase CT images (Combined model) were developed and validated through fivefold cross‐validation using a logistic regression classifier. Model performance was evaluated using receiver operating characteristic (ROC) analysis. The predictive performance was also compared between the Combined model and human readers.ResultsFour, five, and five features were selected from NCP, AP, and VP CT images for the development of radiomic models, respectively. The NCP, AP, and VP models exhibited areas under the curve (AUCs) of 0.748 (95% confidence interval [CI], 0.620–0.852), 0.749 (95% CI, 0.620–0.852), and 0.790 (95% CI, 0.665–0.884) in the validation dataset, respectively. The Combined model based on three‐phase CT images outperformed the NCP, AP, and VP models (all p < 0.05), yielding an AUC of 0.882 (95% CI, 0.773–0.951) in the validation dataset. The Combined model displayed noninferior performance compared to two senior radiologists; however, it outperformed two junior radiologists (p = 0.004 and 0.001, respectively).ConclusionThe Combined model based on radiomic features extracted from three‐phase CT images achieved radiologist‐level performance and could be used as promising noninvasive tool to differentiate PSP from SMPN.

CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma

To predict the Fuhrman grade of clear cell renal cell carcinoma (ccRCC) with a machine learning classifier based on single- or three-phase computed tomography (CT) images. Patients with pathologically proven ccRCC from February 1, 2009 to September 31, 2018 who were not treated were retrospectively collected for machine learning-based analysis. The texture features were extracted and ranked from precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP) and three-phase CT images, and open-source gradient boosting from the decision tree library of CatBoost was used to establish a machine learning classifier to differentiate low- from high-grade ccRCC. The performances of machine learning classifiers based on features from single- and three-phase CT images were compared with each other. A total of 231 patients with 232 pathologically proven ccRCC lesions were retrospectively collected. 35, 36, 41, and 22 Features were extracted and ranked from PCP, CMP, NP, and three-phase CT images, respectively. The machine learning model based on three-phase CT images [area under the ROC curve (AUC) = 0.87] achieved the best diagnostic performance for differentiating low- from high-grade ccRCC, followed by single-phase NP (AUC = 0.84), CMP (AUC = 0.80), and PCP images (AUC = 0.82). Machine learning classifiers can be promising noninvasive techniques to differentiate low- and high-Fuhrman nuclear grade ccRCC, and classifiers based on three-phase CT images are superior to those based on features from each single phase.

Evaluation of the radiation dose exposure and associated cancer risks in patients having preoperative parathyroid localization.

OBJECTIVE The study aimed to evaluate the total effective and organ absorbed radiation doses associated with three- and four-phase parathyroid computed tomography (CT) and sestamibi scans used for the preoperative localisation of parathyroid adenomas in a cohort of patients with primary hyperparathyroidism at a single institution. We aimed to assess the risk of cancer incidence for the organs demonstrating the highest absorbed doses for the different imaging techniques, and more specifically determine the risk for our cohort of patients. METHODS Fifty patients with primary hyperparathyroidism had both multiphase CT and sestamibi scans. The Imaging Performance Assessment of CT Scanners (ImPACT) calculator was used to estimate the patient-effective and organ-absorbed radiations doses for all the CT examinations. For sestamibi scans, the US Nuclear Regulatory Commission NUREG/CR-6345 publication was used to estimate the dose for each patient. The attributable risks of cancer were calculated using the Health Protection Agency HPA-CRCE-028 publication. RESULTS The mean patient total effective doses were 15.9%±2.8mSv, 20.2%±2.8 mSv and 5.6±0.24mSv for three-phase CT, four-phase CT and sestamibi examinations, respectively. In our cohort, the highest attributable lifetime risk was for lung cancer (0.03%) after multiphase CT. This compared with a tenfold lower risk for thyroid cancer (0.003%). After sestamibi, the highest risk was for colon cancer (0.06%). CONCLUSIONS Multiphase CT is associated with a higher radiation dose and thus a higher potential risk of cancer, but this risk is low in the older population that constituted the majority of our cohort.

Segmentation of multi-phase X-ray computed tomography images

X-ray computed tomography (CT) is useful for non-destructively visualising internal features of non-transparent objects. In addition, it provides quantitative information about the geometry and spatial distribution of an object’s constituent materials. However, X-ray CT images include blurs and noises. Herein, the authors focus on the partial volume effect that causes blurs depending on image resolution. Artificial materials (e.g., thin wire and threads) and natural materials (e.g., oil sand) were observed using a microfocus X-ray CT scanner. A maximum likelihood thresholding method considering the partial volume effect based on histogram data was applied for segmenting two- and three-phase X-ray CT images. Relative errors in measured cross-sectional areas of aluminium wires and nylon threads were evaluated for verifying the segmentation method. Occupancy ratios of each phase of oil sand to its bulk volume were quantified. The introduced segmentation method estimates the volume of each constituent of two- and three-phase porous materials with reasonable accuracy.

99mTc-Methylene diphosphonate SPECT/CT as the one-stop imaging modality for the diagnosis of osteoid osteoma

The aim of this study was to evaluate the utility of Tc-methylene diphosphonate (Tc-MDP) single-photon emission tomography (SPECT)/computed tomography (CT) for the diagnosis of osteoid osteoma and compare the same with three-phase planar bone scintigraphy (BS) and CT alone. Data of 31 patients (age: 20.6±13.2 years; male: 80.6%) who had undergone Tc-MDP BS with SPECT/CT for clinically and/or radiographically suspected osteoid osteoma were retrospectively evaluated. Planar BS images were analyzed by an experienced nuclear medicine physician. CT images were evaluated by an experienced radiologist. SPECT/CT images were evaluated by the nuclear medicine physician and radiologist in consensus. On the basis of the diagnostic confidence the interpreters used a scoring scale of 1-3, in which 1 is negative for osteoid osteoma, 2 is equivocal, and 3 is positive for osteoid osteoma. For the calculation of sensitivity, specificity, and predictive values for planar BS, CT, and SPECT/CT an interpretive score of 2 or higher was taken as positive for osteoid osteoma. Receiver operating characteristic curve analysis was performed and the area under the curve was calculated and compared. Histopathology and microbiology/clinical imaging follow-up was used as the reference standard. There were nine equivocal lesions on planar BS and five equivocal lesions on CT, but none on SPECT/CT. The sensitivity, specificity, and accuracy of SPECT/CT were all 100%; those of CT were 77.8, 92.3, and 83.8% and those of planar BS were 100, 38.4, and 74.1%, respectively. On comparison, the area under the curve of SPECT/CT was significantly larger than that of planar BS (1.00 vs. 0.761; P=0.005) and CT (1.00 vs. 0.872; P=0.044). However, no significant difference was seen between planar BS and CT (0.761 vs. 0.872; P=0.236). Tc-MDP SPECT/CT shows excellent diagnostic accuracy for osteoid osteoma and can be used as a one-stop imaging modality for the same. It is superior to planar BS and CT alone for the diagnosis of suspected osteoid osteoma.

Imaging in gastroenterology.

Imaging in gastroenterology.

Multiphase renal CT in the evaluation of renal masses: is the nephrographic phase necessary?

The aim of this study is to investigate the feasibility of eliminating the nephrographic phase from the four-phase renal computed tomography (CT) imaging to a three-phase protocol without affecting its diagnostic value. Thirty patients undergoing four-phase renal CT scans for assessment of renal lesions (>10 mm) were included in the study. A three-phase renal CT, without nephrographic phase, had similar diagnostic ability to a four-phase renal CT in the detection and characterization of renal lesions. A three-phase CT (plain, corticomedullary, and excretory phase) is therefore adequate in the clinical diagnosis of renal lesions.

Essential role of using virtual pyeloscopy in the diagnosis of small satellite renal pelvic tumour in solitary kidney patient

Virtual pyeloscopy (VP) plays an important role in the organ-sparing nephroscopic tumour resection and traditional pole resection, especially when a fibreoptic ureteroscopic examination cannot be performed. A 67-year-old man developed macroscopic hematuria. Abdominal computed tomography (CT) and cystoscopy revealed a lower calix renal stone and a 20-mm lower pyelon mass suggesting transitional cell cancer. An additional small satellite lesion in the pelvico-ureteral junction was supposedly present. Three-phase MultiDetector CT with VP showed a solitary 3 to 4-mm renal pelvic lesion beyond the known calix stone and lower pole mass. In our case, VP played an important role in the organ-sparing nephroscopic tumour resection and traditional pole resection.

Essential role of using virtual pyeloscopy in the diagnosis of small satellite renal pelvic tumour in solitary kidney patient

Virtual pyeloscopy (VP) plays an important role in the organ-sparing nephroscopic tumour resection and traditional pole resection, especially when a fibreoptic ureteroscopic examination cannot be performed. A 67-year-old man developed macroscopic hematuria. Abdominal computed tomography (CT) and cystoscopy revealed a lower calix renal stone and a 20-mm lower pyelon mass suggesting transitional cell cancer. An additional small satellite lesion in the pelvico-ureteral junction was supposedly present. Three-phase MultiDetector CT with VP showed a solitary 3 to 4-mm renal pelvic lesion beyond the known calix stone and lower pole mass. In our case, VP played an important role in the organ-sparing nephroscopic tumour resection and traditional pole resection.

Radiation dose reduction from low-kilovoltage liver computed tomography using multidetector rowcomputed tomography

The primary goal of this study was to ascertain the reduction in radiation dose when conducting low-kilovoltage liver computed tomography (CT) examinations using multidetector row CT (MDCT). Fifty patients under follow-up who were diagnosed with liver cancer were examined using a 16-slice MDCT scanner and volume CT dose index (CTDI(vol)) measured according to the methods of examination, which were based on a three-phase CT scan conducted in the previous year and a four-phase CT scan was done a year later. Scanning parameters were 120 kVp-140 mA s, 120 kVp-120 mA s and 80 kVp-280 mA s. The CTDI(vol) was decreased to ∼47% at 80 kVp -280 mA s. The results indicate that low-kilovoltage liver CT is a useful means of reducing radiation doses.

Hepatic contrast medium enhancement at computed tomography and its correlation with various body size measures

When the same dose of iodine is given to all patients when performing abdominal computed tomography (CT) there may be a wide inter-individual variation in contrast medium (CM) enhancement of the liver. To evaluate if any of the measures body height (BH), body mass index (BMI), lean body mass (LBM), ideal body weight (IBW), and body surface area (BSA) correlated better than body weight (BW) with hepatic enhancement, and to compare the enhancement when using iodixanol and iomeprol. One hundred patients referred for standard three-phase CT examination of abdomen were enrolled. Body weight and height were measured at the time of the CT examination. Forty grams of iodine (iodixanol 320 mg I/mL or iomeprol 400 mg I/mL) was injected at a rate of 1.6 g-I/s, followed by a 50 mL saline flush. The late arterial phase was determined by using a semi-automatic smart prep technique with a scan delay of 20 s. The hepatic parenchymal phase started automatically 25 s after the late arterial phase. CM concentration was estimated by placement of regions of interest in aorta (native and late arterial phase) and in liver (native and parenchymal phase). BW (r = -0.51 and -0.64), LBM (r = -0.54 and -0.59), and BSA (r = -0.54 and -0.65) showed the best correlation coefficients with aortic and hepatic parenchymal enhancement, respectively, without any significant differences between the measures. Comparing iodixanol and iomeprol there was no significant difference in aortic enhancement. The liver enhancement was significantly higher (P < 0.05) using iodixanol than iomeprol. To achieve a consistent hepatic enhancement, CM dose may simply be adjusted to body weight instead of using more complicated calculated parameters based on both weight and height.