Diagnosis of focal liver lesions from ultrasound images using a pretrained residual neural network.

OBJECTIVE: This study aims to develop a ResNet50-based deep learning model for focal liver lesion (FLL) classification in ultrasound images, comparing its performance with other models and prior research. METHODOLOGY: We retrospectively collected 581 ultrasound images from the Chulabhorn Hospital's HCC surveillance and screening project (2010-2018). The dataset comprised five classes: non-FLL, hepatic cyst (Cyst), hemangioma (HMG), focal fatty sparing (FFS), and hepatocellular carcinoma (HCC). We conducted 5-fold cross-validation after random dataset partitioning, enhancing training data with data augmentation. Our models used modified pre-trained ResNet50, GGN, ResNet18, and VGG16 architectures. Model performance, assessed via confusion matrices for sensitivity, specificity, and accuracy, was compared across models and with prior studies. RESULTS: ResNet50 outperformed other models, achieving a 5-fold cross-validation accuracy of 87 ± 2.2%. While VGG16 showed similar performance, it exhibited higher uncertainty. In the testing phase, the pretrained ResNet50 excelled in classifying non-FLL, cysts, and FFS. To compare with other research, ResNet50 surpassed the prior methods like two-layered feed-forward neural networks (FFNN) and CNN+ReLU in FLL diagnosis. CONCLUSION: ResNet50 exhibited good performance in FLL diagnosis, especially for HCC classification, suggesting its potential for developing computer-aided FLL diagnosis. However, further refinement is required for HCC and HMG classification in future studies.

Assessing the variability and correlation between SUV and ADC parameters of head and neck cancers derived from simultaneous PET/MRI: A single-center study.

OBJECTIVE: Intratumoral heterogeneity is associated with poor outcomes in head and neck cancer (HNC) patients owing to chemoradiotherapy resistance. [18 F]-FDG positron emission tomography (PET) / Magnetic Resonance Imaging (MRI) provides spatial information about tumor mass, allowing intratumor heterogeneity assessment through histogram analysis. However, variability in quantitative PET/MRI parameter measurements could influence their reliability in assessing patient prognosis. Therefore, to use standardized uptake value (SUV) and apparent diffusion coefficient (ADC) parameters for assessing tumor response, this study aimed to measure SUV and ADC's variability and assess their relationship in HNC. METHODS: First, ADC variability was measured in an in-house diffusion phantom and in five healthy volunteers. The SUV variability was only measured with the NEMA phantom using a clinical imaging protocol. Furthermore, simultaneous PET/MRI data of 11 HNC patients were retrospectively collected from the National Cyclotron and PET center in Chulabhorn Hospital. Tumor contours were manually drawn from PET images by an experienced nuclear medicine radiologist before tumor volume segmentation. Next, SUV and ADC's histogram were used to extract statistic variables of ADC and SUV: mean, median, min, max, skewness, kurtosis, and 5th , 10th , 25th , 50th , 75th , 90th , and 95th percentiles. Finally, the correlation between the statistic variables of ADC and SUV, as well as Metabolic Tumor volume and Total Lesion Glycolysis parameters was assessed using Pearson's correlation. RESULTS: This pilot study showed that both parameters' maximum coefficient of variation was 13.9% and 9.8% in the phantom and in vivo, respectively. Furthermore, we found a strong and negative correlation between SUVmax and ADVmed (r = -0.75, P = 0.01). CONCLUSION: The SUV and ADC obtained by simultaneous PET/MRI can be potentially used as an imaging biomarker for assessing intratumoral heterogeneity in patients with HNC. The low variability and relationship between SUV and ADC could allow multimodal prediction of tumor response in future studies.

Non-contrast based approach for liver function quantification using Bayesian-based intravoxel incoherent motion diffusion weighted imaging: A pilot study.

PURPOSE: Liver cirrhosis disrupts liver function and tissue perfusion, detectable by magnetic resonance imaging (MRI). Assessing liver function at the voxel level with 13-b value intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) could aid in radiation therapy liver-sparing treatment for patients with early impairment. This study aimed to evaluate the feasibility of IVIM-DWI for liver function assessment and correlate it with other multiparametric (mp) MRI methods at the voxel level. METHOD: This study investigates the variability of apparent diffusion coefficient (ADC) derived from 13-b value IVIM-DWI and B1-corrected dual flip angle (DFA) T1 mapping. Experiments were conducted in-vitro with QIBA and NIST phantoms and in 10 healthy volunteers for IVIM-DWI. Additionally, 12 patients underwent an mp-MRI examination. The imaging protocol included a 13-b value IVIM-DWI sequence for generating IVIM parametric maps. B1-corrected DFA T1 pulse sequence was used for generating T1 maps, and Gadoxatate low temporal resolution dynamic contrast-enhanced (LTR-DCE) MRI was used for generating the Hepatic extraction fraction (HEF) map. The Mann-Whitney U test was employed to compare IVIM-DWI parameters (Pure Diffusion, Dslow ; Pseudo diffusion, Dfast ; and Perfusion Fraction, Fp ) between the healthy volunteer and patient groups. Furthermore, in the patient group, statistical correlations were assessed at a voxel level between LTR-DCE MRI-derived HEF, T1 post-Gadoxetate administration, ΔT1%, and various IVIM parameters using Pearson correlation. RESULTS: For-vitro measurements, the maximum coefficient of variation of the ADC and T1 parameters was 12.4% and 16.1%, respectively. The results also showed that Fp and Dfast were able to distinguish between healthy liver function and mild liver function impairment at the global level, with p = 0.002 for Fp and p < 0.001 for Dfast . Within the patient group, these parameters also exhibited a moderate correlation with HEF at the voxel level. CONCLUSION: Overall, the study highlighted the potential of Dfast and Fp for detecting liver function impairment at both global and pixel levels.

Effectiveness of smartphone applications for magnetic resonance imaging learning among radiological technology students: An alternative tool for enhancing knowledge.

BACKGROUND: Magnetic resonance imaging (MRI) education is crucial in the undergraduate radiological technology curriculum. Traditional approaches, such as lectures and observation, are insufficient for enhancing student understanding. Educational tools dedicated to MRI learning are needed to improve students' knowledge and comprehension. OBJECTIVES: To develop an application for MRI learning via smartphones, and to assess students' satisfaction with the application and its effectiveness as a self-learning tool for radiological technology students. METHODS: The MRI learning application was developed using the Thunkable platform, covering MRI theory, equipment, pulse sequences, parameters, MRI safety, and artifacts. Seventy-three undergraduate radiological technology students were recruited and divided into app-users (n = 37) and non-users (n = 36). Pre- and post-tests, comprising 20 multiple-choice questions, were created and utilized to assess the effectiveness of the application. Pre- and post-test scores were then analyzed and compared between the two groups using a student's t-test. Finally, user satisfaction with the MRI learning application was assessed using a questionnaire with a five-point Likert scale. RESULTS: The mean pre-test and post-test scores for the app-users group were 12.65 ± 3.24 and 13.95 ± 3.41, respectively, while those for the non-users group were 12.94 ± 2.99 and 13.94 ± 2.74, respectively. The mean post-test score was significantly increased after using the MRI learning application (P = 0.01). However, there was no significant difference in the mean scores of the pre- and post-tests between the two groups. All participants expressed a high level of satisfaction with the application (4.32 ± 0.11 points). CONCLUSION: The developed smartphone application for MRI learning has the potential to enhance students' knowledge. The survey results indicated a high level of satisfaction among users. Thus, the MRI learning application could serve as an alternative tool for radiological technology students seeking to deepen their understanding of MRI-related subjects.

Determination of hepatic extraction fraction with gadoxetate low-temporal resolution DCE-MRI-based deconvolution analysis: validation with ALBI score and Child-Pugh class.

INTRODUCTION: In this study, we aimed to investigate the feasibility of gadoxetate low-temporal resolution (LTR) DCE-MRI for voxel-based hepatic extraction fraction (HEF) quantification for liver sparing radiotherapy using a deconvolution analysis (DA) method. METHODS: The accuracy and consistency of the deconvolution implementation in estimating liver function was first assessed using simulation data. Then, the method was applied to DCE-MRI data collected retrospectively from 64 patients (25 normal liver function and 39 cirrhotic patients) to generate HEF maps. The normal liver function patient data were used to measure the variability of liver function quantification. Next, a correlation between HEF and ALBI score (a new model for assessing the severity of liver dysfunction) was assessed using Pearson's correlation. Differences in HEF between Child-Pugh score classifications were assessed for significance using the Kruskal-Wallis test for all patient groups and Mann-Whitney U-test for inter-groups. A statistical significance was considered at a P-value <0.05 in all tests. RESULTS: The results showed that the implemented method accurately reproduced simulated liver function; root-mean-square error between estimated and simulated liver response functions was 0.003, and the coefficient-of-variance of HEF was <20%. HEF correlation with ALBI score was r = -0.517, P < 0.0001, and HEF was significantly decreased in the cirrhotic patients compared to normal patients (P < 0.0001). Also, HEF in Child-Pugh B/C was significantly lower than in Child-Pugh A (P = 0.024). CONCLUSION: The study demonstrated the feasibility of gadoxetate LTR-DCE MRI for voxel-based liver function quantification using DA. HEF could distinguish between different grades of liver function impairment and could potentially be used for functional guidance in radiotherapy.