Objective: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection. Materials and Methods: This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively. Results: Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05). Conclusion The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.

Objective: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection. Materials and Methods: This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively. Results: Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05). Conclusion The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.

Objective: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection. Materials and Methods: This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively. Results: Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05). Conclusion The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.

Objective: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection. Materials and Methods: This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively. Results: Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05). Conclusion The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.

Objective: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection. Materials and Methods: This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively. Results: Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05). Conclusion The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.

Purpose: The aim of this study was to demonstrate the effectiveness of a machine learning-based radiomics model for distinguishing tumor response and overall survival in patients with unresectable colorectal liver metastases (CRLM) treated with targeted biological therapy. Methods: We prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy. We evaluated the diagnostic performance of machine learning-based radiomics model in tumor response of liver MRI compared with the guidelines for the Response Evaluation Criteria in Solid Tumors. Overall survival was evaluated using the Kaplan-Meier analysis and compared to the Cox proportional hazard ratios following univariate and multivariate analyses. Results: Performance measurement of the trained model through metrics showed the accuracy of the machine learning model to be 76.5%, and the area under the receiver operating characteristic curve was 0.857 (95% confidence interval [CI], 0.605–0.976; P < 0.001). For the patients classified as non-progressing or progressing by the radiomics model, the median overall survival was 17.5 months (95% CI, 12.8–22.2), and 14.8 months (95% CI, 14.2–15.4), respectively (P = 0.431, log-rank test). Conclusion Machine learning-based radiomics models could have the potential to predict tumor response in patients with unresectable CRLM treated with biologic therapy.

Background: This study aimed to compare the effects of applying a vibrating foam roller before resistance exercise versus after resistance exercise on changes in serum muscle damage markers, muscle stiffness, and range of motion. This study also aimed to provide foundational data for optimizing the timing of vibrating foam roller application to enhance recovery after resistance exercise in practical settings. Methods: Twelve healthy adult males were recruited as participants. Each participant was subjected to three interventions in a random order with a washout period of at least 5 days: vibration foam rolling before resistance exercise, vibration foam rolling after resistance exercise, and resistance exercise without vibration foam rolling. Blood creatine kinase, knee flexion range of motion, and muscle stiffness were measured before, immediately after, 24 hours after, and 48 hours after exercise and foam rolling protocols. Results: Creatine kinase levels in vibration foam rolling after resistance exercise were significantly lower than those in vibration foam rolling before resistance exercise at 24 and 48 hours post-exercise. Muscle stiffness was significantly lower immediately and 24 hours post-exercise in vibration foam rolling after resistance exercise than in vibration foam rolling before resistance exercise and resistance exercise without vibration foam rolling. Knee flexion range of motion was significantly lower in resistance exercise without vibration foam rolling than in vibration foam rolling after resistance exercise at 24 and 48 hours post-exercise. Conclusions The application of vibration foam rolling after resistance exercise was more effective than that before exercise in decreasing muscle damage markers, reducing muscle stiffness, and improving the range of motion.

Background: We aimed to evaluate whether composite blood biomarkers including aldo-keto reductase family 1 member B10 (AKR1B10) and cytokeratin 18 (CK-18; a nonalcoholic steatohepatitis [NASH] marker) have clinically applicable performance for the diagnosis of NASH, advanced liver fibrosis, and high-risk NASH (NASH+significant fibrosis). Methods: A total of 116 subjects including healthy control subjects and patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD) were analyzed to assess composite blood-based and imaging-based biomarkers either singly or in combination. Results: A composite blood biomarker comprised of AKR1B10, CK-18, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) showed excellent performance for the diagnosis of, NASH, advanced fibrosis, and high-risk NASH, with area under the receiver operating characteristic curve values of 0.934 (95% confidence interval [CI], 0.888 to 0.981), 0.902 (95% CI, 0.832 to 0.971), and 0.918 (95% CI, 0.862 to 0.974), respectively. However, the performance of this blood composite biomarker was inferior to that various magnetic resonance (MR)-based composite biomarkers, such as proton density fat fraction/MR elastography- liver stiffness measurement (MRE-LSM)/ALT/AST for NASH, MRE-LSM+fibrosis-4 index for advanced fibrosis, and the known MR imaging-AST (MAST) score for high-risk NASH. Conclusion Our blood composite biomarker can be useful to distinguish progressive forms of NAFLD as an initial noninvasive test when MR-based tools are not available.

MRI plays an important role in abdominal imaging because of its ability to detect and characterize focal lesions. However, MRI examinations have several challenges, such as comparatively long scan times and motion management through breath-holding maneuvers. Techniques for reducing scan time with acceptable image quality, such as parallel imaging, compressed sensing, and cutting-edge deep learning techniques, have been developed to enable problem-solving strategies. Additionally, free-breathing techniques for dynamic contrast-enhanced imaging, such as extra-dimensional-volumetric interpolated breath-hold examination, golden-angle radial sparse parallel, and liver acceleration volume acquisition Star, can help patients with severe dyspnea or those under sedation to undergo abdominal MRI. We aimed to present various advanced abdominal MRI techniques for reducing the scan time while maintaining image quality and free-breathing techniques for dynamic imaging and illustrate cases using the techniques mentioned above. A review of these advanced techniques can assist in the appropriate interpretation of sequences.

Pancreatic cancer is usually detected through contrast-enhanced CT and MRI. However, pancreatic cancer is occasionally overlooked because of its small size or is misdiagnosed as other conditions due to atypical imaging features that present diagnostic challenges. Considering the rapid growth and poor prognosis associated with pancreatic cancer, the ability to accurately detect and differentiate pancreatic lesions is crucial for appropriate surgical intervention. Reviewing diverse challenging cases of pancreatic cancer at an early stage and other mimicking lesions may help us accurately interpret the imaging features of pancreatic cancer on CT and MRI scans. Therefore, we aimed to illustrate various imaging features of pancreatic cancer and its mimicking lesions and provide valuable insights for differential diagnosis.