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.

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.

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.

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.