Comparison of utility of deep learning reconstruction on 3D MRCPs obtained with three different k-space data acquisitions in patients with IPMN

High-precision MRI of liver and hepatic lesions on gadoxetic acid-enhanced hepatobiliary phase using a deep learning technique

PURPOSE

The purpose of this study was to investigate whether the high-precision magnetic resonance (MR) sequence using modified Fast 3D mode wheel and Precise IQ Engine (PIQE), that was collected in a wheel shape with sequential data filling in the k-space in the phase encode-slice encode plane, is feasible for breath-hold (BH) three-dimensional (3D) T1-weighted imaging of the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI in comparison to the compressed sensing (CS) sequence using Advanced Intelligent Clear-IQ Engine (AiCE).

METHODS

This retrospective study included 54 patients with focal hepatic lesions who underwent dynamic contrast-enhanced MRI. Both standard HBP images using CS with AiCE and high-precision HBP images using modified Fast 3D mode wheel and PIQE were obtained. Image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated using the Wilcoxon signed-rank test. p values of < 0.05 were considered to be statistically significant.

RESULTS

Scores for image noise, conspicuity of liver contours and intrahepatic structures, and overall image quality in high-precision HBP imaging using modified Fast 3D mode wheel and PIQE were significantly higher than those in HBP imaging using CS and AiCE (all p < 0.001). There was no significant difference in the presence of artifact and motion-related blurring. There were no significant differences between the sequences in SNR (p = 0.341) or CNR (p = 0.077). The detection rate of focal hepatic lesions was 71.4-85.3% in CS with AiCE, and 82.2-95.8% in modified Fast 3D mode wheel and PIQE.

CONCLUSION

A high-precision MR sequence using a modified Fast 3D mode wheel and PIQE is applicable for the HBP of BH 3D T1-weighted imaging.

Utility of under-sampled scans with iterative reconstruction and high-frequency preserving transform for high spatial resolution magnetic resonance cholangiopancreatography

PURPOSE

Under-sampled scans with iterative reconstruction and high-frequency preserving transform (Us-IRHF) can increase the acquisition speed without degrading the image quality by recovering image information from under-sampled data. We investigate the clinical applicability of high spatial resolution magnetic resonance cholangiopancreatography (MRCP) images without extending the scanning time using Us-IRHF.

METHODS

A slit phantom was scanned with conventional- (without Us-IRHF), Us-IR- (without HF), and Us-IRHF scanning. The matrix size was 320 × 320 for Us-IR- and Us-IRHF- and 288 × 208 for conventional scanning. Modulation transfer function (MTF) focused on the 1.0 lp/cm gauge for each scanning was calculated. For clinical study we acquired respiratory-triggered 3D MRCP scans with and without Us-IRHF (U+-, U-MRCP) in 41 patients. The matrix size was 320 × 320 for U+- and 288 × 208 for U-MRCP. The acquisition time and the relative duct-to-periductal contrast ratios (RCs) for the right- and left intrahepatic bile-, the common bile-, and the main pancreatic duct were recorded. Visualization of each duct and overall image quality was scored on 5-point confidence scales. For visualization of each duct the score ranged from 1 (not visible) to 5 (visible with excellent details), for the image quality, it ranged from 1 (undiagnostic) to 5 (excellent). Superiority for the qualitative visualization score and non-inferiority for the RC values with prespecified margins were assessed.

RESULTS

Phantom study showed that compared to the conventional- and Us-IR (without HF) images, the MTF for the Us-IRHF image revealed the highest response. For clinical study, the mean acquisition time was 161 s for U+- and 165 s for U-MRCP. For all ducts, the RC value of U+MRCP was non-inferior to U-MRCP and the qualitative visualization score assigned to U+MRCP was superior to U-MRCP.

CONCLUSION

Us-IRHF improved the image quality of high spatial resolution MRCP without extending the scanning time.

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients

PURPOSE

Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in different body fields. However, there have been no reports regarding the utility of DLR for image quality and T-factor assessment improvements on T2-weighted imaging (T2WI), short inversion time (TI) inversion recovery (STIR) imaging, and unenhanced- and contrast-enhanced (CE) 3D fast spoiled gradient echo (GRE) imaging with and without CS in comparison with thin-section multidetector-row CT (MDCT) for non-small cell lung cancer (NSCLC) patients. The purpose of this study was to determine the utility of DLR for improving image quality and the appropriate sequence for T-category assessment for NSCLC patients.

METHODS

As subjects for this study, 213 pathologically diagnosed NSCLC patients who underwent thin-section MDCT and MR imaging as well as T-factor diagnosis were retrospectively enrolled. SNR of each tumor was calculated and compared by paired t-test for each sequence with and without DLR. T-factor for each patient was assessed with thin-section MDCT and all MR sequences, and the accuracy for T-factor diagnosis was compared among all sequences and thin-section CT by means of McNemar's test.

RESULTS

SNRs of T2WI, STIR imaging, unenhanced thin-section Quick 3D imaging, and CE-thin-section Quick 3D imaging with DLR were significantly higher than SNRs of those without DLR (P < 0.05). Diagnostic accuracy of STIR imaging and CE-thick- or thin-section Quick 3D imaging was significantly higher than that of thin-section CT, T2WI, and unenhanced thick- or thin-section Quick 3D imaging (P < 0.05).

CONCLUSION

DLR is thus considered useful for image quality improvement on MR imaging. STIR imaging and CE-Quick 3D imaging with or without CS were validated as appropriate MR sequences for T-factor evaluation in NSCLC patients.

Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T

BACKGROUND

We aimed to determine the capabilities of compressed sensing (CS) and deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) for improving image quality while reducing examination time on female pelvic 1.5-T magnetic resonance imaging (MRI).

METHODS

Fifty-two consecutive female patients with various pelvic diseases underwent MRI with T1- and T2-weighted sequences using CS and PI. All CS data was reconstructed with and without DLR. Signal-to-noise ratio (SNR) of muscle and contrast-to-noise ratio (CNR) between fat tissue and iliac muscle on T1-weighted images (T1WI) and between myometrium and straight muscle on T2-weighted images (T2WI) were determined through region-of-interest measurements. Overall image quality (OIQ) and diagnostic confidence level (DCL) were evaluated on 5-point scales. SNRs and CNRs were compared using Tukey's test, and qualitative indexes using the Wilcoxon signed-rank test.

RESULTS

SNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.010). CNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.003). OIQ of T1WI and T2WI obtained using CS with DLR were higher than that using CS without DLR or conventional PI (p < 0.001). DCL of T2WI obtained using CS with DLR was higher than that using conventional PI or CS without DLR (p < 0.001).

CONCLUSION

CS with DLR provided better image quality and shorter examination time than those obtainable with PI for female pelvic 1.5-T MRI.

RELEVANCE STATEMENT

CS with DLR can be considered effective for attaining better image quality and shorter examination time for female pelvic MRI at 1.5 T compared with those obtainable with PI.

KEY POINTS

Patients underwent MRI with T1- and T2-weighted sequences using CS and PI. All CS data was reconstructed with and without DLR. CS with DLR allowed for examination times significantly shorter than those of PI and provided significantly higher signal- and CNRs, as well as OIQ.

Compressed sensing with deep learning reconstruction: Improving capability of gadolinium-EOB-enhanced 3D T1WI

PURPOSE

The purpose of this study was to determine the utility of compressed sensing (CS) with deep learning reconstruction (DLR) for improving spatial resolution, image quality and focal liver lesion detection on high-resolution contrast-enhanced T1-weighted imaging (HR-CE-T1WI) obtained by CS with DLR as compared with conventional CE-T1WI with parallel imaging (PI).

METHODS

Seventy-seven participants with focal liver lesions underwent conventional CE-T1WI with PI and HR-CE-T1WI, surgical resection, transarterial chemoembolization, and radiofrequency ablation, followed by histopathological or >2-year follow-up examinations in our hospital. Signal-to-noise ratios (SNRs) of liver, spleen and kidney were calculated for each patient, after which each SNR was compared by means of paired t-test. To compare focal lesion detection capabilities of the two methods, a 5-point visual scoring system was adopted for a per lesion basis analysis. Jackknife free-response receiver operating characteristic (JAFROC) analysis was then performed, while sensitivity and false positive rates (/data set) for consensus assessment of the two methods were also compared by using McNemar's test or the signed rank test.

RESULTS

Each SNR of HR-CE-T1WI was significantly higher than that of conventional CE-T1WI with PI (p < 0.05). Sensitivities for consensus assessment showed that HR-CE-MRI had significantly higher sensitivity than conventional CE-T1WI with PI (p = 0.004). Moreover, there were significantly fewer FP/cases for HR-CE-T1WI than for conventional CE-T1WI with PI (p = 0.04).

CONCLUSION

CS with DLR are useful for improving spatial resolution, image quality and focal liver lesion detection capability of Gd-EOB-DTPA enhanced 3D T1WI without any need for longer breath-holding time.

Diffusion weighted imaging with reverse encoding distortion correction: Improvement of image quality and distortion for accurate ADC evaluation in in vitro and in vivo studies

PURPOSE

The purpose of this in vivo study was to determine the effect of reverse encoding direction (RDC) on apparent diffusion coefficient (ADC) measurements and its efficacy for improving image quality and diagnostic performance for differentiating malignant from benign tumors on head and neck diffusion-weighted imaging (DWI).

METHODS

Forty-eight patients with head and neck tumors underwent DWI with and without RDC and pathological examinations. Their tumors were then divided into two groups: malignant (n = 21) and benign (n = 27). To determine the utility of RDC for DWI, the difference in the deformation ratio (DR) between DWI and T2-weighted images of each tumor was determined for each tumor area. To compare ADC measurement accuracy of DWIs with and without RDC for each patient, ADC values for tumors and spinal cord were determined by using ROI measurements. To compare DR and ADC between two methods, Student's t-tests were performed. Then, ADC values were compared between malignant and benign tumors by Student's t-test on each DWI. Finally, sensitivity, specificity and accuracy were compared by means of McNemar's test.

RESULTS

DR of DWI with RDC was significantly smaller than that without RDC (p < 0.0001). There were significant differences in ADC between malignant and benign lesions on each DWI (p < 0.05). However, there were no significant difference of diagnostic accuracy between the two DWIs (p > 0.05).

CONCLUSION

RDC can improve image quality and distortion of DWI and may have potential for more accurate ADC evaluation and differentiation of malignant from benign head and neck tumors.

Using deep learning models in magnetic resonance cholangiopancreatography images to diagnose common bile duct stones

BACKGROUNDS AND AIMS

Magnetic resonance cholangiopancreatography (MRCP) plays a significant role in diagnosing common bile duct stones (CBDS). Currently, there are no studies to detect CBDS by using the deep learning (DL) model in MRCP. This study aimed to use the DL model You Only Look Once version 5 (YOLOv5) to diagnose CBDS in MRCP images and verify its validity compared to the accuracy of radiologists.

METHODS

By collecting the thick-slab MRCP images of patients diagnosed with CBDS, 4 submodels of YOLOv5 were used to train and validate the performance. Precision, recall rate, and mean average precision (mAP) were used to evaluate model performance. Analyze possible reasons that may affect detection accuracy by validating MRCP images in 63 CBDS patients and comparing them with radiologist detection accuracy. Calculate the correctness of YOLOv5 for detecting one CBDS and multiple CBDS separately.

RESULTS

The precision of YOLOv5l (0.970) was higher than that of YOLOv5x (0.909), YOLOv5m (0.874), and YOLOv5s (0.939). The mAP did not differ significantly between the 4 submodels, with the following results: YOLOv5l (0.942), YOLOv5x (0.947), YOLO5s (0.927), and YOLOv5m (0.946). However, in terms of training time, YOLOv5s was the fastest (4.8 h), detecting CBDS in only 7.2 milliseconds per image. In 63 patients the YOLOv5l model detected CBDS with an accuracy of 90.5% compared to 92.1% for radiologists, analyzing the difference between the positive group successfully identified and the unidentified negative group not. The incorporated variables include common bile duct diameter > 1 cm (p = .560), combined gallbladder stones (p = .706), maximum stone diameter (p = .057), combined cholangitis (p = .846), and combined pancreatitis (p = .656), and the number of CBDS (p = .415). When only one CBDS was present, the accuracy rate reached 94%. When multiple CBDSs were present, the recognition rate dropped to 70%.

CONCLUSION

YOLOv5l is the model with the best results and is almost as accurate as the radiologist's detection of CBDS and is also capable of detecting the number of CBDS. Although the accuracy of the test gradually decreases as the number of stones increases, it can still be useful for the clinician's initial diagnosis.

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65-75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.

Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging

The application of machine learning (ML) and deep learning (DL) in radiology has expanded exponentially. In recent years, an extremely large number of studies have reported about the hepatobiliary domain. Its applications range from differential diagnosis to the diagnosis of tumor invasion and prediction of treatment response and prognosis. Moreover, it has been utilized to improve the image quality of DL reconstruction. However, most clinicians are not familiar with ML and DL, and previous studies about these concepts are relatively challenging to understand. In this review article, we aimed to explain the concepts behind ML and DL and to summarize recent achievements in their use in the hepatobiliary region.

Reverse encoding distortion correction for diffusion-weighted MRI: Efficacy for improving image quality and ADC evaluation for differentiating malignant from benign areas in suspected prostatic cancer patients

PURPOSE

The purpose of this study was to determine the influenceof reverse encoding distortion correction (RDC) on ADC measurement and its efficacy for improving image quality and diagnostic performance for differentiating malignant from benign prostatic areas on prostatic DWI.

METHODS

Forty suspected prostatic cancer patients underwent DWI with or without RDC (i.e. RDC DWI or DWI) using a 3 T MR system as well as pathological examinations. The pathological examination results indicated 86 areas were malignant while 86 out of 394 areas were computationally selected as benign. SNR for benign areas and muscle and ADCs for malignant and benign areas were determined by ROI measurements on each DWI. Moreover, overall image quality was assessed with a 5-point visual scoring system on each DWI. Paired t-test or Wilcoxon's signed rank test was performed to compare SNR and overall image quality for DWIs. ROC analysis was then used to compare the diagnostic performance, and sensitivity (SE), specificity (SP) and accuracy (AC) of ADC were compared between two DWI by means of McNemar's test.

RESULTS

SNR and overall image quality of RDC DWI showed significant improvements when compared with those of DWI (p < 0.05). Areas under the curve (AUC), SP and AC of DWI RDC DWI (AUC: 0.85, SP: 72.1%, AC: 79.1%) were significantly better than those of DWI (AUC: 0.79, p = 0.008; SP: 64%, p = 0.02; AC: 74.4%, p = 0.008).

CONCLUSION

RDC technique has the potential to improve image quality and ability to differentiate malignant from benign prostatic areas on DWIs of suspected prostatic cancer patients.