Iterative Motion Correction Technique with Deep Learning Reconstruction for Brain MRI: A Volunteer and Patient Study.

The aim of this study was to investigate the effect of iterative motion correction (IMC) on reducing artifacts in brain magnetic resonance imaging (MRI) with deep learning reconstruction (DLR). The study included 10 volunteers (between September 2023 and December 2023) and 30 patients (between June 2022 and July 2022) for quantitative and qualitative analyses, respectively. Volunteers were instructed to remain still during the first MRI with fluid-attenuated inversion recovery sequence (FLAIR) and to move during the second scan. IMCoff DLR images were reconstructed from the raw data of the former acquisition; IMCon and IMCoff DLR images were reconstructed from the latter acquisition. After registration of the motion images, the structural similarity index measure (SSIM) was calculated using motionless images as reference. For qualitative analyses, IMCon and IMCoff FLAIR DLR images of the patients were reconstructed and evaluated by three blinded readers in terms of motion artifacts, noise, and overall quality. SSIM for IMCon images was 0.952, higher than that for IMCoff images (0.949) (p < 0.001). In qualitative analyses, although noise in IMCon images was rated as increased by two of the three readers (both p < 0.001), all readers agreed that motion artifacts and overall quality were significantly better in IMCon images than in IMCoff images (all p < 0.001). In conclusion, IMC reduced motion artifacts in brain FLAIR DLR images while maintaining similarity to motionless images.

Super-resolution Deep Learning Reconstruction for 3D Brain MR Imaging: Improvement of Cranial Nerve Depiction and Interobserver Agreement in Evaluations of Neurovascular Conflict.

RATIONALE AND OBJECTIVES: To determine if super-resolution deep learning reconstruction (SR-DLR) improves the depiction of cranial nerves and interobserver agreement when assessing neurovascular conflict in 3D fast asymmetric spin echo (3D FASE) brain MR images, as compared to deep learning reconstruction (DLR). MATERIALS AND METHODS: This retrospective study involved reconstructing 3D FASE MR images of the brain for 37 patients using SR-DLR and DLR. Three blinded readers conducted qualitative image analyses, evaluating the degree of neurovascular conflict, structure depiction, sharpness, noise, and diagnostic acceptability. Quantitative analyses included measuring edge rise distance (ERD), edge rise slope (ERS), and full width at half maximum (FWHM) using the signal intensity profile along a linear region of interest across the center of the basilar artery. RESULTS: Interobserver agreement on the degree of neurovascular conflict of the facial nerve was generally higher with SR-DLR (0.429-0.923) compared to DLR (0.175-0.689). SR-DLR exhibited increased subjective image noise compared to DLR (p ≥ 0.008). However, all three readers found SR-DLR significantly superior in terms of sharpness (p < 0.001); cranial nerve depiction, particularly of facial and acoustic nerves, as well as the osseous spiral lamina (p < 0.001); and diagnostic acceptability (p ≤ 0.002). The FWHM (mm)/ERD (mm)/ERS (mm-1) for SR-DLR and DLR was 3.1-4.3/0.9-1.1/8795.5-10,703.5 and 3.3-4.8/1.4-2.1/5157.9-7705.8, respectively, with SR-DLR's image sharpness being significantly superior (p ≤ 0.001). CONCLUSION: SR-DLR enhances image sharpness, leading to improved cranial nerve depiction and a tendency for greater interobserver agreement regarding facial nerve neurovascular conflict.

Spontaneous rupture of a uterine leiomyoma accompanied by a hematoma appearing as a cystic lesion on imaging: A case report.

The rupture of a uterine leiomyoma is a rare complication. We report a case of ruptured leiomyoma that formed a hematoma that was initially suggestive of an ovarian origin. Magnetic resonance imaging revealed intact ovaries and a cystic lesion adjacent to leiomyomas. During surgery, the cystic lesion was found to be a hematoma caused by a rupture of the leiomyoma.

Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction.

The aim of this study was to investigate whether super-resolution deep learning reconstruction (SR-DLR) is superior to conventional deep learning reconstruction (DLR) with respect to interobserver agreement in the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI. This retrospective study included 39 patients who underwent 1.5T cervical spine MRI. T2-weighted sagittal images were reconstructed with SR-DLR and DLR. Three blinded radiologists independently evaluated the images in terms of the degree of neuroforaminal stenosis, depictions of the vertebrae, spinal cord and neural foramina, sharpness, noise, artefacts and diagnostic acceptability. In quantitative image analyses, a fourth radiologist evaluated the signal-to-noise ratio (SNR) by placing a circular or ovoid region of interest on the spinal cord, and the edge slope based on a linear region of interest placed across the surface of the spinal cord. Interobserver agreement in the evaluations of neuroforaminal stenosis using SR-DLR and DLR was 0.422-0.571 and 0.410-0.542, respectively. The kappa values between reader 1 vs. reader 2 and reader 2 vs. reader 3 significantly differed. Two of the three readers rated depictions of the spinal cord, sharpness, and diagnostic acceptability as significantly better with SR-DLR than with DLR. Both SNR and edge slope (/mm) were also significantly better with SR-DLR (12.9 and 6031, respectively) than with DLR (11.5 and 3741, respectively) (p < 0.001 for both). In conclusion, compared to DLR, SR-DLR improved interobserver agreement in the evaluations of neuroforaminal stenosis using 1.5T cervical spine MRI.

Comparison of 1.5 T and 3 T magnetic resonance angiography for detecting cerebral aneurysms using deep learning-based computer-assisted detection software.

PURPOSE: To compare the diagnostic performance of 1.5 T versus 3 T magnetic resonance angiography (MRA) for detecting cerebral aneurysms with clinically available deep learning-based computer-assisted detection software (EIRL aneurysm® [EIRL_an]), which has been approved by the Japanese Pharmaceuticals and Medical Devices Agency. We also sought to analyze the causes of potential false positives. METHODS: In this single-center, retrospective study, we evaluated the MRA scans of 90 patients who underwent head MRA (1.5 T and 3 T in 45 patients each) in clinical practice. Overall, 51 patients had 70 aneurysms. We used MRI from a vendor not included in the dataset used to create the EIRL_an algorithm. Two radiologists determined the ground truth, the accuracy of the candidates noted by EIRL_an, and the causes of false positives. The sensitivity, number of false positives per case (FPs/case), and the causes of false positives were compared between 1.5 T and 3 T MRA. Pearson's χ2 test, Fisher's exact test, and the Mann‒Whitney U test were used for the statistical analyses as appropriate. RESULTS: The sensitivity was high for 1.5 T and 3 T MRA (0.875‒1), but the number of FPs/case was significantly higher with 3 T MRA (1.511 vs. 2.578, p < 0.001). The most common causes of false positives (descending order) were the origin/bifurcation of vessels/branches, flow-related artifacts, and atherosclerosis and were similar between 1.5 T and 3 T MRA. CONCLUSION: EIRL_an detected significantly more false-positive lesions with 3 T than with 1.5 T MRA in this external validation study. Our data may help physicians with limited experience with MRA to correctly diagnose aneurysms using EIRL_an.

Clinical Impact of Deep Learning Reconstruction in MRI.

Deep learning has been recognized as a paradigm-shifting tool in radiology. Deep learning reconstruction (DLR) has recently emerged as a technology used in the image reconstruction process of MRI, which is an essential procedure in generating MR images. Denoising, which is the first DLR application to be realized in commercial MRI scanners, improves signal-to-noise ratio. When applied to lower magnetic field-strength scanners, the signal-to-noise ratio can be increased without extending the imaging time, and image quality is comparable to that of higher-field-strength scanners. Shorter imaging times decrease patient discomfort and reduce MRI scanner running costs. The incorporation of DLR into accelerated acquisition imaging techniques, such as parallel imaging or compressed sensing, shortens the reconstruction time. DLR is based on supervised learning using convolutional layers and is divided into the following three categories: image domain, k-space learning, and direct mapping types. Various studies have reported other derivatives of DLR, and several have shown the feasibility of DLR in clinical practice. Although DLR efficiently reduces Gaussian noise from MR images, denoising makes image artifacts more prominent, and a solution to this problem is desired. Depending on the training of the convolutional neural network, DLR may change the imaging features of lesions and obscure small lesions. Therefore, radiologists may need to adopt the habit of questioning whether any information has been lost on images that appear clean. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Acceleration of knee magnetic resonance imaging using a combination of compressed sensing and commercially available deep learning reconstruction: a preliminary study.

PURPOSE: To evaluate whether deep learning reconstruction (DLR) accelerates the acquisition of 1.5-T magnetic resonance imaging (MRI) knee data without image deterioration. MATERIALS AND METHODS: Twenty-one healthy volunteers underwent MRI of the right knee on a 1.5-T MRI scanner. Proton-density-weighted images with one or four numbers of signal averages (NSAs) were obtained via compressed sensing, and DLR was applied to the images with 1 NSA to obtain 1NSA-DLR images. The 1NSA-DLR and 4NSA images were compared objectively (by deriving the signal-to-noise ratios of the lateral and the medial menisci and the contrast-to-noise ratios of the lateral and the medial menisci and articular cartilages) and subjectively (in terms of the visibility of the anterior cruciate ligament, the medial collateral ligament, the medial and lateral menisci, and bone) and in terms of image noise, artifacts, and overall diagnostic acceptability. The paired t-test and Wilcoxon signed-rank test were used for statistical analyses. RESULTS: The 1NSA-DLR images were obtained within 100 s. The signal-to-noise ratios (lateral: 3.27 ± 0.30 vs. 1.90 ± 0.13, medial: 2.71 ± 0.24 vs. 1.80 ± 0.15, both p < 0.001) and contrast-to-noise ratios (lateral: 2.61 ± 0.51 vs. 2.18 ± 0.58, medial 2.19 ± 0.32 vs. 1.97 ± 0.36, both p < 0.001) were significantly higher for 1NSA-DLR than 4NSA images. Subjectively, all anatomical structures (except bone) were significantly clearer on the 1NSA-DLR than on the 4NSA images. Also, in the former images, the noise was lower, and the overall diagnostic acceptability was higher. CONCLUSION: Compared with the 4NSA images, the 1NSA-DLR images exhibited less noise, higher overall image quality, and allowed more precise visualization of the menisci and ligaments.

Commercially Available Deep-learning-reconstruction of MR Imaging of the Knee at 1.5T Has Higher Image Quality Than Conventionally-reconstructed Imaging at 3T: A Normal Volunteer Study.

PURPOSE: This study aimed to evaluate whether the image quality of 1.5T magnetic resonance imaging (MRI) of the knee is equal to or higher than that of 3T MRI by applying deep learning reconstruction (DLR). METHODS: Proton density-weighted images of the right knee of 27 healthy volunteers were obtained by 3T and 1.5T MRI scanners using similar imaging parameters (21 for high resolution image and 6 for normal resolution image). Commercially available DLR was applied to the 1.5T images to obtain 1.5T/DLR images. The 3T and 1.5T/DLR images were compared subjectively for visibility of structures, image noise, artifacts, and overall diagnostic acceptability and objectively. One-way ANOVA and Friedman tests were used for the statistical analyses. RESULTS: For the high resolution images, all of the anatomical structures, except for bone, were depicted significantly better on the 1.5T/DLR compared with 3T images. Image noise scored statistically lower and overall diagnostic acceptability scored higher on the 1.5T/DLR images. The contrast between lateral meniscus and articular cartilage of the 1.5T/DLR images was significantly higher (5.89 ± 1.30 vs. 4.34 ± 0.87, P < 0.001), and also the contrast between medial meniscus and articular cartilage of the 1.5T/DLR images was significantly higher (5.12 ± 0.93 vs. 3.87 ± 0.56, P < 0.001). Similar image quality improvement by DLR was observed for the normal resolution images. CONCLUSION: The 1.5T/DLR images can achieve less noise, more precise visualization of the meniscus and ligaments, and higher overall image quality compared with the 3T images acquired using a similar protocol.

Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction.

PURPOSE: To compare image quality and interobserver agreement in evaluations of neuroforaminal stenosis between 1.5T cervical spine magnetic resonance imaging (MRI) with deep learning reconstruction (DLR) and 3T MRI without DLR. METHODS: In this prospective study, 21 volunteers (mean age: 42.4 ± 11.9 years; 17 males) underwent cervical spine T2-weighted sagittal 1.5T and 3T MRI on the same day. The 1.5T and 3T MRI data were used to reconstruct images with (1.5T-DLR) and without (3T-nonDLR) DLR, respectively. Regions of interest were marked on the spinal cord to calculate non-uniformity (NU; standard deviation/signal intensity × 100), as an indicator of image noise. Two blinded radiologists evaluated the images in terms of the depiction of structures, artifacts, noise, overall image quality, and neuroforaminal stenosis. The NU value and the subjective image quality scores were compared between 1.5T-DLR and 3T-nonDLR using the Wilcoxon signed-rank test. Interobserver agreement in evaluations of neuroforaminal stenosis for 1.5T-DLR and 3T-nonDLR was evaluated using Cohen's weighted kappa analysis. RESULTS: The NU value for 1.5T-DLR was 8.4, which was significantly better than that for 3T-nonDLR (10.3; p < 0.001). Subjective image scores were significantly better for 1.5T-DLR than 3T-nonDLR images (p < 0.037). Interobserver agreement (95% confidence intervals) in the evaluations of neuroforaminal stenosis was significantly superior for 1.5T-DLR (0.920 [0.916-0.924]) than 3T-nonDLR (0.894 [0.889-0.898]). CONCLUSION: By using DLR, image quality and interobserver agreement in evaluations of neuroforaminal stenosis on 1.5T cervical spine MRI could be improved compared to 3T MRI without DLR.

Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer.

PURPOSE: To assess the possibility of reducing the image acquisition time for diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) by denoising with deep learning-based reconstruction (dDLR). METHODS: Seventeen patients with prostate cancer who underwent DWIBS by 1.5 T magnetic resonance imaging with a number of excitations of 2 (NEX2) and 8 (NEX8) were prospectively enrolled. The NEX2 image data were processed by dDLR (dDLR-NEX2), and the NEX2, dDLR-NEX2, and NEX8 image data were analyzed. In qualitative analysis, two radiologists rated the perceived coarseness, conspicuity of metastatic lesions (lymph nodes and bone), and overall image quality. The contrast-to-noise ratios (CNRs), contrast ratios, and mean apparent diffusion coefficients (ADCs) of metastatic lesions were calculated in a quantitative analysis. RESULTS: The image acquisition time of NEX2 was 2.8 times shorter than that of NEX8 (3 min 30 s vs 9 min 48 s). The perceived coarseness and overall image quality scores reported by both readers were significantly higher for dDLR-NEX2 than for NEX2 (P = 0.005-0.040). There was no significant difference between dDLR-NEX2 and NEX8 in the qualitative analysis. The CNR of bone metastasis was significantly greater for dDLR-NEX2 than for NEX2 and NEX8 (P = 0.012 for both comparisons). The contrast ratios and mean ADCs were not significantly different among the three image types. CONCLUSIONS: dDLR improved the image quality of DWIBS with NEX2. In the context of lymph node and bone metastasis evaluation with DWIBS in patients with prostate cancer, dDLR-NEX2 has potential to be an alternative to NEX8 and reduce the image acquisition time.

Clinical feasibility of an abdominal thin-slice breath-hold single-shot fast spin echo sequence processed using a deep learning-based noise-reduction approach.

BACKGROUND: T2-weighted imaging (T2WI) is a key sequence of MRI studies of the pancreas. The single-shot fast spin echo (single-shot FSE) sequence is an accelerated form of T2WI. We hypothesized that denoising approach with deep learning-based reconstruction (dDLR) could facilitate accelerated breath-hold thin-slice single-shot FSE MRI, and reveal the pancreatic anatomy in detail. PURPOSE: To assess the image quality of thin-slice (3 mm) respiratory-triggered FSE T2WI (Resp-FSE) and breath-hold fast advanced spin echo with and without dDLR (BH-dDLR-FASE and BH-FASE, respectively) at 1.5 T. MATERIALS AND METHODS: MR images of 42 prospectively enrolled patients with suspected pancreaticobiliary disease were obtained at 1.5 T. We qualitatively and quantitatively evaluated image quality of BH-dDLR-FASE related to BH-FASE and Resp-FSE. RESULTS: The scan time of BH-FASE was significantly shorter than that of Resp-FSE (30 ± 4 s and 122 ± 25 s, p < 0.001). Qualitatively, dDLR significantly improved BH-FASE image quality, and the image quality of BH-dDLR-FASE was significantly better than that of Resp-FSE; as quantitative parameters, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of BH-dDLR-FASE were also significantly better than those of Resp-FSE. The BH-dDLR-FASE sequence covered the entire pancreas and liver and provided overall image quality rated close to excellent. CONCLUSIONS: The dDLR technique enables accelerated thin-slice single-shot FSE, and BH-dDLR-FASE seems to be clinically feasible.

Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes.

OBJECTIVES: To investigate whether deep learning reconstruction (DLR) provides improved cervical spine MR images using a 1.5 T unit in the evaluation of degenerative changes without increasing imaging time. METHODS: This study included 21 volunteers (age 42.4 ± 11.9 years; 17 males) who underwent 1.5 T cervical spine sagittal T2-weighted MRI. From the imaging data with number of acquisitions (NAQ) of 1 or 2, images were reconstructed with DLR (NAQ1-DLR) and without DLR (NAQ1) or without DLR (NAQ2), respectively. Two readers evaluated the images for depiction of structures, artifacts, noise, overall image quality, spinal canal stenosis, and neuroforaminal stenosis. The two readers read studies blinded and randomly. Values were compared between NAQ1-DLR and NAQ1 and between NAQ1-DLR and NAQ2 using the Wilcoxon signed-rank test. RESULTS: The analyses showed significantly better results for NAQ1-DLR compared with NAQ1 and NAQ2 (p < 0.023), except for depiction of disc and foramina by one reader and artifacts by both readers in the comparison between NAQ1-DLR and NAQ2. Interobserver agreements (Cohen's weighted kappa [97.5% confidence interval]) in the evaluation of spinal canal stenosis for NAQ1-DLR/NAQ1/NAQ2 were 0.874 (0.866-0.883)/0.778 (0.767-0.789)/0.818 (0.809-0.827), respectively, and those in the evaluation of neuroforaminal stenosis were 0.878 (0.872-0.883)/0.855 (0.849-0.860)/0.852 (0.845-0.860), respectively. CONCLUSIONS: DLR improved the 1.5 T cervical spine MR images in the evaluation of degenerative spine changes. KEY POINTS: • Two radiologists demonstrated that deep learning reconstruction reduced the noise in cervical spine sagittal T2-weighted MR images obtained using a 1.5 T unit. • Reduced noise in deep learning reconstruction images resulted in a clearer depiction of structures, such as the spinal cord, vertebrae, and zygapophyseal joint. • Interobserver agreement in the evaluation of spinal canal stenosis and foraminal stenosis on cervical spine MR images was significantly improved using deep learning reconstruction (0.874 and 0.878, respectively) versus without deep learning (0.778-0.818 and 0.852-0.855, respectively).

Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography.

PURPOSE: The purpose of this study was to evaluate whether deep learning reconstruction (DLR) improves the image quality of intracranial magnetic resonance angiography (MRA) at 1.5 T. MATERIALS AND METHODS: In this retrospective study, MRA images of 40 patients (21 males and 19 females; mean age, 65.8 ± 13.2 years) were reconstructed with and without the DLR technique (DLR image and non-DLR image, respectively). Quantitative image analysis was performed by placing regions of interest on the basilar artery and cerebrospinal fluid in the prepontine cistern. We calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for analyses of the basilar artery. Two experienced radiologists evaluated the depiction of structures (the right internal carotid artery, right ophthalmic artery, basilar artery, and right superior cerebellar artery), artifacts, subjective noise and overall image quality in a qualitative image analysis. Scores were compared in the quantitative and qualitative image analyses between the DLR and non-DLR images using Wilcoxon signed-rank tests. RESULTS: The SNR and CNR for the basilar artery were significantly higher for the DLR images than for the non-DLR images (p < 0.001). Qualitative image analysis scores (p < 0.003 and p < 0.005 for readers 1 and 2, respectively), excluding those for artifacts (p = 0.072-0.565), were also significantly higher for the DLR images than for the non-DLR images. CONCLUSION: DLR enables the production of higher quality 1.5 T intracranial MRA images with improved visualization of arteries.

Breath-hold 3D magnetic resonance cholangiopancreatography at 1.5 T using a deep learning-based noise-reduction approach: Comparison with the conventional respiratory-triggered technique.

OBJECTIVES: To assess the image quality of conventional respiratory-triggered 3-dimentional (3D) magnetic resonance cholangiopancreatography (Resp-MRCP) and breath-hold 3D MRCP (BH-MRCP) with and without denoising procedure using deep learning-based reconstruction (dDLR) at 1.5 T. METHODS: Forty-two patients underwent MRCP at 1.5 T MRI. The following imaging sequences were performed: Resp-MRCP and BH-MRCP. We applied the dDLR method to the BH-MRCP data (BH-dDLR-MRCP). As a qualitative analysis, two radiologists rated the visibility of the proximal common bile duct (CBD), pancreaticobiliary junction, distal main pancreatic duct, cystic duct, and right and left hepatic ducts. Artifacts and overall image quality were also rated. The signal-to-noise ratios (SNRs), contrast ratios, and contrast-to-noise ratios (CNRs) of the CBD images were calculated for quantitative analysis. RESULTS: BH-MRCP was successfully performed in a single BH. The qualitative and quantitative measurements for BH-dDLR-MRCP were significantly higher than for BH-MRCP (P < 0.02 and P < 0.001, respectively), and the qualitative measurements for BH-dDLR-MRCP were equivalent to or higher than for Resp-MRCP (P = 0.048-1.000). The SNRs and CNRs for BH-dDLR-MRCP were significantly higher than for Resp-MRCP (P < 0.001 and P = 0.001, respectively). CONCLUSION: dDLR is useful and clinically feasible for BH-MRCP at 1.5 T MRI, and enables rapid imaging without loss of image quality compared to conventional Resp-MRCP.

Pulmonary postmortem computed tomography of bacterial pneumonia and pulmonary edema in patients following non-traumatic in-hospital death.

In this study, we compared the postmortem computed tomography (PMCT) findings among nonpathological lungs, lungs with bacterial pneumonia, and lungs with pulmonary edema in patients following non-traumatic in-hospital death. We studied 104 consecutive adult patients (208 lungs) who died in our tertiary care hospital and underwent PMCT and pathological autopsy (both within 2.5 days after death), and were pathologically diagnosed with nonpathological lungs, bacterial pneumonia, and pulmonary edema. Thirteen pulmonary features were assessed on the CT scans. We also examined the association between the time elapsed since death and the pulmonary findings. We observed increased lung opacities with horizontal plane formation, diffuse opacities, diffuse bronchovascular bundle thickening, symmetric opacities to the contralateral lung, and decreased segmental opacities with time elapsed since death (Cochran-Armitage test for trend). Multiple logistic regression revealed that the presence of opacities without horizontal plane formation or centrilobular opacities, and the absence of diffuse bronchovascular bundle thickening were associated with histopathological pneumonia, whereas the presence of opacities with horizontal plane formation, diffuse opacities, and interlobular septal thickening were associated with histopathological pulmonary edema. In conclusion, specific pulmonary PMCT findings increased with time elapsed since death, and some lung findings may facilitate the diagnosis of bacterial pneumonia and pulmonary edema.

Single-energy metal artifact reduction technique for reducing metallic coil artifacts on post-interventional cerebral CT and CT angiography.

PURPOSE: To evaluate the effects of the single-energy metal artifact reduction (SEMAR) algorithm on image quality of cerebral CT and CT angiography (CTA) for patients who underwent intracranial aneurysm coiling. METHODS: Twenty patients underwent cerebral CT and CTA using a 320-detector row CT after intracranial aneurysm coiling. Images with and without application of the SEMAR algorithm (SEMAR CT and standard CT images, respectively) were reconstructed for each patient. The images were qualitatively assessed by two independent radiologists in a blinded manner for the depiction of anatomical structures around the coil, delineation of the arteries around the coil, and the depiction of the status of coiled aneurysms. Artifact strength was quantitatively assessed by measuring the standard deviation of attenuation values around the coil. RESULTS: The strength of artifacts measured in SEMAR CT images was significantly lower than that in standard CT images (25.7 ± 10.2 H.U. vs. 80.4 ± 67.2 H.U., p < 0.01, Student's paired t test). SEMAR CT images were significantly improved compared with standard CT images in the depiction of anatomical structures around the coil (p < 0.01, the sign test), delineation of the arteries around the coil (p < 0.01), and the depiction of the status of coiled aneurysms (p < 0.01). CONCLUSION: The SEMAR algorithm significantly reduces metal artifacts from intracranial aneurysm coiling and improves visualization of anatomical structures and arteries around the coil, and depiction of the status of coiled aneurysms on post-interventional cerebral CT.

Detection of liver metastasis: is diffusion-weighted imaging needed in Gd-EOB-DTPA-enhanced MR imaging for evaluation of colorectal liver metastases?

PURPOSE: We compared diagnostic ability for detecting hepatic metastases between gadolinium ethoxy benzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) on a 1.5-T system, and determined whether DWI is necessary in Gd-EOB-DTPA-enhanced MRI for diagnosing colorectal liver metastases. MATERIALS AND METHODS: We assessed 29 consecutive prospectively enrolled patients with suspected metachronous colorectal liver metastases; all patients underwent surgery and had preoperative Gd-EOB-DTPA-enhanced MRI. Overall detection rate, sensitivity for detecting metastases and benign lesions, positive predictive value, and diagnostic accuracy (Az value) were compared among three image sets [unenhanced MRI (DWI set), Gd-EOB-DTPA-enhanced MRI excluding DWI (EOB set), and combined set]. RESULTS: Gd-EOB-DTPA-enhanced MRI yielded better overall detection rate (77.8-79.0 %) and sensitivity (87.1-89.4 %) for detecting metastases than the DWI set (55.9 % and 64.7 %, respectively) for one observer (P < 0.001). No statistically significant difference was seen between the EOB and combined sets, although several metastases were newly detected on additional DWI. CONCLUSIONS: Gd-EOB-DTPA-enhanced MRI yielded a better overall detection rate and higher sensitivity for detecting metastases compared with unenhanced MRI. Additional DWI may be able to reduce oversight of lesions in Gd-EOB-DTPA-enhanced 1.5-T MRI for detecting colorectal liver metastases.

Fate of hypointense lesions on Gd-EOB-DTPA-enhanced magnetic resonance imaging.

PURPOSE: To investigate the natural outcome and clinical implication of hypointense lesions in the hepatobiliary phase of Gd-EOB-DTPA-enhanced MRI. MATERIALS AND METHODS: Forty patients underwent Gd-EOB-DTPA-enhanced MRI for preoperative evaluation of HCC. Hypointense lesions in the hepatobiliary phase that were hypovascular 5mm of more were extracted for follow-up. We performed a longitudinal study retrospectively for these lesions regardless of whether classical HCC developed or emerged in a different area from that of the lesions being followed. RESULTS: Thirty one patients displayed 130 hypointense lesions on MRI and only nine showed no hypointense lesions. In total, 17 (13.1%) of 130 hypointense lesions on MRI developed into classical HCC. The cumulative rates for these lesions to develop into classical HCC were 3.2% at 1 year, 11.1% at 2 years and 15.9% at 3 years. The total occurrence rates of classical HCC (25.8% at 1 year, 52.6% at 2 years and 76.4% at 3 years) were higher compared to those regarding only occurrence of classical HCC from hypointense lesions on MRI (10.0% at 1 year, 35.6% at 2 years and 44.6% at 3 years), although no significant difference was observed (p=0.073). CONCLUSIONS: Hypointense lesions that are detected in the hepatobiliary phase of Gd-EOB-DTPA-enhanced MRI have some malignant potential, although treating these lesions aggressively in patients who already have HCC may be too severe.

Detection of hepatocellular carcinoma by Gd-EOB-DTPA-enhanced liver MRI: comparison with triple phase 64 detector row helical CT.

PURPOSE: To compare the diagnostic performance of Gd-EOB-DTPA-enhanced MRI with that of triple phase 64-MDCT in the detection of hepatocellular carcinoma (HCC). PATIENTS AND METHODS: Thirty-four patients with 52 surgically proven lesions underwent Gd-EOB-DTPA-enhanced MRI and triple phase 64-MDCT. Two observers independently evaluated MR and CT imaging on a lesion-by-lesion basis. Sensitivity, positive and negative predictive values and reproducibility were evaluated. The diagnostic accuracy of each modality was assessed with alternative-free response receiver operating characteristic (ROC) analysis. RESULTS: Both observers showed higher sensitivity in detecting lesions with MRI compared to CT, however, only the difference between the two imaging techniques for observer 2 was significant (P=0.034). For lesions 1cm or smaller, MRI and CT showed equal sensitivity (both 62.5%) with one observer, and MRI proved superior to CT with the other observer (MRI 75% vs. CT 56.3%), but the latter difference was not significant (P=0.083). The difference in positive and negative predictive value between the two imaging techniques for each observer was not significant (P>0.05). The areas under the ROC curve for each observer were 0.843 and 0.861 for MRI vs. 0.800 and 0.833 for CT and the differences were not significant. Reproducibility was higher using MRI for both observers, but the result was not significant (MRI 32/33 vs. CT 29/33, P=0.083). CONCLUSION: Gd-EOB-DTPA-enhanced MRI tended to show higher diagnostic accuracy, sensitivity and reproducibility compared to triple phase 64-MDCT in the detection of hepatocellular carcinoma, however statistical significance was not achieved.

MR imaging of the biliary tract with Gd-EOB-DTPA: effect of liver function on signal intensity.

OBJECTIVE: To quantitatively evaluate the signal intensity of the biliary tract in gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance (MR) imaging and to investigate the effect of liver function on the signal intensity of the biliary tract. MATERIALS AND METHODS: A total of 32 patients with and without chronic liver disease (normal liver group, n = 15; chronic liver disease group, n = 17) were included in this study. All patients were prospectively enrolled for evaluation of known or suspected focal liver lesions. In the chronic liver disease group, the etiologies were chronic hepatitis C virus infection (n = 12) and chronic hepatitis B virus infection (n = 5). The median Child-Pugh score was 5 (range, 5-7). Each patient received the standard dose of Gd-EOB-DTPA (0.025 mmol/kg of body weight). Post-contrast T1-weighted MR images were obtained at 5, 10, 15, 20, 25, and 30 min after administration of Gd-EOB-DTPA. Maximum signal intensities (SIs) of the right and left hepatic ducts, common hepatic duct, and common bile duct were measured. Relative signal intensity was calculated as follows: relative SI = maximum SI(bileduct)/mean SI(muscle). Serum albumin level, serum total bilirubin level, prothrombin time, indocyanine green retention rate at 15 min (ICG-R15), and estimated glomerular filtration rate were entered into regression analysis. RESULTS: The signal intensity of the bile duct reached a peak 30 min after administration of Gd-EOB-DTPA. The mean relative signal intensity of the right and left hepatic ducts at the peak time point was not significantly different between the two groups, while increase in signal intensity was delayed in the chronic liver disease group. The mean relative signal intensity of the common hepatic duct and that of the common bile duct at the peak time point were significantly different between the two groups (Wilcoxon rank-sum test, P = 0.03, respectively). Stepwise regression analysis revealed that ICG-R15 was a significant predictor of the signal intensity of the bile duct (right and left hepatic ducts, P = 0.04; common hepatic duct, P = 0.008; common bile duct, P = 0.003). CONCLUSIONS: The results of our study demonstrate that the presence of chronic liver disease significantly affects the signal intensity of the bile duct in Gd-EOB-DTPA-enhanced MR imaging. ICG-R15 was only a significant predictor of the signal intensity of the bile duct. The signal intensity of the bile duct may reflect underlying liver function.