Faster acquisition of magnetic resonance imaging sequences of the knee via deep learning reconstruction: a volunteer study

AIM

To evaluate whether deep learning reconstruction (DLR) can accelerate the acquisition of magnetic resonance imaging (MRI) sequences of the knee for clinical use.

MATERIALS AND METHODS

Using a 1.5-T MRI scanner, sagittal fat-suppressed T2-weighted imaging (fs-T2WI), coronal proton density-weighted imaging (PDWI), and coronal T1-weighted imaging (T1WI) were performed. DLR was applied to images with a number of signal averages (NSA) of 1 to obtain 1DLR images. Then 1NSA, 1DLR, and 4NSA images were compared subjectively, and by noise (standard deviation of intra-articular water or medial meniscus) and contrast-to-noise ratio between two anatomical structures or between an anatomical structure and intra-articular water.

RESULTS

Twenty-seven healthy volunteers (age: 40.6 ± 11.9 years) were enrolled. Three 1DLR image sequences were obtained within 200 s (approximately 12 minutes for 4NSA image). According to objective evaluations, PDWI 1DLR images showed the smallest noise and significantly higher contrast than 1NSA and 4NSA images. For fs-T2WI, smaller noise and higher contrast were observed in the order of 4NSA, 1DLR, and 1NSA images. According to the subjective analysis, structure visibility, image noise, and overall image quality were significantly better for PDWI 1DLR than 1NSA images; moreover, the visibility of the meniscus and bone, image noise, and overall image quality were significantly better for 1DLR than 4NSA images. Fs-T2WI and T1WI 1DLR images showed no difference between 1DLR and 4NSA images.

CONCLUSION

Compared to PDWI 4NSA images, PDWI 1DLR images were of higher quality, while the quality of fs-T2WI and T1WI 1DLR images was similar to that of 4NSA images.

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.

Evaluation of the relations between reproduction-related pituitary and ovarian hormones and abdominal fat area-related variables determined with computed tomography in overweight or obese women who have undergone bariatric surgery: a cross-sectional study

Background

An understanding of the associations between midregion fat depots and systemic hormone levels will be crucial for developing health-promotion messages aimed at overweight or obese women. However, related research in this area is rare. The present study was performed to identify and quantify fat-related reproduction pituitary and ovarian hormones in overweight or obese women.

Methods

A total of 250 eligible overweight or obese women scheduled to undergo laparoscopic sleeve gastrectomy (LSG) from a single center were retrospectively included in this study. Computed tomography (CT) images at the level of the umbilicus were selected, and abdominal fat areas were measured and calculated. The reproduction-related pituitary and ovarian hormones were also measured. The correlations among the parameters were examined using Spearman correlation test. Multiple linear regression analysis was performed after log and β-transformation of the hormone levels and fat area-related variables.

Results

Positive correlations were detected for prolactin (PRL) with total fat area (TFA) [β=0.045; P=0.029; 95% confidence interval (CI): 0.004-0.085] and subcutaneous fat area (SFA) (β=0.066; P=0.023; 95% CI: 0.009-0.123), whereas estradiol showed a negative correlation with visceral fat area (VFA) (β=-0.056, P=0.005; 95% CI: -0.096 to -0.017) and relative VFA (rVFA) (β=-0.068; P=0.001; 95% CI: -0.109 to -0.027) and a positive correlation with SFA (β=0.036; P=0.042; 95% CI: 0.001-0.071). Progesterone (PROG) was negatively correlated with both VFA (β=-0.037; P=0.002; 95% CI: -0.061 to -0.013) and rVFA (β=-0.039; P=0.002; 95% CI: -0.063 to -0.014). The final results revealed that TFA was increased by 3.1% and SFA was increased by 4.7% with a doubling of PRL concentration; VFA was reduced by 2.5% and rVFA was reduced by 2.6% with a doubling of PROG concentration; and VFA was reduced by 3.8%, rVFA was reduced by 4.6%, and SFA was increased by 2.5% with a doubling of estradiol concentration.

Conclusions

There exist certain associations between some reproduction-related pituitary and ovarian hormones and fat areas. Our findings provide new insights into the associations between midregion fat depots and systemic hormone levels in overweight or obese women.

Differentiation between pleomorphic adenoma and schwannoma in the parapharyngeal space: histogram analysis of apparent diffusion coefficient

OBJECTIVES

To elucidate the differences between pleomorphic adenomas and schwannomas occurring in the parapharyngeal space by histogram analyses of apparent diffusion coefficient (ADC) values measured with diffusion-weighted MRI.

METHODS

This retrospective study included 29 patients with pleomorphic adenoma and 22 patients with schwannoma arising in the parapharyngeal space or extending into the parapharyngeal space from the parotid region. Using pre-operative MR images, ADC values of tumor lesions showing the maximum diameter were measured. The regions of interest for ADC measurement were placed by contouring the tumor margin, and the histogram metrics of ADC values were compared between pleomorphic adenomas and schwannomas regarding the mean, skewness, and kurtosis by Wilcoxon's rank sum test. Subsequent to the primary analysis which included all lesions, we performed two subgroup analyses regarding b-values and magnetic field strength used for MRI.

RESULTS

The mean ADC values did not show significant differences between pleomorphic adenomas and schwannomas for the primary and subgroup analyses. Schwannomas showed higher skewness (p = 0.0001) and lower kurtosis (p = 0.003) of ADC histograms compared with pleomorphic adenomas in the primary analysis. Skewness was significantly higher in schwannomas in all the subgroup analyses. Kurtosis was consistently lower in schwannomas but did not reach statistical significance in one subgroup analysis.

CONCLUSIONS

Skewness and kurtosis showed significant differences between pleomorphic adenomas and schwannomas occupying the parapharyngeal space, but the mean ADC values did not. Our results suggest that the skewness and kurtosis of ADC histograms may be useful in differentiating these two parapharyngeal tumors.

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.

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.

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.

Value of apparent diffusion coefficient on MRI for prediction of histopathological type in anal fistula cancer

The main histopathological types of anal fistula cancers are mucinous adenocarcinoma and tubular adenocarcinoma. The purpose of this study was to investigate the utility of the apparent diffusion coefficient (ADC) value in magnetic resonance imaging (MRI) to determine the histopathological type of an anal fistula cancer, and to investigate the relationship between ADC values and histopathological type (mucinous type or tubular carcinoma), clinical information, and surgical findings. We retrospectively identified 69 patients diagnosed with anal fistula cancer at our hospital from January 2013 to December 2021. Among them, we selected the patients diagnosed using the same 1.5-T MRI machine, underwent surgery, and a pathological sample was obtained during the operation. Finally, these 25 patients were selected for the analysis since they underwent the imaging scan using the same MRI machine. The ADC value was compared between mucinous and tubular adenocarcinomas, and between tumors at the Tis-T1-T2 and T3-T4 stages. Finally, 25 patients were selected. The mean age of the 25 patients included in the analysis was 60.8 ± 13.3 years and all were males. The median ADC of anal fistula cancers was 1.97 × 10-3 mm2/s for mucinous adenocarcinomas and 1.36 × 10-3 mm2/s for tubular adenocarcinomas; this difference was statistically significant (P < .01). Furthermore, the median ADC was 1.62 × 10-3 mm2/s for tumors in Tis-T1-T2 stages and 2.01 × 10-3 mm2/s for T3-T4 tumors (P = .02). The ADC value in MR images may predict the histopathological type and depth of anal fistula cancers. Also, the different ADC values between Tis-T1-T2 and T3-T4 tumors could help predict the classification of progression.

The contralateral effects of anticipated stimuli on brain activity measured by ERP and fMRI

The present study examined the effects of unilateral stimulus presentation on the right hemisphere preponderance of the stimulus-preceding negativity (SPN) in the event-related potential (ERP) experiment, and aimed to elucidate whether unilateral stimulus presentation affected activations in the bilateral anterior insula in the functional magnetic resonance imaging (fMRI) experiment. Separate fMRI and ERP experiments were conducted using visual and auditory stimuli by manipulating the position of stimulus presentation (left side or right side) with the time estimation task. The ERP experiment revealed a significant right hemisphere preponderance during left stimulation and no laterality during the right stimulation. The fMRI experiment revealed that the left anterior insula was activated only in the right stimulation of auditory and visual stimuli whereas the right anterior insula was activated by both left and right stimulations. The visual condition retained a contralateral dominance, but the auditory condition showed a right hemisphere dominance in a localized area. The results of this study indicate that the SPN reflects perceptual anticipation, and also that the anterior insula is involved in its occurrence.

Pulse Sequences and Reconstruction in Fast MR Imaging of the Liver

The liver moves with respiratory motion. Respiratory motion causes image artifacts as MRI is a motion-sensitive imaging modality; thus, MRI scan speed improvement has been an important technical development target for liver MRI for years. Recent pulse sequence and image reconstruction technology advancement has realized a fast liver MRI acquisition method. Such new technologies allow us to obtain liver MRI in a shorter time, particularly, within breath-holding time. Other benefits of new the technology and the higher spatial resolution liver MRI within a given scan time are improved slice coverage and smaller pixel size. In this review, MRI pulse sequence and reconstruction technologies to accelerate scan speed for T₁- and T₂-weighted liver MRI will be discussed. Technologies that reduce scan time while keeping image contrast, SNR and image spatial resolution are needed for fast MRI acquisition. We will discuss the progress of MRI acquisition methods, the enabling technology, established applications, current trends, and the future outlook.

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.

Radiation Protection of the Eye Lens in Fluoroscopy-guided Interventional Procedures

The medical staff involved in fluoroscopy-guided procedures are at potential risks of radiation-induced cataract. Therefore, proper monitoring of the lens doses is critical, and radiation protection should be provided to the maximum extent that is reasonably achievable. The collar dosimeter is necessary to avoid underestimation of the lens dose, and the third dosimeter behind the protective eyewear would be helpful for those who are likely to exceed the dose limit. The reduction of the patient doses will correspondingly reduce the staff doses. Proper placement of the ceiling-mounted shields and minimization of the face-to-glass gap are the keys to effective shielding. The optimization of procedures and devices that help maintain a distance from the irradiated area and to prevent the looking-up posture will substantially reduce the lens dose.

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.

Computed Tomography and Clinical Analysis of Ovarian Mucinous Tumors in Adolescent Patients

STUDY OBJECTIVE

To investigate the clinical and computed tomography features of ovarian mucinous tumors in children and adolescents.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective analysis of clinical and preoperative computed tomography (CT) data was performed in 59 patients who were 20 years or younger with ovarian mucinous tumors confirmed by histopathology. Patients' age, medical history, symptoms, tumor marker levels, and CT imaging findings were recorded.

INTERVENTIONS

None.

MAIN OUTCOME MEASURES

Identification of the clinical and CT features of ovarian mucinous tumors in pediatric and adolescent patients.

RESULTS

There were 41 mucinous cystadenomas in 39 cases, 12 borderline mucinous cystadenomas in 11 cases, and 9 mucinous cystadenocarcinomas in 9 patients. A total of 55 tumors were multilocular (55/62, 88.7%), including fewer than 10 loculations in 23 tumors, 10-20 loculations in 17 tumors, and over 20 loculations in 15 tumors. Eleven borderline mucinous cystadenomas were multilocular (11/12, 91.7%), with over 10 loculations in 7 tumors (7/12, 58.3%). Twelve tumors appeared as multilocular cystic-solid (12/62, 19.4%), and a case of mucinous cystadenocarcinoma was predominantly solid (1/62, 1.6%). Seventeen tumors showed honeycomb sign and stained glass appearance. Six mural nodules, with sizes ranging from 1.1 to 3.5 cm (average: 2.8 cm), were found in borderline mucinous cystadenoma and mucinous cystadenocarcinoma.

CONCLUSIONS

The CT findings of ovarian mucinous tumors in children are characteristic. The preoperative CT is helpful in making differential diagnoses.

MDS cells impair osteolineage differentiation of MSCs via extracellular vesicles to suppress normal hematopoiesis

Myelodysplastic syndrome (MDS) is a clonal disorder of hematopoietic stem cells (HSCs), characterized by ineffective hematopoiesis and frequent progression to leukemia. It has long remained unresolved how MDS cells, which are less proliferative, inhibit normal hematopoiesis and eventually dominate the bone marrow space. Despite several studies implicating mesenchymal stromal or stem cells (MSCs), a principal component of the HSC niche, in the inhibition of normal hematopoiesis, the molecular mechanisms underlying this process remain unclear. Here, we demonstrate that both human and mouse MDS cells perturb bone metabolism by suppressing the osteolineage differentiation of MSCs, which impairs the ability of MSCs to support normal HSCs. Enforced MSC differentiation rescues the suppressed normal hematopoiesis in both in vivo and in vitro MDS models. Intriguingly, the suppression effect is reversible and mediated by extracellular vesicles (EVs) derived from MDS cells. These findings shed light on the novel MDS EV-MSC axis in ineffective hematopoiesis.

Whole-lesion histogram analysis of apparent diffusion coefficient for the assessment of non-mass enhancement lesions on breast MRI

Objectives

To investigate the application of apparent diffusion coefficient (ADC) histogram analysis in differentiating between benign and malignant breast lesions detected as non-mass enhancement on MRI.

Materials and Methods

A retrospective study was conducted for 25 malignant and 26 benign breast lesions showing non-mass enhancement on breast MRI. An experienced radiologist without prior knowledge of the pathological results drew a region of interest (ROI) outlining the periphery of each lesion on the ADC map. A histogram was then made for each lesion. Following a univariate analysis of 18 summary statistics values, we conducted statistical discrimination after hierarchical clustering using Ward’s method. A comparison between the malignant and the benign groups was made using multiple logistic regression analysis and the Mann-Whitney U test. A P -value of less than 0.05 was considered statistically significant.

Results

Univariate analysis for the 18 summary statistics values showed the malignant group had greater entropy (P < 0.001) and lower uniformity (P < 0.001). While there was no significant difference in mean and skewness values, the malignant group tended to show a lower mean (P = 0.090) and a higher skewness (P = 0.065). Hierarchical clustering of the 18 summary statistics values identified four values (10th percentile, entropy, skewness, and uniformity) of which the 10th percentile values were significantly lower for the malignant group (P = 0.035).

Conclusions

Whole-lesion ADC histogram analysis may be useful for differentiating malignant from benign lesions which show non-mass enhancement on breast MRI.

Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features

Objectives

We aimed to investigate the influence of magnetic resonance fingerprinting (MRF) dictionary design on radiomic features using in vivo human brain scans.

Methods

Scan-rescans of three-dimensional MRF and conventional T1-weighted imaging were performed on 21 healthy volunteers (9 males and 12 females; mean age, 41.3 ± 14.6 years; age range, 22–72 years). Five patients with multiple sclerosis (3 males and 2 females; mean age, 41.2 ± 7.3 years; age range, 32–53 years) were also included. MRF data were reconstructed using various dictionaries with different step sizes. First- and second-order radiomic features were extracted from each dataset. Intra-dictionary repeatability and inter-dictionary reproducibility were evaluated using intraclass correlation coefficients (ICCs). Features with ICCs > 0.90 were considered acceptable. Relative changes were calculated to assess inter-dictionary biases.

Results

The overall scan-rescan ICCs of MRF-based radiomics ranged from 0.86 to 0.95, depending on dictionary step size. No significant differences were observed in the overall scan-rescan repeatability of MRF-based radiomic features and conventional T1-weighted imaging (p = 1.00). Intra-dictionary repeatability was insensitive to dictionary step size differences. MRF-based radiomic features varied among dictionaries (overall ICC for inter-dictionary reproducibility, 0.62–0.99), especially when step sizes were large. First-order and gray level co-occurrence matrix features were the most reproducible feature classes among different step size dictionaries. T1 map-derived radiomic features provided higher repeatability and reproducibility among dictionaries than those obtained with T2 maps.

Conclusion

MRF-based radiomic features are highly repeatable in various dictionary step sizes. Caution is warranted when performing MRF-based radiomics using datasets containing maps generated from different dictionaries.

Key Points

• MRF-based radiomic features are highly repeatable in various dictionary step sizes.

• Use of different MRF dictionaries may result in variable radiomic features, even when the same MRF acquisition data are used.

• Caution is needed when performing radiomic analysis using data reconstructed from different dictionaries.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08555-3.

[An improved driving ability after attention function training for safe driving via use of a day rehabilitation service in a patient with cerebral infarction]

In recent years, the assessment of and support for the safety of driving for people with higher brain dysfunction to allow them to resume car driving have become issues to be addressed in Japan. It is difficult to determine whether or not people with higher brain dysfunction may safely resume car driving; in addition, methods of supporting this resumption have not been established. To support people with higher brain dysfunction and allow them to live at home in areas where public means of transportation may be insufficient, initiatives promoting the resumption of car driving are necessary in healthcare sectors, including day rehabilitation facilities. We provided support to a patient with an attention disorder due to left thalamic infarction, with the aim of achieving sufficient independence to drive a car, in a day rehabilitation facility. We herein report this case from the perspective of a speech-language-hearing therapist. The patient was a right-handed man in his 60s who had higher brain dysfunction with attention disorder as the main symptom. No marked motor paralysis of the extremities was observed. Use of a day rehabilitation service was started approximately two months after the onset of symptoms. Rehabilitation and support aimed at the resumption of car driving were provided approximately one month after the start of the day rehabilitation service use. To determine whether or not the patient was fit to drive a car, higher brain function tests for the intellectual function, attention function, and frontal function, as well as a theoretical evaluation based on the Stroke Drivers' Screening Assessment Japanese Version (J-SDSA) and monitoring of daily behaviors were performed. In addition, after the patient was given permission from an attending physician to drive a car on the condition that the patient did not drive fast and the patient's wife always accompanied him while driving, a safety assessment was also performed. As a result, approximately 10 months later, the J-SDSA theoretical evaluation score showed a passing grade, in contrast to the failing grade he had previously earned. Furthermore, errors in performing household activities due to a decreased attention function became unremarkable with respect to daily behaviors; therefore, we determined, together with the attending physician, that the patient now had sufficient independence to drive a car. In our day rehabilitation facility, the number of requests for advice on car driving from people with higher brain dysfunction living in the community had been increasing. Multisectoral assessments, training, and instruction should be continued in collaboration with attending physicians, other facilities located within the community, and driving schools in order to support people with higher brain dysfunction and help them once again be able to drive a car.

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.

[A patient with mesenteric lymphoma who developed amyotrophic lateral sclerosis and sepsis]

We treated a patient with mesenteric lymphoma who concomitantly developed amyotrophic lateral sclerosis (ALS). The patient died of urinary tract infection nine months after the onset of ALS. We herein report the changes in the patient's condition and the sequence of events until death from the viewpoint of a physiotherapist. The patient was a 69-year-old woman who developed mesenteric lymphoma in September of X year and perceived weakness in the toes in November of X year. She showed signs of upper and lower motor neuron disorders, and electrophysiologic testing revealed denervation in three areas of the spinal cord. In March of X+1 year, she was diagnosed with definite ALS based on the Awaji criteria. In April of X+1 year, she began to receive continuous home healthcare, specifically outpatient rehabilitation. No remarkable bulbar palsy was observed soon after the initiation of rehabilitation; however, manual muscle testing revealed strengths in the lower and upper limbs of 1 and 3-5, respectively, indicating muscle weakness and muscle atrophy. She developed exacerbation of neurological symptoms in the upper limbs, bulbar palsy, and respiratory muscle paralysis during rehabilitation. The ALS Functional Rating Scale-Revised indicated a decreased tendency to X [please define X]. In July of X+1 year, the mesenteric lymphoma had enlarged, resulting in the development of ureteric obstruction and ultimately causing hydronephrosis. Urinary tract infection and sepsis were diagnosed, and she was hospitalized. Although her fever temporarily subsided following ceftriaxone administration, she ultimately died due to a systemic inflammatory response syndrome three days after hospitalization. The mean period between the ALS onset and death is reportedly 40.6±33.1 months. The rate of ALS progression differs among individuals. Malignant tumors and paraneoplastic neurological syndrome may be involved in rapidly worsening neurological symptoms. Patients who concomitantly develop motor neuron disorders and malignant tumors are likely to have a higher risk of developing serious conditions associated with the exacerbation of neurological symptoms and complications. Our patient had several diseases that affected her survival prognosis; however, the sharing of information regarding her condition among healthcare professionals may have been insufficient. The primary physician responsible for treating each disease should cooperate with physiotherapists and other paramedical staff who have frequent opportunities to talk to patients in daily clinical practice. In geriatric patients in particular, such an environment is essential.

Effects of Gadolinium Deposition in the Brain on Motor or Behavioral Function: A Mouse Model

Background Recent studies showing gadolinium deposition in multiple organs have raised concerns about the safety of gadolinium-based contrast agents (GBCAs). Purpose To explore whether gadolinium deposition in brain structures will cause any motor or behavioral alterations. Materials and Methods This study was performed from July 2019 to December 2020. Groups of 17 female BALB/c mice were each repeatedly injected with phosphate-buffered saline (control group, group A), a macrocyclic GBCA (group B), or a linear GBCA (group C) for 8 weeks (5 mmol per kilogram of bodyweight per week for GBCAs). Brain MRI studies were performed every other week to observe the signal intensity change caused by the gadolinium deposition. After the injection period, rotarod performance test, open field test, elevated plus-maze test, light-dark anxiety test, locomotor activity assessment test, passive avoidance memory test, Y-maze test, and forced swimming test were performed to assess the locomotor abilities, anxiety level, and memory. Among-group differences were compared by using one-way or two-way factorial analysis of variance with Tukey post hoc testing or Dunnett post hoc testing. Results Gadolinium deposition in the bilateral deep cerebellar nuclei was confirmed with MRI only in mice injected with a linear GBCA. At 8 weeks, contrast ratio of group C (0.11; 95% CI: 0.10, 0.12) was higher than that of group A (-2.1 × 10^-3; 95% CI: -0.011, 7.5 × 10^-3; P < .001) and group B (2.7 × 10^-4; 95% CI: -8.2 × 10^-3, 8.7 × 10^-3; P < .001). Behavioral analyses showed that locomotor abilities, anxiety level, and long-term or short-term memory were not different in mice injected with linear or macrocyclic GBCAs. Conclusion No motor or behavioral alterations were observed in mice with brain gadolinium deposition. Also, the findings support the safety of macrocyclic gadolinium-based contrast agents. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Chen in this issue.

Coronary artery calcium score may be a novel predictor of COVID-19 prognosis: a retrospective study

Background

Although several studies have reported an association between atherosclerosis-related diseases and COVID-19, the relationship between COVID-19 severity and atherosclerosis progression remains unclear. The aim of this study is to determine the coronary artery calcium score (CACS) prognostic value in patients with COVID-19 using indices such as deterioration in oxygenation and CT images of the chest.

Methods

This was a single-centre retrospective study of 53 consecutive patients with COVID-19 in Narita who were admitted to our hospital between March 2020 and August 2020. CACS was calculated based on non-gated CT scans of the chest performed on admission day. The patients were divided into the following two groups based on CACS: group 1 (CACS ≥180, n=11) and group 2 (CACS <180, n=42). Following univariate analysis of the main variables, multivariate analysis of variables that may be associated with COVID-19 progression was performed.

Results

Multivariable logistic regression analysis of age, sex, smoking history, diabetes, hypertension, dyslipidaemia, number of days from symptom onset to hospitalisation and CACS of ≥180 was performed. It revealed that unlike CACS of <180, CACS of ≥180 is associated with exacerbation of oxygenation or CT images of the chest during hospitalisation (OR: 12.879, 95% CI: 1.399 to 380.401). Furthermore, this model of eight variables showed good calibration (Hosmer-Lemeshow p=0.119).

Conclusion

CACS may be a prognosis marker of COVID-19 severity. Although coronary artery calcification is not typically assessed in pneumonia cases, it may provide a valuable clinical indicator for predicting severe COVID-19 outcomes.

Clinical and CT features of ovarian torsion in infants, children and adolescents

OBJECTIVE

To investigate the clinical features and computed tomography (CT) findings of pediatric ovarian torsion.

METHODS

A retrospective analysis of the clinical and CT data of 61 newborns, infants, children, and adolescents with ovarian torsion confirmed by histopathology was performed.

RESULTS

Clinical features included abdominal mass, abdominal pain, nausea and vomiting, and fever. The tumor marker α-fetoprotein was increased in five cases. Ovarian enlargement was found in 26 cases, and follicles were detected in the peripheral region of the ovary in 21 cases. Twenty-one cases presented as solid mixed-density masses on CT images. A total of 30 cases of ovarian torsion were associated with a benign ovarian mass. Among 27 cases of cystic or predominantly cystic masses, the mass had a thickened wall in 26 cases and showed an uneven density in 23 cases. Among all 61 patients, a torsed pedicle was detected in 47 cases. A torsed ovary or mass exhibited mild contrast enhancement in seven cases. Uterine deviation toward the involved side, blurred fat space around lesions, and pelvic free fluid were also found.

CONCLUSION

Pediatric ovarian torsion presents a relatively characteristic CT appearance. Correct diagnosis can be established based on clinical and imaging features.

Voice, rhythm, and beep stimuli differently affect the right hemisphere preponderance and components of stimulus-preceding negativity

The present study investigated whether auditory stimuli with different contents affect right laterality and the components of stimulus-preceding negativity (SPN). A time-estimation task was performed under voice, rhythm, beep, and control conditions. The SPN interval during which participants anticipated the stimulus was divided into quarters to define early and late SPNs. Early and late components of SPN were also extracted using a principal component analysis. The anticipation of voice sounds enhanced the early SPN and the early component, which reflected the anticipation of language processing. Beep sounds elicited the right hemisphere preponderance of the early component, the early SPN, and the late SPN. The rhythmic sound tended to attenuate the amplitude compared with the two other stimuli. These findings further substantiate the existence of separate early and late components of the SPN. In addition, they suggest that the early component reflects selective anticipatory attention toward differing types of auditory feedback.

In vitro Demonstration of Melanoma Metastasis in Lymph Nodes of Prepared Specimens Using a Light-emitting Diode-based Multispectral Photoacoustic Ultrasound Imaging System

Although an excellent photoacoustic (PA) ultrasound method has been reported for the evaluation of lymph node melanoma metastasis in animal experiments, it remains to be evaluated in clinical trials. Recently, we performed PA ultrasound assessment using light-emitting diodes to detect metastatic melanoma in the lymph nodes of specimens prepared for microscopic examination. The PA effect was not obvious in amelanotic melanoma, but was seen in melanotic melanoma by PA imaging (PAI) and histopathological correlation in cases of primary melanotic melanoma accompanied by metastatic lymph nodes, including the coexistence of amelanotic melanoma and melanotic melanoma. Clinical workup should be performed with not only PAI but also conventional ultrasonography in cases with metastasis related to amelanotic transformation, which would likely be missed by PAI alone.

Application of CT texture analysis to assess the localization of primary aldosteronism

We performed present study to investigate whether the localization of primary aldosteronism (PA) can be predicted using quantitative texture analysis on unenhanced computed tomography (CT). Plain CT data of 82 PA patients (54 unilateral (right-sided:left-sided = 24:30), 28 bilateral) were analyzed retrospectively. After semi-automatically setting the region of interest to include the whole adrenal gland, texture analyses were performed with or without a Laplacian of Gaussian filter with various spatial scaling factors (SSFs). Logistic regression analysis was performed using the extracted histogram-based texture features to identify parameters capable of predicting excessive aldosterone production. The result of adrenal venous sampling served as gold standard in present study. As a result, logistic regression analysis indicated that the mean gray level intensity (p = 0.026), the mean value of the positive pixels (p = 0.003) in the unfiltered image, and entropy (p = 0.027) in the filtered image (SSF: 2 mm) were significant parameters. Using the model constructed by logistic regression analysis and the optimum cutoff value, the localization of PA (three multiple choices of left, right or bilateral) was determined with an accuracy of 67.1% (55/82). CT texture analysis may provide a potential avenue for less invasive prediction of the localization of PA.

Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis

We conducted a feasibility study to predict malignant glioma grades via radiomic analysis using contrast-enhanced T1-weighted magnetic resonance images (CE-T1WIs) and T2-weighted magnetic resonance images (T2WIs). We proposed a framework and applied it to CE-T1WIs and T2WIs (with tumor region data) acquired preoperatively from 157 patients with malignant glioma (grade III: 55, grade IV: 102) as the primary dataset and 67 patients with malignant glioma (grade III: 22, grade IV: 45) as the validation dataset. Radiomic features such as size/shape, intensity, histogram, and texture features were extracted from the tumor regions on the CE-T1WIs and T2WIs. The Wilcoxon-Mann-Whitney (WMW) test and least absolute shrinkage and selection operator logistic regression (LASSO-LR) were employed to select the radiomic features. Various machine learning (ML) algorithms were used to construct prediction models for the malignant glioma grades using the selected radiomic features. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the prediction models in the primary dataset. The selected radiomic features for all folds in the LOOCV of the primary dataset were used to perform an independent validation. As evaluation indices, accuracies, sensitivities, specificities, and values for the area under receiver operating characteristic curve (or simply the area under the curve (AUC)) for all prediction models were calculated. The mean AUC value for all prediction models constructed by the ML algorithms in the LOOCV of the primary dataset was 0.902 ± 0.024 (95% CI (confidence interval), 0.873-0.932). In the independent validation, the mean AUC value for all prediction models was 0.747 ± 0.034 (95% CI, 0.705-0.790). The results of this study suggest that the malignant glioma grades could be sufficiently and easily predicted by preparing the CE-T1WIs, T2WIs, and tumor delineations for each patient. Our proposed framework may be an effective tool for preoperatively grading malignant gliomas.

Factors associated with the size of the adhesio interthalamica based on 3.0-T magnetic resonance images

BACKGROUND

Adhesio interthalamica (AI) is a small structure connecting bilateral thalami.

PURPOSE

To evaluate the effects of patient age, sex, and lateral diameter of the third ventricle on the long diameter of the AI using multivariate analyses based on magnetic resonance (MR) images obtained with 3.0-T scanners.

MATERIAL AND METHODS

This clinical retrospective study included images of 153 patients who underwent MR examination using 3.0-T scanners. The long diameter of the AI and lateral diameter of the third ventricle were measured on images in the mid-sagittal plane and axial plane at the anterior commissure, respectively. Univariate and multivariate analyses were performed.

RESULTS

AI was observed in 138 patients (70 men, 68 women; mean age = 63.7 ± 13.7 years; mean AI size =5.34 ± 1.63 mm). By univariate analyses, patient age (r = -0.262, P = 0.002), sex ( P = 0.010), and lateral diameter of the third ventricle (r = -0.642, P < 0.001) were significantly associated with the long diameter of the AI. With multiple linear regression analyses with a stepwise selection of parameters, only the lateral diameter of the third ventricle (estimate = -0.432, P < 0.001) was significantly associated with the long diameter of the AI. The lateral diameter of the third ventricle was longer in patients without AI (15 patients) than in those with AI ( P = 0.006).

CONCLUSION

The lateral diameter of the third ventricle was a major factor negatively associated with the long diameter of the AI.

Deep learning for staging liver fibrosis on CT: a pilot study

OBJECTIVES

To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images.

METHODS

This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference. To improve robustness, additional images for training data were generated by rotating or parallel shifting the images, or adding Gaussian noise. Supervised training was used to minimise the difference between the liver fibrosis stage and the fibrosis score obtained from deep learning based on CT images (F_DLCT score) output by the model. Testing data were input into the trained DCNNs to evaluate their performance.

RESULTS

The F_DLCT scores showed a significant correlation with liver fibrosis stage (Spearman's correlation coefficient = 0.48, p < 0.001). The areas under the receiver operating characteristic curves (with 95% confidence intervals) for diagnosing significant fibrosis (≥ F2), advanced fibrosis (≥ F3) and cirrhosis (F4) by using F_DLCT scores were 0.74 (0.64-0.85), 0.76 (0.66-0.85) and 0.73 (0.62-0.84), respectively.

CONCLUSIONS

Liver fibrosis can be staged by using a deep learning model based on CT images, with moderate performance.

KEY POINTS

• Liver fibrosis can be staged by a deep learning model based on magnified CT images including the liver surface, with moderate performance. • Scores from a trained deep learning model showed moderate correlation with histopathological liver fibrosis staging. • Further improvement are necessary before utilisation in clinical settings.

Deep learning with convolutional neural network in radiology

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

Quantitative computed tomography texture analyses for anterior mediastinal masses: Differentiation between solid masses and cysts

OBJECTIVES

To investigate whether solid anterior mediastinal masses could be differentiated from cysts using quantitative computed tomography (CT) texture analyses in unenhanced CT (UECT) or contrast enhanced CT (CECT).

MATERIALS AND METHODS

This clinical retrospective study included 76 UECT images (40 men and 36 women, 28 cystic (mean diameter, 29.5 mm) and 48 solid (mean diameter, 48.2 mm)) and 84 CECT images (45 men and 39 women, 26 cystic (mean diameter, 31.4 mm) and 58 solid (mean diameter, 51.4 mm)) of anterior mediastinal masses, which were diagnosed histopathologically or using imaging criteria. Polygonal regions of interest were placed on these masses. CT histogram analyses for images of masses with or without filtration (Laplacian of Gaussian filters with various spatial scaling factors) were performed. DeLong's test was performed to compare areas under the curve (AUC) with receiver operating characteristic analyses.

RESULTS

From logistic regression analyses with a stepwise procedure, a combination of the mean in unfiltered images (mean0; i.e., CT attenuation) and mean in filtered images featuring coarse texture for UECT (AUC = 0.869) and the combination of mean0 and entropy in filtered images featuring fine texture for CECT (AUC = 0.997) were found to predict better the internal characteristics of anterior mediastinal masses. In UECT and CECT, diagnostic performance using these combinations tended to be high compared to mean0 alone (AUC = 0.780 [p = 0.033] and AUC = 0.983 [p = 0.130], respectively).

CONCLUSION

Solid anterior mediastinal masses can be differentiated from cysts using quantitative CT texture analyses with moderate and high diagnostic performance in UECT and CECT, respectively.

Liver Fibrosis: Deep Convolutional Neural Network for Staging by Using Gadoxetic Acid-enhanced Hepatobiliary Phase MR Images

Purpose To investigate the performance of a deep convolutional neural network (DCNN) model in the staging of liver fibrosis using gadoxetic acid-enhanced hepatobiliary phase magnetic resonance (MR) imaging. Materials and Methods This retrospective study included patients for whom input data (hepatobiliary phase MR images, static magnetic field of the imaging unit, and hepatitis B and C virus testing results available, either positive or negative) and reference standard data (liver fibrosis stage evaluated from biopsy or surgical specimens obtained within 6 months of the MR examinations) were available were assigned to the training (534 patients) or the test (100 patients) group. For the training group (54, 53, 81, 113, and 233 patients with fibrosis stages F0, F1, F2, F3, and F4, respectively; mean patient age, 67.4 ± 9.7 years; 388 men and 146 women), MR images with three different section levels were augmented 90-fold (rotated, parallel-shifted, brightness-changed and contrast-changed images were generated; a total of 144 180 images). Supervised training was performed by using the DCNN model to minimize the difference between the output data (fibrosis score obtained through deep learning [F_DL score]) and liver fibrosis stage. The performance of the DCNN model was evaluated in the test group (10, 10, 15, 20, and 45 patients with fibrosis stages F0, F1, F2, F3, and F4, respectively; mean patient age, 66.8 years ± 10.7; 71 male patients and 29 female patients) with receiver operating characteristic (ROC) analyses. Results The F_DL score was correlated significantly with fibrosis stage (Spearman rank correlation coefficient: 0.63; P < .001). Fibrosis stages F4, F3, and F2 were diagnosed with areas under the ROC curve of 0.84, 0.84, and 0.85, respectively. Conclusion The DCNN model exhibited a high diagnostic performance in the staging of liver fibrosis. ^© RSNA, 2017 Online supplemental material is available for this article.

Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study

Purpose To investigate diagnostic performance by using a deep learning method with a convolutional neural network (CNN) for the differentiation of liver masses at dynamic contrast agent-enhanced computed tomography (CT). Materials and Methods This clinical retrospective study used CT image sets of liver masses over three phases (noncontrast-agent enhanced, arterial, and delayed). Masses were diagnosed according to five categories (category A, classic hepatocellular carcinomas [HCCs]; category B, malignant liver tumors other than classic and early HCCs; category C, indeterminate masses or mass-like lesions [including early HCCs and dysplastic nodules] and rare benign liver masses other than hemangiomas and cysts; category D, hemangiomas; and category E, cysts). Supervised training was performed by using 55 536 image sets obtained in 2013 (from 460 patients, 1068 sets were obtained and they were augmented by a factor of 52 [rotated, parallel-shifted, strongly enlarged, and noise-added images were generated from the original images]). The CNN was composed of six convolutional, three maximum pooling, and three fully connected layers. The CNN was tested with 100 liver mass image sets obtained in 2016 (74 men and 26 women; mean age, 66.4 years ± 10.6 [standard deviation]; mean mass size, 26.9 mm ± 25.9; 21, nine, 35, 20, and 15 liver masses for categories A, B, C, D, and E, respectively). Training and testing were performed five times. Accuracy for categorizing liver masses with CNN model and the area under receiver operating characteristic curve for differentiating categories A-B versus categories C-E were calculated. Results Median accuracy of differential diagnosis of liver masses for test data were 0.84. Median area under the receiver operating characteristic curve for differentiating categories A-B from C-E was 0.92. Conclusion Deep learning with CNN showed high diagnostic performance in differentiation of liver masses at dynamic CT. ^© RSNA, 2017 Online supplemental material is available for this article.

Impact of hepatocellular carcinoma heterogeneity on computed tomography as a prognostic indicator

We assessed the relationship between the heterogeneity of HCC on preoperative non-contrast-enhanced CT and patient prognosis. The heterogeneity of CT images from 122 patients was assessed and texture feature parameters such as mean, standard deviation (SD), entropy, mean of the positive pixels (MPP), skewness, and kurtosis were obtained using filtration. The relationship between CT texture features and 5-year overall survival (OS) or disease-free survival (DFS) was assessed. Multivariate regression analysis was performed to evaluate the independence of texture feature from clinical or pathological parameters. The Kaplan-Meier curves for OS or DFS was significantly different between patient groups dichotomized by cut-off values for all CT texture parameters with filtration at at least one filter level. Multivariate regression analysis showed the independence of most CT texture parameters on clinical and pathological parameters for OS with filtration at at least one filter level and without filtration except kurtosis. SD, entropy, and MPP with coarse filter, and skewness without filtration showed a significant correlation for DFS. CT texture features of non-contrast-enhanced CT images showed a relationship with HCC prognosis. Multivariate regression analysis showed the possibility of CT texture feature increase the prognostic prediction of HCC by clinical and pathological information.

Gadoxetate disodium-induced tachypnoea and the effect of dilution method: a proof-of-concept study in mice

OBJECTIVES

To directly investigate the rapid respiratory effect of gadoxetate disodium in an experimental study using mice.

METHODS

After confirming the steady respiratory state under general anaesthesia, eight mice were injected with all test agents in the following order: phosphate-buffered saline (A, control group), 1.25 mmol/kg of gadoteridol (B) or gadopentetate dimeglumine (C), or 0.31 mmol/kg of gadoxetate disodium (D, E). The experimenter was not blinded to the agents. The injection dose was fixed as 100 μL for Groups A-D and 50 μL for Group E. We continuously monitored and recorded respiratory rate (RR), peripheral oxygen saturation (SpO₂), and heart rate. The time-series changes from 0 to 30 s were compared by the linear mixed method RESULTS: Groups D and E showed the largest RR increase (20.6 and 20.3 breaths/min, respectively) and were significantly larger compared to Group A (3.36 breaths/min, both P<0.001). RR change of Groups D and E did not differ. RR change of Groups B and C was smaller (0.72 and 12.4 breaths/min, respectively) and did not differ statistically with Group A. Significant bradycardia was observed only in Group C (P<0.001). SpO₂ was constant in all groups.

CONCLUSIONS

Gadoxetate disodium causes a rapid tachypnoea without significant change of SpO₂ and heart rate regardless of the dilution method.

KEY POINTS

• Injection of gadoxetate disodium causes tachypnoea. • Dilution method did not alter the rapid respiratory effect of gadoxetate disodium. • The respiratory effect of gadoxetate disodium was larger than other contrast agents.

A pulmonary metastatic model of murine melanoma assessed by magnetic resonance imaging

Immune checkpoint inhibitors and kinase inhibitors have improved prognosis of malignant melanoma (MM) patients. However, these therapies cannot completely overcome the metastasis of MM. Thus, development of new therapy against metastasis should be required. A first step towards this goal, the aim of this study, is to establish a model of pulmonary metastasis from primary cutaneous MM and a monitoring system. B16-F10, a murine melanoma cell line, was subcutaneously injected into the pinna of mice. The pinna was excised when the lesion was detected. A metastatic nodule on T2-weighted imaging was detected 4 weeks after resection of the pinna. Lung metastases were observed in 37.5% (6/16) of the specimens. We established a novel murine model of the high pulmonary metastasis of MM. The MRI was useful for observations of the growth of the metastatic lesions in the lungs without dissection.

Precision of quantitative computed tomography texture analysis using image filtering: A phantom study for scanner variability

Quantitative computed tomography (CT) texture analyses for images with and without filtration are gaining attention to capture the heterogeneity of tumors. The aim of this study was to investigate how quantitative texture parameters using image filtering vary among different computed tomography (CT) scanners using a phantom developed for radiomics studies.A phantom, consisting of 10 different cartridges with various textures, was scanned under 6 different scanning protocols using four CT scanners from four different vendors. CT texture analyses were performed for both unfiltered images and filtered images (using a Laplacian of Gaussian spatial band-pass filter) featuring fine, medium, and coarse textures. Forty-five regions of interest were placed for each cartridge (x) in a specific scan image set (y), and the average of the texture values (T(x,y)) was calculated. The interquartile range (IQR) of T(x,y) among the 6 scans was calculated for a specific cartridge (IQR(x)), while the IQR of T(x,y) among the 10 cartridges was calculated for a specific scan (IQR(y)), and the median IQR(y) was then calculated for the 6 scans (as the control IQR, IQRc). The median of their quotient (IQR(x)/IQRc) among the 10 cartridges was defined as the variability index (VI).The VI was relatively small for the mean in unfiltered images (0.011) and for standard deviation (0.020-0.044) and entropy (0.040-0.044) in filtered images. Skewness and kurtosis in filtered images featuring medium and coarse textures were relatively variable across different CT scanners, with VIs of 0.638-0.692 and 0.430-0.437, respectively.Various quantitative CT texture parameters are robust and variable among different scanners, and the behavior of these parameters should be taken into consideration.

Resectable primary pleural myxoid liposarcoma with a pedicle: report of a rare case and literature review

Primary pleural myxoid liposarcoma is a rare tumor. Here, we report a primary myxoid liposarcoma occupying the majority of the left thoracic cavity with features suggesting invasion. Computed tomography (CT) at medical check-up incidentally revealed a bulky inhomogeneous fatty mass. The tumor's large size made a prediction of its resectability by preoperative CT difficult. The patient underwent an operation, which revealed that the tumor was attached to the pleura with a thin pedicle; the tumor was resected completely. Few therapies for pleural liposarcoma other than resection are available; hence, surgery should be considered even if the tumor's size implies invasion on radiological imaging. In this case report, we discuss the imaging findings of this case with a review of the related literature.

Quantitative computed tomography texture analysis for estimating histological subtypes of thymic epithelial tumors

OBJECTIVES

To investigate whether high-risk thymic epithelial tumor (TET) (HTET) can be differentiated from low-risk TET (LTET) using computed tomography (CT) quantitative texture analysis.

MATERIALS AND METHODS

The data of 39 patients (mean age, 58.6±14.1 years) (39 unenhanced CT (UECT) and 33 contrast-enhanced CT (CECT)) who underwent thymectomy for TET were retrospectively analyzed. A region of interest was placed to include the entire TET within the slice at its maximum diameter. Texture analysis was performed for images with or without a Laplacian of Gaussian filter (with various spatial scaling factors [SSFs]). Two radiologists evaluated the visual heterogeneity of TET using a 3-point scale.

RESULTS

The mean in the unfiltered image (mean0u) and entropy in the filtered image (SSF: 6mm) (entropy6u) for UECT, and the mean in the unfiltered image (mean0c) for CECT were significant parameters for differentiating between HTET and LTET as determined by logistic regression analysis. The area under the receiver operating characteristics curve (AUC) for differentiating HTET from LTET using mean0u, entropy6u, and mean0c was 0.75, 0.76, and 0.89, respectively. And the combination of mean0u and entropy6u allowed AUC of 0.87. Entropy6u provided a higher diagnostic performance compared with visual heterogeneity analysis (p≤0.018).

CONCLUSION

Using CT quantitative texture analysis, HTET can be differentiated from LTET with a high diagnostic performance.

Computed tomography and magnetic resonance imaging of a plexiform angiomyxoid myofibroblastic tumor: a case report

Background

Plexiform angiomyxoid myofibroblastic tumor (PAMT) is a very rare mesenchymal tumor of the stomach. Here we report a case of pathologically confirmed PAMT with an unique cyst formation.

Case presentation

A 55-year-old male with a 10-year history of a gastric subepithelial tumor underwent computed tomography (CT) and magnetic resonance imaging (MRI). Two cysts were observed in the tumor, and the cyst wall showed moderately high intensity on T2-weighted images compared with the gastric wall. On dynamic study, the cyst wall showed a gradual enhancement pattern, and prominent enhancement was observed in the delayed phase. Laparoscopic partial gastric resection was performed, and a pathological diagnosis of PAMT was rendered.

Conclusion

We present a rare case of gastric PAMT, which was uniquely presented as cysts. One of the cysts in the tumor had an epithelial wall lining, which had never been reported before in gastric mesenchymal tumor, in addition to partial glandular structure. We reviewed our case, focusing on radiologic-pathologic correlation, and suggested hypothesis of cyst formation. According to our findings, PAMT with cyst formation would be included of differential diagnosis of gastric subepithelial tumors.

The natural history of streptozotocin-stimulated non-alcoholic steatohepatitis mice followed by Gd-EOB-DTPA-enhanced MRI: Comparison with simple steatosis mice

PURPOSE

To clarify the development of HCC, temporal change of steatosis and Gd-EOB-DTPA enhancement of non-alcoholic steatohepatitis (NASH) model mice by magnetic resonance imaging (MRI).

MATERIALS AND METHODS

All animal experiments were approved by the institution's Animal Research Committee. MRI was performed on six NASH and six simple steatosis (SS) model mice every 2weeks from the ages of 8weeks to 16weeks. The sequential changes in the number and size of the focal liver lesions detected on Gd-EOB-DTPA-enhanced MRI were evaluated. Additionally, the hepatic fat fraction (HFF), contrast-to-noise ratio (CNR) and relative enhancement (RE) were calculated at each time point. The temporal changes and correlations in these parameters were evaluated.

RESULTS

All alive NASH model mice demonstrated focal liver lesions from week 10, at the latest. Number of the lesions increased with time, and all the lesion enlarged with time. All the lesions larger than 1mm were confirmed as hepatocellular carcinoma (HCC) pathologically. While the HFF remained constant in NASH model mice, HFF in SS model mice dramatically increased with time. CNR of the NASH model mice remained constant through the study period, while CNR in SS model mice decreased with time. Although no correlation was seen in NASH model mice, the HFF showed a negative correlation against CNR and RE in SS model mice.

CONCLUSION

Development of HCC was observed using Gd-EOB-DTPA-enhanced MRI only in NASH model mice. Degree of steatosis and hepatic enhancement by Gd-EOB-DTPA was both constant in NASH model mice, while steatosis increased and hepatic enhancement decreased with time in SS model mice.

The effects of bolus supplementation of branched-chain amino acids on skeletal muscle mass, strength, and function in patients with rheumatic disorders during glucocorticoid treatment

OBJECTIVES

To test the effects of bolus supplementation of branched-chain amino acids (BCAA) on skeletal muscle mass, strength, and function in patients with rheumatic disorders taking glucocorticoid (GC).

METHODS

Patients with rheumatic disorders treated with prednisolone (≥10 mg/day) were randomized to ingest additional daily 12 g of BCAA (n = 9) or not (n = 9) for 12 weeks. At baseline, and 4, 8, and 12 weeks, they underwent bioelectrical impedance analysis, muscle strength and functional tests, and computed tomography analysis for cross-sectional area of mid-thigh muscle.

RESULTS

Disease activities of the patients were well controlled and daily GC dose was similarly reduced in both groups. Limb muscle mass was recovered in both groups. Whole-body muscle mass and muscle strength and functional mobility were increased only in BCAA (+) group. The effects of BCAA supplementation on recovering skeletal muscle mass were prominent in particular muscles including biceps femoris muscle.

CONCLUSIONS

This trial is the first-in-man clinical trial to demonstrate that BCAA supplementation might be safe and, at least in part, improve skeletal muscle mass, strength, and function in patients with rheumatic disorders treated with GC.