Two Case Studies on Inferior Mesenteric Artery Embolization for Primary Postpartum Hemorrhage.

Postpartum hemorrhage (PPH) remains a leading cause of maternal mortality. Transcatheter arterial embolization (TAE) is an effective treatment option for PPH. Among the vessels embolized, the inferior mesenteric artery (IMA) is usually not the first choice for TAE, because it is a rare source of bleeding in PPH. In this report, we describe our experience with two patients with PPH, in whom the IMA was selected on the basis of contrast-enhanced computed tomography (CECT) findings, and prompt hemostasis was achieved with TAE. CECT can provide useful clues as to the culprit artery based on the anatomical location of the hematoma and extravasation. These useful findings of CECT allow prompt TAE of rare sources of bleeding in PPH, such as the IMA.

The usefulness of vertebral needle targeting simulation training system using ray-summation imaging: experimental study.

PURPOSE: Using the multi-detector computed tomography and related three-dimensional imaging technology, we developed a vertebral needle targeting simulation training system named spinal needling intervention practice using ray-summation imaging (SNIPURS). Herein, we assessed the utility of SNIPURS by evaluating changes in the learning curves of SNIPURS trainees. METHODS: Twenty-one examinees were enrolled: seven experienced operators (expert group), seven trainees with coaching (coaching group), and seven trainees without coaching (non-coaching group). They performed six tests of vertebral needle targeting simulation on the workstation-generated spinal ray-summation images of six patients with vertebral fractures. In each test, they determined the bilateral trans-pedicular puncture points and angles on two thoracic and two lumbar vertebrae on ray-summation imaging (i.e., 8 simulations per test). The coaching group received coaching by a trainer after Tests 1 and 4, while the others did not. Scores were given based on the trans-pedicular pathway (1 point) or not (0 point). Eight virtual needles were evaluated in each of Tests 1-6. RESULTS: Among the three groups, the expert group had the highest average scores on Tests 1-4 (expert: 3.86, 6.57, 7.43, and 7.57; coaching: 1.86, 6.14, 6, and 6.29; and non-coaching: 1.14, 4.14, 4.71, and 4.86). The coaching group's scores caught up with the expert groups' average scores on Tests 5 and 6, whereas those of the non-coaching group did not (expert and coaching: 7.86 and 8.00, non-coaching: 5.86 and 7.14). All examinees in the expert and coaching groups achieved a perfect score on the final Test 6, whereas three of the seven non-coaching trainees did not. CONCLUSION: SNIPURS might be suitable for vertebral needle targeting training. The coaching provided during SNIPURS training helped the trainees to acquire the spinal puncture techniques in PVP.

Spontaneous Muscle Hematoma in Japanese Patients with Severe COVID-19 Treated with Unfractionated Heparin: Two Case Reports.

In hospitalized coronavirus disease 2019 (COVID-19) patients, anticoagulation therapy is administered to prevent thrombosis. However, anticoagulation sometimes causes bleeding complications. We herein report two Japanese cases of severe COVID-19 in which spontaneous muscle hematomas (SMH) developed under therapeutic anticoagulation with unfractionated heparin. Although the activated partial prothrombin time was within the optimal range, contrast-enhanced computed tomography (CECT) revealed SMH in the bilateral iliopsoas muscles in both cases, which required emergent transcatheter embolization. Close monitoring of the coagulation system and the early diagnosis of bleeding complications through CECT are needed in severe COVID-19 patients treated with anticoagulants.

Risk of radiation pneumonitis in patients with emphysema after stereotactic body radiotherapy for non-small cell lung cancer assessed by quantitative CT.

Quantitative CT assessment of patients with pulmonary emphysema is used to measure pulmonary function. The present study evaluated whether the quantitative CT value can accurately estimate the risk of radiation pneumonitis (RP) after stereotactic body radiotherapy (SBRT) for non-small cell lung cancer (NSCLC) in patients with and without emphysema. A total of 80 patients with stage I NSCLC receiving SBRT at a dose of 50 or 60 Gy in five fractions at our hospital between November 2003 and October 2015 were included in the analysis. A total of 33 (41%) patients were diagnosed with emphysema on CT examination. Dosimetric parameters, quantitative CT percentage value of low attenuation area (LAA%) in the whole lung, and average whole lung CT density values were used to examine the risk of RP. Among the 80 patients, 26 (33%) and 3 (4%) experienced Grade 1 and Grade 2 RP, respectively, during the median observation period of 18.8 (1.8-106.8) months. The RP rate for patients with a LAA% (<-910 HU) of ≤25% was significantly higher than that of subjects with LAA% (<-910 HU) >25% (P=0.037). The RP rate in subjects with an average HU value of >-790 HU was significantly higher compared with that of patients with ≤-790 HU (P=0.036). Age (hazard ratio [HR]=2.46; P=0.03) and average HU (HR=3.39; P=0.02) were significantly associated with RP, whereas mean lung dose was not identified to be significant in multivariate analysis. The quantitative CT value was associated with RP after SBRT.

A breathing movement sensor for chest radiography during inspiration in children aged less than 3 years: a prospective randomized controlled study.

Chest radiographs should be obtained at the peak of inspiration so that radiological findings can be precisely interpreted. However, this is not easily achieved, particularly in young children who do not follow the instruction to hold their breath. We developed a sensor that detects the breathing movements and conducted a randomized controlled study to determine whether the sensor would increase the proportion of chest radiographs obtained in the inspiration phase. We recruited 124 infants and children aged less than 3 years, who visited the pediatric department of a general hospital in Tokyo, Japan, and allocated them into one of two groups: with-sensor and without-sensor groups. Overall, 81% of all images were obtained during inspiration. The proportion of chest radiographs taken during inspiration was not statistically different between the two groups (81% vs. 82%). In the with-sensor group, radiologic technologists were able to obtain chest radiographs of the same quality while not observing the chest movement, but the sensor. The use of the sensor did not increase the proportion of chest radiographs taken in the inspiration phase in this study. However, this null result may indicate the possibility of utilizing the sensor for automatizing chest radiography in the future.

A Fundamental Study Assessing the Diagnostic Performance of Deep Learning for a Brain Metastasis Detection Task.

PURPOSE: Increased use of deep convolutional neural networks (DCNNs) in medical imaging diagnosis requires determinate evaluation of diagnostic performance. We performed the fundamental investigation of diagnostic performance of DCNNs using the detection task of brain metastasis. METHODS: We retrospectively investigated AlexNet and GoogLeNet using 3117 positive and 37961 negative MRI images with and without metastasis regarding (1) diagnostic biases, (2) the optimal K number of K-fold cross validations (K-CVs), (3) the optimal positive versus negative image ratio, (4) the accuracy improvement curves, (5) the accuracy range prediction by the bootstrap method, and (6) metastatic lesion detection by regions with CNNs (R-CNNs). RESULTS: Respectively, AlexNet and GoogLeNet had (1) 50 ± 4.6% and 50 ± 4.9% of the maximal mean ± 95% confidence intervals (95% CIs) measured with equal-sized negative versus negative image datasets and positive versus positive image datasets, (2) no less than 10 and 4 of K number in K-CVs fell within the respective maximum biases of 4.6% or 4.9%, (3) 74% of the highest accuracy with equal positive versus negative image ratio dataset and 91% of that with four times of negative-to-positive image ratio dataset, (4) the accuracy improvement curves increasing from 69% to 74% and 73% to 88% as positive versus negative pairs of the training images increased from 500 to 2495, (5) at least nine and six out of 10-CV result sets essential to predict the accuracy ranges by the bootstrap method, and (6) 50% and 45% of metastatic lesion detection accuracies by R-CNNs. CONCLUSIONS: Our research presented methodological fundamentals to evaluate diagnostic features in the visual recognition of DCNNs. Our series will help to conduct the accuracy investigation of computer diagnosis in medical imaging.

The safety and efficacy of percutaneous vertebroplasty for patients over 90 years old.

OBJECTIVE: To retrospectively analyze the safety and efficacy of percutaneous vertebroplasty (PVP) for patients aged 90 or over. MATERIALS AND METHODS: We analyzed 130 consecutive patients with osteoporotic vertebral fractures who underwent a first-time PVP between May 2015 and September 2017 at our institution. We divided them into the elder patient group aged 90 years or over (n = 21) and the younger patient group under 90 years (n = 109). We compared the two groups' background, treatments, and outcomes using univariate analyzes and the log rank test. RESULTS: A significant difference was observed in dementia (19% in the younger group vs. 48% in the elder group, p < 0.01). No significant difference was revealed in the procedure time or the rate of complications. The post-PVP mobility function and the pain level were significantly improved compared to before PVP in both groups (p < 0.01 each). No significant differences were observed between the two groups in the recurrence of vertebral fracture after treatment (17% vs. 14%) or the 1-year survival rate (79% vs. 86%), respectively. CONCLUSION: The results of our analyzes suggested that a PVP can safely and effectively contribute to pain relief as well as the restoration of ambulation for patients aged 90 or over.

Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences.

PURPOSE: The confusion of MRI sequence names could be solved if MR images were automatically identified after image data acquisition. We revealed the ability of deep learning to classify head MRI sequences. MATERIALS AND METHODS: Seventy-eight patients with mild cognitive impairment (MCI) having apparently normal head MR images and 78 intracranial hemorrhage (ICH) patients with morphologically deformed head MR images were enrolled. Six imaging protocols were selected to be performed: T2-weighted imaging, fluid attenuated inversion recovery imaging, T2-star-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient mapping, and source images of time-of-flight magnetic resonance angiography. The proximal first image slices and middle image slices having ambiguous and distinctive contrast patterns, respectively, were classified by two deep learning imaging classifiers, AlexNet and GoogLeNet. RESULTS: AlexNet had accuracies of 73.3%, 73.6%, 73.1%, and 60.7% in the middle slices of MCI group, middle slices of ICH group, first slices of MCI group, and first slices of ICH group, while GoogLeNet had accuracies of 100%, 98.1%, 93.1%, and 94.8%, respectively. AlexNet significantly had lower classification ability than GoogLeNet for all datasets. CONCLUSIONS: GoogLeNet could judge the types of head MRI sequences with a small amount of training data, irrespective of morphological or contrast conditions.