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Abstract To determine whether temporal subtraction (TS) CT obtained with non-rigid image registration improves detection of various bone metastases during serial clinical follow-up examinations by numerous radiologists. Six board-certified radiologists retrospectively scrutinized CT images for patients with history of malignancy sequentially. These radiologists selected 50 positive and 50 negative subjects with and without bone metastases, respectively. Furthermore, for each subject, they selected a pair of previous and current CT images satisfying predefined criteria by consensus. Previous images were non-rigidly transformed to match current images and subtracted from current images to automatically generate TS images. Subsequently, 18 radiologists independently interpreted the 100 CT image pairs to identify bone metastases, both without and with TS images, with each interpretation separated from the other by an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Compared with interpretation without TS images, interpretation with TS images was associated with a significantly higher mean figure of merit (0.710 vs. 0.658; JAFROC analysis, P = 0.0027). Mean sensitivity at lesion-based was significantly higher for interpretation with TS compared with that without TS (46.1% vs. 33.9%; P = 0.003). Mean false positive count per subject was also significantly higher for interpretation with TS than for that without TS (0.28 vs. 0.15; P

PURPOSEWe aimed to evaluate mid- to long-term results of endovascular treatment for portal vein thrombosis (PVT) after living-donor liver transplantation (LDLT).METHODSThirty cases (14 males, 16 females; age range, 0.67–65 years) who underwent endovascular treatment including thrombolysis, angioplasty, stent placement, and/or collateral embolization for PVT after LDLT from 2001 to 2017 were retrospectively reviewed. Clinical and procedural data were collected and analyzed regarding the patency of the PVT site at the last follow-up date (PVT-free persistency) using Log-rank test. Results were considered statistically significant at P < 0.05.RESULTSMedian follow-up was 120 months. The technical success rate was 80% (n=24). Patency rates at 1 week and 1, 3, 6, 12, 36, and 60 months were 73%, 59%, 55%, 51%, 51%, 51%, and 51% for primary patency and 80%, 70%, 66%, 66%, 66%, 61%, and 61% for assisted patency after secondary endovascular treatment. PVT-free persistency rates regarding the subgroups were as follows: children under 12 years vs. adults, 50% vs. 68% (P = 0.42); acute vs. nonacute, 76% vs. 46% (P = 0.10); localized vs. extensive, 90% vs. 50% (P = 0.035); transileocolic approach vs. percutaneous-transhepatic approach, 71% vs. 54% (P = 0.39); and thrombolysis-based treatment vs. non-thrombolysis-based treatment, 71% vs. 44% (P = 0.12), respectively. Among technically successful cases, PVT-free persistency rate was 94% for those with hepatopetal flow in the peripheral portal vein vs. 17% for those without hepatopetal flow (P < 0.001). The only major complication occurring was pleural hemorrhage (n=1). Minor complications (i.e., fever) occurred in 18 patients (60%).CONCLUSIONIn conclusion, mid- to long-term portal patency following endovascular treatment was approximately 50%–60% in PVT patients after LDLT. PVT site patency over three months after the first endovascular treatment, localized PVT, and hepatopetal flow in the peripheral portal vein were identified as key prognostic factors for mid- to long-term portal patency.

Pancreatic β-cell mass (BCM) has a central importance in the pathophysiology of diabetes mellitus. Recently, pancreatic β-cell-specific imaging, especially positron emission tomography (PET) with exendin-based probes, has emerged for non-invasive evaluation of BCM. We developed a novel exendin-based probe labeled with fluorine-18, [18F]FB(ePEG12)12-exendin-4 (18F-Ex4) for PET imaging. We subsequently conducted a first-in-human phase 1 study of 18F-Ex4 PET/computed tomography (CT) and investigated the safety and utility for visualizing the pancreas. Six healthy male subjects were enrolled in this study. A low dose (37.0 MBq) of 18F-Ex4 PET/CT was administered (first cohort: n = 2), and subsequently a higher dose (74.0 MBq) was administered (second cohort: n = 4). In the first and second cohorts, 38.6 ± 4.8 and 71.1 ± 4.8 MBq of 18F-Ex4 were administered, respectively. No serious adverse events were observed in both groups. Only one participant in the first cohort showed transient hypoglycemia during the PET scans. 18F-Ex4 PET/CT successfully visualized the pancreas in all participants. The mean standardized uptake value of the pancreas was found to be higher than that in the surrounding organs, except for the bladder and kidney, during the observation. Dosimetry analyses revealed the effective systemic doses of 18F-Ex4 as 0.0164 ± 0.0019 mSv/MBq (first cohort) and 0.0173 ± 0.0020 mSv/MBq (second cohort). 18F-Ex4 PET/CT demonstrated the safety and utility for non-invasive visualization of the pancreas in healthy male subjects. 18F-Ex4 is promising for clinical PET imaging targeting pancreatic β cells.

Purpose: To determine the reproducibility and productivity of reduced dose chest computed tomography (CT) using a nodule detection task. Materials and methods: Eighty-eight consecutive non-contrast CT examinations were performed using an automatic exposure system with a reference standard deviation of 8.5. Simulated raw data of a reduced dose scan (standard deviation at 21 and 29) were generated with a dose simulator. Original and simulated raw data were reconstructed to series of 7-mm-thick images (Original, Simulation A, Simulation B). In the first part of the reading experiment, three readers independently interpreted these images (88 cases × 3 series) and recorded the size, type, and location of the pulmonary nodules. The reading time for every case was recorded. In the second part of the experiment, the repeated interpretation of standard dose images was performed by two readers. Concordance or discordance of nodule detection between the first and the repeated reading result was assessed. Results: A statistically significant difference in the detected nodule counts for lesions less than 5 mm by one reader was observed in simulation B images. Discordance of the interpretation result was found only in ground-glass nodules larger than 5 mm detected by one reader in simulation B images. There was no statistically significant difference in the reading time among the three image types. Conclusion: Simulated standard deviation 21 images can reproduce the image interpretation result of original images, whereas simulated standard deviation 29 images may compromise the accuracy of nodule assessment. The effect on the reading time was not observed with dose reduction simulation. Keywords: Computed tomography, Radiation dose reduction, Automatic exposure control, Pulmonary nodules

This study aimed to identify the characteristics of the vascular network in the superficial subcutaneous layer of the breast and to analyze differences between breasts with cancer and contralateral unaffected breasts using vessel branching points (VBPs) detected by three-dimensional photoacoustic imaging with a hemispherical detector array. In 22 patients with unilateral breast cancer, the average VBP counts to a depth of 7 mm below the skin surface were significantly greater in breasts with cancer than in the contralateral unaffected breasts (p

Abstract Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article offers a perspective on the basic concepts of CNN and its application to various radiological tasks, and discusses its challenges and future directions in the field of radiology. Two challenges in applying CNN to radiological tasks, small dataset and overfitting, will also be covered in this article, as well as techniques to minimize them. Being familiar with the concepts and advantages, as well as limitations, of CNN is essential to leverage its potential in diagnostic radiology, with the goal of augmenting the performance of radiologists and improving patient care. Key Points • Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. • Convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a backpropagation algorithm. • Familiarity with the concepts and advantages, as well as limitations, of convolutional neural network is essential to leverage its potential to improve radiologist performance and, eventually, patient care.

Purpose: To investigate the effect of breastfeeding on IVIM and non-Gaussian diffusion MRI in the breast. Materials and methods: An IRB approved prospective study enrolled seventeen volunteers (12 in lactation and 5 with post-weaning, range 31–43 years; mean 35.4 years). IVIM (fIVIM and D*) and non-Gaussian diffusion (ADC0 and K) parameters using 16 b values, plus synthetic apparent diffusion coefficients (sADCs) from 2 key b values (b = 200 and 1500 s/mm2) were calculated using regions of interest. ADC0 maps of the whole breast were generated and their contrast patterns were evaluated by two independent readers using retroareolar and segmental semi-quantitative scores. To compare the diffusion and IVIM parameters, Wilcoxon signed rank tests were used between pre- and post-breastfeeding and Mann-Whitney tests were used between post-weaning and pre- or post-breastfeeding. Results: ADC0 and sADC values significantly decreased post-breastfeeding (1.90 vs. 1.72 × 10−3 mm2/s, P

Background: A breast-specific photoacoustic imaging (PAI) system prototype equipped with a hemispherical detector array (HDA) has been reported as a promising system configuration for providing high morphological reproducibility for vascular structures in living bodies. Methods: To image the vasculature of human limbs, a newly designed PAI system prototype (PAI-05) with an HDA with a higher density sensor arrangement was developed. The basic device configuration mimicked that of a previously reported breast-specific PAI system. A new imaging table and a holding tray for imaging a subject's limb were adopted. Results: The device’s performance was verified using a phantom. Contrast of 8.5 was obtained at a depth of 2 cm, and the viewing angle reached up to 70 degrees, showing sufficient performance for limb imaging. An arbitrary wavelength was set, and a reasonable PA signal intensity dependent on the wavelength was obtained. To prove the concept of imaging human limbs, various parts of the subject were scanned. High-quality still images of a living human with a wider size than that previously reported were obtained by scanning within the horizontal plane and averaging the images. The maximum field of view (FOV) was 270 mm × 180 mm. Even in movie mode, one-shot 3D volumetric data were obtained in an FOV range of 20 mm in diameter, which is larger than values in previous reports. By continuously acquiring these images, we were able to produce motion pictures. Conclusion: We developed a PAI prototype system equipped with an HDA suitable for imaging limbs. As a result, the subject could be scanned over a wide range while in a more comfortable position, and high-quality still images and motion pictures could be obtained.

The purpose of this study was to assess whether homology-based emphysema quantification (HEQ) is significantly associated with lung cancer risk. This retrospective study was approved by our institutional review board. We included 576 patients with lung nodules (317 men and 259 women; age, 66.8 ± 12.3 years), who were selected from a database previously generated for computer-aided diagnosis. Of these, 283 were diagnosed with lung cancer, whereas the remaining 293 showed benign lung nodules. HEQ was performed and percentage of low-attenuation lung area (LAA%) was calculated on the basis of computed tomography scans. Statistical models were constructed to estimate lung cancer risk using logistic regression; sex, age, smoking history (Brinkman index), LAA%, and HEQ were considered independent variables. The following three models were evaluated: the base model (sex, age, and smoking history); the LAA% model (the base model + LAA%); and the HEQ model (the base model + HEQ). Model performance was assessed using receiver operating characteristic analysis and the associated area under the curve (AUC). Differences in AUCs among the models were evaluated using Delong's test. AUCs of the base, LAA%, and HEQ models were 0.585, 0.593, and 0.622, respectively. HEQ coefficient was statistically significant in the HEQ model (P = 0.00487), but LAA% coefficient was not significant in the LAA% model (P = 0.199). Delong's test revealed significant difference in AUCs between the LAA% and HEQ models (P = 0.0455). In conclusion, after adjusting for age, sex, and smoking history (Brinkman index), HEQ was significantly associated with lung cancer risk.

Purpose: To examine the feasibility and potential difficulties of automatically generating radiologic reports (RRs) to articulate the clinically important features of brain magnetic resonance (MR) images.Materials and Methods: We focused on examining brain atrophy by using magnetization-prepared rapid gradient-echo (MPRAGE) images. The technology was based on multi-atlas whole-brain segmentation that identified 283 structures, from which larger superstructures were created to represent the anatomic units most frequently used in RRs. Through two layers of data-reduction filters, based on anatomic and clinical knowledge, raw images (~10 MB) were converted to a few kilobytes of human-readable sentences. The tool was applied to images from 92 patients with memory problems, and the results were compared to RRs independently produced by three experienced radiologists. The mechanisms of disagreement were investigated to understand where machine–human interface succeeded or failed.Results: The automatically generated sentences had low sensitivity (mean: 24.5%) and precision (mean: 24.9%) values; these were significantly lower than the inter-rater sensitivity (mean: 32.7%) and precision (mean: 32.2%) of the radiologists. The causes of disagreement were divided into six error categories: mismatch of anatomic definitions (7.2 ± 9.3%), data-reduction errors (11.4 ± 3.9%), translator errors (3.1 ± 3.1%), difference in the spatial extent of used anatomic terms (8.3 ± 6.7%), segmentation quality (9.8 ± 2.0%), and threshold for sentence-triggering (60.2 ± 16.3%).Conclusion: These error mechanisms raise interesting questions about the potential of automated report generation and the quality of image reading by humans. The most significant discrepancy between the human and automatically generated RRs was caused by the sentence-triggering threshold (the degree of abnormality), which was fixed to z-score >2.0 for the automated generation, while the thresholds by radiologists varied among different anatomical structures.

We prospectively examined the variability of non-Gaussian diffusion magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) measurements with different numbers of b-values and excitations in normal breast tissue and breast lesions. Thirteen volunteers and fourteen patients with breast lesions (seven malignant, eight benign; one patient had bilateral lesions) were recruited in this prospective study (approved by the Internal Review Board). Diffusion-weighted MRI was performed with 16 b-values (0-2500 s/mm2 with one number of excitations [NEX]) and five b-values (0-2500 s/mm2, 3 NEX), using a 3T breast MRI. Intravoxel incoherent motion (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion (theoretical apparent diffusion coefficient [ADC] at b value of 0 sec/mm2 [ADC0] and kurtosis [K]) parameters were estimated from IVIM and Kurtosis models using 16 b-values, and synthetic apparent diffusion coefficient (sADC) values were obtained from two key b-values. The variabilities between and within subjects and between different diffusion acquisition methods were estimated. There were no statistical differences in ADC0, K, or sADC values between the different b-values or NEX. A good agreement of diffusion parameters was observed between 16 b-values (one NEX), five b-values (one NEX), and five b-values (three NEX) in normal breast tissue or breast lesions. Insufficient agreement was observed for IVIM parameters. There were no statistical differences in the non-Gaussian diffusion MRI estimated values obtained from a different number of b-values or excitations in normal breast tissue or breast lesions. These data suggest that a limited MRI protocol using a few b-values might be relevant in a clinical setting for the estimation of non-Gaussian diffusion MRI parameters in normal breast tissue and breast lesions.

We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.

[This corrects the article DOI: 10.1371/journal.pone.0146548.].

We aimed to describe the development of an inference model for computer-aided diagnosis of lung nodules that could provide valid reasoning for any inferences, thereby improving the interpretability and performance of the system. An automatic construction method was used that considered explanation adequacy and inference accuracy. In addition, we evaluated the usefulness of prior experts' (radiologists') knowledge while constructing the models. In total, 179 patients with lung nodules were included and divided into 79 and 100 cases for training and test data, respectively. F-measure and accuracy were used to assess explanation adequacy and inference accuracy, respectively. For F-measure, reasons were defined as proper subsets of Evidence that had a strong influence on the inference result. The inference models were automatically constructed using the Bayesian network and Markov chain Monte Carlo methods, selecting only those models that met the predefined criteria. During model constructions, we examined the effect of including radiologist's knowledge in the initial Bayesian network models. Performance of the best models in terms of F-measure, accuracy, and evaluation metric were as follows: 0.411, 72.0%, and 0.566, respectively, with prior knowledge, and 0.274, 65.0%, and 0.462, respectively, without prior knowledge. The best models with prior knowledge were then subjectively and independently evaluated by two radiologists using a 5-point scale, with 5, 3, and 1 representing beneficial, appropriate, and detrimental, respectively. The average scores by the two radiologists were 3.97 and 3.76 for the test data, indicating that the proposed computer-aided diagnosis system was acceptable to them. In conclusion, the proposed method incorporating radiologists' knowledge could help in eliminating radiologists' distrust of computer-aided diagnosis and improving its performance.

Compressed sensing (CS) reconstructions of under-sampled measurements generate missing data based on assumptions of image sparsity. Non-contrast time-of-flight MR angiography (TOF-MRA) is a good candidate for CS based acceleration, as MRA images feature bright trees of sparse vessels over a well-suppressed anatomical background signal. A short scan time derived from CS is beneficial for patients of moyamoya disease (MMD) because of the frequency of MR scans. The purpose of this study was to investigate the reliability of TOF-MRA with CS in the evaluation of MMD. Twenty-two patients were examined using TOF-MRA with CS (CS-TOF) and parallel imaging (PI-TOF). The acceleration factors were 3 (CS3) and 5 (CS5) for CS-TOF, and 3 (PI3) for PI-TOF. Two neuroradiologists evaluated the MMD grading according to stenosis/occlusion scores using the modified Houkin's system, and the visibility of moyamoya vessels (MMVs) using a 3-point scale. Concordance was calculated with Cohen's κ. The numbers of MMVs in the basal ganglia were compared using Bland-Altman analysis and Wilcoxon's signed-rank tests. MRA scan times were 4:07, 3:53, and 2:42 for PI3, CS3, and CS5, respectively. CS-reconstruction completed within 10 minutes. MMD grading and MMV visibility scales showed excellent correlation (κ > .966). Although the number of MMVs was significantly higher in CS3 than in PI3 (p < .0001) and CS5 (p < .0001), Bland-Altman analysis showed a good agreement between PI3, CS3, and CS5. Compressed sensing can accelerate TOF-MRA with improved visualization of small collaterals in equivalent time (CS3) or equivalent results in a shorter scan time (CS5).

We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. The best averaged validation accuracies of CADx were 55.9%, 68.0%, and 62.4% for the conventional method, the DCNN method with transfer learning, and the DCNN method without transfer learning, respectively. For image size of 56, 112, and 224, the best averaged validation accuracy for the DCNN with transfer learning were 60.7%, 64.7%, and 68.0%, respectively. DCNN was better than the conventional method for CADx, and the accuracy of DCNN improved when using transfer learning. Also, we found that larger image sizes as inputs to DCNN improved the accuracy of lung nodule classification.

BACKGROUND:The heterogeneous distribution of emphysema is a key feature of chronic obstructive pulmonary disease (COPD) patients that typically is evaluated using high-resolution chest computed tomography (HRCT). Oxygen-enhanced pulmonary magnetic resonance imaging (OEMRI) is a new method to obtain information regarding regional ventilation, diffusion, and perfusion in the lung without radiation exposure. We aimed to compare OEMRI with HRCT for the assessment of heterogeneity in COPD patients. METHODS:Forty patients with stable COPD underwent quantitative HRCT, OEMRI, and pulmonary function tests, including arterial blood gas analysis. OEMRI was also performed on nine healthy control subjects. We measured the severity of emphysema (percent low attenuation volume; LAV%) in whole lungs and the standard deviations (SDs) of the LAV% values of 10 isovolumetric partitions (SD-LAV) as an index of cranial-caudal heterogeneity. Similarly, relative enhancement ratios of oxygen (RERs) in whole lungs from OEMRI and SD-RER were analyzed. RESULTS:COPD patients showed a lower mean RER than control subjects (12.6% vs 22.0%, p

Dynamic physiological and anatomical changes for delivery may adversely induce various specific non-obstetric complications during pregnancy and puerperal period. These complications can be fatal to both the mother and the fetus, thus a precise and early diagnosis ensued by an early treatment is essential. Along with ultrasonography, computed tomography (CT) and magnetic resonance imaging (MRI) have assumed an increasing role in the diagnosis. This article aims to discuss the pathophysiology of these complications, the indications for CT and MRI, and the imaging findings.

OBJECTIVE:The purpose of this study was to investigate the relationship between visual score of emphysema and homology-based emphysema quantification (HEQ) and evaluate whether visual score was accurately predicted by machine learning and HEQ. MATERIALS AND METHODS:A total of 115 anonymized computed tomography images from 39 patients were obtained from a public database. Emphysema quantification of these images was performed by measuring the percentage of low-attenuation lung area (LAA%). The following values related to HEQ were obtained: nb0 and nb1. LAA% and HEQ were calculated at various threshold levels ranging from -1000 HU to -700 HU. Spearman's correlation coefficients between emphysema quantification and visual score were calculated at the various threshold levels. Visual score was predicted by machine learning and emphysema quantification (LAA% or HEQ). Random Forest was used as a machine learning algorithm, and accuracy of prediction was evaluated by leave-one-patient-out cross validation. The difference in the accuracy was assessed using McNemar's test. RESULTS:The correlation coefficients between emphysema quantification and visual score were as follows: LAA% (-950 HU), 0.567; LAA% (-910 HU), 0.654; LAA% (-875 HU), 0.704; nb0 (-950 HU), 0.552; nb0 (-910 HU), 0.629; nb0 (-875 HU), 0.473; nb1 (-950 HU), 0.149; nb1 (-910 HU), 0.519; and nb1 (-875 HU), 0.716. The accuracy of prediction was as follows: LAA%, 55.7% and HEQ, 66.1%. The difference in accuracy was statistically significant (p = 0.0290). CONCLUSION:LAA% and HEQ at -875 HU showed a stronger correlation with visual score than those at -910 or -950 HU. HEQ was more useful than LAA% for predicting visual score.

Background The relationship between the signal from retrograde venous flow on magnetic resonance angiography (MRA) and retrograde upward flow from the left brachiocephalic vein has not been explored. Purpose To reveal the frequency of jugular venous reflux using MRA and nuclear venography in patients being evaluated for cerebral volume and blood flow. Material and Methods A total of 229 patients with cognitive disturbance who had undergone brain magnetic resonance imaging (MRI) and single-photon emission computed tomography (SPECT) on the same day to evaluate cerebral blood flow were evaluated. Jugular venous reflux was measured on MRA and nuclear venography, which was conducted just after injection of N-isopropyl-123I-p-iodoamphetamine for the SPECT study. Results MRA showed jugular reflux in seven patients on the right side, and in 22 on the left. Nuclear venography showed jugular reflux in six patients on the right side, and in 20 on the left. Conclusion Jugular venous reflux was observed mostly on the left side. Retrograde flow was observed on both MRA and nuclear venography in half of the cases, with the rest only on one of the modalities.

In Compressed Sensing (CS) of MRI, optimization of the regularization parameters is not a trivial task. We aimed to establish a method that could determine the optimal weights for regularization parameters in CS of time-of-flight MR angiography (TOF-MRA) by comparing various image metrics with radiologists' visual evaluation. TOF-MRA of a healthy volunteer was scanned using a 3T-MR system. Images were reconstructed by CS from retrospectively under-sampled data by varying the weights for the L1 norm of wavelet coefficients and that of total variation. The reconstructed images were evaluated both quantitatively by statistical image metrics including structural similarity (SSIM), scale invariant feature transform (SIFT) and contrast-to-noise ratio (CNR), and qualitatively by radiologists' scoring. The results of quantitative metrics and qualitative scorings were compared. SSIM and SIFT in conjunction with brain masks and CNR of artery-to-parenchyma correlated very well with radiologists' visual evaluation. By carefully selecting a region to measure, we have shown that statistical image metrics can reflect radiologists' visual evaluation, thus enabling an appropriate optimization of regularization parameters for CS.

Moyamoya disease (MMD) is a chronic, progressive, cerebrovascular occlusive disease that causes abnormal enlargement of collateral pathways (moyamoya vessels) in the region of the basal ganglia and thalamus. Cerebral revascularization procedures remain the preferred treatment for patients with MMD, improving the compromised cerebral blood flow (CBF). However, voxel based analysis (VBA) of revascularization surgery for MMD based on data from pre- and postoperative data has not been established. The latest algorithm called as Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) has been introduced for VBA as the function of statistical parametric mapping (SPM8), and improved registration has been achieved by SPM8 with DARTEL. In this study, VBA was conducted to evaluate pre- and postoperative single photon emission computed tomography (SPECT) images for MMD by SPM8 with DARTEL algorithm, and the results were compared with those from SPM8 without DARTEL (a conventional method). Thirty-two patients with MMD who underwent superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery as the first surgery were included and all patients underwent pre- and postoperative 3D T1-weighted imaging and SPECT. Pre- and postoperative SPECT images were registered to 3D T1-weighted images, then VBA was conducted. Postoperative SPECT showed more statistically increased CBF areas in the bypassed side cerebral hemisphere by using SPM8 with DARTEL (58,989 voxels; P

Polymeric micelle of the A3B-type lactosome comprising (poly(sarcosine))3-b-poly(l-lactic acid) was labeled with 111In. The 111In-labeled A3B-type lactosome was administered to the model mice bearing meningeal dissemination and bone metastasis at mandible. With single-photon emission computed tomography (SPECT) imaging, the meningeal dissemination was identified successfully by 111In-labeled A3B-type lactosome, which was superior to 201TlCl in regard of the imaging contrast. The 111In-labeled A3B-type lactosome was also potential in imaging selectively of bone metastasis at mandible, whilst a nonspecific imaging of the whole bone was obtained by the SPECT imaging using 99mTc-HMDP. The polymeric micelle of the A3B-type lactosome was therefore found to be effective as a vehicle of 111In to be targeted to meningeal dissemination and bone metastasis.

A new technique - Z-spectrum Analysis Provides Proton Environment Data (ZAPPED) - was used to map cross-relaxing free and restricted protons in nine healthy subjects plus two brain tumor patients at 3T. First, MT data were acquired over a wide symmetric range of frequency offsets, and then a trio of quantitative biomarkers, i.e., the apparent spin-spin relaxation times (T2,f, T2,r) in both free and restricted proton pools as well as the restricted pool fraction Fr, were mapped by fitting the measured Z-spectra to a simple two-Lorentzian compartment model on a voxel-by-voxel basis. The mean restricted exchangeable proton fraction, Fr, was found to be 0.17 in gray matter (GM) and 0.28 in white matter (WM) in healthy subjects. Corresponding mean values for apparent spin-spin relaxation times were 785 µs (T2,f) and 17.7 µs (T2,r) in GM, 672 µs (T2,f) and 23.4 µs (T2,r) in WM. The percentages of Ff and Fr in GM are similar for all ages, whereas Fr shows a tendency to decrease with age in WM among healthy subjects. The patient ZAPPED images show higher contrast between tumor and normal tissues than traditional T2-weighted and T1-weighted images. The ZAPPED method provides a simple phenomenological approach to estimating fractions and apparent T2 values of free and restricted MT-active protons, and it may offer clinical useful information.

Photoacoustic tomography is a recently developed imaging modality that can provide high spatial-resolution images of hemoglobin distribution in tissues such as the breast. Because breast cancer is an angiogenesis-dependent type of malignancy, we evaluated the clinical acceptability of breast tissue images produced using our first prototype photoacoustic mammography (PAM) system in patients with known cancer. Post-excisionally, histological sections of the tumors were stained immunohistochemically (IHC) for CD31 (an endothelial marker) and carbonic anhydrase IX (CAIX) (a marker of hypoxia). Whole-slide scanning and image analyses were used to evaluate the tumor microvessel distribution pattern and to calculate the total vascular perimeter (TVP)/area for each lesion. In this clinical study, 42 lesions were primarily scanned using PAM preoperatively, three of which were reported to be benign and were excluded from statistical analysis. Images were produced for 29 out of 39 cancers (visibility rate = 74.4%) at the median depth of 26.5 (3.25-51.2) mm. Age, menopausal status, body mass index, history of neoadjuvant treatment, clinical stage and histological tumor angiogenesis markers did not seem to affect the visibility. The oxygen saturation level in all of the measured lesions was lower than in the subcutaneous counterpart vessels (Wilcoxon test, p value

Oxygen causes an increase in the longitudinal relaxation rate of tissues through its T1-shortening effect owing to its paramagnetic properties. Due to such effects, MRI has been used to study oxygen-related signal intensity changes in various body parts including cerebrospinal fluid (CSF) space. Oxygen enhancement of CSF has been mainly studied using MRI sequences with relatively longer time resolution such as FLAIR, and T1 value calculation. In this study, fifteen healthy volunteers were scanned using fast advanced spin echo MRI sequence with and without inversion recovery pulse in order to dynamically track oxygen enhancement of CSF. We also focused on the differences of oxygen enhancement at sulcal and ventricular CSF. Our results revealed that CSF signal after administration of oxygen shows rapid signal increase in both sulcal CSF and ventricular CSF on both sequences, with statistically significant predominant increase in sulcal CSF compared with ventricular CSF. CSF is traditionally thought to mainly form from the choroid plexus in the ventricles and is absorbed at the arachnoid villi, however, it is also believed that cerebral arterioles contribute to the production and absorption of CSF, and controversy remains in terms of the precise mechanism. Our results demonstrated rapid oxygen enhancement in sulcal CSF, which may suggest inhaled oxygen may diffuse into sulcal CSF space rapidly probably due to the abundance of pial arterioles on the brain sulci.

To evaluate the accuracy of advanced non-linear registration of serial lung Computed Tomography (CT) images using Large Deformation Diffeomorphic Metric Mapping (LDDMM).FIFTEEN CASES OF LUNG CANCER WITH SERIAL LUNG CT IMAGES (INTERVAL: 62.2±26.9 days) were used. After affine transformation, three dimensional, non-linear volume registration was conducted using LDDMM with or without cascading elasticity control. Registration accuracy was evaluated by measuring the displacement of landmarks placed on vessel bifurcations for each lung segment. Subtraction images and Jacobian color maps, calculated from the transformation matrix derived from image warping, were generated, which were used to evaluate time-course changes of the tumors.The average displacement of landmarks was 0.02±0.16 mm and 0.12±0.60 mm for proximal and distal landmarks after LDDMM transformation with cascading elasticity control, which was significantly smaller than 3.11±2.47 mm and 3.99±3.05 mm, respectively, after affine transformation. Emerged or vanished nodules were visualized on subtraction images, and enlarging or shrinking nodules were displayed on Jacobian maps enabled by highly accurate registration of the nodules using LDDMM. However, some residual misalignments were observed, even with non-linear transformation when substantial changes existed between the image pairs.LDDMM provides accurate registration of serial lung CT images, and temporal subtraction images with Jacobian maps help radiologists to find changes in pulmonary nodules.

Background : A breast-specific photoacoustic imaging (PAI) system prototype equipped with a hemispherical detector array (HDA) has been reported as a promising system configuration for providing high morphological reproducibility for vascular structures in living bodies. Methods : To image the vasculature of human limbs, a newly designed PAI system prototype (PAI-05) with an HDA with a higher density sensor arrangement was developed. The basic device configuration mimicked that of a previously reported breast-specific PAI system. A new imaging table and a holding tray for imaging a subject's limb were adopted. Results : The device’s performance was verified using a phantom. Contrast of 8.5 was obtained at a depth of 2 cm, and the viewing angle reached up to 70 degrees, showing sufficient performance for limb imaging. An arbitrary wavelength was set, and a reasonable PA signal intensity dependent on the wavelength was obtained. To prove the concept of imaging human limbs, various parts of the subject were scanned. High-quality still images of a living human with a wider size than that previously reported were obtained by scanning within the horizontal plane and averaging the images. The maximum field of view (FOV) was 270 mm × 180 mm. Even in movie mode, one-shot 3D volumetric data were obtained in an FOV range of 20 mm in diameter, which is larger than values in previous reports. By continuously acquiring these images, we were able to produce motion pictures. Conclusion : We developed a PAI prototype system equipped with an HDA suitable for imaging limbs. As a result, the subject could be scanned over a wide range while in a more comfortable position, and high-quality still images and motion pictures could be obtained.