Search

Recent disasters have shown the existence of large variance in recovery trajectories across cities that have experienced similar damage levels. Case studies of such events reveal the high complexity of the recovery process of cities, where inter-city dependencies and intra-city coupling of social and physical systems may affect the outcomes in unforeseen ways. Despite the large implications of understanding the recovery processes of cities after disasters for many domains including critical services, disaster management, and public health, little work have been performed to unravel this complexity. Rather, works are limited to analyzing and modeling cities as independent entities, and have largely neglected the effect that inter-city connectivity may have on the recovery of each city. Large scale mobility data (e.g. mobile phone data, social media data) have enabled us to observe human mobility patterns within and across cities with high spatial and temporal granularity. In this paper, we investigate how inter-city dependencies in both physical as well as social forms contribute to the recovery performances of cities after disasters, through a case study of the population recovery patterns of 78 Puerto Rican counties after Hurricane Maria using mobile phone location data. Various network metrics are used to quantify the types of inter-city dependencies that play an important role for effective post-disaster recovery. We find that inter-citysocialconnectivity, which is measured by pre-disaster mobility patterns, is crucial for quicker recovery after Hurricane Maria. More specifically, counties that had more influx and outflux of people prior to the hurricane, were able to recover faster. Our findings highlight the importance of fostering the social connectivity between cities to prepare effectively for future disasters. This paper introduces a new perspective in the community resilience literature, where we take into account the inter-city dependencies in the recovery process rather than analyzing each community as independent entities.

Disaster risk management, including response and recovery, are essential elements of sustainable development. With the recent increase in natural hazards, the importance of techniques to understand, model and predict the evacuation and returning behavior of affected individuals is rising. Studies have found that influence from real world social ties affects mobility decisions during disasters. Despite the rapid spread of social media platforms, little has been quantitatively understood about the influence of social ties on online social media on such decisions. Information provided by who at what timing influences users’ decision-making process by how much during disasters? In this study, we answer these research questions by proposing a data-driven framework that can predict post-disaster mobility decisions and simultaneously unravel the influence of various information on online social media. More specifically, our method quantifies the influence of information provided by different types of social media accounts on the peoples’ decisions to return or stay displaced after evacuation. We tested our approach using real world data collected from more than 13 million unique Twitter users during Hurricane Sandy. Experiments verified that we can improve the predictive accuracy of return and displacement behavior, and also quantify the influence of online information. In contrast to popular beliefs, it was found that information posted by the crowd influenced the decisions more than information disseminated by official accounts. Improving our understanding of influence dynamics on online social media could provide policy makers with insights on how to disseminate information on social media more effectively for better disaster response and recovery, which may contribute towards building sustainable urban systems.

Agent-based simulations, combined with large scale mobility data, have been an effective method for understanding urban scale human dynamics. However, collecting such large scale human mobility datasets are especially difficult during rare events (e.g., natural disasters), reducing the performance of agent-based simulations. To tackle this problem, we develop an agent-based model that can simulate urban dynamics during rare events by learning from other cities using inverse reinforcement learning. More specifically, in our framework, agents imitate real human-beings' travel behavior from areas where rare events have occurred in the past (source area) and produce synthetic people movement in different cities where such rare events have never occurred (target area). Our framework contains three main stages: 1) recovering the reward function, where the people's travel patterns and preferences are learned from the source areas; 2) transferring the model of the source area to the target areas; 3) simulating the people movement based on learned model in the target area. We apply our approach in various cities for both normal and rare situations using real-world GPS data collected from more than 1 million people in Japan, and show higher simulation performance than previous models.

Unsupervised translation has become a popular task in natural language processing (NLP) due to difficulties in collecting large scale parallel datasets. In the urban computing field, place embeddings generated using human mobility patterns via recurrent neural networks are used to understand the functionality of urban areas. Translating place embeddings across cities allow us to transfer knowledge across cities, which may be used for various downstream tasks such as planning new store locations. Despite such advances, current methods fail to translate place embeddings across domains with different scales (e.g. Tokyo to Niigata), due to the straightforward adoption of neural machine translation (NMT) methods from NLP, where vocabulary sizes are similar across languages. We refer to this issue as the domain imbalance problem in unsupervised translation tasks. We address this problem by proposing an unsupervised translation method that translates embeddings by exploiting common hierarchical structures that exist across imbalanced domains. The effectiveness of our method is tested using place embeddings generated from mobile phone data in 6 Japanese cities of heterogeneous sizes. Validation using landuse data clarify that using hierarchical anchors improves the translation accuracy across imbalanced domains. Our method is agnostic to input data type, thus could be applied to unsupervised translation tasks in various fields in addition to linguistics and urban computing.

Economic loss caused by natural disasters is increasing in many cities around the world. There is an increasing demand for a method that effectively measures the fragility of people flow to appropriately plan the future investment into infrastructure. Conventional methods measure the fragility of urban systems using infrastructure data such as the road and railway networks. However, these methods are costly to perform, cannot directly measure the disruption on human activities caused by disasters, nor can they be applied for individual disasters. Here, we propose a novel method that quantifies the fragility of cities through detecting the delay in commuting activities using GPS data collected from smartphones. Because commuting activities are daily routines for many people, commuting flow has little day-to-day fluctuation, which makes it an appropriate metric for detecting anomalies and disruption in urban systems. This method can be utilized in any city in the world regardless of differences in network structures or population distribution, as long as people commute on a daily basis. We validate our method in various cities for snowfall and typhoons using real datasets in Japan, and show that intuitive results can be obtained. Our method's reliability is clarified by comparing the results with conventional metrics. We also present useful analyses and applications of CityFlowFragility for urban planning and disaster management.

Following publication of the original article [1], the author reported to remove the name from the acknowledgements in the original article.

Real-time estimation of people distribution immediately after a disaster is directly related to disaster reduction and is also highly beneficial in society. Recently, traffic estimation research has been actively performed using data assimilation techniques for observation data obtained from mobile phones. However, there has been no research on data assimilation technique using real-time gridded aggregated observation data obtained from mobile phones, which are available and can be used to estimate population flow and distribution in a metropolitan area during a large-scale disaster. In this research, population distribution in an urban area during a disaster was estimated using gridded aggregated observation data obtained from mobile phones, using particle filter. The experimental results indicated that the particle filters enabled high-precision real-time estimation in the Kanto district.

Understanding individual and crowd dynamics in urban environments is critical for numerous applications, such as urban planning, traffic forecasting, and location-based services. However, researchers have developed travel demand models to accomplish this task with survey data that are expensive and acquired at low frequencies. In contrast, emerging data collection methods have enabled researchers to leverage machine learning techniques with a tremendous amount of mobility data for analyzing and forecasting people’s behaviors. In this study, we developed a reinforcement learning-based approach for modeling and simulation of people mass movement using the global positioning system (GPS) data. Unlike traditional travel demand modeling approaches, our method focuses on the problem of inferring the spatio-temporal preferences of individuals from the observed trajectories, and is based on inverse reinforcement learning (IRL) techniques. We applied the model to the data collected from a smartphone application and attempted to replicate a large amount of the population’s daily movement by incorporating with agent-based multi-modal traffic simulation technologies. The simulation results indicate that agents can successfully learn and generate human-like travel activities. Furthermore, the proposed model performance significantly outperforms the existing methods in synthetic urban dynamics.

Large-scale disasters often trigger mass evacuation due to significant damages to urban systems. Understanding the evacuation and reentry (return) process of affected individuals is crucial for disaster management. Moreover, measuring the heterogeneity in the individuals' post-disaster behavior with respect to their socio-economic characteristics is essential for policy making. Recent studies have used large-scale location datasets collected from mobile devices to analyze post-disaster mobility patterns. Despite the availability of such data and the societal importance of the problem, no studies have focused on how income inequality affects the equity in post-disaster mobility. To overcome these research gaps, we overlay mobility data with income information from census to quantify the effects of income inequality on evacuation and reentry behavior after disasters, and the resulting spatial income segregation. Spatio-temporal analysis using location data of more than 1.7 million mobile phone users from Florida affected by Hurricane Irma reveal significant effects of income inequality on evacuation behavior. Evacuees with higher income were more likely to evacuate from affected areas and reach safer locations with less damage on housing and infrastructure. These differences were common among evacuees from both inside and outside mandatory evacuation zones. As a result of such effects of inequality, significant spatial income segregation was observed in the affected areas. Insights on the effects of income inequality on post-disaster mobility and spatial segregation could contribute to policies that better address social equity in pre-disaster preparation and post-disaster relief.

Predicting the evacuation decisions of individuals before the disaster strikes is crucial for planning first response strategies. In addition to the studies on post-disaster analysis of evacuation behavior, there are various works that attempt to predict the evacuation decisions beforehand. Most of these predictive methods, however, require real time location data for calibration, which are becoming much harder to obtain due to the rising privacy concerns. Meanwhile, web search queries of anonymous users have been collected by web companies. Although such data raise less privacy concerns, they have been under-utilized for various applications. In this study, we investigate whether web search data observed prior to the disaster can be used to predict the evacuation decisions. More specifically, we utilize a "session-based query encoder" that learns the representations of each user's web search behavior prior to evacuation. Our proposed approach is empirically tested using web search data collected from users affected by a major flood in Japan. Results are validated using location data collected from mobile phones of the same set of users as ground truth. We show that evacuation decisions can be accurately predicted (84%) using only the users' pre-disaster web search data as input. This study proposes an alternative method for evacuation prediction that does not require highly sensitive location data, which can assist local governments to prepare effective first response strategies., Comment: Accepted in ACM KDD 2019

Catering to the humanitarian needs of hurricane-affected residents is the most challenging part for the emergency management agencies. These agencies typically follow a centralized help disbursement model by collecting donations and disbursing them to the needful through their employees or registered volunteers. The time required to move goods and volunteers to the place of need poses a survival challenge to emergency hit residents especially during the initial few days after the emergency. We propose and design a social-media (specifically Twitter) aided hyperlocal help-network by utilizing the tweets to identify users who require help and those who are willing to provide it. We also analyze tweets related to road damage, traffic jam, etc. to sense the current state of road infrastructure. We propose to match the help seekers and those who are willing to help, taking into consideration their spatial proximity and then provide the fastest working route for the help-provider to reach the matched help-seeker. Numerical experiments performed on hurricane Sandy Twitter dataset shows the effectiveness of the proposed approach as we are able to satisfy the need of more than 80% of help-seekers by matching them to appropriate help-offerer within a 24-hour duration after posting the request for help tweet with a maximum travel distance of 10 km.

Recently, the frequency and intensity of weather-related disasters are increasing and are becoming more ubiquitous, often devastating vulnerable rural areas. To prepare for speedy and effective first response, we need a flood detection method that works much faster and is able to cover a wider area compared to conventional methods that use CCTV cameras and low cost sensors, which are costly to distribute ubiquitously in all areas with possible flood threats. With the spread of mobile phones, we are able to obtain real time anonymized location information of individuals in a ubiquitous, low cost, and a continuous manner from users that have agreed to provide their location data for disaster relief purposes. Here we propose a novel method that infers flooded areas in real time by detecting anomalous behaviors of individuals using mobile phone location data. We are motivated in applying our method to rural areas that are costly to cover using cameras and sensors. To overcome the sparseness of mobile phone location signals in such rural areas, our method combines mobile phone location data with terrain information including the digital elevation model and river trajectory data. We evaluated our method using real world data from 2 severe floods in the rural parts of Japan and verified that our method is more accurate and has numerous advantages compared to conventional methods. This work presents the potential use of mobile phone data as a complementary, if not an alternative method for flood detection especially in rural areas.

This paper is the first work to replicate and simulate urban dynamics by learning individuals' decision-making processes and creating human-like agents from GPS data. We develop a novel agent model by learning from historical data via reinforcement learning techniques. We test our methodology in different scenarios at the citywide level using real world smartphone GPS data. Simulation results show that our agents can successfully learn and generate human-like travel activities. Furthermore, the performance of synthetic urban dynamics significantly outperforms existing methods.

Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuation behavior remains limited because of the lack of empirical evidence on the evacuation movement of individuals across multiple disaster instances. Here we investigate the GPS trajectories of a total of more than 1 million anonymized mobile phone users whose positions were tracked for a period of 2 months before and after four of the major earthquakes that occurred in Japan. Through a cross comparative analysis between the four disaster instances, we find that in contrast to the assumed complexity of evacuation decision making mechanisms in crisis situations, an individual's evacuation probability is strongly dependent on the seismic intensity that they experience. In fact, we show that the evacuation probabilities in all earthquakes collapse into a similar pattern, with a critical threshold at around seismic intensity 5.5. This indicates that despite the diversity in the earthquakes profiles and urban characteristics, evacuation behavior is similarly dependent on seismic intensity. Moreover, we found that probability density functions of the distances that individuals evacuate are not dependent on seismic intensities that individuals experience. These insights from empirical analysis on evacuation from multiple earthquake instances using large scale mobility data contributes to a deeper understanding of how people react to earthquakes, and can potentially assist decision makers to simulate and predict the number of evacuees in urban areas with little computational time and cost. This can be achieved by utilizing only the information on population density distribution and seismic intensity distribution, which can be observed instantaneously after the shock.

Understanding the daily movement of humans in space and time on different granularity levels is of critical value for urban planning, transport management, health care and commercial services. However, population's daily travel behavior data was collected by travel surveys that are infrequent, expensive, and disable to reflect changes in transportation. The demand for capturing, modeling and reproducing human travel behavior in different scenarios pose a challenge on the significant delays. In this study, we propose an inverse reinforcement learning based formulation for training an agent model that enables modeling complex decision-making with consideration of a dynamic environment on the urban granularity level. The modeling framework is based on the Markov decision process to represent an individual's decision making. To obtain the travel behavior characteristics of real humans, we apply the proposed approach to a real-time GPS dataset collected via a smart phone application with more than 2 million daily users to model the people travel behavior for different daily scenarios (i.e., weekdays, weekends, and national holidays) in the Tokyo metropolitan area. It is found that the developed model can generate individual's daily travel plan. In addition, by aggregating the agent travel behavior on the city-wide scale, the urban daily travel demand can be obtained and used for estimate the hourly population distribution. The result of this work can also be regarded as a synthetic mobility dataset, avoiding many of the privacy concerns surrounding real GPS data.

Mid-level disasters that frequently occur, such as typhoons and earthquakes, heavily affect human activities in urban areas by causing severe congestion and economic loss. Predicting the irregular movement of individuals following such disasters is crucial for managing urban systems. Past survey results show that mid-level disasters do not force many individuals to evacuate away from their homes, but do cause irregular movement by significantly delaying the movement timings, resulting in severe congestion in urban transportation. We propose a novel method that predicts such irregularity of individuals' movements in several mid-level disasters using various types of features including the victims' usual movement patterns, disaster information, and geospatial information of victims' locations. Using real GPS data of 1 million people in Tokyo, we show that our method can predict mobility delay with high accuracy

Large scale disasters cause severe social disorder and trigger mass evacuation activities. Managing the evacuation shelters efficiently is crucial for disaster management. Kumamoto prefecture, Japan, was hit by an enormous (Magnitude 7.3) earthquake on 16th of April, 2016. As a result, more than 10,000 buildings were severely damaged and over 100,000 people had to evacuate from their homes. After the earthquake, it took the decision makers several days to grasp the locations where people were evacuating, which delayed of distribution of supply and rescue. This situation was made even more complex since some people evacuated to places that were not designated as evacuation shelters. Conventional methods for grasping evacuation hotspots require on-foot field surveys that take time and are difficult to execute right after the hazard in the confusion.We propose a novel framework to efficiently estimate the evacuation hotspots after large disasters using location data collected from smartphones. To validate our framework and show the useful analysis using our output, we demonstrated the framework on the Kumamoto earthquake using GPS data of smartphones collected by Yahoo Japan. We verified that our estimation accuracy of evacuation hotspots were very high by checking the located facilities and also by comparing the population transition results with newspaper reports. Additionally, we demonstrated analysis using our framework outputs that would help decision makers, such as the population transition and function period of each hotspot. The efficiency of our framework is also validated by checking the processing time, showing that it could be utilized efficiently in disasters of any scale. Our framework provides useful output for decision makers that manage evacuation shelters after various kinds of large scale disasters.

Real-time estimation of human mobility following a massive disaster will play a crucial role in disaster relief. Because human mobility in massive disasters is quite different from their usual mobility, real-time human location data is necessary for precise estimation. Due to privacy concerns, real-time data is anonymized and a popular form of anonymization is population distribution. In this paper, we aim to estimate human mobility following an unprecedented disaster using such population distribution data. To overcome technical obstacles including high dimensionality, we propose novel particle filter by devising proposal distribution. Our proposal distribution provides states considering both prediction model and acquired observation. Therefore, particles maintain high likelihood. In the experiments, our methods realized more accurate estimation than the baselines, and its estimated mobility was consistent with the survey researches. The computational cost is significantly low enough for real-time operations. The GPS data collected on the day of the Great East Japan Earthquake is used for the evaluation.

ObjectivesThis study noninvasively examined total creatine (CR) of the myocardium in dilated cardiomyopathy (DCM) or hypertrophic cardiomyopathy (HCM) using proton magnetic resonance spectroscopy (1H-MRS).BackgroundAbnormalities in CR metabolism in failing hearts have been reported. A biochemical study suggested that myocardial metabolic changes are very similar in DCM and HCM despite the different heart failure (HF) mechanisms.MethodsUsing cardiac-gated 1H-MRS with magnetic resonance image (MRI)-guided point-resolved spectroscopy (PRESS) localization, we quantitatively measured septal CR. Patients with either DCM (n = 11) or HCM (n = 7) and age-matched normal subjects (n = 14) were examined.ResultsMyocardial CR was significantly lower in DCM patients (16.1 ± 4.5 μmol/g wet weight [range 10.2 to 22.9], p < 0.05) than that in subjects with normal hearts (27.6 ± 4.1 μmol/g [range 21.4 to 36.2]). Myocardial CR in HCM patients (22.6 ± 8.1 μmol/g [range 12.2 to 34.5]) was significantly lower than that in subjects with normal hearts (p < 0.05) but was significantly higher than that in DCM patients (p < 0.05). In 18 patients with either DCM or HCM, myocardial CR correlated positively with left ventricular ejection fraction (LVEF) (y = 0.22x + 9.8, r = 0.73, p = 0.0006) but correlated negatively with plasma B-type natriuretic peptide (BNP) levels (y = −0.012x + 22.4, r = −0.54, p = 0.022).ConclusionsThis study showed that 1H-MRS can noninvasively detect CR depletion associated with the severity of HF in cardiomyopathy.

Objectives This study examined the changes in myocardial energy metabolism during myocardial ischemia after “remote preconditioning” and investigated the involvement of adenosine receptors in the mechanisms of this effect. Background Recent studies have indicated that a brief period of ischemia and reperfusion (ischemic preconditioning, PC) in a remote organ reduces myocardial infarct size (IS) protecting against subsequent sustained myocardial ischemia. However, the mechanisms of “remote PC” remain unclear. We assessed myocardial energy metabolism during sustained myocardial ischemia and reperfusion after renal PC (RPC), in comparison with that after myocardial PC (MPC) in open-chest rabbits. It has been established that adenosine receptors are involved in the mechanisms of MPC. Methods Rabbits that had been anesthetized with halothane were divided into six groups. The control (CNT) group underwent 40-min coronary occlusion followed by 120 min reperfusion. Before the procedure, the MPC group underwent an additional protocol of 5 min coronary artery occlusion and 20 min reperfusion, and the RPC group received a 10 min episode of renal artery occlusion and 20 min reperfusion. In additional experimental groups, 8 sulfophenyltheophylline (SPT, 10 mg/kg), an adenosine receptor inhibitor, was intravenously injected before the 40 min myocardial ischemia (SPT, MPC + SPT and RPC + SPT groups, respectively). Myocardial levels of phosphocreatine (PCr), ATP and intracellular pH (pHi) were measured by 31P-NMR spectroscopy. Results RPC and MPC delayed the decreases in ATP levels, preserved pHi during 40-min myocardial ischemia and resulted in better recovery of ATP and PCr during 120 min reperfusion compared with the controls. SPT abolished the improvement in myocardial energy metabolism and the reduction in myocardial IS caused by MPC or RPC. Myocardial IS in the CNT (n = 8), MPC (n = 9), RPC (n = 9), SPT (n = 6), MPC + SPT (n = 8) and RPC + SPT (n = 8) groups averaged 42.8 ± 3.5%, 18.2 ± 1.8%∗, 19.6 ± 1.3%∗, 44.9 ± 5.0%, 35.6 ± 2.7% and 34.8 ± 3.6% of the area at risk (∗p < 0.05 vs. CNT), respectively. Conclusions PC in a remote organ, similar to MPC, improved myocardial energy metabolism during ischemia and reperfusion and reduced IS in vivo by an adenosine-dependent mechanism in rabbits.

BACKGROUND The metabolic changes of myocardial ischemia in patients with coronary artery disease assessed by 31P magnetic resonance spectroscopy (MRS) have been reported previously. A significant decrease in the ratio of phosphocreatine (PCr) to ATP during handgrip exercise in a group of patients with severe coronary artery disease has been demonstrated. However, there are no reports at present that directly compare cardiac 31P MRS data with exercise 201Tl myocardial scintigraphy, now established as one of the most important clinical methods to assess myocardial ischemia. The purpose of this study was to investigate whether 31P MRS with handgrip exercise testing is able to detect myocardial ischemia, demonstrated by exercise 201Tl scintigraphy. METHODS AND RESULTS Twenty-seven patients with severe stenosis of the left anterior descending coronary artery (> or = 75%) and 11 normal control subjects composed the present study. Patients were divided into two groups on the basis of exercise 201Tl scintigraphy: a reversible 201Tl defect group (RD[+]) who demonstrated redistribution at the late image and a fixed 201Tl defect group (RD[-]). While lying supine within the magnet, subjects performed handgrip exercise at 30% of maximal force once in every two cardiac cycles. 31P MR spectra were collected before and during handgrip exercise. Data were corrected for the saturation factor. ANOVA revealed significant differences among the three groups with respect to the mean +/- SD PCr/ATP ratio at rest (control, 1.85 +/- 0.28 > RD(+), 1.60 +/- 0.19 > RD(-), 1.24 +/- 0.30; P < .05). The PCr/ATP ratio decreased significantly from 1.60 +/- 0.19 at rest to 0.96 +/- 0.28 during exercise (P < .001) in the RD(+) group (n = 15). However, in the RD(-) group (n = 12), the ratio did not change significantly during handgrip exercise (1.24 +/- 0.30 at rest versus 1.19 +/- 0.28 during exercise). Similarly, the ratio did not change in the control group (n = 11) (1.85 +/- 0.28 at rest versus 1.90 +/- 0.23 during exercise). CONCLUSIONS Contrary to normal subjects or patients with fixed thallium defects, the PCr/ATP ratio was significantly altered by exercise in patients with reversible thallium defects. These results suggest that 31P MRS with handgrip exercise testing is a sensitive method for detecting myocardial ischemia.

A deployable radiator (DPR) on the Engineering Test Satellite-VIII (ETS-VIII) was launched to a geo stationary orbit using H-IIA rocket on 18 Dec. 2006, carrying a deployable radiator (DPR). Deployable radiators are anticipated for extension of the radiator area on satellites to mitigate increased heat generation. The DPR is an experimental apparatus on the ETSVIII; it has a reservoir-embedded loop heat pipe (RELHP) for heat transfer from the experimental heat load to the DPR radiator. The RELHP reservoir is embedded in the evaporator for reliable function of the Loop Heat Pipe (LHP). This paper describes the DPR and the RELHP characteristics on the ETS-VIII from its launch operation to the beginning of the test under an orbital environment.

To assess quantitatively phosphocreatine (PCr), adenosine triphosphate (ATP) and total creatine (CR) in asynergic regions of ischemic human myocardium using phosphorus (31P) and proton magnetic resonance spectroscopy (1H MRS). Patients were divided into two groups: 31P MRS and 1H MRS. In each group, patients were subdivided into three groups using echocardiography: a normokinetic [WN(P) (n = 6) in 31P MRS, WN(H) (n = 10) in 1H MRS], a hypokinetic [WH(P) (n = 13), WH(H) (n = 7)], and a- or dyskinetic wall motion groups [WA(P) (n = 14), WA(H) (n =6)]. They were compared with normal subjects of each group [CNP (n = 10), CN(H) (n = 10)]. 31P MRS spectra were obtained from the anterior and apical regions of the left ventricle by slice-selected 1D CSI. 1H MRS spectra were obtained from the 2 x 2 x 2-cm voxel set in the left ventricular wall by the PRESS method. In the 31P MRS group, myocardial PCr was significantly lower in the WH(P) and WA(P) groups than in the CN(P) group, but myocardial PCr in the WN(P) group did not differ from that in CN(P). A difference in ATP could not be detected among the four groups. In the 1H MRS group, myocardial CR was significantly lower in the WH(H) and WA(H) groups than in the CN(H) group. Myocardial CR in the WNH group did not differ from that in the CN(H). The noninvasive 31P and 1H MRS approach is useful in the assessment of metabolite reduction associated with wall motion abnormality.

Background 31 P metabolite measurements in the human heart by magnetic resonance spectroscopy (MRS) have been reported previously. By use of a method in which metabolite content was quantified with reference to a standard located outside the chest, it has become possible to measure the content of phosphocreatine (PCr) and ATP in vivo in the human heart. In this study, PCr and ATP contents were measured by 31 P MRS and compared in human myocardium with reversible ischemia or scar diagnosed by exercise thallium scintigraphy. Methods and Results Forty-one subjects with stenosis of the left anterior descending coronary artery (>50%) and 11 healthy control subjects (C) composed the present study group. Patients were divided into two groups on the basis of exercise 201 Tl scintigraphy: a reversible 201 Tl defect group (RD[+], n=29) who demonstrated redistribution at late image and a fixed 201 Tl defect group (RD[−], n=12). While the subjects lay supine within the magnet, 31 P MR spectra were obtained from the anterior and apical regions of the left ventricle by slice-selected one-dimensional chemical shift imaging. For metabolite quantification, a standard was placed at the center of the surface coil. ANOVA revealed significant differences among the three groups with respect to the mean (±SD) PCr at rest (C, 12.14±4.25 >RD[+], 7.64±3.00 >RD[−], 3.94±2.21 μmol/g wet heart tissue, P RD[−], 4.35±1.52 μmol/g wet heart tissue, P Conclusions Compared with healthy control subjects, PCr content decreased significantly in patients with both reversible and fixed 201 Tl defects, and ATP content decreased significantly in subjects with fixed thallium defects. These results suggest that the measurement of ATP content in the human heart by 31 P MRS is a clinically important method for the evaluation of myocardial viability.

We measured and compared left ventricular mass in 20 patients by ultrafast computed tomography (UFCT), magnetic resonance imaging (MRI), and contrast left ventriculography (LVG). Left ventricular mass was calculated by UFCT and MRI in two ways: (1) excluding papillary muscles and trabeculae (LV mass), and (2) including papillary muscles and trabeculae (LV mass + PM&T) by Simpson's method. Left ventricular mass excluding papillary muscles and trabeculae (LV mass) in LVG was calculated by Rackley's method by biplane angiocardiography. LV mass was significantly larger in LVG than in MRI and UFCT (p < 0.01). Although LV mass was significantly larger in MRI than in UFCT (p < 0.01), there was no significant difference in LV mass + PM&T between UFCT and MRI. Interobserver and intraobserver variability showed good correlation of coefficient in both UFCT and MRI. We therefore conclude that left ventricular mass is best measured by including papillary muscles and trabeculae by Simpson's method in UFCT or MRI.