Dynamic Chemical Shift Imaging for Image-Guided Thermal Therapy

Magnetic resonance temperature imaging (MRTI) is recognized as a noninvasive means to provide temperature imaging for guidance in thermal therapies. The most common method of estimating temperature changes in the body using MR is by measuring the water proton resonant frequency (PRF) shift. Calculation of the complex phase difference (CPD) is the method of choice for measuring the PRF indirectly since it facilitates temperature mapping with high spatiotemporal resolution. Chemical shift imaging (CSI) techniques can provide the PRF directly with high sensitivity to temperature changes while minimizing artifacts commonly seen in CPD techniques. However, CSI techniques are currently limited by poor spatiotemporal resolution. This research intends to develop and validate a CSI-based MRTI technique with intentional spectral undersampling which allows relaxed parameters to improve spatiotemporal resolution. An algorithm based on autoregressive moving average (ARMA) modeling is developed and validated to help overcome limitations of Fourier-based analysis allowing highly accurate and precise PRF estimates. From the determined acquisition parameters and ARMA modeling, robust maps of temperature using the k-means algorithm are generated and validated in laser treatments in ex vivo tissue. The use of non-PRF based measurements provided by the technique is also investigated to aid in the validation of thermal damage predicted by an Arrhenius rate dose model.

THE MULTIPLE LOCATION TIME WEIGHTED INDEX: USING PATIENT ACTIVITY SPACES TO CALCULATE PRIMARY CARE SERVICE AREAS

Geographic health planning analyses, such as service area calculations, are hampered by a lack of patient-specific geographic data. Using the limited patient address information in patient management systems, planners analyze patient origin based on home address. But activity space research done sparingly in public health and extensively in non-health related arenas uses multiple addresses per person when analyzing accessibility. Also, health care access research has shown that there are many non-geographic factors that influence choice of provider. Most planning methods, however, overlook non-geographic factors influencing choice of provider, and the limited data mean the analyses can only be related to home address. This research attempted to determine to what extent geography plays a part in patient choice of provider and to determine if activity space data can be used to calculate service areas for primary care providers. During Spring 2008, a convenience sample of 384 patients of a locally-funded Community Health Center in Houston, Texas, completed a survey that asked about what factors are important when he or she selects a health care provider. A subset of this group (336) also completed an activity space log that captured location and time data on the places where the patient regularly goes. Survey results indicate that for this patient population, geography plays a role in their choice of health care provider, but it is not the most important reason for choosing a provider. Other factors for choosing a health care provider such as the provider offering “free or low cost visits”, meeting “all of the patient’s health care needs”, and seeing “the patient quickly” were all ranked higher than geographic reasons. Analysis of the patient activity locations shows that activity spaces can be used to create service areas for a single primary care provider. Weighted activity-space-based service areas have the potential to include more patients in the service area since more than one location per patient is used. Further analysis of the logs shows that a reduced set of locations by time and type could be used for this methodology, facilitating ongoing data collection for activity-space-based planning efforts.

A biophysical model of synaptic plasticity and metaplasticity can account for the dynamics of the backward shift of hippocampal place fields.

Hippocampal place cells in the rat undergo experience-dependent changes when the rat runs stereotyped routes. One such change, the backward shift of the place field center of mass, has been linked by previous modeling efforts to spike-timing-dependent plasticity (STDP). However, these models did not account for the termination of the place field shift and they were based on an abstract implementation of STDP that ignores many of the features found in cortical plasticity. Here, instead of the abstract STDP model, we use a calcium-dependent plasticity (CaDP) learning rule that can account for many of the observed properties of cortical plasticity. We use the CaDP learning rule in combination with a model of metaplasticity to simulate place field dynamics. Without any major changes to the parameters of the original model, the present simulations account both for the initial rapid place field shift and for the subsequent slowing down of this shift. These results suggest that the CaDP model captures the essence of a general cortical mechanism of synaptic plasticity, which may underlie numerous forms of synaptic plasticity observed both in vivo and in vitro.

DUAL ENERGY COMPUTED TOMOGRAPHY FOR PROTON THERAPY TREATMENT PLANNING

Proton radiation therapy is gaining popularity because of the unique characteristics of its dose distribution, e.g., high dose-gradient at the distal end of the percentage-depth-dose curve (known as the Bragg peak). The high dose-gradient offers the possibility of delivering high dose to the target while still sparing critical organs distal to the target. However, the high dose-gradient is a double-edged sword: a small shift of the highly conformal high-dose area can cause the target to be substantially under-dosed or the critical organs to be substantially over-dosed. Because of that, large margins are required in treatment planning to ensure adequate dose coverage of the target, which prevents us from realizing the full potential of proton beams. Therefore, it is critical to reduce uncertainties in the proton radiation therapy. One major uncertainty in a proton treatment is the range uncertainty related to the estimation of proton stopping power ratio (SPR) distribution inside a patient. The SPR distribution inside a patient is required to account for tissue heterogeneities when calculating dose distribution inside the patient. In current clinical practice, the SPR distribution inside a patient is estimated from the patient’s treatment planning computed tomography (CT) images based on the CT number-to-SPR calibration curve. The SPR derived from a single CT number carries large uncertainties in the presence of human tissue composition variations, which is the major drawback of the current SPR estimation method. We propose to solve this problem by using dual energy CT (DECT) and hypothesize that the range uncertainty can be reduced by a factor of two from currently used value of 3.5%. A MATLAB program was developed to calculate the electron density ratio (EDR) and effective atomic number (EAN) from two CT measurements of the same object. An empirical relationship was discovered between mean excitation energies and EANs existing in human body tissues. With the MATLAB program and the empirical relationship, a DECT-based method was successfully developed to derive SPRs for human body tissues (the DECT method). The DECT method is more robust against the uncertainties in human tissues compositions than the current single-CT-based method, because the DECT method incorporated both density and elemental composition information in the SPR estimation. Furthermore, we studied practical limitations of the DECT method. We found that the accuracy of the DECT method using conventional kV-kV x-ray pair is susceptible to CT number variations, which compromises the theoretical advantage of the DECT method. Our solution to this problem is to use a different x-ray pair for the DECT. The accuracy of the DECT method using different combinations of x-ray energies, i.e., the kV-kV, kV-MV and MV-MV pair, was compared using the measured imaging uncertainties for each case. The kV-MV DECT was found to be the most robust against CT number variations. In addition, we studied how uncertainties propagate through the DECT calculation, and found general principles of selecting x-ray pairs for the DECT method to minimize its sensitivity to CT number variations. The uncertainties in SPRs estimated using the kV-MV DECT were analyzed further and compared to those using the stoichiometric method. The uncertainties in SPR estimation can be divided into five categories according to their origins: the inherent uncertainty, the DECT modeling uncertainty, the CT imaging uncertainty, the uncertainty in the mean excitation energy, and SPR variation with proton energy. Additionally, human body tissues were divided into three tissue groups – low density (lung) tissues, soft tissues and bone tissues. The uncertainties were estimated separately because their uncertainties were different under each condition. An estimate of the composite range uncertainty (2s) was determined for three tumor sites – prostate, lung, and head-and-neck, by combining the uncertainty estimates of all three tissue groups, weighted by their proportions along typical beam path for each treatment site. In conclusion, the DECT method holds theoretical advantages in estimating SPRs for human tissues over the current single-CT-based method. Using existing imaging techniques, the kV-MV DECT approach was capable of reducing the range uncertainty from the currently used value of 3.5% to 1.9%-2.3%, but it is short to reach our original goal of reducing the range uncertainty by a factor of two. The dominant source of uncertainties in the kV-MV DECT was the uncertainties in CT imaging, especially in MV CT imaging. Further reduction in beam hardening effect, the impact of scatter, out-of-field object etc. would reduce the Hounsfeld Unit variations in CT imaging. The kV-MV DECT still has the potential to reduce the range uncertainty further.

Survey of shift and work tolerance of City of Houston emergency medical service personnel

This study of ambulance workers for the emergency medical services of the City of Houston studied the factors related to shiftwork tolerance and intolerance. The EMS personnel work a 24-hour shift with rotating days of the week. Workers are assigned to A, B, C, D shift, each of which rotate 24-hours on, 24-hours off, 24-hours on and 4 days off. One-hundred and seventy-six male EMTs, paramedics and chauffeurs from stations of varying levels of activity were surveyed. The sample group ranged in age from 20 to 45. The average tenure on the job was 8.2 years. Over 68% of the workers held a second job, the majority of which worked over 20 hours a week at the second position.^ The survey instrument was a 20-page questionnaire modeled after the Folkard Standardized Shiftwork Index. In addition to demographic data, the survey tool provided measurements of general job satisfaction, sleep quality, general health complaints, morningness/eveningness, cognitive and somatic anxiety, depression, and circadian types. The survey questionnaire included an EMS-specific scaler of stress.^ A conceptual model of Shiftwork Tolerance was presented to identify the key factors examined in the study. An extensive list of 265 variables was reduced to 36 key variables that related to: (1) shift schedule and demographic/lifestyle factors, (2) individual differences related to traits and characteristics, and (3) tolerance/intolerance effects. Using the general job satisfaction scaler as the key measurement of shift tolerance/intolerance, it was shown that a significant relationship existed between this dependent variable and stress, number of years working a 24-hour shift, sleep quality, languidness/vigorousness. The usual amount of sleep received during the shift, general health complaints and flexibility/rigidity (R$\sp2$ =.5073).^ The sample consisted of a majority of morningness-types or extreme-morningness types, few evening-types and no extreme-evening types, duplicating the findings of Motohashi's previous study of ambulance workers. The level of activity by station was not significant on any of the dependent variables examined. However, the shift worked had a relationship with sleep quality, despite the fact that all shifts work the same hours and participate in the same rotation schedule. ^

Neuromuscular disuse and atrophy: {\it In vivo\/} magnetic resonance studies of intramuscular lipid metabolism and fluid shift changes

We postulated that neuromuscular disuse results in deleteriously affected tissue-vascular fluid exchange processes and subsequently damages the important oxidative bioenergetic process of intramuscular lipid metabolism. The in-depth research reported in the literature is somewhat limited by the ex vivo nature and sporadic time-course characterization of disuse atrophy and recovery. Thus, an in vivo controlled, localized animal model of disuse atrophy was developed in one of the hindlimbs of laboratory rabbits (employing surgically implanted tetrodotoxin (TTX)-filled mini-osmotic pump-sciatic nerve superfusion system) and tested repeatedly with magnetic resonance (MR) throughout the 2-week period of temporarily induced disuse and during the recovery period (following explantation of the TTX-filled pump) for a period of 3 weeks. Controls consisted of saline/"sham"-implanted rabbit hindlimbs. The validity of this model was established with repeated electrophysiologic nerve conduction testing using a clinically appropriate protocol and percutaneously inserted small needle stimulating and recording electrodes. Evoked responses recorded from proximal (P) and distal (D) sites to the sciatic nerve cuff in the TTX-implanted group revealed significantly decreased (p $<$ 0.001) proximal-to-distal (P/D) amplitude ratios (as much as 50-70% below Baseline/pre-implanted and sham-implanted group values) and significantly increased (p $<$ 0.01) differential latency (PL-DL) values (as much as 1.5 times the pre- and sham-implanted groups). By Day 21 of recovery, observed P/D and PL-DL levels matched Baseline/sham-implemented levels. MRI-determined cross-sectional area (CSA) values of Baseline/pre-implanted, sham- or TTX-implanted, and recovering/explanted and the corresponding contralateral hindlimb tibialis anterior (TA) muscles normalized to tibial bone (TB) CSA (in TA/TB ratios) revealed that there was a significant decline (indicative of atrophic response) from pre- and sham-implanted controls by as much as 20% (p $<$ 0.01) at Day 7 and 50-55% (p $<$ 0.001) at Day 13 of TTX-implantation. In the non-implanted contralaterals, a significant increase (indicative of hypertrophic response) by as much as 10% (p $<$ 0.025) at Day 7 and 27% (p $<$ 0.001) at Day 13 + TTX was found. The induced atrophic/hypertrophic TA muscles were observed to be fully recovered by Day 21 post-explantation as evidenced by image TA/TB ratios. End-point biopsy results from a small group of rabbits revealed comprehensive atrophy of both Type I and Type II fibers, although the heterogeneity of the response supports the use of image-guided, volume-localized proton magnetic resonance spectroscopy (MRS) to noninvasively assess tissue-level metabolic changes. MRS-determined results of a 0.25cc volume of tissue within implanted limb TA muscles under resting/pre-ischemic, ischemic-stressed, and post-ischemic conditions at timepoints during and following disuse atrophy/recovery revealed significantly increased intramuscular spectral lipid levels, as much as 2-3 times (p $<$ 0.01) the Baseline/pre-implanted values at Day 7 and 6-7 times (p $<$ 0.001) at Day 13 + TTX, which approached normal levels (compared to pre- and sham-implanted groups) by Day 21 of post-explanation recovery. (Abstract shortened by UMI.) ^

The effect of the shift from HMO to PPO plans on the prevalence of hospital bad debt in the insured population: A case study in two Houston-area hospitals

In spite of the dramatic increase and general concern with U.S. hospital bad debt expense (AMNews, January 12, 2004; Philadelphia Business Journal, April 30, 2004; WSJ, July 23, 2004), there appears to be little available analysis of the precise sources and causes of its growth. This is particularly true in terms of the potential contribution of insured patients to bad debt expense in light of the recent shift in managed care from health maintenance organization (HMO) plans to preferred provider organization (PPO) plans (Kaiser Annual Survey Report, 2003). This study examines and attempts to explain the recent dramatic growth in bad debt expense by focusing on and analyzing data from two Houston-area hospital providers within one healthcare system. In contrast to prior studies in which self-pay was found to be the primary source of hospital bad debt expense (Saywell, R. M., et al., 1989; Zollinger, T. W., 1991; Weissman, Joel S., et al., 1999), this study hypothesizes that the growing hospital bad debt expense is mainly due to the shifting trend away from HMOs to PPOs as a conscious decision by employers to share costs with employees. Compared to HMO plans, the structure of PPOs includes higher co-pays, coinsurance, and deductibles for the patient-pay portion of medical bills, creating the potential for an increase in bad debt for hospital providers (from a case study). This bad debt expense has a greater impact in the community hospital than in the Texas Medical Center hospital. ^

The multiple location time weighted index: Using patient activity spaces to calculate primary care service areas

Geographic health planning analyses, such as service area calculations, are hampered by a lack of patient-specific geographic data. Using the limited patient address information in patient management systems, planners analyze patient origin based on home address. But activity space research done sparingly in public health and extensively in non-health related arenas uses multiple addresses per person when analyzing accessibility. Also, health care access research has shown that there are many non-geographic factors that influence choice of provider. Most planning methods, however, overlook non-geographic factors influencing choice of provider, and the limited data mean the analyses can only be related to home address. This research attempted to determine to what extent geography plays a part in patient choice of provider and to determine if activity space data can be used to calculate service areas for primary care providers. ^ During Spring 2008, a convenience sample of 384 patients of a locally-funded Community Health Center in Houston, Texas, completed a survey that asked about what factors are important when he or she selects a health care provider. A subset of this group (336) also completed an activity space log that captured location and time data on the places where the patient regularly goes. ^ Survey results indicate that for this patient population, geography plays a role in their choice of health care provider, but it is not the most important reason for choosing a provider. Other factors for choosing a health care provider such as the provider offering "free or low cost visits", meeting "all of the patient's health care needs", and seeing "the patient quickly" were all ranked higher than geographic reasons. ^ Analysis of the patient activity locations shows that activity spaces can be used to create service areas for a single primary care provider. Weighted activity-space-based service areas have the potential to include more patients in the service area since more than one location per patient is used. Further analysis of the logs shows that a reduced set of locations by time and type could be used for this methodology, facilitating ongoing data collection for activity-space-based planning efforts. ^