William R. Hendee

Selection of pulse sequences producing maximum tissue contrast in magnetic resonance imaging

The importance of spin density [N(H)] and spin-lattice (T1) and spin-spin (T2) relaxation in the characterization of tissue by nuclear magnetic resonance (NMR) is clearly recognized. This work considers which optimized pulse sequences provide the best tissue discrimination between a given pair of tissues. The effects of tissue spin density and machine-imposed minimum rephasing echo times (TEMIN) for achieving maximum signal tissue contrast are discussed. A long TEMIN sacrifices T1-dependent contrast in saturation recovery (SR) and inversion recovery (IR) pulse sequences so that spin-echo (SE) becomes the optimum sequence to provide tissue contrast, due to T2 relaxation. Pulse sequences providing superior performance may be selected based on spin density and T1 and T2 ratios for a given pair of tissues. Selection of the preferred pulse sequence and interpulse delay times to produce maximum tissue contrast is strongly dependent on knowledge of tissue spin densities as well as T1 and T2 characteristics. As the spin density ratio increases, IR replaces SR as the preferred sequence and SE replaces IR and SR as the pulse sequence providing superior contrast. To select the optimal pulse sequence and interpulse delay times, an accurate knowledge of tissue spin density, T1 and T2 must be known for each tissue.

Improvement of Lesion Detection in Scintigraphic Images by SVD Techniques for Resolution Recovery

The properties of singular value decomposition (SVD) are used to implement an SVD spatial domain pseudoinverse restoration filter. This type of filter is attractive for poor imaging conditions (low spatial resolution, high image noise) and is thus appealing for nuclear medicine images. The method might offer some advantages over more traditional frequency domain filter techniques since the restoration is performed on a local rather than global basis. High-contrast thyroid phantom images collected at different count densities and low-contrast liver phantom images were processed with the SVD filter. Restored images yielded improved spatial resolution, lesion contrast, and signal-to-noise ratio. The SVD pseudoinverse restoration filter implemented as an interactive process permits the operator to terminate filtering at a stage where the visually best image is obtained compared to the original data. Processed images suggest that the technique may have potential for improving lesion detection in nuclear medicine.

Phase detection and contrast loss in magnetic resonance imaging

Several recent articles have assessed the relative efficiency of nuclear magnetic resonance (NMR) pulse sequences. One consideration that has received little attention is the effect on image contrast of displaying images without information on the sign of the reconstructed signals. The radiofrequency receivers currently used on most NMR imaging systems are quadrature detectors that preserve both the magnitude and sign of the NMR signal. Usually, however, sign or phase information is not used in the final image presentation. We point out that in imaging sequences that may have negative signals, such as inversion recovery, this loss of sign information produces a reduction in contrast between some tissues in an NMR image. We discuss the tissue parameters and interpulse delay times that result in contrast loss in inversion recovery and indicate the extent of contrast loss. We point out that for some tissues with unequal hydrogen spin densities, the region of contrast loss coincides with the region where maximum contrast would occur if sign information were preserved.

Cross-Sectional Anatomic Images by Gamma Ray Transmission Scanning

60Co gamma-ray transmission data were measured along linear scan paths at a number of angular orientations with respect to the patient and submitted to a computer software program. Reconstructed images are displayed as digital density printouts and as isodensity contours on an x-y plotter or oscilloscope screen. Image resolution is limited primarily by factors such as collimation and the amount of transmission data collected; with the rather rudimentary apparatus at disposal, a spatial resolution better than 5 mm has been achieved.

Computerized patient contours using the scanning arm of a compound B‐scanner

Full utilization of the precision of newer radiation therapy devices requires patient contours drawn with greater accuracy than is possible with the conventional lead wire technique. Polaroid photographs can introduce large errors due to distortion and small image size. Techniques including electromechanical or optical devices and CT scans offer improved accuracy, but often at added expense. A method for obtaining contours has been developed which utilizes a treatment planning minicomputer (equipped with an analog-to-digital converter and plotter) and a commercially available ultrasound B-scanning arm. Voltages corresponding to the X-Y position of the tip of the scanning arm are fed from the scanner to the A/D interface, smoothed, scaled, and plotted. The resulting drawing is a full scale external patient contour. The accuracy of this method is compared to alternative techniques.

The University of Colorado Radiology Adult Dose-Risk Smartcard

The Department of Radiology at the University of Colorado School of Medicine in Denver developed the Adult Dose-Risk Smartcard shown in Figure 1 to provide a convenient, pocket-sized reference card communicating the effective doses and radiation risks of common adult radiologic examinations. The smartcard was distributed to University of Colorado radiologists, referring physicians, medical physicists, and attendees at the recent 1-day Colorado Radiation Safety Symposium: Risk and Dose Optimization in Radiology. The card is intended to facilitate radiation risk consultations and improve patient satisfaction by simplifying essential facts on radiation dose and risk to a level understandable by referring physicians and their patients. This allows patients to make more informed decisions about the relative risks of radiologic examinations compared with the medical risk caused by refusing a recommended imaging procedure. Estimates of effective doses for most adult procedures come from the published literature, many from the article “Effective Doses in Radiology and Nuclear Medicine: a Catalog” by Mettler et al [1]. Mammographic dose (and risk) estimates come from recent articles by Hendrick et al [2,3]. Most estimates of cancer risk from the low-dose radiation exposures in the smartcard are based on the latest report from the International Commission on Radiological Protection [4], whose radiation risk stimates are age averaged (for dults aged 18-65 years) and gender averaged, yielding an overall risk for fatal cancer induction of 4.1% (a 0.041 probability factor) per sievert or 0.0041% (a 0.000041 probability factor) per millisievert. Age-dependent estimates of mammographic risks specific to female patients are based on the National Academy of Sciences Biological Effects of Ionizing Radiation report [3,5]. Both International Commission on Radiological Protection [4] and Biological Effects of Ionizing Radiation [5] risk estimates for solid tumors assume a linear no-threshold relationship between radiation dose and cancer risk to extrapolate the high-dose, high-linear energy transfer exposures (eg, to atomic bomb survivors, in whom subsequent radiation-induced cancers have been documented) to the low-dose, low-linear energy transfer exposures from diagnostic radiology examinations, for which no direct cancer-causing effect has been documented in humans. There is good evidence from studies of atomic bomb survivors that organ doses 100 mSv result in a small, but statistically significant, increase in cancer risk. The risks stated on the University of Colorado smartcard assume a linear no-threshold model to extrapolate the dose-risk relationship down to the low doses used in diagnostic examinations. These risk estimates are conservative in terms of protecting patients and may overestimate rather than underestimate radiation risk from medical examinations. To put doses and risks in perspective, the smartcard permits the

Computational analysis and dosimetric evaluation of a commercial irregular‐fields computer program

The proper evaluation of the accuracy of a new computer program for radiation-therapy dosimetry requires consideration of both the mathematical algorithm used in the program and the performance required in clinical applications. As an example, our evaluation of the irregular-fields dosimetry program currently marketed by SHM for their Rad-8 system is described. The evaluation begins with an explanation of the mathematical computation described. The evaluation begins with an explanation of the mathematical computation method, with emphasis on the points where the calculation differs from previous methods. Next, the procedure for setting up the beam data file is discussed. Finally, a step-by-step procedure is described in which calculated doses are compared with measured doses, using a Varian Clinac-4 with lead flattening filter, and the limits within which a +/- 5% accuracy is attainable are estimated. Some sources of error and areas for possible improvements are mentioned.

Ultraviolet-induced changes in residual thermoluminescence from gamma-irradiated lithium fluoride

Exposing y-irradiated LiF (TLD-100) to ultraviolet light after the dosimetric glow peak at 2 10°C has been recorded enhances the residual thermoluminescence measured during a second readout. The residual thermoluminescence exhibits a narrow peak centered at 200OC and a broad peak beginning at about 23OoC. The area under the narrow peak increases with both the radiation dose and the exposure to ultraviolet. Possibly, LiF measurements of high doses of radiation could be confirmed by measuring residual thermoluminescence after

Experimentation: An Introduction to Measurement Theory and Experiment Design

1. Approach to Laboratory Work. 2. Measurement and Uncertainty. 3. Statistics of Observation. 4. Scientific Thinking and Experimenting. 5. Experiment Design. 6. Experiment Evaluation. 7. Writing Scientific Reports. Appendix 1. Mathematical Properties of the Gaussian or Normal Distribution. Appendix 2. The Principle of Least Squares. Appendix 3. Difference Tables an the Calculus of Finite Differences. Appendix 4. Specimen Experiment. Bibliography. Answers to Problems. Index.

The impact of future technology on oncologic diagnosis: Oncologic imaging and diagnosis

Over the past few years, the discipline of medical imaging has entered an evolutionary period that reflects primarily the introduction of computers and digital technology into the imaging process. Clinical applications of this evolution realized to date (e.g., transmission computed tomography, ultrasound and quantitative nuclear medicine) are only indicative of future developments that promise to increase the contributions of medical imaging in a very substantial manner. This increase in the area of oncologic diagnosis is one of the more exciting possibilities existing in medicine today.