Sons, 1995. $29.95; pp 149: paperback As stated in the Preface to the first edition of MRI: Basic Principles and Applications, Drs. Brown and Semelka seek to present thr basic concepts ol MRI in a fashion that is comprehensible to a wide range of readers. The authors have successfully met this goal by combining a mixture of well-written text, supporting images, and a few equations where necessary to support the fundamental concepts of MFX A highlight is the use of images to support many fundamental concepts. This frequently allows the authors to avoid the use of unnecessary depth in physics, which would be inappropriate for clinical scientists.The book is organized inLo five sections: basic NMR/MIU physics, spatial localization and imaging techniques, more elaborate imaging techniques and contrast mechanisms, hardware, and finally contrast agents. Each section is further subdillded into a small number of chapters that facilitate the dissection of the complexities into manageable pieces. The chapters are, in general, succinct and present the material in an approachable manner appropriate to those with a limited mathematical background. The authors are to be commended on meeting their goal of simplifylng a number of difficult topics and reducing them to this largely nonmathematical foundation.The book's coverage of pulse sequences begins with standard spin-echo, inversion-recovery, and gradient-echo methods, and leads eventually to magnctization preparation and echo-planar imaging. Methods such as spatial presaturation, magnetization transfer, and fat suppression are dealt with as separate topics and described in the context of Additional Measurement Techniques. The separation of these as supplemental methods to be combined with traditional techniques is both appropriate and well executed. In the case of magnetization transfer, however, only a minor description is employed and no images in support of the concepts are provided, which is unfortunate considering MTs applicability in MRA and post-contrast-agent imaging.The book, in its entirety, is comprehensive at a superficial level and moves rapidly from topic to topic. Within an easy three evening reading, it provides a full-gamut description of MRI. While the text provides essentially nothing new, and duplicates material that can be found elsewhere, it does so in a concise manner. This condensation provides a single source for easy recall, which wdl make it a useful addition to many. However, a detailed reference list within each chapter would be useful in directing those interested to more complete material on the topics presented.While the book has several strengths, a few minor criticisms are appropriate. In the basic physics section, the Boltzman distribution is presented and summarized as an excess of 1:106 protons in the lower energy state. Unfortunately, this is true only at a single, unstated field strength, temperature [assumed body temperature), and volume, and hereby prevents the authors from demonstrating its vanability, for example, with field st...

MR imaging is an excellent modality for diagnosis of acute appendicitis and exclusion of diseases requiring surgical/interventional treatment. Therefore MR imaging is useful for triage of pregnant patients with acute abdominal and pelvic pain.

The gradual upward displacement of the appendix during pregnancy was confirmed. MRI can be used for determination of the appendix localization in pregnant patients. Further studies with a larger number of patients will be helpful to answer this clinically relevant question.

Women with mostly mammographically dense fibroglandular tissue (breast density, BD) have a 4- to 6-fold increased risk for breast cancer compared to women with little BD. BD is most frequently estimated from 2-dimensional (2-D) views of mammograms by a histogram segmentation approach (HSM) and more recently by a mathematical algorithm consisting of mammographic imaging parameters (MATH). Two non-invasive clinical magnetic resonance imaging (MRI) protocols: 3-D gradient-echo (3DGRE) and short tau inversion recovery (STIR) were modified for 3-D volumetric reconstruction of the breast for measuring fatty and fibroglandular tissue volumes by a Gaussian-distribution curve-fitting algorithm. Replicate breast exams (N= 2 to 7 replicates in 6 women) by 3DGRE and STIR were highly reproducible for all tissue-volume estimates (coefficients of variation <5%). Reliability studies compared measurements from four methods, 3DGRE, STIR, HSM, and MATH (N=95 women) by linear regression and intra-class correlation (ICC) analyses. Rsqr, regression slopes, and ICC, respectively, were (I) 0.76–0.86, 0.8–1.1, and 0.87–0.92 for %-gland tissue, (II) 0.72–0.82, 0.64–0.96, and 0.77–0.91, for glandular volume, (III) 0.87–0.98, 0.94–1.07, and 0.89–0.99, for fat volume, and (IV) 0.89–0.98, 0.94–1.00, and 0.89–0.98, for total breast volume. For all values estimated, the correlation was stronger for comparisons between the two MRI than between each MRI vs. mammography, and between each MRI vs. MATH data than between each MRI vs. HSM data. All ICC values were >0.75 indicating that all four methods were reliable for measuring BD and that the mathematical algorithm and the two complimentary non-invasive MRI protocols could objectively and reliably estimate different types of breast tissues.

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor-intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R(2) = 0.93) and %-density (R(2) = 0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies.