Quantitative Imaging in the Thorax (Hardcover)

This book offers a comprehensive review of quantitative imaging, from extraction of features to creating AI and machine specific algorithms. Quantitative imaging is the extraction of quantifiable features from images for the assessment of normality, severity, and degree of change. It includes the development, standardization, and optimization of protocols, data analysis, and display/reporting methods to allow for validation of image derived metrics. The radiology report is comprised of both qualitative and quantitative data. The former is narrative, detail oriented, easier for patients and other providers to understand, but difficult to summarize and hard to relate to outcome measures. Quantitative imaging on the other hand is objective and can be used in complex statistical calculations, but cannot really stand alone and needs stringent quality measures in order to preserve accuracy and reproducibility. With an increasing focus on AI and machine learning and the need to build patientand disease specific algorithms, this book offers a common ground for physicians, radiologists and data scientists to reference and further advance the field.

 

The main purpose of this book is to provide details of quantitative imaging from extraction of data from CT and MR images and incorporation into models for clinical translation and application. Individual chapters feature experts from around the world and covers all aspects of quantitative imaging in the thorax, from screening to diagnosis and management. This book also features chapters dedicated to radiomics and radiogenomics.

 

This is an ideal guide for radiologists, physicians, and data scientists working towards a common ground in quantitative imaging.

Ritu R. Gill, MD, MPH, is Chief of Cardiothoracic Radiology and  Professor of Radiology at Columbia University Medical Center, NY.  She is a subspecialty-trained radiologist with expertise in Cardiothoracic Imaging and Oncoradiology and holds a Master’s Degree in Public Health with a concentration in Clinical Effectiveness.

Dr. Gill’s scholarly work centers on the integration of quantitative imaging metrics with clinical and pathological parameters to develop prognostic models for thoracic malignancies. Her research seeks to advance the role of imaging as a biomarker in precision oncology, with the goal of improving disease characterization, treatment selection, and survival outcomes in patients with lung and other chest cancers.

In addition to her research and clinical practice, Dr. Gill is actively engaged in academic leadership, education, and editorial work. She has contributed to numerous peer-reviewed publications, book chapters, and educational initiatives in radiology and oncology. As a book editor, Dr. Gill brings a multidisciplinary perspective that bridges imaging science, clinical medicine, and public health, fostering collaborations that drive innovation and evidence-based care in thoracic imaging and oncologic radiology.
 

Prabhakar Rajiah, MBBS, MD, FRCR, FACR, FACC, FAHA, FSCCT, FSCMR, FNASCI, FSABI, is a Professor of Radiology and Consultant Cardiovascular & Thoracic Radiologist at the Mayo Clinic in Rochester, Minnesota. His clinical and academic work centers on structural heart disease, multienergy and photon-counting CT, quantitative imaging, and advanced cardiovascular CT and MRI techniques.

Dr. Rajiah is an internationally recognized authority in his field, with a scholarly record that includes more than 250 peer-reviewed publications, 75 book chapters, and editorial leadership of five textbooks and special journal issues. He serves as Deputy Editor of the Journal of Thoracic Imaging and as Associate Editor for Radiology, Radiology: Cardiothoracic Imaging, and RadioGraphics.

His contributions to cardiovascular imaging and radiologic education have been recognized with numerous distinctions, including the RSNA Research Trainee Prize, the Roentgen Radiology Fellow Research Award, multiple RSNA Honored Educator Awards, and the RSNA Lifetime Honored Educator Award.