For decades, cross-sectional biomedical images have been generated from various modalities,
including computed tomography (CT), three-dimensional tomosynthesis, ultrasound, magnetic
resonance imaging (MRI), single photon emission computed tomography (SPECT), and positron
emission tomography (PET). Many advanced quantitative imaging methods have been developed,
such as perfusion MRI/CT, diffusion tensor/weighted MRI, functional MRI (fMRI), ultrasound/MR
elastography, dynamic PET, and dynamic contrast enhanced MRI. There is great variability
across imaging platforms, imaging techniques, postprocessing software, and imaging
readers. There is an unmet clinical need for improving the value and practicality
of quantitative biomedical imaging.
Systematic reviews of a quantitative biomedical imaging method will improve researchers'
understanding and skills in utilizing these methods. For example, arterial spin labeling
(ASL) is a noninvasive MRI modality capable of measuring blood perfusion without the
use of a contrast agent. E. Vaghefi and B. Pontré reviewed the technical aspects of
ASL and their implications for its optimum adaptation for retinal blood perfusion
monitoring, as well as ASL application in human ocular blood flow assessment.
Cross-system/site/vendor/platform/software/reader comparisons of a quantitative biomedical
imaging method are crucial. C. Brodén et al. compared 3D-CT to standard radiostereometric
analysis for measuring migration of acetabular cups in total hip arthroplasty. G.
J. Pelgrim et al. evaluated the capability of MRI and CT to perform myocardial perfusion
quantification, previously only achievable with PET.
Reproducibility and reliability assessments of quantitative biomedical imaging methods
are necessary steps. The current 2D tagging cardiac MRI technique requires multiple
breath holds to cover the whole heart and cannot show the 3D motion of the left ventricle.
Y. Amano et al. evaluated the feasibility of fast 3-breath-hold 3D tagging for the
assessment of the circumferential strain in patients with hypertrophic myocardial
diseases. A. R. Yu et al. investigated the optimal PET energy window for 124I PET
based on image characteristics of reconstructed PET. The energy window of 350~750 keV
was proposed as the optimal energy window, although 400~590 keV was obtained as the
highest noise equivalent count rate.
Pathophysiological validation and computer simulation are crucial to understanding
a quantitative biomedical imaging method. U. Klose et al. confirmed the strong dependence
of the whole brain apparent diffusion coefficient (ADC) MRI histograms on the age
of the examined subjects. The proposed model can be used to characterize changes of
the whole brain ADC histogram in certain diseases under consideration of age effects.
Z. Krajnc et al. quantitatively evaluated growth plates around the knees in adolescent
soccer players utilizing the diffusion-weighted MRI. Diffusion-weighted imaging measurements
indicate increased cellularity in growth plates around knees in football players most
prominent in proximal tibia medial region after intense training. D. Shimamoto et
al. evaluated whether the diagnostic performance of Gd-EOB-DTPA-enhanced MRI in evaluating
liver function and pathology is improved by considering liver volume. They found that
the inclusion of liver volume may improve Gd-EOB-DTPA-based predictions of liver function
but not predictions of liver pathology. F. F. Schröder et al. analyzed preoperative
CT images of patients who underwent pancreatoduodenectomy (PD) or pylorus preserving
PD and investigated predictors for postoperative pancreatic fistula and postoperative
severe complications.
Physical and virtual phantom for quality check/assurance will improve our knowledge
of a quantitative biomedical imaging method. Characterization of lesion formation
and restoration by imaging features is a novel field of research in multiple sclerosis.
R. K. Verma et al. investigated statistical differences with MR perfusion imaging
features that reflect the dynamics of Gadolinium uptake in multiple sclerosis lesions
using dynamic texture parameter analysis. Brain tissue segmentation in MRI is useful
for a wide range of applications. F. Baselice et al. proposed a brain joint segmentation
and classification algorithm based on proton density and relaxation times, instead
of the acquired gray level image.
Computer assisted analysis and diagnosis will facilitate the clinical adoption of
single or multiparametric/modality imaging methods. In order to accurately diagnose
acute appendicitis, K. B. Kim et al. proposed a method to extract the appendix automatically
by using a series of image processing and self-organizing maps that learn typical
shape patterns of the appendix from US. The analysis and interpretation of high-resolution
CT images of the chest in the presence of interstitial lung disease are a time-consuming
task which requires experience. V. Vasconcelos et al. proposed a computer-aided diagnosis
(CAD) scheme to assist radiologists in the differentiation of lung patterns associated
with interstitial lung disease and with healthy lung parenchyma. Indirect immunofluorescence
is the gold standard for the diagnosis of autoimmune diseases. A. B. Elgaaied et al.
introduced the AIDA Project (Autoimmunity: Diagnosis Assisted by Computer) developed
in the framework of an Italy-Tunisia, cross-border cooperation and its preliminary
results. J. Jeong et al. proposed a CAD algorithm with a simplified false-positive
reduction scheme for microcalcification clusters in reconstructed digital breast tomosynthesis
images. M. Larobina et al. investigated the feasibility of automatically training
supervised methods, such as k-nearest neighbor and principal component discriminant
analysis, to segment the four subcortical brain structures: caudate, thalamus, pallidum,
and putamen. Their results demonstrate that atlas-guided training is an effective
way to automatically define a representative and reliable training dataset, thus giving
supervised methods the chance to successfully segment brain MRI images without the
need for user interaction.
Development of a translational imaging method from preclinical to clinical patient
care may require additional steps to simplify the paradigm, to improve image quality,
or to reengineer the hardware. The usefulness of ADC MRI as a quantitative imaging
tool has motivated several studies that have investigated the reliability and reproducibility
of ADC estimates. M. Alipoor et al. proposed a new experiment design method that is
based on minimizing the determinant of the covariance matrix of the estimated parameters.
S. Aootaphao et al. proposed the X-ray scattering correction method for improving
soft tissue images on the large flat panel detector of portable cone beam CT (CBCT).
The reconstructed images with their proposed scatter correction show significant improvement
on image quality. Thus, the proposed scatter correction technique has a high potential
to detect soft tissues in the brain. S.-S. Han et al. evaluated the availability of
software-based correction of mandibular plane for the vertical measurement of the
mandible in CBCT. G. Wang et al. developed high-field permanent magnetic circuit of
1.2 T and 1.5 T with novel magnetic focusing and curved surface correction. They have
obtained high quality images of mice using their small animal micro-MRI instruments.
Imaging-based big data and network systems may be used to promote hardware and software
standards in quantitative biomedical imaging. Using the data from the Osteoarthritis
Initiative, M. Zhang et al. developed a rapid cartilage damage quantification method
for the lateral tibiofemoral compartment using MRI. M. Zhang et al. evaluated the
influence of MRI sequence on the relationship between bone marrow lesions volume and
pain. They compared quantitative assessments of bone marrow lesions on intermediate-weighted
fat suppressed (IW FS) turbo spin echo and 3-dimensional dual echo steady state (3D
DESS) sequences. They found that bone marrow lesion quantification on IW FS offers
better validity and statistical power than bone marrow lesion quantification on a
3D DESS sequence.
Finally, it is crucial to develop novel quantitative biomedical imaging biomarkers
for diseases, such as shear-wave ultrasound elastography (SWE). SWE is thought to
be useful for quantitatively evaluating tissue hardness. However, it remains unclear
what types of pathology affect tissue hardness. T. Fukuhara et al. elucidated the
correlation between shear-wave velocity (SWV) and fibrosis in thyroid. Small muscle
cells of the cavernosum play an important role in erection. J.-J. Zhang et al. investigated
the feasibility of shear-wave US elastography on evaluating the level of small muscle
cells in penis quantitatively. Liver disease associated with cystic fibrosis (CFLD)
is the second cause of mortality in these patients. T. Cañas et al. found that shear-wave
elastography in the right hepatic lobe is a noninvasive technique useful to detect
CFLD in their sample of patients. They found that splenic SWV values are higher in
cystic fibrosis patients, without any clinical consequence. Last but not least, V.
Rajagopalan and E. P. Pioro studied the potential role of brain parenchymal fraction
as a relatively simple quantitative MRI measure for distinguishing amyotrophic lateral
sclerosis phenotypes.
Guang Jia
Steven B. Heymsfield
Jinyuan Zhou
Guang Yang
Yukihisa Takayama