Recent trends in medical imaging systems
Recent trends in medical imaging systems
The enormity of changes in
the field of medical
imaging technology is hard to fathom. In an article on
this topic, author James H. Thrall, M.D., chairman emeritus, Department of
Radiology, at Massachusetts General Hospital, Boston, stated, “For the better
part of 100 years, physics was the dominant scientific basis of radiology, and
X-ray attenuation was the paramount measurable parameter.” And furthermore,
today, “the richness of measurable parameters has taken medical imaging beyond
organ anatomy and pathology into the realms of physiology, pharmacology and
cellular and molecular biology,”[1] he explained.
The frenetic pace of change
in healthcare today creates challenges of its own. Such challenges come in a
myriad of forms, from new policies and legislation to novel use cases to new
technology innovations. Yet, especially in technology innovation, several key
trends are emerging in new modalities, mobile, the Internet of
Things (IoT) and big data that hold the promise of
significant positive impacts for radiology professionals.
Mobile and IoT:
Mobile technology has made
its way to healthcare. While many mobile healthcare applications exist, there
are not many U.S. Food and Drug Administration (FDA)-approved medical imaging
mobile applications. The majority of those that have been approved are used
primarily for reference purposes.
In the medical imaging
arena, many applications received 510(k) premarket notification from the FDA.
Common-use cases include 3-D viewing, clinical collaboration and easy picture archiving and communication
system (PACS) connectivity, which enables access to
radiology reports and referral studies.
In contrast, the IoT has
not yet been leveraged successfully in imaging. One potential area, however, is
using the IoT to enhance the reading room experience for radiologists by
controlling the screen brightness and contrast parameters, including the room
lighting, based on the radiologist’s choices and reading protocols. Large
imaging independent software vendors (ISVs) are investing in cloud-based IoT
platforms which will eventually enable integration of their medical devices
across the world. These platforms support integration with patient home
monitoring, wellness planning, ambulatory and diagnostic workflows. IoT platforms
are also helping complex imaging workstations to outsource their high intensive
imaging algorithms to the cloud, and utilizing cloud-based applications to
reduce the footprint on end-users’ devices. These platforms allow third-party
software vendors or IT departments of healthcare organizations to customize and
build their own IoT and analytics applications according to their needs.
Another technology trend
that may emerge in the future is interconnectivity of medical imaging devices.
These devices may be able to connect with one or multiple vendors and enable
asset and fleet management, including finance-based operational analysis. Some
of these integrations exist where device vendors are able to remotely perform
preventive maintenance on their devices. The IoT has the capability to cater to
multiple vendors and to undoubtedly become a trend in the future.
Big Data and Analytics
Tools:
Big data analytics has
gained prominence in the medical imaging arena in recent years for its critical
contribution to the care continuum, along with other electronic health record
(EHR) data in context. With the American College of Radiology’s (ACR) Imaging
3.0 initiative and the current focus on value-based healthcare delivery,
analytics tools will go well beyond gathering operational metrics. From
outcomes to reimbursements, protocols to patient experiences, the impact of
data analytics will be far-reaching.
For example, analytics are
extensively used to detect specific patterns identified with specific pathology.
The imaging algorithms are capable of deriving metrics using intensive analysis
of patterns in a given digital image, and output scores that complement the
analyses made by the radiologist, which can be useful for quick diagnosis.
The future of analytics in
diagnostic imaging data is promising. We can look forward to new and
interesting features in the radiology information system (RIS) and PACS
systems, especially those in the cloud, which might include analytics and
impressive reports on operational and clinical data.
Enterprise-wide key
performance indicators (KPIs) are derived from existing data sources inclusive
of DICOM/HL7 transactions, log scraping, flat files, registries, database
transactions, network parsing and configurations. These KPIs are customized to
suit the needs of the provider, for clinical efficiency, operational
efficiency, and overall patient comfort and
care. They have been used
for resource optimization since the start of the decade, but with the increased
complexities of devices, and increased number of modalities, software systems
and mobile devices, something as simple as time taken for a clinician to
contact a technician on an average in a month could be easily measured and
improved.
Read the article “Understanding How Big Data Will Change Healthcare.”
Trending Modalities:
Mammography. Digital
breast tomosynthesis (DBT), or 3-D mammography, is the exciting new
standard in breast imaging due to its dramatic improvement in lesion visibility
and in early cancer detection. With DBT, a series of images are generated along
the breast instead of combining two projections of images. DBT enables
radiologists to view each tissue layer independently, which reduces the number
of errors as well as the number of recalls.[2] The combination of DBT with
ultrasound and MRI enhances diagnostic accuracy even further. The push will
continue as well to remove radiation from mammography, without compromising the
information and image quality.
3-D Ultrasonic Holography. This
technology gained traction this year and is anticipated to continue its growth
in 2017. Because ultrasonic holography does not use dangerous radiation, it is
ideal for preventive and post-operative examinations in breast cancer patients.
The resolution of generated images is high in comparison to those of a normal
ultrasound. In addition, the images are easily reproducible and allow automated
computer-based data interpretation.
BIOMEDICAL
APPLICATIONS IN NANOTECHNOLOGY:
Introduction:
Nanotechnology has many definitions and applications.
However, all definitions highlight the design and development of highly ordered
bottom–up nanostructured materials that offer specific responses when exposed
to certain stimuli (Saji et al. 2010). Surface
chemistry and physics “tune” the applications of nanosized materials. The
concentration of atoms on the surface of these systems represents up to 90% of
their total mass and results in enhanced reactivity. In this sense, modifying
the surface of a nanomaterial in different ways can produce materials with
distinct biological properties and functionalities for a specific end
application and with improved solubility under physiological conditions (Gupta
et al. 2007;
Kasemo 2002).
Biomedical
applications of metal oxide nanoparticles:
Metal oxide nanoparticles have been employed to construct
several medical devices. The magnetic properties of iron oxide have been used
for therapeutic and diagnostic purposes, such as contrast agents for magnetic
resonance imaging, magnetic particle imaging, and ultrasonic techniques (e.g.
magneto-motive ultrasound (Oh et al. 2006),
photoacoustic imaging, and magnetic particle hyperthermia (Gupta and
Gupta 2005; Liu et
al. 2016b). The
electronic structure of zinc oxide (ZnO) is useful for biomedical applications;
for example, the intrinsic fluorescence of ZnO nanowires has been employed to
image cancer cells (Hong et al. 2011). To this
end, functionalization of the surface of ZnO nanowires increases their
solubility in water and their biocompatibility and reduces their cellular
toxicity. Functionalization of the ZnO surface with specific biomolecules
creates photosensitive biosensors (Liu et al. 2006).
Metal nanoparticles:
The strong optical absorption related to the surface
plasmon resonance of noble metals makes them suitable for constructing
molecular contrast devices (Liao et al. 2006;
Bhattacharya and Mukherjee 2008).
Absorption and scattering in the visible and near-infrared regions have
stimulated the application of materials containing metal nanoparticles in the
fields of sensing and diagnosis. Gold nanoparticles can be deposited on
appropriate substrates or added to substrate formulation to enhance
luminescence (Bhattacharya and Mukherjee 2008). The
application of this technology depends on the size and geometry of the
particles because these determine their absorption/scattering properties. Gold
nanorods absorb in the near-infrared and have been used to monitor the blood
flow in vivo using photoacoustic imaging (Wang et al. 2005). The
literature contains examples of applications that used gold nanocages,
nanoshells, and nanospheres (Liao et al. 2006). It is
possible to modify the surface of gold nanoparticles with sulfur-containing
compounds because gold and sulfur have a high chemical affinity (Schmidt and
Healy 2009; Moyano
and Rotello 2011).
Modification of gold nanoparticles with biospecific compounds enhances binding
to specific tissues (Faraji and Wipf 2009). For
example, surface-labeled gold nanoshells have been used to target cancer cells
in vitro (Bhattacharya and Mukherjee 2008) and the
results confirmed by optical microscopy.
Carbon
nanotubes:
physical and chemical properties of carbon nanotubes
(CNTs) have motivated their application in several areas of science.
Modification of the surface of these particles and their functionalization with
biological molecules at the molecular level has increased their use in
nanobiotechnology (Yang et al. 2007; Prato et
al. 2008; Sharma et
al. 2016). These
modified particles provide well-dispersed samples that are compatible with
physiological conditions (Williams et al. 2002). In this
context, nanotubes might be useful drug delivery vehicles because their
nanometer size enables them to move easily inside the body (Pastorin et
al. 2006; Faraji
and Wipf 2009
Liposo
Liposomes and nanobiotechnology:
mes are small artificial lipid-bilayer spherical vesicles
that were first reported by Bangham and Horne (1964).
Liposomes with different properties can be achieved by tuning their
composition, surface charge, and size. The rigidity and fluidity of the bilayer
can also be tailored by choosing specific lipids (Akbarzadeh et al. 2013). These
artificial membrane models can be classified on the basis of their diameter.
Small unilamellar vesicles range in size from 20 to 100 nm, whereas large
unilamellar vesicles (LUVs) range from 200 to 1000 nm. The vesicles consist of
a single lipid bilayer and an internal aqueous cavity
Nanotechnology
to engineer the surface of metallic implants:
nanootechnology has also found applications in tissue and
implant engineering. The possibility to enhance the surface area of the
material and to tune the roughness of its surface at the nanometric scale
should yield better biological responses of osteogenic cells and effective
mechanical contact between tissue and implant. Titanium and its alloys are
considered to be the most attractive materials for bone replacement
applications (Rack and Qazi 2006). The
widespread use of this metal is due to its improved mechanical properties, high
resistance to corrosion, low surface reactivity, and acceptable
biocompatibility in vivo and in vitro
Wettability determines how cells and fluids interact with surfaces.
Wettability refers to the ability of a fluid to spread on a given surface. It
is related to the equilibrium of forces acting at the solid–liquid interface
and is governed by the topography of the surface (Quéré 2008). In
biomaterials science, wettability is assessed by measuring contact angles (θ)
between a liquid drop and the surface (Menzies and Jones 2010). When
this liquid is water, surfaces where the water droplets spontaneously spread
over the surface (θ < 90°) are considered to be hydrophilic; if θ
is >90°, the surface is considered to be hydrophobic. However, this
classical limit between a hydrophilic and a hydrophobic surface has been
reviewed due to the specific structure of water molecules at the interface
(Berg et al. 1994;
Vogler 1998). The
value of θ is measured at the solid–liquid–gas interface and is univocally
fixed by the chemical nature of the different phases and the equilibrium forces
acting among these phases. Contact angles are mathematically correlated by the
Young–Dupré equation (Kwok and Neumann 1999), namely,
cos(θ) = (γ SG − γ SL)γ LG,
where γ is the interfacial tension between the solid (S), liquid (L), and gas
(G) phases. In particular, γ SG is called
the surface free energy (SFE) of a solid. SFE is an important parameter because
it can be determined by using chemical models that depend not only on γ SL and γ LG,
but also on specific intermolecular forces. Hence, the total SFE can be seen to
be the result of the combination of dispersive forces (γ SGd)
and polar forces (γ SGp) (Kwok and
Neumann 1999). Thus,
the SFE of a biomaterial selectively determines how either the polar or the
non-polar portion of proteins and cell membranes interact with the surfaces.
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