35 research outputs found
Bone Material Analogues for PET/MRI Phantoms
Purpose: To develop bone material analogues that can be used in construction
of phantoms for simultaneous PET/MRI systems.
Methods: Plaster was used as the basis for the bone material analogues tested
in this study. It was mixed with varying concentrations of an iodinated CT
contrast, a gadolinium-based MR contrast agent, and copper sulfate to modulate
the attenuation properties and MRI properties (T1 and T2*). Attenuation was
measured with CT and 68Ge transmission scans, and MRI properties were measured
with quantitative ultrashort echo time pulse sequences. A proof-of-concept
skull was created by plaster casting.
Results: Undoped plaster has a 511 keV attenuation coefficient (~0.14 cm-1)
similar to cortical bone (0.10-0.15 cm-1), but slightly longer T1 (~500 ms) and
T2* (~1.2 ms) MR parameters compared to bone (T1 ~ 300 ms, T2* ~ 0.4 ms).
Doping with the iodinated agent resulted in increased attenuation with minimal
perturbation to the MR parameters. Doping with a gadolinium chelate greatly
reduced T1 and T2*, resulting in extremely short T1 values when the target T2*
values were reached, while the attenuation coefficient was unchanged. Doping
with copper sulfate was more selective for T2* shortening and achieved
comparable T1 and T2* values to bone (after 1 week of drying), while the
attenuation coefficient was unchanged.
Conclusions: Plaster doped with copper sulfate is a promising bone material
analogue for a PET/MRI phantom, mimicking the MR properties (T1 and T2*) and
511 keV attenuation coefficient of human cortical bone
Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI
Non-invasive prostate cancer detection from MRI has the potential to
revolutionize patient care by providing early detection of
clinically-significant disease (ISUP grade group >= 2), but has thus far shown
limited positive predictive value. To address this, we present an MRI-based
deep learning method for predicting clinically significant prostate cancer
applicable to a patient population with subsequent ground truth biopsy results
ranging from benign pathology to ISUP grade group~5. Specifically, we
demonstrate that mixed supervision via diverse histopathological ground truth
improves classification performance despite the cost of reduced concordance
with image-based segmentation. That is, where prior approaches have utilized
pathology results as ground truth derived from targeted biopsies and
whole-mount prostatectomy to strongly supervise the localization of clinically
significant cancer, our approach also utilizes weak supervision signals
extracted from nontargeted systematic biopsies with regional localization to
improve overall performance. Our key innovation is performing regression by
distribution rather than simply by value, enabling use of additional pathology
findings traditionally ignored by deep learning strategies. We evaluated our
model on a dataset of 973 (testing n=160) multi-parametric prostate MRI exams
collected at UCSF from 2015-2018 followed by MRI/ultrasound fusion (targeted)
biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating
that deep networks trained with mixed supervision of histopathology can
significantly exceed the performance of the Prostate Imaging-Reporting and Data
System (PI-RADS) clinical standard for prostate MRI interpretation